Aerobic Fitness and Healthy Brain Aging - DiVA portal

Aerobic Fitness and Healthy Brain Aging

Cognition, Brain Structure, and Dopamine

Lars Jonasson

Department of Radiation Sciences Umeå 2017

This work is protected by the Swedish Copyright Legislation (Act 1960:729) Dissertation for PhD ISBN: 978-91-7601-753-1 ISSN: 0346-6612 New Series No. 1908 The cover image is based on an oil painting by Lars Jonasson. It symbolizes how physical movement elicits physiological factors that may enter the brain to influence its function. Electronic version available at: http://umu.diva-portal.org/ Printed by: UmU Print Service, Umeå University Umeå, Sweden 2017

Dedicated to Olivia, my daughter

She embodies how learning about the world can be so enjoyable, yet at times also be quite frustrating. She shows how learning sometimes happens quickly, yet is oftentimes slow. Regardless, as long as there is a desire, the learning process will gradually develop into new knowledge about the world. Much like science.

Acknowledgements

CJ, I will be forever grateful to you for these last four years. We have had a lot of fun while preparing for, collecting, and trying to understand the data from the PHIBRA project. You have always radiated curiosity and been eager to dig into the details. You have also been supportive and flexible when needed the most (Olivia will also understand one day and be just as grateful as me). When you moved to Copenhagen you were missed, not because our research was compromised, which it wasn’t, but because of your colorful character spreading laughter here on level 3. It has been great to be part of the ”Boraxbekk Brain Buddies” together with Emma, Frida, Hanna, and Pär.

Katrine, I am also happy for having had you as a co-supervisor. Confident and with good judgment, always on the point, and providing just the spot-on comments. You also show how much could in reality be done within a 24-hour day-night cycle.

Lars N, your fascinating research was the reason why I came to Umeå in the first place. Coming here, I did not know how you were as a person or how you would be to work with. All I can say is that there would not have been a single reason for me not wanting to come here. I feel very fortunate to be a part of the environment you have created at UFBI.

Arthur, thank you for sharing your expertise and for being one of the great pioneers in this field. Kirk, thank you for the time I could spend in Pittsburgh in the BACH lab. Those weeks gave me invaluable perspectives.

Pär F, I am happy for the friendship that evolved. It was really social and stimulating to share rooms at CEDAR and working on the PHIBRA project together. When you feel like imaging again there is at least one good design left on the whiteboard over here that could possibly be appreciated even by Poldrack...

I want to thank everyone at CEDAR and DDB. It’s been a joy to spend the days working under the same roof as all of you. After long periods of keen work, surely someone will arrange something out of the ordinary, throwing Hawaii parties, sour herring “feasts”, or just Peter putting on a blinking x-mas rain-deer sweater. Wonderful. I cannot add all of your names here because you are so many. Just know that I really appreciated to be around all of you. I have to give special thanks to the Friday badminton crew though. Bosse, Lotta, Peter, and Sören, ending every Friday with badminton and sauna (males only) is the perfect way to wrap up the week. Big thanks also to Erling for persuading me to start using R statistics, and to Maria J for letting me probe her mind for statistical formulas and packages. Also big thanks to the graduate school and Johan Lundberg for giving me the opportunity to spread my research across the globe at conferences and visiting Kirk. Thanks also to the other graduate school people for good times, particularly Daniel, Elisabeth, Eva, Helena, Markus, and Martin.

I also want to thank everyone at UFBI for being part in creating a great environment for doing real cognitive neuroscience research. Micael A, the pipeline wizard, thank you for always being willing to share your knowledge, and for having become such a close friend. Anders W, the other programming wizard, you have also been so helpful. Anders L, you really make statistics fun, and having you around is a joy. Sara P, I’m always happy when spending time with you, my first and still coolest friend in Umeå. Amar, Anders B, Anders E, Carola, Filip, Fredrik, Greger, Linnea, Micael S, Pär N, Sara S, Urban and everyone else in the environment. Finally, thanks to Johan for all the remarkably insightful comments and thoughts during the lab meetings over the years, and for organizing and keeping everything together (also Micael S).

The staff and researchers at Diagnostic Radiology have also been great. Linda, if ever to clone a human being.. Nina my radioactive cognitive neuroscience roomie and molecular expert. Anna joining the crew with her strong radioactive and resonating skills and new ideas. Torbjörn with whom I’ve enjoyed many corridor chats to contrast otherwise silent days, and occassionaly also longer bouts, struggling with brain perfusion. Susanna being on the clinical side of dopamine. Mats and Kajsa taking perfect care of the PHIBRA participants and their imaging sessions. Big thanks also to the staff at Nuclear Medicine and the staff at the 3 Tesla scanner for being eager and able to make everything run smoothly.

The COBRA team. Big thanks to the PIs for inviting me in, Lars N, Katrine, Lars B, Martin, and Ulman. The level of skill and knowledge present during those meetings and that is being shared amongst the crew is really amazing. It is intellectual enjoyment at its very best. Special thanks to Goran and Ylva. It was a true pleasure to work more closely together with you on the dopamine manuscripts. You both have brilliant minds and personalities. I really hope there will be more of that in the future. Alireza, Anders E, Anders W, Andreas, Anna, Doug, Jan, Micael, Neda, Nina, thank you.

I also want to acknowledge the Department of Psychology, where I finished my undergraduate studies and where I still feel at home. In the beginning of my graduate studies I was actually the only graduate student at Diagnostic Radiology. Instead I got to be called “the adopted PhD student at Psychology” taking part in the activities of the PhD students there. I want to thank both the people in charge and certain PhD students for that. Erik M, Johan P, and Dr. Andreas S. Gustaf, happy you joined. Also thanks to Camilla for giving me the opportunity to lecture at the department’s programs. Again, many more names should be written but I need the space for the actual thesis.

Finally, my family, Östen, Lena, Mattis, Sandra, Anna for being good role models and always being encouraging. Thanks also to Olivia, that despite her young age often suggesting we go to the University to do some work together. I am never late to encourage such proposals.

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Table of Contents

Abstract ...................................................................................... iiiAbbreviations ............................................................................. ivList of Publications ...................................................................... vSammanfattning ......................................................................... vi

Fysisk träning, hjärnan och kognitiva funktioner .................................................. viSummering av resultat ........................................................................................... viiSlutsats ................................................................................................................... viii

Introduction ................................................................................ 1Brain and cognition in aging ..................................................................................... 1Exercise in aging ....................................................................................................... 4Exercise and cognition ............................................................................................. 4Exercise and the brain .............................................................................................. 6

Micro level effects of exercise ............................................................................. 6Macro level effects of exercise ........................................................................... 11

Models linking exercise, brain, and cognition ........................................................ 19Aims ........................................................................................................................ 20

Materials and methods ............................................................... 21Study population ..................................................................................................... 21Aerobic fitness ........................................................................................................ 22Cognitive test battery .............................................................................................. 23Magnetic resonance imaging .................................................................................. 23Positron emission tomography .............................................................................. 25

The partial volume effect ................................................................................. 25Statistical analyses .................................................................................................. 27

Results ....................................................................................... 29Study I ..................................................................................................................... 29Study II ................................................................................................................... 33Study III .................................................................................................................. 35

Discussion ................................................................................. 37Cognitive improvements ........................................................................................ 38Effects on brain structure ....................................................................................... 39

Hippocampus ................................................................................................... 39Prefrontal cortex ............................................................................................... 41

Effects on the dopaminergic system ...................................................................... 46Methodological considerations and limitations .................................................... 53

Longitudinal imaging ...................................................................................... 53Limitations ........................................................................................................ 54

Future directions .................................................................................................... 55Active vs. passive mechanisms ........................................................................ 56Aerobic exercise vs. other forms of exercise .................................................... 56Concurrent cognitive stimulation ..................................................................... 57

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Time of optimal plasticity ................................................................................ 58Conclusion ................................................................................. 59References ................................................................................. 61

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Abstract

Background Performing aerobic exercise and maintaining high levels of aerobic fitness may have positive effects on both brain structure and function in older adults. Despite decades of research however, there is still a rather poor understanding of the neurocognitive mechanisms explaining the positive effects of aerobic exercise on cognition. Changes in prefrontal gray matter as well as dopaminergic neurotransmission in striatum are both candidate neurocognitive mechanisms. The main aims of this thesis are: 1. To investigate the effects of aerobic exercise and fitness on cognition and magnetic resonance imaging (MRI) derived gray matter volumes using data from a 6 month physical exercise intervention in older adults (Study I). 2. To simulate the effect of atrophy in longitudinal positron emission tomography (PET) which could pose a challenge to interpreting changes in longitudinal PET imaging (Study II). 3. To study the influence of aerobic exercise and fitness on the dopamine D2-receptor (D2R) system in striatum using [11C]raclopride PET as a potential mechanism for improved cognition (Study III).

Results In Study I, aerobic exercise was found to improve cognitive performance in a broad, rather than domain-specific sense. Moreover, aerobic fitness was related to prefrontal cortical thickness, and improved aerobic fitness over 6 months was related to increased hippocampal volume. In Study II, we identified areas in the striatum vulnerable to the effect of shrinkage, which should be considered in longitudinal PET imaging. Finally, in Study III, the effect of being aerobically fit, and improving fitness levels was found to impact dopaminergic neurotransmission in the striatum, which in turn mediated fitness-induced improvements in working memory updating performance.

Conclusion The findings in this thesis provide novel evidence regarding the neurocognitive mechanisms of aerobic exercise-induced improvements in cognition, and impacts the interpretation of longitudinal PET imaging. Performing aerobic exercise and staying aerobically fit at an older age have positive effects on cognition and brain systems important for memory and cognition. Specifically, fitness-induced changes to the dopaminergic system stands out as one novel neurocognitive mechanism explaining the positive effects of aerobic fitness on working-memory performance in healthy older adults.

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Abbreviations ACC anterior cingulate cortex BDNF brain-derived neurotrophic factor BOLD blood-oxygenation-level dependent BPND non-displaceable binding potential of [11C]raclopride D1R dopamine D1-family of receptors (D1, D5) D2R dopamine D2-family of receptors (D2, D3, D4) DA dopamine DAT dopamine transporter dlPFC dorsolateral prefrontal cortex IFG inferior frontal gyrus IGF-1 insulin-like growth factor 1 MFG middle frontal gyrus MRI magnetic resonance imaging mRNA messenger ribonucleic acid NOS nitric oxide synthase PET positron emission tomography PFC prefrontal cortex PHIBRA physical influences on brain in aging ROI region of interest SN substantia nigra VBM voxel-based morphometry VEGF vascular endothelial growth factor vlPFC ventrolateral prefrontal cortex VTA ventral tegmental area

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List of Publications The following manuscripts and published articles will be included in this thesis and will be referred to by their roman numerals.

I. Jonasson, L. S., Nyberg, L., Kramer, A. F., Lundquist, A., Riklund, K., & Boraxbekk, C. J. (2017). Aerobic exercise intervention, cognitive performance, and brain structure: Results from the Physical Influences on Brain in Aging (PHIBRA) Study. Frontiers in Aging Neuroscience, 8(Article 336), 1–15.

II. Jonasson, L. S., Axelsson, J., Riklund, K., & Boraxbekk, C. J. (2017). Simulating effects of brain atrophy in longitudinal PET imaging with an anthropomorphic brain phantom. Physics in Medicine and Biology, 62(13), 5213–5227.

III. Jonasson, L. S., Nyberg, L., Kramer, A. F., Axelsson, J., Riklund, K., Boraxbekk, C.J. (2017). Aerobic fitness influences working memory updating via the striatal dopaminergic system in older adults. (manuscript)

All papers are reproduced with permission from the copyright holders.

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Sammanfattning

För inte allt för länge sedan trodde man att hjärnan inte förändrades positivt när man väl blivit äldre. Denna uppfattning har ersatts av ett forskningsfält som studerar hjärnans plasticitet, dvs. hjärnans livslånga förmåga att förändras i respons till den miljö vi dagligen möter. Denna syn på hjärnan är mycket positiv då den öppnar för att vi kan förändras, förbättras och att vi även till viss mån kan välja hur vi vill förändras. Trots detta ser vi i dagens samhälle att åldersrelaterade kognitiva nedsättningar är ett problem för många äldre människor. Detta kan vara mycket frustrerande för individen. Samtidigt ökar den åldrande delen av befolkning och detta ställer stora krav på samhället och åldrandevården. Det finns med andra ord starka skäl till att förstå hjärnans plasticitet för att människor ska kunna göra val som påverkar deras hjärnor positivt. I projektet Physical Influences on Brain in Aging (PHIBRA) har vi undersökt den plasticitet som sker till följd av fysisk träning i 60 äldre människor (64-78 år) i Västerbotten. Hälften av dem utförde aerobisk träning, 3 dagar i veckan under 6 månader, och hälften utförde styrka, balans och stretching träning under lika lång tid.

Fysisk träning, hjärnan och kognitiva funktioner

Under de senaste årtiondena har mycket forskning ägnats åt att studera effekterna av fysisk aktivitet på den kognitiva hälsan hos äldre människor. Flertalet epidemiologiska studier har visat att de människor som är och har varit fysiskt aktiva även har en minskad risk av att drabbas av åldersrelaterad kognitiv nedsättning och demens-problematik. Det finns i dagsläget ett stort antal studier som undersökt fysisk aktivitets påverkan på hjärnan och där har man kunnat påvisa flertalet positiva förändringar i hjärnans struktur samt funktion. Detta till trots är majoriteten av dessa studier tvärsnittsstudier. De starkaste evidensen kommer dock av att utföra faktiska interventioner likt PHIBRA. Dessa är mycket viktiga för att förstå de neurokognitiva mekanismerna av att vara fysiskt aktiv, dvs. vad mer precist det är som sker i hjärnan och som kan påverka kognitiva funktioner positivt. Dessutom kan interventioner öka förståelsen för exakt vilka kognitiva funktioner som påverkas. Delstudierna i denna avhandling svarar således på mer specifika frågeställningar. För att besvara dessa användes bland annat magnetkamera (MR) undersökningar för att titta på hjärnans struktur, samt positron emissions tomografi (PET) för att undersöka hjärnans dopaminsystem.

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Summering av resultat

I Studie I studerade vi specifika kognitiva funktioner, som till exempel arbetsminne, mental snabbhet, eller problemlösning. Trots att tidigare forskning har visat positiva effekter på kognitiva funktioner råder fortfarande en stor osäkerhet över vilka funktioner som påverkas och främst hur de påverkas. Vi studerade även förändringar i områden i pannloben som är viktiga för att en människa ska, t.ex. klara av att planera, hantera information och kontrollera sitt beteende. Dessutom undersöktes förändringar i hippocampus som är A och O för minnet. Detta gjordes med hjälp av MR.

Våra analyser visade på en förbättring i flertalet kognitiva funktioner över tid i båda grupper, men där de individer som tränat aerobisk träning generellt förbättrades mer. I hjärnan såg vi att de som hade en högre syreupptagningsförmåga innan studien började hade tjockare områden av grå substans i pannloben, vilket skulle kunna indikera att deras hjärnor inte åldrats lika mycket. Relativt andra deltagare var förbättring av syreupptagningsförmågan över 6 månader kopplat till en ökad storlek på hippocampus vilket visar att denna mycket viktiga struktur även kan öka i storlek på kort tid hos äldre människor.

I Studie II använde vi en hjärnmodell som vi designat, en så kallad fantom. Denna fantom använde vi för att undersöka hur våra PET mätningar påverkas när ett specifikt område i en individs hjärna förändras i storlek eller om antalet dopamin-receptorer förändras. Resultaten visade att förändringar av volymen i striatum kan bli ett problem när longitudinella PET data skall tolkas, särskilt i vissa mer utsatta områden. Vidare visade resultaten att de mätningar vi skulle utföra i Studie III inte skulle påverkas nämnvärt av de fysiologiska förändringar som kan förväntas på 6 månader.

I Studie III studerades med hjälp av en PET kamera dopaminsystemet i de basala ganglierna, mer exakt i striatum. Dopaminsystemet i striatum är viktigt för pannlobsfunktionerna som studerades i Studie I, men är också viktigt för motorik, beroenden, motivation och belöning. Våra resultat visade likt i Studie I att de som hade en bättre syreupptagningsförmåga före studiens början även hade fler tillgängliga dopaminreceptorer i striatum. Förändringarna i syreupptagningsförmågan över 6 månader indikerade att mängden dopamin hade ökat. Denna ökning av dopamin åtföljdes av en förbättrad arbetsminnesförmåga till följd av träningen.

Någon skillnad mellan de två träningsgrupperna kunde inte påvisas med någon av de mätningar som gjordes på hjärnan. Förklaringar till det kan vara många. Den kanske mest troliga är att även kontrollgruppen utförde en form

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av träning som gav positiva effekter på hjärnan inklusive dess kognitiva funktioner. Vidare så kan även förändrade vardagsrutiner påverka individen. Exempelvis att ta trapporna i stället för hissen kan ha bidragit med mer aerobisk träning i vardagen även för kontrollgruppen och skulle kunna vara en förklaring till deras förbättrade syreupptagningsförmåga över sex månader.

Slutsats Flertalet resultat inom ramen för denna avhandling visar på att det är positivt för ett antal viktiga system i hjärnan att bibehålla en god syreupptagningsförmåga även i högre åldrar exempelvis genom aerobisk träning. Det är sannolikt att olika processer i hjärnan påverkas olika snabbt. Exempel på snabbare processer är t.ex. förändrade dopaminnivåer eller ökningen av nya nervceller och blodkärl i hippocampus. Sannolikt påverkar aerobisk träning även mer långsamma processer och som kan ha en roll för strukturella förändringar i pannloben och andra områden i hjärnbarken och bibehållen hjärnhälsa. Dessa inkluderar minskad inflammation, oxidativ stress och minskat blodtryck. Slutsatsen från i avhandlingen inkluderade studier samt den genomgångna litteraturen visar att fysisk aktivitet är bra för hjärnan och dess kognitiva funktioner.

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Introduction As we are approaching old age, a decline in memory and other cognitive functions is normal, with concurrent decreases in brain health 1,2. Brain aging and declining cognitive function are considered intricately linked 3. Everyone would probably prefer to age successfully, maintaining a healthy brain and youth-like cognitive vitality throughout life. Unfortunately, not everyone experiences aging in such a way. Understanding the mechanisms explaining healthy brain aging is thus important to be able to affect the aging process and to promote healthy brain aging. Factors explaining individual differences in brain health are only partly genetic in origin 4, but also involve various life-style factors 5, such as physical activity 6. In other words, it appears that certain behaviors are more beneficial than others. The mechanisms however, are not completely understood.

In this thesis, the effects of aerobic exercise and fitness on brain health and cognition in older adults are studied. The main focus is understanding potential mechanisms causing such effects.

Brain and cognition in aging As we approach an old age, considerable reductions in gray matter tissue volumes have normally occurred 1,2,7,8. The age-related volume loss results primarily due to loss of dendritic arbors and synaptic connections 9, rather than on the loss of neurons 10, and may also involve damage to myelin and the vascular system. The functional consequence of a degenerating central nervous system is generally related to reduced cognitive functioning 3,11–13.

There appears to be some consensus that prefrontal and medial temporal regions are particularly sensitive to age-related decline in brain health, whereas sensory cortices are less vulnerable 9,14. These same regions serve cognitive functions exhibiting an accelerated rate of decline from about the age of 60 to 70 3,15–18, namely episodic memory and executive functions.

The hippocampus, located within the medial temporal lobes, is critical for spatial memory and navigation 19,20, and the formation of episodic memories 21, i.e. memories of life events that can be consciously retrieved 22,23. Remembering where one has put one’s keys is one example of such a memory. Episodic memory function has been found to predict dementia 10 years prior to the actual diagnosis 24. The degeneration of hippocampus is a hallmark of many dementias, Alzheimer’s disease in particular 25,26, and can be partly responsible for age-related loss of memory function also in healthy adults 15.

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Processing in prefrontal regions are crucial for executive functions enabling cognitive control of behavior 27,28. Executive function is not a completely unitary construct, and can be decomposed into constituent components according to function, e.g. inhibition, shifting, and working memory updating 29. Working in synchrony, executive processes enable self-control and self-monitoring, interference resolution, the ability to plan behavior, and integrate information from different dimensions for the realization of internal goals 27. Declining prefrontal brain health is consequently believed to cause declining executive function 3,30. Hence, an understanding of factors able to preserve brain health in these important yet vulnerable regions, could potentially be used to delay the impact of cognitive decline.

I believe the revised Scaffolding theory of aging and cognition (STAC-R) 12 captures the complexity of the aging process. The model provides an overview of potentially important factors in the aging-cognition connection, hence I have chosen to illustrate the model in Figure I. In short, across life, an individual will be exposed to various factors that may either enrich or deplete neural resources. Examples of enriching factors include education, intellectual engagement, and physical exercise. Depleting factors include stress, vascular disease, or certain genes. These elements will then interact with the biological aging process to influence brain structure and function. Age-related influences in brain structure include increased amyloid burden, reduced cortical thickness, brain volume, white matter integrity, and dopamine depletion. These changes affect brain function, as evident in altered neural processing both within specific regions and in the communication between regions. The changes in brain structure and function then decides the level of cognitive function and rate of cognitive change. This first part of the model summarizes the previous paragraphs of this thesis. There are however two important parts of the model left, and both relate to the concept of plasticity.

Plasticity enables the brain to change in response to a challenging environment, e.g. to improve behavioral functions. One example of plasticity that has been thoroughly studied is when a new memory is learned. To accommodate a new memory, existing synaptic connections need to be altered, or new synapses created. A whole cascade of cellular events may be involved in this process, including numerous transcription factors, neural growth factors, and neurotransmitters 31. In STAC-R, individuals may use compensatory scaffolding, a concept involving alterations in neural processing to maintain cognitive function in the face of declining brain health. In contrast, individuals not exhibiting typical age-related neurobiological decline do not need compensatory scaffolding to preserve cognitive functioning, as predicted by the theory on brain maintenance 3. In one experiment, Düzel and colleagues revealed subgroups of older adults that exhibited behavioral and neural activation patterns suggesting

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brain maintenance, compensatory scaffolding, or declining memory function without compensatory scaffolding 32. In other words, one group of older adults had similar activation patterns and recollection memory performance as young adults, indicating brain maintenance. Another group of individuals had high memory recollection and showed increased prefrontal, parietal, and medial temporal activation when compared with a group having worse performance. The former group was believed to successfully utilize compensatory scaffolding to improve memory function, whereas the other group failed to do so. The prefrontal areas exhibiting increased activation in the high-memory group also showed less gray matter atrophy in those same areas. Hence, the ability to compensate might depend on some level of structural integrity in the PFC 32. This possibility is interesting, as the status of brain structure not only influences cognitive function directly, but also modulate the ability for compensatory scaffolding. The concept of compensatory scaffolding clearly captures the fact that the brain is plastic throughout life. Accordingly, the last part of the model posits that various interventions can be implemented to influence plasticity, leading to compensatory scaffolding at an old age. Such interventions include social or intellectual engagement, cognitive training, or physical exercise. Although interventions in STAC-R focus on compensatory scaffolding, interventions could also been seen as providing neural enrichment without compensation and relate to brain maintenance 3,33,34.

Figure I. The STAC-R model of brain and cognitive aging. Reproduced with permission (Reuter-Lorenz and Park, 2014)12

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Exercise in aging Before reviewing the literature on exercise, it is important to distinguish between different concepts commonly studied. Physical activity is any type of activity involving the skeletal muscles which increases energy expenditure. Physical exercise is a planned form of PA with the goal to improve physical fitness. Physical fitness on the other hand relates to some level of skill or health attribute that can be measured, e.g. vO2peak 35. Motor fitness includes factors such as flexibility, movement speed, balance or fine coordination 36. In the present thesis, there is a particular focus on aerobic fitness, i.e. the capacity to consume oxygen for use in producing energy during exertion. Aerobic fitness is commonly estimated with VO2max or VO2peak tests 37.

Several prospective studies have found that being physically active reduces the risk of suffering cognitive decline 38,39, be diagnosed with dementia 38,40, Alzheimer’s disease 38, and even Parkinson’s disease 41. This indicates that physical exercise has neuroprotective effects and the potential to preserve brain health and cognitive function in old age. Although important, large scale prospective or epidemiological studies have a limited ability of providing knowledge about the underlying brain mechanisms explaining why being physically active entail such effects. Interventions trying to isolate the specific mechanisms from different forms of physical exercise are of great importance.

Accordingly, over the last decades, there has been a growing interest in understanding the mechanisms by which physical activity and exercise influences cognitive functioning and brain health 37,42–44.

Exercise and cognition What are the cognitive changes occurring after prolonged periods of aerobic exercise? A pronounced answer to this question has eluded researchers for decades. Research relating physical activity and exercise to cognitive functions began with observations that acute bouts of exercise at different intensities and durations influenced cognitive test performance, e.g. 45, both positively and negatively (see the 1986 review by Tomporowski and Ellis 46). Already 30 years ago several researchers were also conducting controlled aerobic exercise interventions, measuring cognition using well-controlled laboratory tasks in order to understand the if, what, where, and how exercise could influence cognition 47,48. However, a clear pattern of result has yet to emerge.

Often one or more cognitive tasks have improved following an aerobic exercise intervention, but equally often other tasks have not improved 49–52. This is also true for tasks belonging to the same cognitive domain 50,52. To increase generalizability and convincingly demonstrate transfer of training to a given

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cognitive domain, creating a factor using several tasks tapping that domain is important as it increases the robustness of the measure 53. This approach is largely absent from the aerobic exercise literature, but see 54,55. The lack of robust factors reflecting the function of cognitive domains may partly explain why studies diverge. Notwithstanding, tasks belonging to the same cognitive domain may still involve different processing requirements 56, and vary in cognitive load or difficulty. Hence, individual tasks can still be informative, albeit with some loss of generalizability. Further, individual tasks may be more or less sensitive to variations in, e.g. neural processing 57 probably due to variations in the amount or type of processing required. How specifically exercise may influence the brain and consequently cognition is under question 43.

One approach to circumvent the problem with individual studies generalizing results using single tasks to cognitive domains is to conduct meta-analyses. A number of meta-analyses have also been conducted, including studies made on adults above 18 years of age 58,59, including also children 60,61, or healthy older adults 62–65. These have demonstrated improvements in executive function 58,62, working memory 59, memory 58, “attention and processing speed“ 58, controlled processing 62, processing speed 63, visual and auditory attention 63, and motor function 63. In these same meta-analyses, null effects were observed on domains of working memory 58,63, executive functions 63, memory 59,63, processing speed 62, perception 63, inhibition 63, and visuospatial ability 62. As is evident, the effects of aerobic exercise on cognition are inconclusive based on these meta-analyses. This became even more evident when a recent Cochrane systematic review found no effect at all from aerobic exercise or aerobic fitness in healthy older adults in any cognitive domain, incl. several memory and attentional domains, executive function, inhibition, working memory, motor function, perception, and processing speed 64. Conversely, one of the most recent meta-analyses included only randomized controlled trials with supervised exercise protocols in adults above age 50 65. They found improvements on all cognitive domains tested, including memory, executive function, working memory and attention. In addition to aerobic exercise, resistance training, combined aerobic and resistance training, and tai chi all improved cognition, whereas yoga showed a trend. However, considerably fewer studies involved tai chi and yoga.

There are several potential reasons explaining why no clear picture of exercise-induced effects on specific cognitive functions have yet emerged. The original research studies exhibit large between-study variations in study protocols, type of control group, cognitive tests used, as well as differences in study populations, a problem recognized also by others 59,61,66. The choice of exercise performed by an active control group may be particularly important considering that various forms of physical exercise when analyzed together was found to benefit cognition 65 (see also 67–69. It is plausible to believe that true exercise-induced effects on

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cognition from aerobic exercise will partly overlap with true effects from resistance exercise. Statistical tests may consequently indicate that a cognitive function has not improved from aerobic exercise, when in fact both aerobic and active control groups have improved. Furthermore, the meta-analyses differ in inclusion criteria, and in the assignment of tasks to different domains. For instance, whereas Angevaren et al. 63, categorized trail making part A as a processing speed task, Smith et al. 58, placed it in the attention domain. This is also true for individual studies, e.g. categorizing the digit-symbol task as depending on visuospatial ability 70 or on processing speed 50.

Several theories have been proposed, trying to predict which cognitive functions would benefit from aerobic exercise, e.g. the speed of information processing 51, visuospatial ability 70, controlled and effortful processing 71, memory and learning 72, and executive control processing 50,73. Neither has received strong support from exercise interventions in humans, at least on the behavioral level. In sum, due to the mixed results evident in both individual studies and meta-analyses, it is not possible to confirm with any confidence whether any specific cognitive abilities improve from performing aerobic exercise. Individual studies with robust cognitive test batteries enabling the creation of factors to reflect domains of cognitive functioning is necessary to better understand if and how exercise influences cognition.

Exercise and the brain The study of brain plasticity arising from exercise may critically inform about the underlying mechanisms leading to healthy brain aging 6,37,42 which are evident in large epidemiological datasets 38,39,74. Stillman et al. 42 systematically described the overarching question which needs an answer to understand how physical exercise influences brain structure and function. What are the cellular and molecular events elicited by exercise (Micro level)? How are these translated to larger scale functional and structural changes in the brain (Macro level)? What impact do these structural and functional changes have on cognitive functioning (Behavioral level)?

Micro level effects of exercise The micro level cellular and molecular effects elicited by exercise may involve the most basic cellular responses, e.g. altered levels of growth factors and neurotransmitters, dendritic restructuring, growth of new synaptic connections and neuroreceptors, neurogenesis, and angiogenesis. Research into each of these processes are important but is well beyond the scope of this thesis to describe in detail. Performing aerobic exercise likely triggers the induction of a number of growth factors in muscle tissue 37,75–77, as well as release of calcium from bone tissue 78. When crossing the blood-brain barrier these peripherally induced

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substances may trigger a cascade of cellular events to induce both structural and functional plastic changes in the brain. Hence, even if nearly impossible to measure the transfer from the periphery to the central nervous system in humans, it will be assumed that the majority of exercise-induced structural and functional plastic responses detectable in humans are in one way or another related to peripherally triggered growth factors, e.g. insulin-like growth factor-1 (IGF-1) and vascular endothelial growth factor (VEGF), or other molecular events, e.g. increased calcium and nitric oxide synthase (NOS). Perhaps the most studied neural growth factor in relation to exercise is the brain-derived neurotrophic growth factor (BDNF) 79. BDNF is involved in neurogenesis, in activity-dependent synaptic function modulation, neural growth, and neurotransmitter release 80. Increased BDNF levels from voluntary wheel-running have been observed both in the hippocampus and in the cortex in animals 81,82, but is dependent on IGF-1, at least in the hippocampus 83. BDNF also influences the functionality of dopaminergic neurons in basal ganglia. In substantia nigra (SN) and ventral tegmental area (VTA) dopamine function is affected, e.g. by BDNF mediated increases in dopamine uptake 84. In the striatum, dopamine binding to D1-D2 receptor heteromers increases the expression of BDNF to stimulate neuronal growth 85. The influence of BDNF on the dopaminergic system suggests that the exercise-induced increase in BDNF may indeed lead to plasticity in the dopaminergic system following exercise.

Positron emission tomography in physical exercise studies Micro level processes on the cellular and molecular level can be most readily studied in animals 37. However, advancements in positron emission tomography (PET) imaging enables studying certain molecular events also in humans 86,87, e.g. neurotransmitter release, density of neuroreceptors, or glucose metabolism, to name a few possibilities. A recent review concluded that PET studies in the physical exercise literature are so far sparse, reasons thereof including the high costs associated with PET, and the exposure to radiation inherent in PET imaging 87. Nevertheless, Boecker and Drzezga also concluded that PET offers unique opportunities to probe human brain function in relation to exercise 87. There are many technical challenges associated with PET. For example, due to the volume-dependency of PET measures 88,89, and typical age-related atrophy 90, changes in the volume of brain regions over time must be considered when interpreting changes in receptor binding following an intervention.

Up until now, most exercise studies utilizing PET have investigated glucose metabolism, but also the opiodergic system. Associations between faster gait speed and higher glucose metabolism in prefrontal, cingulate, and parietal areas have been observed in older women 91. Interestingly, in another study, global glucose uptake was found to decrease as exercise intensity increased from 30, 55, and 75% of VO2max 92. Further, the decrease in glucose uptake was substantially

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larger in the dorsal ACC in individuals with higher exercise capacity, leading the authors to hypothesize that trained individuals may have an improved ability to utilize lactate as an additional energy source in the brain. Displacement studies have also been conducted. For instance, two hours of endurance exercise compared to rest increased release of opiodergic peptides in several prefrontal, orbitofrontal, cingulate, temporal, and insular regions 93. Further, this increase in opiodergic peptides was positively associated with euphoria ratings, providing evidence for the opiod theory of “Runner’s high” in human subjects. As will be discussed in more detail in subsequent paragraphs, PET has also been utilized to study the dopaminergic system 94–98.

Dopamine Dopamine (DA), characterized as a neurotransmitter by Arvid Carlsson and colleagues 99, is a neurotransmitter belonging to the catecholamine family. It is primarily synthesized in the SN and VTA, from where dopaminergic innervation of the striatum and cortex originates. There are several distinct dopaminergic pathways that serve specific behavioral functions (Figure II). Processing within these pathways occur in corticostriatal and striatonigral loops 100, connecting distinct cortical and striatal regions via thalamus. These loops can be divided according to function into limbic, associative (or executive), and sensorimotor pathways 100–102. Limbic striatum forms loops with orbitofrontal cortex, medial prefrontal cortex, and ventral anterior cingulate cortex (ACC) and is implicated in mood, motivation, addiction, and memory. Associative striatum forms loops with the dorsolateral prefrontal cortex (dlPFC) and dorsal ACC, and is critical for executive functions. One theory posits that age-related reduction in dopaminergic function and cognitive decline are intricately linked 13. Finally, sensorimotor striatum is connected primarily to the sensory and motor cortices, and is involved in the coordination and control of movement. DA levels in the striatum are elevated both with increasing speed 103,104 and angular posture in running animals 104.

For dopamine to influence neural processes relating to limbic, associative, and sensorimotor functions it must first bind to its’ target receptors, belonging either to the dopamine D1-receptor (D1R) family (D1, D5), or the D2-receptor (D2R) family (D2, D3, D4). Both types are abundant in the basal ganglia, whereas the primary type in the prefrontal cortex is D1R. When dopamine binds to these receptors the striatal and prefrontal cortex appears to have somewhat opposing roles. The D1R and D2R in striatum serve as gatekeepers and are critically involved in the updating of information in working memory 105,106, as well as in shifting current goals 107. In the PFC, D1R’s are involved in stabilizing and maintaining current information in working memory 108. The updating vs. maintenance model of working memory is described in more detail in Figure III.

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Figure II. The organization of basal ganglia and cortical projections. The color schematics are according to function (red = limbic, green = associative, blue = sensory and motor). The striatum not only receives projections from the SN or VTA, but also projects back and is able to influence both SN/VTA as well as distal striatal processes via these loops (see enlargement where limbic input reaches neuron b which in turn projects to dorsal/posterior striatal area). DL-PFC = dorsolateral prefrontal cortex, IC = internal capsule, OMPFC = orbital and medial prefrontal cortex, S = shell of nucleus accumbens, SNc = substantia nigra pars compacta, SNr = substantia nigra pars reticulata, VTA = ventral tegmental area.

From Haber, S. N., Fudge, J. L., & McFarland, N. R. (2000). Striatonigrostriatal pathways in primates form an ascending spiral from the shell to the dorsolateral striatum. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 20(6), 2369–82. Reprinted with permission 100.

Figure III. Gate function of striatum to update information into working memory. In the first scenario, the gate to the cortex is closed and information is maintained in the frontal cortex. When dopamine (DA) from the substantia nigra parc compacta (SNc) activates striatal D2, the indirect ”NoGo” pathway is activated. Inhibitory signals to the globus pallidus externa (GPe) is sent. This releases the inhibtory input from GPe to globus pallidus interna (GPi) and the subthalamic nucleus (STN). The release of inhibotory input from GPe to GPi,

and the excitatory input from STN to GPi increases the inhibitory input to the thalamus. When thalamus is not firing, information in the frontal cortex is maintained and not updated. In the second scenario, the gate is open and information in the cortex is updated. When DA from SNc activates D1, the direct ”Go” pathway inhibits the firing of GPi. Consequently the inhibitory firing on the thalamus is released, and information is updated in the frontal cortex. From Frank, M. J., & O’Reilly, R. C. (2006). A mechanistic account of striatal dopamine function in human cognition: psychopharmacological studies with cabergoline and haloperidol. Behavioral Neuroscience, 120(3), 497–517. © 2006, American Psychological Association. Reprinted with permission.

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Dopamine levels exhibit an inverted-U shape relationship to executive functions, where insufficient as well as excessive levels impair cognitive performance 109. For instance, higher levels of prefrontal dopamine may be advantageous for memory maintenance, but may cause difficulties in cognitive flexibility, and vice versa. Furthermore, Dopamine is critically involved in neuroplasticity, where activation of D1R in both the hippocampus 110 and prefrontal cortex 111 is necessary for the late phases of long-term potentiation (LTP), an important plastic mechanism enabling the brain to adapt and learn. D2R on the other hand is often related to long-term depression, although the specific action of both D1R and D2R could vary slightly at different DA concentrations 112. Finally, dopamine binding to D2R may reduce neural noise by amplifying the signal from the most salient neural ensemble, while silencing neighboring neurons 113. In sum, the dopaminergic neurotransmitter system is vital for the function of several executive and memory processes.

Considering the role of the dopaminergic system in neuroplasticity 110, memory 114–116, executive functions 105,106,109,117, and age-related cognitive decline 13,115,118, improved dopamine function could be one important neurocognitive mechanism explaining the multitude of positive effects observed on cognitive and brain function from aerobic exercise 119,120.

A review of the literature shows that the bulk of research exploring the dopaminergic system in relation to aerobic exercise has been conducted on rodents with access to running-wheels. These studies implicate that several aspects of the dopaminergic system may be altered. For instance, dopamine levels in striatum increases both from acute 103,121 and prolonged periods of wheel running 122,123. This increase has been suggested to depend on calcium being transported from the skeletal muscles via the blood into the brain, where it activates calcium/calmodulin dependent dopamine synthesis 78. Elevated levels of dopamine may not only be explained by increased synthesis, but also by reduced dopamine transporter (DAT) action. DAT is involved in dopamine reuptake in the striatum, and reduced DAT action would consequently allow DA to persist longer in the synaptic cleft. In fact, prolonged periods of running have caused DAT internalization or down-regulation 124,125. Corroborating evidence suggesting reduced DAT activity showed that, although basal synaptic DA levels did not increase from 8 weeks of wheel running, the DA response to an amphetamine challenge was blunted, whereas the DA concentrations remained elevated for a longer period of time 126. Unfortunately, dopamine levels in these animal studies have not been related to any cognitive test, hence it is difficult to translate the functional value of these alterations to a hypothesis linking exercise-induced dopamine increases to improved cognitive functioning in humans.

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Research in humans is still sparse when it comes to exercise and dopamine. The available studies have reported increased peripheral dopamine after an intense anaerobic sprint 127, and with increased physical activity levels over 6 months 128. Finally, the neurocognitive benefits from aerobic exercise varied depending on a genetic polymorphism influencing prefrontal synaptic dopamine levels 129. These studies are clearly indicative of dopamine being involved in physical activity and exercise also in humans, although more direct evidence of increased dopamine in the human brain is lacking, where the only positron emission tomography (PET) study investigating DA release after acute exercise failed to detect increased DA in the striatum 94.

The dopamine receptor, D2R, has also been studied in human samples. Dang et al. found that physical activity (average steps per day measured with pedometers for ten days) reduced the age-related reduction in D2R specifically in the ventral striatum. 95 Further, two interventions reported increased density of D2R in the striatum after eight weeks of endurance training in four Parkinson’s Disease patients 97, and after combined endurance and resistance training in 19 methamphetamine users 96. Despite providing initial evidence, the two studies both had small or fairly small sample sizes and investigated populations with known dopaminergic deficits. Whether the results can be translated to the healthy older population is uncertain. An increase in striatal D2R has been observed in aged mice given access to running-wheels 130,131. Conversely others have found down-regulated D2R in younger mice 122, or no effect in healthy mice but an increase in PD mice 132. This raises an analogous question as with the human exercise-D2R studies, namely whether young, old, and mice with pathology exhibit a different response to exercise.

Although increasing the density of D2R on the cell surface results directly from performing aerobic exercise, indirect effects may also influence D2R density. In one model focusing on the effect of exercise on PD, a considerable benefit from performing aerobic exercise is the induction of several neuroprotective effects on dopaminergic neurons, hence increasing their survival 120. In the long-run, increasing survival of dopaminergic neurons would also preserve dopaminergic receptors located on the post-synaptic cell surface, and hypothetically, dopamine may diffuse and bind to more distally located dopamine receptors to improve function. The previous two human exercise interventions imaging D2R both used the PET ligand [18F]fallypride, which is insensitive to changes in dopamine levels 133,134 compared to [11C]raclopride 135,136.

Macro level effects of exercise Both the hippocampus and PFC are susceptible to atrophy in aging 1,137, are linked to dementia pathology 25, are associated with cognitive decline 3,30, and are

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affected by physical activity 128,138–140 and aerobic fitness 34,33,141,142. A focus on structural changes in these regions as a function of physical activity and aerobic exercise is thus justified in this thesis. Studies investigating gray matter structure in older adults are listed in Table 1 (cross-sectional) and Table 2 (longitudinal). These studies utilize magnetic resonance imaging (MRI) sequences to measure brain structure.

Table I. List of studies investigating brain gray matter structure in humans with cross-sectional or prospective data.

Ref Sample Measures n Age (range) Findings

143 HF F: 2MST B: FS

69 68 (50-85) V and TH in frontal, temporal, parietal, and occipital lobes.

144 HF PA: ActiGraph B: FS

50 68 (50-83) Thalamus and ventral diencephalon, not other subcortical, incl. HPC, caudate.

139 Older PA: SR B: VBM

45 72 (57-86) GM bilateral HPC, paraHPC, SFG, FP, insular cortex, r MFG precentral gyri, l IFG, thalamus.


331 75 Precuneus GM. Uncorrected clusters mainly in parietal and temporal gyri.

6 Young to Old

F: Physical indicators B: FS

308 62 (25-80) PCC and precuneus identified as ROIs from rsfMRI analysis. Larger GM-fitness only in PCC. Accumulated fitness over 10 years showed a trend with PCC GM.

146 Older & AD

PA: SR B: TBM

43 39

79 (69-89) 82 (73-95)

PA 9 years prior to MRI session, not PA the same year as MRI, associated with larger total brain V. No relation to voxels.

147 Older PA: 10y SR B: FS

52 69 (55-79) PA: superior frontal and pericalcarine cortex. High PA moderated age effect on MTL. Not lateral PFC, global GM and WM, HPC, or striatum.

148 Older & early AD

F: VO2peak B: FAST

64 57

73 (6.3) 74 (6.7)

Fitness associated with larger normalized whole brain V, and WM V in early AD, not in non-demented. Fitness not associated with cognition after controlling for age.

149 Older F: VO2max B: VBM

55 67 (55-79) Fitness moderated effect of age on GM loss primarily in prefrontal, superior parietal and temporal cortices, and in anterior and transverse WM tracts.

150 Young to Old

PA: Pedometer B: FIRST

44 48 (23-80) Age x PA did not interact with ventral striatum, caudate, or putamen V.

151 Young & Old

PA: SR B: VBM

95 19-82 PA associated with HPC and paraHPC GM irrespective of age, only midlife-older (>40y) had increased WM in PCC and precuneus.

152 Older F: VO2peak B: FS

86 64 (6) Fitness associated with larger HPC V only in women.

153 HRT women

F: VO2peak B: VBM

54 70 (58-80) Fitness associated with greater GM in left IFG, right IFG/Insula, left PHPCG, and subgenual cortex. Greatest effect in individuals with long HRT duration.

154 Older F: VO2peak B: FIRST

165 67 (59-81) Fitness associated with larger left and right HPC Vs. Left HPC V partially mediated a fitness-spatial memory association.

140 Older PA: Blocks walked B: VBM

299 78 (70-90) PA associated with greater frontal, occipital, and temporal GM Vs, including HPC 9 years later.

138 Older PA: MET F: Max watt or lactate B: VBM

75 61 (50-78) PA associated with greater GM Vs in prefrontal, cingulate, occipito-temporal, and cerebellar regions. PA, not fitness, was related to memory encoding.

142 Young & Old

F: VO2max B: VBM

20 40

23 (20-28) 72 (65-81)

Fitness: medial-temporal, anterior parietal, and inferior frontal GM, not WM.

155 Older PA: SR B: FSL+in-house

638-691

73 (0.7) PA measured 3 years prior to the imaging session. PA: GM, less atrophy, and less WM lesions.

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156 Older PA: 10y SR B: FS

44 44

74 71

PA: not HPC V. PA-stress interaction: lifetime stress more severe effect on HPC V and memory when low exercise.

157 Older PA: 2w SR into kcal/week B: TBM

226 78 (4) PA: posterior corona radiata WM tract. BMI, even after controlling for physical activity and education, with V in frontal, temporal, parietal, and occipital regions.

158 Older & early AD

F: VO2peak B: VBM

67 71

73 (6) 74 (6)

Fitness: no GM or WM in non-demented older. Uncorrected IFG GM, occipital, lentiform, lingual WM. Fitness in early AD: postcentral gyrus WM, uncorrected GM and WM in frontal, temporal, parietal, and occipital regions. Small-V correction on the MTL revealed positive association to WM in HPC and GM in PHPCG.

159 Young to Old

PA: SR B: FS & VBM

834 50 (25-83) PA was measured 5.9 years prior to the imaging session. FS: PA during sports showed uncorrected positive, and PA during work negative, associations with GM in HPC, PFC, and temporal lobe. PA during sports associated with reduced global WM. VBM: No PA dimensions (sports, leisure, work) and whole-brain. VBM-ROI: PA during sports and ACC GM, not HPC, PHPCG, caudate, putamen, pallidum, PFC, temporal cortex.

160 CAD F: VO2peak B: VBM

30 65 (7) Fitness: GM in l caudate, putamen, temporal pole, in r HPC, putamen, and planum temporale. Putamen GM at baseline predicted change in fitness over 6 months.

161 MCI PA: ActiGraph B: MRI

310 71 (65-94) Moderate-intensity PA, not light-intensity or total PA: HPC. Moderate PA had an indirect effect on memory via HPC.

162 Older women

Dancers vs. non-dancers B: VBM

28 29

73 (4) 73 (4)

No difference dancers/non-dancers in GM. Uncorrected VBM: dancers GM in medial, middle, and superior frontal gyri. ROI analysis on HPC revealed no differences.

163 Older PA: SR B: VBM/SEM

414 69 (60-87) Inadequate PA vs. fitness-enhancing PA: lateral PFC, orbitofrontal, inferior temporal, primary visual cortex, inferior parietal lobule, HPC, caudate, and putamen. No other ROI was tested.

164 Older F: VO2peak B: FS

68 70 (61-75) Fitness: cortical and total GM, but only in men. No relation between fitness and HPC, amygdala, caudate, putamen, or brainstem.

165 Older active vs. health edu.

PA: 2y SR B: fully deformable algorithm

20 10

81 (4) 82 (3)

No difference in total GM or WM hyperintensities between individuals that had remained physically active 2 years after a PA intervention and individuals who had remained sedentary after a health education intervention. However, functional activations in the dorsolateral prefrontal cortex were higher in the PA group during digit-symbol substitution performance.

166 Older PA: 21y earlier + active vs sedentary B: VBM

32 43

73 (4) 72 (4)

Midlife PA (21 years earlier): total cerebral GM. PA at old age: MFG GM, and uncorrected analyses showed additional clusters in insula, parietal, precentral, temporal, and occipital areas.


68 ~73 (5) PA: no GM, neither considered alone, nor in interaction with APOE4 risk.

168 Young to Old

PA: SR B: FS + PCA

331 NA (19-79) Brain age difference from chronological age from PA: caudal ACC, amygdala, NACC, rostral middle frontal, rostral ACC and PCC, paracentral, inferior and transverse temporal, r insular regions precuneus, left MTG.

169 Older F: VO2peak B: FIRST

158 67 (59-81) Fitness, not PA, was positively associated with HPC, spatial memory, and frequency of forgetting.

170 Amnestic MCI

F: VO2max B: VBM

22 69 (57-80) Fitness: GM in l inferior parietal, superior and middle frontal, right medial, superior and orbitofrontal regions. No covariates, e.g. age.

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171 Young & Old

F: VO2max B: VBM

9 24

27 (21-35) 74 (~5)

Older athletes (>15y endurance training): GM r superior parietal lobule/secondary sensorimotor cortex, cuneus, and in CER, WM in precuneus, inferior temporal subgyral and occipital subgyral. VO2max: GM precuneus, WM occipital and frontal lobes in the athlete group..

172 Older PA: SR MET & Accel. B: Subfields

90 67 (6.0) Total walking activity and low-intensity walking: shape of subiculum, but only in women.

173 Older F: VO2max B: FIRST

179 67 (59-81) Fitness: caudate V, not putamen or pallidum. Caudate partially mediated positive association between fitness and task-switching accuracy.

174 Young to Old

PA: 7d SR MET B: FS

203 54 (23-88) PA associated with larger TH in left anterior prefrontal cortex.

141 Older F: VO2max B: VBM

142 66 (58-81) Fitness: GM in prefrontal, motor, cingulate, anterior parietal, and temporal areas. r IFG and PCG mediated fitness-Stroop association. MFG and PCG mediated fitness-spatial working memory association.

175 Young & Old

F: VO2peak B: FS

32 29

21 (18-31) 64 (55-82)

Fitness in older: TH in supramarginal/inferior parietal, precentral, supramarginal, and middle temporal areas, and in precuneus. Fitness associated with thinner cortex in young adults.

176 Midlife to Old

F: VO2max B: FS + B: Voyager

32 55 (45-73) Fitness: TH in insula, medial prefrontal, left precentral, and right postcentral ROIs (not HPC). Additional differences between endurance athletes (>15y) and healthy adults.

177 Older PA: SR B: FIRST

280 73 (6) Habitual compared to non- exercisers had larger NACC and left HPC s.

178 Older F: Cooper PA: SR B: VBM

29 68 (60-85) Fitness-GM in l CER. Fitness-age interaction in temporo-cerebellar, r perisylvian, l pre- post-central.. PA-GM in temporo-occipital cluster, a left perisylvian cluster, and a frontal cluster (FP, SFG, paracingulate). No a priori ROI analyses GM-fitness in ACC, IFG, MFG, FP, angular and supramarginal gyri, and HPC. Only PA and left HPC.

Notes: Unless stated otherwise, reported brain regions are positively associated with fitness or physical activity. Sample: AD = Alzheimer’s disease, CAD = coronary artery disease, HF = heart failure, HRT = hormone replacement therapy, MCI = mild cognitive impairment, PD = Parkinson’s disease. Measures: Accel. = accelerometer, FAST = segmentation in FSL, FIRST = segmentation in FSL. FS = Freesurfer, FSL = https://fsl.fmrib.ox.ac.uk/, MET = metabolic equivalent, PCA = principal components analysis, rsfMRI = resting-state functional magnetic resonance imaging, SEM = structural equation modelling, SR = self-report, TBM = tensor based morphometry, VBM = voxel-based morphometry. Findings: ACC = anterior cingulate cortex, CER = cerebellum, FP = frontal pole, GM = gray matter, HPC = hippocampus, IFG = inferior frontal gyrus, l = left, MFG = middle frontal gyrus, MTG = middle temporal gyrus, MTL = medial temporal lobe, NACC = nucleus accumbens PCC = posterior cingulate cortex, PCG = precentral gyrus, PMC = premotor cortex, r = right, ROI = region of interest, SFG = superior frontal gyrus, SMA = supplementary motor area, STL = superior temporal lobe, TH = thickness, V = volume, WM = white matter.

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Table II. List of studies investigating gray matter structure in humans using longitudinal data.

Ref Type Sample Measures n Age (range) Findings 179 L Older PA: ActiGraph

B: in-house 352 Approx.

79 (4) PA at year 5: GM and WM, and 5-year change in GM and WM. Sedentary behavior predicted WM atrophy.

34 I Older E: 6m Aero vs. control B: VBM

59 67 67 (60-79)

Aero compared to controls: GM in dorsal ACC, SMA, r IFG and MFG, l STL, anterior WM.

33 I

Older E: 12m Aero vs. control B: FIRST

60 60

68 66 (55-80)

Aero and VO2max: anterior HPC V bilaterally. Spatial memory~ absolute VO2max and increased V.

180 I Older E: 6m Aero vs control B: FS+FIRST

52 66 (59-74) Improved fitness: reduced HPC mean diffusivity. Changes in HPC V explained by mean diffusivity, not fitness.

181 I MCI E: 6m Combined Aero vs. control B: VBM

13 9

70 (60-80) 70 (61-76)

Aero exercise compared to controls increased GM in middle and superior frontal cortex, angular cortex, precuneus, and PCC.

182 I Older E: 3m Aero vs. control B: Manual segmentation

21 19

69 (4) 68 (4)

Only Aero: HPC blood flow and blood V. Fitness (VO2VAT) associated with changes to HPC blood flow and blood V, HPC head V. HPC head V accounted for by perfusion.

183 I Older E: 12m Aero, coordination, or control B: Manual

49 69 (62-79) Only motor fitness, not VO2peak: HPC V at baseline. Both Aero and coordination: HPC V. Aero favored l HPC, coordination favored r HPC. Changes in HPC V explained by VO2peak.

184 I Older and MCI

E: 12w walking B: FS

16 14

76 (61-85) 79 (65-88)

Improved fitness: TH in large parts of the frontal cortex (incl. insula), temporal cortex, and parietal cortex, and in lateral occipital cortex. Note: Unclear methods and reporting of results limits the impact of this study.

128 I Midlife-Older

E: 6m Medium vs. low intensity Aero, Control B: VBM

20 21 21

60 63 58 (50-72)

No difference in GM, nor between changes in the lactate step test and GM. Increased PA (MET): GM V in left prefrontal, cingulate, parietal, and occipital cortex

185 I Older & PD

E: 6w balance B: VBM

16 20

65 (7) 63 (7)

Across 4 MRI sessions, behavior in PD correlated with GM changes in ACC, right anterior precuneus, and left ventral PMC, MTG, and IPC. In controls, behavior related to left HPC. Comparing PD to controls, GM in right CER.

186 I Older E: 6m Res, cognition, res+cog, sham B: manual

16 20 21 22

Approx. 70 (7)

Whole-brain cortical TH non-significant. A priori ROI (HPC and PCC): PCC TH increased from Res training compared to non-Res training, and partially mediated improved global cognition.

187 I MCI E: 6m Aero, Res, or control B: FIRST

10 8 11

70-80 Aero compared to controls: HPC V. Change in HPC V negatively associated with verbal memory.

Notes. Unless stated otherwise, reported brain regions are positively associated with fitness or physical activity. Type: I = intervention, L = longitudinal. Sample: MCI = mild cognitive impairment, PD = Parkinson’s disease. Measures: Aero = aerobic exercise, Cog = cognition, FIRST = segmentation in FSL. FS = Freesurfer, FSL = https://fsl.fmrib.ox.ac.uk/, Res = resistance exercise, VBM = voxel-based morphometry. Findings: GM = gray matter, WM = white matter, ACC = anterior cingulate cortex, CER = cerebellum, HPC = hippocampus, IFG = inferior frontal gyrus, IPC = inferior parietal cortex, l = left, MFG = middle frontal gyrus, MRI = magnetic resonance imaging, MTG = middle temporal gyrus, PCC = posterior cingulate cortex, PMC = premotor cortex, r = right, ROI = region of interest, SMA = supplementary motor area, STL = superior temporal lobe, TH = thickness, V = volume.

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Hippocampus Beginning in the 90’s, the pioneering research of Henriette van Praag and colleagues performed on rodents revealed that wheel-running increased neurogenesis in the dentate gyrus of hippocampus 72,188. It was later revealed that running also increased long-term potentiation in the hippocampus, a critical mechanism for memory formation, along with elevated levels of BDNF in the dentate gyrus 189. Some of the more robust findings of aerobic exercise-induced neuroplasticity relates to the hippocampus, and is evident in animals 72,188, young adults 190,191, old adults 33,182, and neuropsychiatric populations 192. Understanding the processes by which aerobic exercise influences the structure and function of the hippocampus has also been the focus of recent reviews 43,193

In human populations, several studies implicate the hippocampus as a target for physical activity-related influences. For instance, older adults with higher aerobic fitness levels had larger hippocampal volumes 154, and a follow-up study showed increased hippocampal volume after 12 months of aerobic exercise compared to active control exercise 33. In another pioneering study, combining animal and human research, Pereira and colleagues found that increased cerebral blood volume (CBV), an index of angiogenesis, increased in the dentate gyrus after 12 weeks of exercise in healthy adults, and that CBV increased proportionally with improved aerobic fitness (VO2max) 190. In the young mice, they found that increased CBV in the dentate gyrus was correlated with increased neurogenesis, showing that angiogenesis occurs together with neurogenesis. In 2015, another group found that aerobic fitness improvements over 12 weeks increased hippocampal perfusion together with increased hippocampal head volume 182. Interestingly, increased perfusion, rather than volume, explained improvements in spatial object recall and recognition. Conversely, in young to middle-aged adults, six weeks aerobic exercise was sufficient for increasing the volume of the hippocampus, but this increase was explained by myelin rather than vascular changes 191.

Contemporary views on the effect of aerobic exercise on plasticity of hippocampal structure and function recognize that a number of factors are involved in exercise-induced neuroplasticity 37,43, e.g. changes in gene expression, increased levels of growth factors (e.g. BDNF, IGF-1, VEGF), neurogenesis, angiogenesis, changes to synaptic elements and dendritic structure, as well as cognitive stimulation during periods of exercise 194. However, additional studies relating for example changes in aerobic fitness and different memory processes to the hippocampus are needed to better understand dose-response relationships, individual differences in the response to exercise, and the type of memory processes influenced by exercise 43.

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Cerebral cortex In comparison to the hippocampus, the cerebral cortex has originally received considerably less attention in the exercise literature. This is peculiar, considering the pivotal role of the cerebral cortex, frontal areas in particular, for higher-order cognition 27,28,195. Higher-order processes, including executive functions, are more difficult to study in animals, compared to e.g. hippocampus-dependent memory processes 37, perhaps explaining the initial focus on the hippocampus. Nonetheless, basing their hypothesis on the supposed association between age-related PFC thinning and cognitive decline 30, and between aerobic exercise and improved executive task performance 62,73, Colcombe and colleagues used voxel-based morphometry (VBM) to study the association between aerobic fitness and cortical gray and white matter 196. Specifically, older adults (55-79y) with higher aerobic fitness had higher gray matter densities in frontal, parietal, and temporal regions, and white matter densities in tracts connecting the frontal and parietal cortices. This was the first evidence in humans showing that staying aerobically fit at an older age could influence brain health in distributed regions implicated in cognitive control. In a follow-up experiment, the volume of the anterior cingulate cortex (ACC), superior temporal lobe, and inferior frontal gyrus (IFG) increased in older adults performing 6 months of aerobic exercise, compared to stretching and toning controls, providing the first evidence that aerobic exercise may also revert typical age-related thinning of cortical areas 34.

Since those early studies of exercise-related influences on the cerebral cortex, a number of studies have now been conducted, approaching the question of exercise-induced plasticity using task-related blood-oxygenation-level dependent (BOLD) responses 67,197,198, resting-state connectivity 199–202, white matter integrity 203–205, or perfusion 6,202. The focus here will be on structural imaging primarily reflecting gray matter. On this note, a recent meta-analysis claimed that 80% of the human brain is influenced by physical activity. However, their analyses were not based on statistical tests, but on an overlay of every reported brain region to a template, resulting in an 80% coverage of the brain 206.

Notwithstanding, cross-sectional investigations have indeed implicated that physical activity and aerobic fitness may affect large parts of the cortex (Table I). For instance, total levels of physical activity were associated with larger volumes in prefrontal, cingulate, occipito-temporal regions, and cerebellum 138. Unfortunately, although aerobic fitness data was available, associations between voxel-based morphometry (VBM) and aerobic fitness were not reported in that study. In another study however 141, VO2max was related to larger prefrontal volumes (the IFG and middle frontal gyrus, MFG), as well as the precentral gyrus. They also found that regions within dlPFC and precentral gyrus mediated the relationships between VO2max and Stroop interference, and between VO2max and

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spatial working memory, suggesting a mechanistic link between fitness, prefrontal brain structure, and cognitive performance.

In addition to Colcombe et al. (2006), only one other intervention comparing aerobic exercise to control training in healthy older adults has studied cortical volume changes from aerobic exercise 128. Here, no differences between 6 months moderate-intensity aerobic, low-intensity aerobic (similar to stretching and toning training), and passive controls in cortical gray matter volumes could be detected 128. However, in that same study, total physical activity changes over 6 months, as indexed by self-report questionnaires and conversion of activities into metabolic equivalent (MET) scores, were positively associated with changes in gray matter volumes in prefrontal, cingulate, parietal, and occipital areas, primarily in the left hemisphere. This pattern of result suggests, rather problematically from the researcher’s perspective, that changes in everyday physical behavior may be an even stronger predictor of change than an intervention manipulating exercise behavior 42.

Collectively, both physical activity and aerobic fitness are consistently associated with brain health in prefrontal, cingulate, inferior parietal, and medial temporal regions. Hence, that being physically active is related to larger brain volumes, in particular in prefrontal and medial temporal regions, could indeed explain why risk for cognitive decline 38,39, and dementia 38,40, is reduced as a result of being physically active. Conversely, the two previous interventions having studied morphological changes in cortical gray matter regions provided differing conclusions 128,34. Additional studies are clearly needed to better understand the timing whereby exercise leads to detectable structural changes also in the cortex.

Striatum Aside from the hippocampus and cortex, the striatum is a key region of interest for several reasons. The striatum exhibits hub-like connectivity to the cortex 100,102,207, is important for serving executive processes 105,109, and has rich innervation of dopamine 100. A few studies have studied the striatum in relation to physical activity and exercise, where cross-sectional evidence indicates that both physical activity 163 and aerobic fitness 160,164,173 may influence striatal volumes, the caudate in particular. However, considering the sheer number of studies utilizing VBM (Table I), GM in the striatum does not stand out as a typical outcome influenced by physical activity or fitness. The only intervention analyzing the striatum on the level of region of interest (ROI) found no change in caudate volume after 6 or 12 months of aerobic compared to stretching and toning exercise 33. Nevertheless, one study showed that aerobic fitness positively influenced task-switching performance via caudate and nucleus accumbens volumes 173. Despite some findings suggesting a direct influence on striatal volumes from aerobic fitness 160,164,173, plasticity on the cellular or molecular scale,

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e.g. altered neuroreceptor levels 130,131, appear more likely than detecting macro scale changes detectable in vivo with MRI. In fact, in a sample of 44 individuals (23-80y), age interacted with physical activity such that older active individuals had more D2R compared to less active in ventral striatum, whereas no interaction appeared for volume 95.

Active vs. passive models Theories explaining the effects of aerobic exercise on cognitive and brain function could be viewed along a passive-active continuum. For instance, some theories are emphasizing the active components of exercise, e.g. the immediate potential for newly born cells in hippocampus to be incorporated into functional networks in the presence of cognitive stimulation 194. Others can be viewed as more passive, e.g. postulating that cognitive functioning is maintained by preventing the deterioration of dopaminergic neurons from oxidative stress 120. There are no clear boundaries between the theories emphasizing the passive or active components of exercise-induced effects on the brain. Yet, the division can be useful to stress the fact that exercise induces both immediate plastic resources, utilized to improve function (active), and neuroprotective effects preventing degeneration and decline over time (passive).

One active theory focusing specifically on physical exercise to improve cognitive function, the neurogenic reserve hypothesis 194, was derived from animal studies focusing on the hippocampus. The theory postulates that plastic resources in the hippocampus, including neurogenesis, increases from aerobic exercise. Newly born neurons survive and are incorporated into functional brain networks if cognitive stimulation is present, such as in an enriched environment 208. This increased reserve may prevent normal loss of memory function occurring at an old age. Although Kempermann focused on the hippocampus, the same logic could possibly be applied to the brain in general considering the similar plastic changes observed in animals or humans in non-hippocampal regions in relation to physical exercise 37. including altered levels of neural growth factors 82 and expression of plasticity-related genes 209, angiogenesis 210, synaptogenesis 130,131, which should consequently alter neural processing and functional brain activations to improve behavior 197.

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Aims The overarching aim for this thesis was to investigate the neurocognitive mechanisms of aerobic fitness and exercise in older adults using data from a 6 months aerobic exercise intervention. The specific aims for the three studies included in this thesis are to:

i. Investigate the effects of aerobic exercise and fitness on cognition, gray matter volume in hippocampus, and cortical thickness in prefrontal and anterior cingulate regions.

ii. Investigate the effects of longitudinal changes in the striatal volume and receptor density on the PET signal in striatum.

iii. To test the dopamine-exercise hypothesis for the first time in healthy older adults by investigating the effects of aerobic exercise on the dopaminergic system in the striatum and its relation to working memory updating.

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Materials and methods

Study population From a local newspaper advertisement (Västerbottens Kuriren) sixty older adults (64-78y) were recruited and randomized into performing six months of aerobic exercise, or active control training consisting of stretching, toning, and balance exercises (See Table III for sample characteristics). These individuals were screened with a physical activity questionnaire and were also interviewed to ensure that they were not already exercising vigorously. For instance, no subject included reported jogging or gym activity. This was to ensure that our training protocol could positively influence their physical fitness, and not act as a substitute for previous exercise behavior. Individuals were excluded if: already exercising, medicated with pharmacological agents known to influence dopamine, suffered a neurological disorder, previously had surgery in the brain or heart, were MRI incompatible, or scored below 27 on the mini-mental state examination (MMSE). After completing baseline assessments, each subject was randomized to perform either aerobic or control exercise. Out of the 60 subjects, one subject in the aerobic exercise group had to discontinue training due to a foot injury, and one subject in the control group due to a knee injury.

Both prior to and immediately following the exercise intervention, study participants went through six days of testing. One day with fitness measurements, three days of cognition, and two days of brain imaging (See Figure IV for the complete study protocol). In this thesis, data from aerobic fitness assessments, cognitive assessments, T1 MRI, and [11C]raclopride PET were analyzed. Diffusion tensor imaging (DTI) was used only to functionally segment the striatum.

Table III. Sample characteristics (Mean±SD).

Aerobic (n = 29)

Control (n = 29) Statistics

Females (%) 52 59 χ2(1) = .279, p = .597

Age (years) 68.40±2.54 68.97±2.91 t(56) = .789, p = .434

Education (years) 13.69±3.49 13.69±4.68 t(56) = 0.0, p = 1.00

BMI 25.98 ±3.33 26.73±3.50 t(56) = -0.809, p = .422

MMSE 28.96±1.16 29.46±0.64 t(40) = 1.977, p = .055

Attendance (%) 85.13±9.16 82.11±10.43 t(55) = 1.227, p = .225

BMI = Body Mass Index, MMSE = Mini-mental state examination

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Figure IV. Design of the PHIBRA project. The second data collection was identical to the first. Only the words or numbers for the episodic memory tasks and backward digitspan were different. The cognitive tests were administered in the exact order as in the boxes representing cognitive testing.

Exercise intervention The intervention included 45-60 minutes supervised group-exercise sessions, three days per week, for six months. Participants in the aerobic group performed walking, jogging, cycling, and cross-training. Heart rate (HR) was continuously monitored during training, and the goal was to exercise at 80% of the estimated maximum heart rate. Participants in the active control group performed circuit training, two laps on ten stations. These stations involved a mix of balance, strength, and stretching exercises at various difficulties and loads.

Aerobic fitness Aerobic fitness was estimated using a cycle ergometer test. Females started at 30W and males at 40W. Every three minutes the resistance increased by 30W.

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Oxygen uptake during the test, VO2 = O2mL/kg*min, was estimated by measuring expired air. The test was terminated at volitional exhaustion or when the self-perceived exertion 211 was rated 15 or higher at baseline, and 17 or higher at follow-up. VO2peak, i.e. aerobic fitness, was the highest VO2 achieved during the test.

Cognitive test battery On three separate days, 21 different tasks were performed, aiming to measure several cognitive constructs; episodic memory (EM), processing speed (PS), working-memory updating (UPD), task-switching (TS), executive functioning (EF), reasoning (RS), and visuospatial ability (VS). These tasks are described in detail in Study I.

In short, the EM tasks included word recognition 212, free recall 213, and paired associates 214; PS included trail-making 2 and 3 215, a computerized version of digit-symbol substitution task from the Wechsler Adult Intelligence Scale-Revised (WAIS-R), and letter comparison; UPD included Letter Memory 216, 2-back 217, and Keep Track 29; TS included trail-making 4 215, Odd-Even 218,219, and Local-Global 220; EF included Automated Operation Span 221, Flanker Task 222, and a computerized version of Backward Digitspan (WAIS-R); RS included Raven’s Progressive Matrices and Letter Sets, and finally; VS included Form Boards, Paper Folding, and Spatial Relations 223,224. The order of administration is shown in Figure IV.

Magnetic resonance imaging MRI is an imaging technique utilizing strong magnetic fields to acquire information about tissue characteristics. In Study I, a 3T General Electrics (GE) MR750 scanner was used. Specifically, structural information on gray matter, white matter, and cerebrospinal fluid volumes in the brain were acquired with a T1 sequence. The T1 images were then processed with the longitudinal Freesurfer segmentation pipeline 225,226. In the longitudinal pipeline, subject-specific averages of segmented time points are produced. Each time point is then segmented a second time, now using information also from the average to reduce the impact of timepoint-specific noise. Hippocampus volume (mm3), and, as defined by 227 using the Destrieux atlas 228, the cortical thickness (mm) of dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (vlPFC), and anterior cingulate cortex (ACC) was obtained. The thickness measure reflects not only cell bodies, but also synapses, dendritic branches, myelin, blood vessels, and glial cells 229.

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To produce functional striatal ROIs used in Study III, another MRI sequence was used for mapping fiber pathways connecting the striatum to the frontal cortex, namely diffusion tensor imaging (DTI). The striatum is connected to specific cortical regions depending on function. The limbic striatum is connected to the orbitofrontal PFC and ventral ACC and is implicated in limbic functions, e.g. motivation, reward, or addiction. The associative striatum is connected to the dlPFC, vlPFC, and dorsal ACC and is involved in the updating of information to working memory or shifting goal-sets. The sensorimotor striatum is connected to the sensory and motor cortices and is important for movement and eye-saccades. The result of the functional segmentation is shown in Figure V.

In DTI, information about the diffusion of water in tissue is acquired 230,231. The logic is that water diffuses differently depending on the surrounding tissue characteristics. Along axons for example, which are myelinated, the water will have a preferred direction along the axon due to the hydrophobic properties of myelin. Hence, it is possible to reconstruct and visualize structural connections between brain regions, large fiber bundles in particular. Using DTI and the fsl software (https://www.fmrib.ox.ac.uk/fsl), probabilistic tractography was performed to segment striatum into limbic, associative, and sensorimotor functional ROIs, as described previously 102. For each striatal voxel, 5000 fibers were tracked 232, and terminated when they reached one of the corresponding cortical target masks, i.e. limbic (mainly orbitofrontal cortex), associative (mainly dorso- and ventrolateral prefrontal cortex), or motor (mainly motor cortex and supplementary motor area) cortical targets 102. Firstly, on the subject level, each voxel in striatum was assigned to the cortical target to which it had the highest number of terminating fibers. Finally, a PHIBRA specific template of the functional organization of striatum was produced by assigning each striatal voxel on the group level to the ROI to which that voxel had the highest number of subjects.

Limbic striatumAssociative striatumSensorimotor striatum

Figure V. DTI-based functional segmenation of striatum into limbic, associative, and sensorimotor striatum.

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Positron emission tomography In PET, to image a process in the living human brain, a molecule with the biological function we wish to study is labelled with a radionuclide, which is referred to as a radioligand or tracer. When this tracer decays, it will emit a positron. When the positron collides with an electron, two gamma rays will be emitted in opposite directions. In the PET scanner, the pair of gamma rays is detected, so called coincidence events. A three-dimensional volume can finally be reconstructed, using information on all detected coincidences.

The partial volume effect When imaging tissue with PET, the limited spatial resolution of the PET scanner causes something known as the partial volume effect (PVE). Tissue concentrations in regions with high uptake spill over to surrounding tissue with low uptake, and vice versa. The result of the PVE is that the estimated signal is underestimated in any ROI with higher uptake than its’ surrounding tissue. The size of the PVE is quantified with the recovery coefficient (RC) index of a given region, i.e. the ratio of estimated signal to the true signal. A consequence of the resolution dependency of the PVE is that the PVE becomes larger with smaller objects. This is exactly what is expected when imaging the striatum in older adults longitudinally 90. Consequently, when estimating individuals’ longitudinal changes in BPND using PET, the estimates could be biased, depending on the presence of change in volume. This can for instance be problematic when seeking to determine the rate of decline in D2R using longitudinal data 233, where the rate of decline would be overestimated due to reduced brain volumes. It could also be problematic when comparing different groups or individuals over time if groups or individuals differ from one another in terms of changes in brain volume, e.g. 234.

Several recent technological advancements have improved image resolution or in other ways reduced the effect of PVE. Development and use of ordered-subsets maximization (OSEM) algorithms with resolution modeling for reconstructing PET data has also improved image resolution considerably 235,236. There are however, potential problems with these algorithms. For example, studies have shown that the PET signal in regions with low tracer uptake may be overestimated 237,238 may be introduced, in particular in regions with low uptake where and overestimation of the signal in such regions. A third niche for development is anatomically based partial volume correction methods. These generally require high-resolution anatomical scans from MRI, and flaws in the anatomical segmentation may introduce substantial artifacts 239,240. In addition, to be optimal, anatomically based methods generally assume homogenous binding

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within anatomically distinct regions. This assumption is probably violated when imaging D2R in the striatum considering observations of rostrocaudal D2R binding gradients within the striatum 241. Even if there is no immediate solution to the PVE problem, knowledge about the magnitude of the PVE, and other potential biases that may arise should be considered when interpreting PET data studied over time, as in PHIBRA. Hence, in Study II, we designed an anthropomorphic striatum brain phantom with the goal to simulate longitudinal changes in the volume and/or BPND of the striatum. Both the volume of the striatum and its’ levels of BPND could be controlled, and the performance of different reconstruction algorithms were compared regarding PVE and BPND -dependent bias. In both Study II and III, the GE Discovery 690 PET/CT was used for acquiring data.

[11C]raclopride In Study III, the radioligand [11C]raclopride was used and which binds specifically to the D2-family of dopamine receptors 86. [11C]raclopride was administered intra-venously. The radioactivity concentration is measured by the PET scanner in units of Bq/ml and is higher in areas with D2R, compared to areas lacking D2R. Nevertheless, there will be [11C]raclopride also in regions without D2R and the distribution of tracer in different regions can be described with compartmental models (Figure VI). For example, once the radioligand is injected, some will be located in plasma, concentration Cplasma, or C0, whereas some will enter tissue, Ctissue. Entering the Ctissue with the transfer rate K1, the ligand will be free, C1. Some ligand will be transferred from a free, C1, to a specifically bound state, C2, via k3. The ligand will also return to Cplasma via k2, and in the case of reversible ligands like [11C]raclopride, ligand will also dissociate from C2 via k4.

Figure VI. Distribution of ligand in tissue and plasma. Tissue in a reference region will contain only C1, whereas target regions with specific binding of tracer contain both C1 and C2.

Reference tissue methods 242 are used to calculate the non-displaceable binding potential (BPND). In Study III, cerebellum gray matter was used as reference tissue, making the assumption that cerebellum has the same characteristics as striatum, except for the D2R receptors in striatum. In a simplified scenario where equilibrium between compartments apply, it is sufficient to subtract the concentration in cerebellum from the concentration in striatum, and dividing by the concentration in cerebellum. This simple logic applies to studies using a bolus

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plus constant infusion protocol 219,243, and applies also in Study II when there is no transfer of radioligand between compartments in the brain phantom. When using a single bolus injection, it is necessary to estimate the transfer constants between compartments. In Study III, the graphical analysis model proposed by Logan (2000) was utilized, using cerebellum as a reference region. In the Logan plot, the distribution volume ratio DVR can be calculated once binding and transfer conditions have reached an equilibrium. BPND is then provided by BPND = DVR - 1.

Statistical analyses All analyses were performed using the R statistical software (http://www.R-project.org). In Study I, confirmatory factor analyses (CFA) were performed using the lavaan structural equation modelling (SEM) package 244 to confirm that each task from the cognitive battery loaded on the intended cognitive component and that the structuring of tasks showed a good fit. In one set of models, the EM, PS, UPD, TS, and EF tasks were included. Based on the outcome of the CFA, a unit-weighted domain general ‘Cognitive score’ composite was computed, including the EM, PS, UPD, and EF tasks. In a second set of models, the RS and VS tasks were analyzed to test whether they formed a unitary, or two separate constructs.

To evaluate the neural and cognitive effects of 6 months aerobic exercise, compared to active control training, pre- to post-intervention changes in cognitive performance, cortical thickness (dlPFC, vlPFC, and ACC), and hippocampus volume were compared using Analysis of Covariance (ANCOVA), controlling for age, sex, and education. In addition, individual differences analyses were conducted using linear regressions, including baseline values of fitness (VO2peak) and brain structure (cortical thickness in dlPFC, vlPFC, ACC, and hippocampal volume). The same set of analyses were performed substituting aerobic fitness for ‘Cognitive score’, EF, and EM respectively. The same set of analyzes were also performed on changes in fitness, brain structure, and cognition. All analyzes were performed with and without controlling for age, sex, and education.

In Study II, linear regressions were used to investigate the size of the partial volume effect (PVE) in the striatum, as well as potential bias arising from variations in BPND. The size ratios between left and right striata were modelled on the corresponding RC ratio. The levels of BPND for each striata were modelled on the corresponding RC.

In Study III, the groups were compared with respect to changes in BPND in the striatum, and in the DTI-derived functional striatal ROIs, using ANCOVA, controlling for age, sex, and education. Individual differences analyses were

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conducted using partial correlations, modelling aerobic fitness at baseline, and 6 months change in fitness, on the striatal ROIs, controlling for age, sex, education, and the volume of the corresponding ROI. In addition, due to the complex functionality of the D2R system in the striatum, it has been recommended that voxelwise analyses be conducted alongside ROI analyses 245. To this end, Statistical Parametric Mapping (SPM) 12 was used, using an uncorrected threshold of p < 0.005, and a cluster size, k > 20.

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Results

Study I Both groups improved their aerobic fitness, but the aerobic group improved more as indicated by a significant group x time interaction (Figure VII). Cognitive performance in several cognitive domains improved substantially following the 6-month intervention. Improvements for the aerobic group, in the order of largest to smallest effect size, were observed on the domain general ‘Cognitive score’ composite, executive function, processing speed, visuospatial ability, and working memory updating. The control group improved on executive function, processing speed, and ‘Cognitive score’. Rather than domain specific, the effect of aerobic exercise was found to be domain general, as indicated by a significant group by time interaction on the ‘Cognitive score’ composite favoring the aerobic group (Figure VIII). There were no group differences on changes in cortical thickness or hippocampus volume.

Individual differences analyses with respect to aerobic fitness (VO2peak), brain structure, and cognition revealed several important associations. Baseline aerobic fitness was associated specifically with larger dlPFC thickness, and a trend also for vlPFC, but not ACC or hippocampus volume (Figure IX). Individuals with larger thickness in dlPFC and vlPFC had a higher ‘Cognitive score’, and individuals with larger hippocampus volume had better episodic memory. Aerobic fitness and cognition were unrelated, and no instances of indirect mediation from aerobic fitness to ‘Cognitive score’ or episodic memory via the corresponding brain region could be detected (analyses not published in Study I).

Following the intervention, relatively larger improvements in aerobic fitness were associated with increased volume of the hippocampus specifically, not to cortical thickness (Figure X). However, change in hippocampus volume was unrelated to change in episodic memory. Finally, changes in ‘Cognitive score’ and dlPFC thickness were positively associated, but only for the aerobic group.

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Figure VII. Baseline and post-intervention measures of aerobic fitness (VO2peak). * p < .05. © 246

Figure VIII. Changes in cognition over time. The y-axes show the standardized z value for the unit-weighted cognitive constructs. The ‘Cognitive Score’ was derived from the episodic memory, processing speed, updating, and executive function tasks. Significance bars are derived from repeated measures analysis of variance between groups. © 246

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Figure IX. Associations between cortical thickness, aerobic fitness, and ‘Cognitive score’. Aerobic fitness was positively associated with dlPFC thickness, r = 0.28. ‘Cognitive score’ was positively associated with thickness in dlPFC, r = 0.33, and vlPFC, r = 0.33. © 246

Figure X. Associations between changes in aerobic fitness, cortical thickness and hippocampus volume. Only the association between changes in aerobic fitness and hippocampus was significant, r = 0.39. © 246

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Study II Several important findings that may aid methodological decisions and interpretations of data in longitudinal PET imaging of the brain were established by using a 3D printed striatum phantom (Figure XI).

Reconstructing data with a high-resolution ordered-subsets maximization algorithm (OSEM) with resolution modelling of the point-spread function (PSF) provided the most accurate data.

The size of the PVE, leading to artificially reduced estimates of BPND when the volume of a structure decreases over time, was lowest using OSEM+PSF. For every 1% decrease in the striatal volume, a corresponding decrease in BPND by approximately 0.08% is artificially introduced. The other reconstruction methods gave larger PVE estimates (1.18-1.49%). Importantly, at BPND levels typically observed when imaging striatal D2R, neither reconstruction introduced a bias arising from variations in BPND (Figure XII). Moreover, the reliability was very high, with intraclass correlation coefficients of 1.0. Hence, holding everything constant, the OSEM+PSF reconstruction will not cause spurious fluctuations in the estimated signal.

Despite not affecting the striatum by more than 0.08%, the size of the PVE was substantially larger in certain areas of the striatum (Figure XIII). Viewed along the anterior-posterior axis, the posterior caudate was particularly vulnerable with approximately 0.7% for every 1% difference, just above 0.2% for anterior regions of the caudate and putamen, and mostly below 0.1% in middle caudate and middle and posterior putamen.

Figure XI. Design of the striatum phantom. The cavity (a) could be filled with a separate radioactive concentration than the striatal inserts (b) attached to a holder (c). Sizes were (e) small, (f) medium, and (g) large, differing by approximately 10%. © Institute of Physics and Engineering in Medicine. Reproduced by permission of IOP Publishing. All rights reserved. 247

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Figure XII. Recovery coefficients at different levels of BPND each reconstruction. The recovery coefficient is depicted on the y-axis, and the levels of BPND and reconstruction methods along the x-axis. © Institute of Physics and Engineering in Medicine. Reproduced by permission of IOP Publishing. All rights reserved. 247

Figure XIII. Visualization of RCs for the OSEM+PSF reconstruction along coronal slices for caudate (green) and putamen (red) subdivisions. The recovery coefficient is depicted on the y-axes, and slices along the x-axes. The darker to lighter shades in (c) visualize the anterior to posterior divisions as indicated by vertical lines in (a) and (b). © Institute of Physics and Engineering in Medicine. Reproduced by permission of IOP Publishing. All rights reserved. 247

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Study III Aerobic fitness level (VO2peak) at baseline was associated with higher BPND in the entire striatum, and accordingly, in each of the functional subdivisions; LST, AST, and SMST, controlling for volume (Figure XIV). The brain stem, serving as a control region in which no relation was expected, was not associated with VO2peak. These associations were minimally affected by including age, sex, and education as covariates. Hence, maintaining higher fitness levels at an old age appears to preserve striatal D2R system integrity.

Following the intervention, both groups exhibited a reduction in striatal BPND, well beyond what could be expected from age-related decline in D2R density. No differences between groups with respect BPND in either of the striatal ROIs were detected. Similarly, voxelwise analyses did not reveal any group differences. The decrease in the size of the three functional striatal ROIs was between 1.42% and 1.76%, hence, the PVE has a negligible impact on the estimates of change in BPND following the intervention based on estimates obtained in Study II, hence volume was not considered.

Change in VO2peak across the 6 months was not associated with changes in BPND in the striatum, nor in the functional ROIs. Conversely, voxelwise analyses revealed a cluster in the anterior caudate, bordering LST and AST, where VO2peak predicted a reduction in BPND (Figure XV). A reduction in BPND is expected not only when D2R density decreases, but also with increased levels of dopamine. As dopamine has higher affinity for D2R it displaces [11C]raclopride, hence lowering BPND.

Higher levels of striatal dopamine should in theory improve performance on tasks of working memory updating. Accordingly, additional analyses did indeed reveal a negative association also between changes in BPND and changes in Letter Memory performance, but not with changes in the other updating tasks. A test of mediation was also performed, showing that VO2peak exerted an indirect effect on Letter Memory via BPND. Hence, improving aerobic fitness over 6 months may benefit working memory updating processes via its effect on dopamine.

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Figure XIV. Aerobic fitness predicts higher D2R availability (BPND) in the striatum at baseline.

Figure XV. Longitudinal change in D2R availability (BPND) in left anterior striatum (A) predicted by changes in VO2peak (B) and Letter Memory performance (C).

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Discussion This dissertation focused on the neurocognitive mechanisms of physical exercise in healthy sedentary older adults performing 6 months of aerobic exercise or stretching and toning control exercise. To probe these mechanisms, longitudinal data from the Physical Influences on Brain in Aging (PHIBRA) project was used. Baseline and post-exercise data were collected, including a large cognitive test battery, structural MRI, D2R imaging with PET, and cycle ergometer data providing an objective measure of aerobic fitness. The work in this dissertation has improved the understanding of neurocognitive mechanisms of aerobic exercise, implicating dopamine in particular.

The major contributions of this thesis are:

i. Demonstrating that the cognitive benefits of performing aerobic exercise in old age are general rather than specific to any cognitive domain. This can explain why previous research has provided mixed results (Study I).

ii. Providing analyses and interpretations of individual difference in cortical and hippocampal brain structure, and D2R in relation to aerobic fitness that may aid in understanding discrepancies in previous research (Study I and III).

iii. Providing a novel method to gain knowledge on how longitudinal changes in striatal brain structure and radioligand uptake influence the estimated PET signal in longitudinal designs (Study II).

iv. Demonstrating dopaminergic changes involving increased DA as a likely neurocognitive mechanism explaining why being aerobically fit and active at an old age is important for cognitive functioning (Study III).

v. Providing an in-depth discussion advocating a view where exercise-induced macro level structural plasticity associated with improved brain health in physically active adults likely depend primarily on passive processes in the PFC and on immediately visible active processes in the hippocampus (Study I), and both passive and active mechanisms on striatal micro level dopamine circuits.

In both Study I and Study III support was provided for the view that performing aerobic exercise at an old age positively influences cognitive and brain function. Performing aerobic exercise or having higher levels of aerobic fitness was related to cognitive, prefrontal, and hippocampal measures (Study I), and the dopaminergic system (Study III). Declining frontal and hippocampal brain

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structure, as well as declining dopamine function have been linked with decreased brain health and ultimately age-related cognitive decline 3,9,12–14. Hence, being aerobically fit and active at an old age could be a factor promoting successful aging towards the latter part of adult life 248. However, understanding the neurocognitive mechanisms promoting these beneficial effects is complicated. This is particularly true for humans, where cognition is uniquely advanced, and methods do not allow probing the micro level changes occurring at the cellular and molecular level 37. Nevertheless, Study I and Study III provided important clues as to how exercise may or may not influence brain health and cognition. I will discuss cognitive, hippocampal, prefrontal, and dopaminergic outcomes separately, and then suggest how the neuroscience of physical exercise could proceed from here.

Cognitive improvements The finding of domain general, rather than specific, improvements favoring aerobic exercisers aids in explaining why the extant literature has reported mixed results 62–64. This pattern of results also indicates that the neurocognitive mechanisms influencing brain structure and function are general rather than restricted to any specific region or process. The specific neurocognitive mechanisms able to produce exercise-induced improvements to cognition will be discussed in relation to hippocampus, PFC, and dopamine in subsequent sections. However, first a few notes will be made in relation to the improvement on the ‘Cognitive score’ specifically.

Of the cognitive domains studied, the largest effects for the aerobic group were detected for executive functioning. Improved executive functioning is likely an integral part in explaining the domain general ‘Cognitive score’ improvements in the aerobic group. This is true not only because the executive function tasks constituted one fourth of the unit-weighted composite, but also because executive functions are domain general, and generally display high correlations to other mental and cognitive functions 249,250. The cognitive improvements detected assimilate the view that the primary benefit of exercise on cognition is on executive processes involved in cognitive control 62,73. Sample specific characteristics and individual differences are likely to influence the exact functional outcome of exercising. On the cognitive level, individual differences in everyday cognitive stimulation may be involved in shaping the exact neurocognitive outcome of being physically active. This could add variance on the sample level, why no specific improvement in any function favored aerobic exercise. The interaction between exercise-induced neural processes and cognitive stimulation is recognized in shaping long-term neurocognitive outcomes in the hippocampus in animals 194,208. Designing interventions

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combining aerobic and cognitive exercise could shed light on how physical exercise interacts with the environment also in humans 181,251,252.

One important issue to consider when interpreting the cognitive outcomes in PHIBRA concerns the control group, as both resistance and balance training have been associated with improved cognitive functioning 66,68,251. Hence, direct comparisons with the active control group could be unfairly strict and cause Type I errors. Nevertheless, the pattern of general rather than specific cognitive improvements remains when interpreting improvements also within the groups. The robustness of the cognitive test battery for measuring transfer effects should be viewed as a considerable strength of Study I 53.

Effects on brain structure

Hippocampus Study I significantly contributed to a growing literature indicating that performing aerobic exercise can reverse the typical pattern of hippocampal atrophy. Specifically, it was shown that the volume of the hippocampus in older adults was positively influenced over six months as a function of aerobic fitness improvement. The existing literature strongly suggests that processes inducing hippocampal plasticity are triggered by aerobic exercise in older adults 33,183,190. However, linear relationships between hippocampus structure and aerobic fitness in old adults have been observed in a fewer number of studies 182,190. Results in Study I indicate the existence of a positive linear relationship, at least within the range of fitness levels measured in the PHIBRA sample. These changes are likely to be observed within weeks or months. Here, Study I provides additional evidence showing that aerobic exercise induces plastic changes 42,43,188,193 in a region involved in age-related cognitive decline and neurodegeneration 9,14, and dementias 25,26.

Neurocognitive mechanisms In Study I, although a positive association between aerobic fitness improvement and hippocampal volume was observed, neither were related to memory improvements. As a result, the functional values of increased hippocampal volume and aerobic fitness are uncertain. One explanation for the lack of any hippocampus-fitness-memory associations is that verbal memory tasks were used in Study I and in other studies failing to observe an association between changes in memory and hippocampal volume 191 or blood volume 190. Conversely, exercise studies finding positive associations between improved memory and the hippocampus utilized spatial memory tasks 33,182. The hippocampus is critically involved in a number of spatial functions 19,20. Hence, structural changes in hippocampus could benefit spatial memory to a higher degree, or at least be more

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sensitive to spatial compared to verbal memory functions. To speculate, from an evolutionary perspective, it also makes sense that spatial abilities could be specifically enhanced. Running likely meant leaving the home ground, perhaps involving striding far distances. To not lose track of the geospatial location and the ability to return home, there may have been an evolutionary advantage if resource demanding plastic alterations were utilized for enhanced spatial functions relating to memory and navigation. Although commonly used in rodent studies, navigation tasks are not commonly used when testing humans. At present, there is a rather poor understanding of whether certain types of hippocampus-dependent memory functions are particularly amenable to exercise-induced improvements 43. Another explanation for the lack of behavioral associations is that there appears to be an excess of newly born neurons after exercise. A large portion of newly born neurons do not survive long, unless incorporated into functional networks in the presence of cognitive enrichment 194,208. Consequently, hippocampal volume measures may be subject to considerable noise when probing its functional value after an intense period of exercise.

Structural mechanisms The volume-based freesurfer measure utilized in Study I is unspecific as to the underlying structural changes. To be able to interpret the underlying changes observed in Study I it is necessary to discuss studies utilizing more detailed methods. Neurogenesis in the dentate gyrus of the hippocampus exists not only in developing humans, but also in adults 253. Running-induced neurogenesis in the dentate gyrus has been demonstrated in numerous animal studies 72,188,208. An increase in the number of neurons may thus have influenced hippocampal volume in Study I. Furthermore, angiogenesis increases with running 254 and occurs together with neurogenesis 255. Vascularization is important for neural processing as oxygen, glucose, and other nutrients are supplied via blood. Vascular changes have been observed also in humans, where cerebral blood volume in the hippocampus increases with improved aerobic fitness 182,190, albeit inconclusive with a recent perfusion study not finding an effect 202. Increased blood volume may even explain increased hippocampal volume 182. Accordingly, the association between hippocampal volume and aerobic fitness in Study I are likely due at least in part, on increased vascularization. Indirect evidence for exercise-induced angiogenesis in older adults is provided by a study using DTI-derived hippocampal mean diffusivity as an index of increased barrier density of gray matter 180. Improved aerobic fitness was associated with decreased diffusivity, i.e. increased density. Similar to 182, reduced diffusivity explained increased hippocampal volume. The functional benefits of increased vascularization are likely due to increased supply of nutrients and energy that can be utilized for neural processing.

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Besides neurogenesis and angiogenesis, animal studies have implicated additional structural and cellular changes that could improve hippocampus dependent functions and/or influence structural measures in humans. Running induces structural alterations in the dentate gyrus and CA1 region of the hippocampus, including increased dendritic spine density, length, and complexity 256, and increased glutamate receptor 5 and N-methyl-D-aspartate (NMDA) messenger ribonucleic acid (mRNA) in the dentate gyrus 189. Such changes may directly influence neural processing and the function of hippocampal networks. Furthermore, preliminary evidence suggests that astrocyte cell size increases in the hippocampus from running 257. The specific function for the astrocytes undergoing change is difficult to pin down however, but several uses could positively influence neural functioning and plasticity, including release and regulation of transmitters, regulation of blood flow, and involvement in LTP 257.

Other remaining issues relates to how much exercise is necessary to produce long-lasting effects both on hippocampus morphology, and hippocampus dependent cognition. Apparently rapid structural alterations may occur in the hippocampus from aerobic exercise in young adults, including a return to baseline values within 6 weeks of exercise-termination 191. In another study on older adults, the increase in hippocampal volume appeared linear with aerobic exercise from baseline, to 6, and 12 months 33. Would effects on the hippocampus accumulated for a longer time also be longer lasting than 6 weeks? One study examined hippocampal volume and mean diffusivity 6 months after the end of a 6 months aerobic exercise intervention in older adults 180. The association between improved fitness and decreased mean diffusivity, was no longer evident after 6 months. However, the aerobic exercise group did not differ from the active control group in terms of fitness after the intervention. Ultimately, comparing groups differing in fitness and hippocampal volume following a long intervention could be more informative when performing long-term follow-ups. Interventions such as PHIBRA are important for this endeavor.

Prefrontal cortex An important contribution to the literature on exercise and PFC structure was provided in Study I. In Study I, the positive association between aerobic fitness and dlPFC thickness at baseline indicates that being aerobically fit is beneficial for dlPFC brain health, although causality can not be determined based on cross-sectional data. An important contribution in Study I involves the longitudinal measures of PFC thickness. To my knowledge, only two studies have previously analyzed changes in PFC gray matter structure in healthy older adults 128,34. We found no group differences in cortical thickness following the intervention, nor any associations between improved aerobic fitness and cortical thickness. This

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pattern of results suggests that the fitness-thickness relationship may evolve slowly, at least compared to the rapid structural changes in the hippocampus 191.

Neurocognitive mechanisms Considering that domain general cognitive control processes are served by the PFC 27, and that aerobic exercisers compared to the active control group improved their ‘Cognitive score’ to a larger degree, indicates that PFC function indeed may have been influenced by aerobic exercise. A blunt explanation for the lack of effects is that measures of cortical thickness in PFC or ACC do not explain cognitive improvements of aerobic exercise, whereas other brain indices do, e.g. task-related BOLD response reflecting altered neural processing 67,197 or connectivity within frontal executive networks 199 (no group differences in resting-state fMRI were observed in PHIBRA 202). Although functional brain changes are likely involved in explaining cognitive improvements from exercise, it is unlikely that the cortical thickness measures do not hold any explanatory power. Both the domain general ‘Cognitive score’ as well as executive function ability were positively associated specifically to cortical thickness in dlPFC and vlPFC at baseline, and executive function also to ACC, but neither to hippocampal volume. Episodic memory on the other hand, was specifically associated with hippocampal volume and not PFC or ACC thickness. This dissociation strongly indicates that both the cortical and subcortical freesurfer measures and cognitive test battery are sensitive to individual differences in brain health and cognition. Importantly, the thickness measures reflects underlying neural processes important for cognitive control 27 and the association between baseline dlPFC structure and general and executive function provides further evidence for the link between aging, declining prefrontal function, and reduced cognitive function 14,30,137. Moreover, relative increases in dlPFC thickness predicted an improved ‘Cognitive score’, showing the sensitivity of both measures to individual differences in change. In light of the dlPFC-cognition association and that aerobic exercisers compared to controls improved on the domain general ‘Cognitive score’ composite, does implicate functionally relevant structural changes related to aerobic exercise in dlPFC. Exercise-induced changes in cortical thickness over 6 months in this sample may simply have been too subtle to detect although functionally relevant structural changes still existed. Alternatively, additional measures of fitness could be needed. Factors unrelated to the actual intervention may also impact cortical structure and function over 6 months as will be discussed next.

Colcombe et al. (2006) observed higher gray matter densities in PFC, ACC, and temporal regions in aerobic exercisers compared to controls after a 6 months long intervention. This result was not replicated by Ruscheweyh et al. (2011) finding no differences between low- or medium-intensity exercise, and passive controls over 6 months. Conversely, Ruscheweyh et al. (2011) found that physical activity

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levels did explain gray matter changes over 6 months in PFC, ACC, parietal, and occipital areas, regions typically related to aerobic fitness (Table I and II). Hypothetically, behavioral changes unrelated to the actual intervention may have exerted an even stronger influence on cortical structure than the actual exercise intervention. Although highly speculative, one explanation for the diverging results could be that changes in everyday physical activities of participants in Ruscheweyh et al. (2011) and in PHIBRA differed from those in Colcombe et al. (2006). If participants in Colcombe et al. (2006) did not alter everyday behavior to the same degree, the analyses of effects from the actual aerobic exercise intervention could become more sensitive. Careful screening of participants’ physical activity behavior during the course of an intervention could prove informative for future projects.

Although individuals that were more aerobically fit at baseline also had larger thickness in dlPFC, mediation analyses did not indicate that aerobic fitness was related to cognitive functioning via its influence on dlPFC. Explaining how aerobic fitness and exercise improves brain health and cognition is not straightforward. Several separate but interacting mechanisms are likely involved. In the following sections, a distinction between immediate or active mechanism and longer-term or passive mechanisms will be made. Active mechanisms can influence prefrontal structure and function directly, e.g. via dendritic reorganization or altered neurotransmitter release. The passive mechanisms, rather than being directly involved with neurotransmission, are more related to longer term maintenance of brain structure 3,12, e.g. neuroprotection.

Active mechanisms The active effects on PFC morphology from aerobic exercise could potentially include angiogenesis 254,258,259, altered synaptic/dendritic organization 257, increases in microglia 260 or astrocytes 257. Running stimulates angiogenesis in the rat motor cortex 258, and frontoparietal cortices 259. The extent by which exercise induces cortical angiogenesis is not thoroughly studied, but might be restricted to regions specifically involved in the skill required for performing a specific type of exercise. In line with this, in young adult rats, angiogenesis occurred with running only in the fore limb region of the motor cortex, and not in the visual cortex 258. However, angiogenesis and elevated VEGF mRNA across the frontoparietal cortices and dorsal striatum was detected after 3 weeks of running in old rats, providing evidence for more widespread angiogenesis. Possibly, cortical angiogenesis may be stronger in aged, compared to young rats 259. The opposite pattern was detected in the hippocampus however, where only younger rats had increased angiogenesis 261. In another experiment, environmental enrichment and running both increased endothelial cell proliferation in the adult rat hippocampus, i.e. markers for angiogenesis, whereas only environmental enrichment increased proliferation in PFC 254. Hence, cortical

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plasticity involving angiogenic factors may require a certain amount of cognitive stimulation during exercise to manifest, and may vary depending on the specific type of stimulation present 258.

Additional factors involving structural plasticity in the cortex have been identified in animal studies. Like in 254, running and acrobatic exercise induced partly overlapping yet differential expression of proteins involved in neurotransmitter release, vesicular function, and structure in the cortex, striatum, and cerebellum 209. Acrobatic exercise involved more structural proteins in motor cortex compared to running, suggestively due to acrobatics increased complexity and demand on motor cortical processing. Running on the other hand, showed widespread increases of proteins primarily involved in neurotransmitter release and vesicular function across the motor cortex, striatum, and cerebellum. This could suggest that aerobic exercise does indeed trigger general neurogenic changes, but that the specific functional expression of these will depend on other behavioral changes 194, e.g. acrobatic challenges. Nevertheless, a recent experiment showed that 12 days of running increased dendritic spine density and length in the rat medial PFC, elevated markers for both pre- and post-synaptic proteins in the hippocampus, medial PFC, orbitofrontal cortex, and perirhinal cortex, and increased astrocytic cell size in all areas but perirhinal cortex 257. Altered astrocyte function could benefit neural processing and underlying functions by release and regulation of transmitters, provision of lactate and glucose, regulation of blood flow, LTP, and protection against oxidative stress 257,262. Brockett et al. also included several cognitive tests, finding that memory and attentional shifting tasks involving medial PFC and orbitofrontal cortex improved, whereas a perirhinal cortex-dependent novel objects task did not. This suggests that there may be some specificity of behavioral effects but it is too soon to state how specific effects may be, as discussed by others 43.

In sum, the active induction of multiple neuroplastic factors from prolonged periods of running could explain improved structural and cognitive measures 37. Altered levels of growth factors, vascularization, plasticity-related proteins, and/or neurotransmitter systems are likely to explain the effects, but the data analyzed in Study I does not allow drawing conclusions relating to the cellular and molecular level.

Passive mechanisms There are likely a number of additional neurocognitive mechanisms involved in being aerobically fit and active that may aid in explaining why physical activity is protective against cognitive decline 39,263, dementia 38,40, Alzheimer’s Disease 264, and potentially other neurological 41 and metabolical 52 disorders. An increase in microglia observed after prolonged running 260,265 could provide neuroprotective effects on cortical brain health as these cells are involved in defense against

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inflammation, in tissue repair, the transport of growth factors, and removal of waste products 265. Inflammation, especially for inactive individuals has been found negative for lateral PFC and hippocampal brain health 163. Similarly, the increase in astrocyte size observed in the prefrontal cortex in the rat following 12 days of running 257 could provide neuroprotective effects, e.g. by reducing oxidative stress or aiding in tissue repair 262. Improved glial function could certainly be beneficial for brain health, but assumes a more passive role. Moreover, the angiogenic effects of exercise may improve vascular health, which in turn reduces inflammation and oxidative stress 266. These benefits are likely to have limited short-term effects on the brain and cognition, but will accumulate over time and should be important for brain maintenance 3.

To speculate, it is plausible to assume that neuroprotective effects may exert an effect detectable in vivo in humans within shorter time in individuals with compromised brain health. The rate of decline increases with age, but is steeper in individuals at risk for neurocognitive dysfunction. For example, reducing inflammation in healthy older adults may only have a limited short-term effect, whereas reducing inflammation in an individual already subject to the neurodegenerative effects of inflammation could have a more immediate effect on brain health. Cortical volume changes in PD 185 and MCI 181 populations have been detected within 6 weeks and 6 months respectively. On that note, it is difficult to know whether the sample of healthy older adults in Colcombe et al. (2006), finding increased PFC density after 6 months of aerobic exercise had lower brain health compared to other samples not detecting cortical changes within 6 months (Ruscheweyh et al., 2011; Study I). Such discrepancies may constitute cases where seemingly similar samples may respond differently to an intervention.

In sum, the structural analyses in Study I and reviewed literature show that being aerobically active and fit at an older age can benefit brain health, and consequently cognition, although the exact mechanisms are not completely understood. The pattern of results also indicate that it may be useful to make a distinction between active mechanisms, those that can influence cognitive function and brain health in short time, from seconds to weeks or months, and passive mechanisms, influencing brain health and cognitive function slowly, and may could even require years to be noticeable. This pattern could be observed only because baseline and longitudinal data were analyzed together, and that two regions exhibiting the opposite longitudinal patterns were analyzed together. Accordingly, this is to my knowledge a unique and important contribution of Study I in this field. A neurocognitive model of aerobic exercise making a distinction between active and passive mechanisms are presented in Figure XVI.

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Figure XVI. A neurocognitive model of aerobic exercise separating active and passive paths to brain health and cognitive status. Results from Study I revealed different patterns of fitness-brain structure relationships in the dlPFC and the hippocampus at baseline and following the intervention. This is best understood by active and passive mechanisms (denoted Active and Passive path in the figure). Performing aerobic exercise increases several growth factors in muscle tissue, including IFG-1 and VEGF, as well as calcium levels. These factors enter the brain by crossing the blood-brain barrier to induce both structural and functional plastic responses in the brain. IGF-1 may increase BDNF expression and stimulate BDNF dependent neurogenesis, synaptogenesis, and dendritic reorganization. These relatively fast processes are abundant in the hippocampus and can cause structural alterations within a short time (Study I). These structural changes may then alter neurotransmission and neural processing to improve cognitive function. Increased vascularization caused by elevated levels of endothelial nitrous oxide synthase and VEGF leads to improved transport of oxygen and nutrition to neurons, thereby improving function. The specific structural and functional changes are modulated by lifestyle factors such as cognitive stimulation. In other words, exercise-induced neuroplastic resources are utilized in neural pathways activated by leisure activities. Individual genotypes will also modulate the specific structural and functional changes to be expected. Additional mechanisms relating to neuroprotection and brain maintenance, including increased levels of astrocytes and microglia, and other neuroprotective factors. These processes are slower, and rather than increasing brain volume, are involved in reducing atrophy and maintaining brain health. The primary action of passive mechanisms in the cortex could explain why baseline dlPFC thickness was predicted by aerobic fitness whereas changes were not (Study I).

Effects on the dopaminergic system Study III provided several novel findings regarding aerobic fitness and the dopaminergic system in striatum. Like the hippocampal and prefrontal analyses

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in Study I, the baseline and longitudinal measures differed also with respect to D2R availability. Consequently, the influence of exercise on D2R may differ in the short- and long-run and depend on both active and passive mechanisms.

Demonstrated for the first time in human subjects, it was shown that individuals with higher levels of objectively measured aerobic fitness also exhibited higher D2R availability in the striatum. This result converges with two recent studies in older adults investigating the effect of physical activity on striatal D2R. The first study found that more active older adults walking above 5600 steps per day did not exhibit the typical age-related reduction in ventral striatal D2R evident in less active adults 150. In the second study, D2R availability in caudate was higher in individuals that reported performing physical activities more intensely 98. The results from Study III indicate that variations in aerobic fitness arising from physical activity could be a critical factor in explaining preserved D2R system integrity at an old age. Importantly, the association remained also when controlling for age, sex, education, and volume. Controlling for volume or attempting partial volume correction is necessary in a cross-sectional analysis when large interindividual differences in volumes are present (Meechai et al., 2015; Rousset et al., 1998; Study II). Such differences could otherwise systematically bias the estimated PET signal and lead to faulty estimates of differences 234.

Finding higher levels of D2R in aerobically more fit individuals is important considering the hypothesized role of decreased dopamine system integrity in age-related cognitive decline 13,118. The functional relevance of higher D2R availability in aerobically fit individuals observed in Study III could be put into question however. Exploratory analyses (not included in Study III) did not reveal any significant associations between D2R availability and any cognitive domain. A subset of episodic memory and executive function tasks were marginally significant or displaying trends, but that is to be expected considering the number of statistical tests conducted. Higher levels of striatal D2R availability, presumably reflecting density, have previously been associated with improved memory functions 114–116. The role of D2R density for other processes has been disputed however. The most rigorous investigation as of yet, imaging 180 individuals between 64-68 years of age with [11C]raclopride, did not detect any associations between D2R availability and latent factors of working memory updating or processing speed 114. Hence, our exploratory analyses relating D2R and cognition in Study III were in line with Nyberg et al. (2016), questioning the importance of D2R availability for cognitive functions. The exploratory baseline analyses should be interpreted with caution however, as there would have been a lack of power for detecting an effect of equal magnitude as that reported for episodic memory in Nyberg et al. (2016).

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Despite the uncertain role for the availability of D2R to cognition, binding of DA to caudate D2R serves executive processes 105,106, including updating information to working memory 269 and cognitive flexibility 107. Specifically activating D2R in human subjects using the D2R agonist bromocriptine has been shown to improve cognitive flexibility 270, a subsumed role for dopamine binding to D2R in striatum 105–107.

Following the intervention, no group by time interactions with respect to D2R availability were observed, neither on the level of ROIs, nor on the level of voxels. This suggests that aerobic exercise in healthy older adults does not influence D2R over the course of 6 months. The result is in contrast with previous animal studies showing increased D2R density after aerobic exercise in aged rodents 130,131, or rats where the age is unknown 271. However, in young rodents, others have not observed an increase in D2R 132, or even found downregulated D2R mRNA 272 and density 122. One possible explanation is that elevated D2R is more robust when brain health is lower. The two previous exercise interventions in human populations imaging D2R included populations with reduced brain health. The first found that 8 weeks of aerobic treadmill running almost doubled D2R availability in the putamen of two PD patients 97. D2R availability for one healthy control participant also performing treadmill running, and two PD controls performing no exercise, did not exhibit increases in D2R. In the other study, young to middle age methamphetamine abusers were randomized to performing aerobic and resistance training (n = 10) or education control (n = 9) for 8 weeks 96. The aerobic group displayed increases in D2R availability across the whole striatum, and a marginally significant increase compared to controls. Both populations have known dopaminergic deficits questioning the generalizability.

Active mechanisms Of note, the two studies reporting increased D2R following exercise used [18F]fallypride 96,97, whereas we used [11C]raclopride in Study III. [18F]fallypride has a high affinity for D2R and provides a good estimate of D2R density 134. [11C]raclopride on the other hand has lower affinity, and is more sensitive to endogenous DA and synaptic levels of DA compared to [18F]fallypride 133,134. Hence, differences between radioligands sensitivity to DA could explain diverging results 134,135. Alternatively, although populations with known DA deficits may exhibit increased D2R density from exercise, healthy older adults may respond differently to exercise, e.g. by increasing synaptic DA levels. Aerobic exercise in rodents has been shown to increase DA 122,123 or reduce re-uptake of synaptic DA 126, likely due to DAT internalization 124,125,271. Further, calcium released from the bone during exercise can enter the brain and activate calcium/calmodulin-dependent DA synthesis to increase DA levels 78. Further, tyrosine hydroxylase (TH) mRNA is an indicator of DA synthesis. Six weeks of running increased TH mRNA levels and reduced the expression of D2 autoreceptors in the SN of rats 271.

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As D2 autoreceptors function to inhibit dopaminergic signaling, finding downregulated D2 autoreceptors along with increased TH suggests that dopaminergic input to striatum increased. Foley and Fleshner (2008) in their model of ‘Central fatigue’ posits that running causes BDNF dependent plastic changes to dopaminergic neurons in the SN and the striatum to increase the dopaminergic capacity of the basal ganglia. An increase in dopamine levels and D2R’s function to delay onset of central fatigue during exercise, i.e. the amount of exercise possible before central resources are depleted. These changes also lead to improved control of corticostriatal circuits via the indirect pathway. It is unclear whether exercise induces these changes in sensorimotor pathways only, or whether the effect is general and also effect limbic and associative pathways.

In Study III, both groups exhibited robust reductions in D2R availability. D2R availability in the striatum decreased by 1.2% for the aerobic exercise group, and 3.1% for the control group. The estimated annual decline in D2R in older adults is between 0.2% and 0.8% 273–276. Hence, the reduction exhibited by both groups were several times greater than the most pessimistic scenario of decline. This suggests that reduced D2R density is not the sole explanation for the observed results, but that increased synaptic DA could be a factor 124–126,271. In fact, improved aerobic fitness was associated with a reduction in D2R availability in anterior caudate, suggestively due to increased levels of DA. Greater supply of DA increases exercise capacity 271, and greater exercise capacity is likely to positively affect aerobic fitness.

DA binding to D2R’s in striatum is important for the updating function of working memory 105,106,269. Increasing DA levels should improve this function if levels are lower than optimal 109. For example, stimulating D2R with bromocriptine benefitted cognitive flexibility only in individuals with a genotype associated with lower levels of DA 270 as can be expected in older adults. Increasing DA with amphetamine modulated n-back performance in older adults via changes in frontostriatal and frontoparietal BOLD variability. In theory, if improvements in aerobic fitness increased striatal DA levels, then reduced D2R availability should predict improvements in updating performance. This interpretation was given support by analyses showing that Letter Memory updating performance improved when D2R availability decreased. Importantly, the decrease in D2R availability indirectly mediated a positive influence from improved aerobic fitness to improved Letter Memory updating. These analyses implicate that increased DA to influence cognition stands out as an immediate neurocognitive mechanism triggered by performing aerobic exercise and/or improving aerobic fitness at an old age.

An alternative explanation for reduced D2R availability as a function of improved fitness is that D2R may be downregulated 122,272 and degraded, especially in the

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presence of increased DA 277. However, there is no clear evidence suggesting that reduced D2R density or synaptic DA would be detrimental to performance in older adults. To my knowledge, only one investigation found impaired cognitive performance with higher D2R densities. They found that implicit, not explicit, sequence learning was better when D2R density was lower. Despite this one study, explaining improved working memory performance by downregulated D2R is problematic. Another line of evidence shows that synthesis of striatal dopamine increases with age 278,279. Whereas reduced synthesis of dopamine in caudate has been linked with improved task-switching performance and neural efficiency in frontoparietal networks in young adults, the function of increased DA synthesis in old adults is uncertain 278. The age-related increase in synthesis has been suggested to be detrimental to cognitive performance 278. Longitudinal data will be necessary to interpret the function of increased synthesis in older adults however. Compensatory processes can be utilized by older adults to improve cognitive functioning by compensating for reduced neural efficiency or function 11,12. It could well be that increased DA synthesis is a compensatory process, and that performing aerobic exercise is one way to increase synaptic levels of DA to improve function. However, increased levels of synaptic DA does not necessarily depend on increased synthesis, but may also depend on internalization or downregulation of DAT which has been evidenced in mice 124,125. In sum, the most likely explanation for why reduced D2R availability in anterior caudate indirectly mediated the fitness-cognition relationship is that synaptic DA increased.

Other studies in humans have also implicated dopamine in physical activity, albeit with a lack of spatial specificity. For instance, peripheral levels of dopamine increased with total physical activity over 6 months 128. High impact anaerobic sprints increased peripheral levels of dopamine, and the absolute level of dopamine after the activity was related to 1 week delayed vocabulary recall 127. Unfortunately, in both studies, it is impossible to know based on peripheral measurements if and where dopamine increased in the brain. Dopamine does not cross the blood-brain barrier 280,281. Although central dopamine cannot be detected peripherally, the association between dopamine levels and memory indicates that peripheral markers might at least correlate with dopaminergic activity in the brain. However, the only PET study imaging changes in dopamine levels with [11C]raclopride after acute exercise found no increases 94. Considering the number of animal studies observing DA release during or after running, it is likely that the protocol utilized by Wang et al. was not sensitive enough.

Further evidence for the involvement of DA as a neurocognitive mechanism explaining why being physically active is beneficial for cognition stems from human genetic studies. The Val158Met polymorphism of the catechol-O-methyltransferase (COMT) gene controls dopamine levels in the PFC. Val carriers

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have lower levels of dopamine due to increased enzymatic activity. In healthy adults engaged in 17 weeks of running exercise, individuals with the Val/Val polymorphism improved in cognitive flexibility to a larger extent from running compared with Met carriers 282. In another study, similar results were observed in older adults, where Val/Val carriers with higher fitness levels performed better on the Flanker task 129. Finally, the DAT1/SLCA6A3 polymorphism is involved in the reuptake of DA in the striatum and where carriers of the A allele have a more active reuptake, and consequently synaptic levels of DA. In adolescents, carriers of the A allele, compared to the C allele, benefitted more from an intense bout of aerobic exercise on task-switching performance 283. Hence, in both the PFC and striatum, individuals with genetically determined lower levels of DA seem to benefit more than those with already higher levels. This suggests that aerobic exercise alters dopaminergic neurotransmission, and likely increases dopamine levels also in humans. This increase should in theory be particularly beneficial for individuals with lower levels of dopamine 109, as can be expected in older adults 13,276,284.

Passive mechanisms Comparatively passive mechanisms may also influence measures of D2R availability over time. The medium-spiny neurons in the striatum expressing D2R receive its’ dopaminergic inputs from the SN/VTA. Factors induced by aerobic exercise can serve neuroprotective functions on SN/VTA dopaminergic neurons. For instance, both BDNF 285 and hypoxia inducible factors (HIF) 286 could be triggered by aerobic exercise to reduce inflammation and increase cell survival in SN. Hyman et al. 287 found that application of BDNF onto SN neurons made them resistant to neurotoxic lesioning, more than doubling survival of dopaminergic neurons. In the long-run, the protection of dopaminergic input to the striatum is hypothesized to increase also D2R density in striatum 120. Consequently, in the short term, increased DA from improving aerobic fitness would reduce [11C]raclopride D2R availability after an intensive period of exercise. Increased DA after 6 months of exercise provided the most plausible explanation for the pattern of result in Study III. In other words, reduced D2R availability being associated both with improved aerobic fitness and working memory updating performance is most readily explained by elevated DA rather than reduced D2R density. In the long-run, increased DA stimulation should preserve D2R system integrity 120, as indicated by the positive association between D2R availability and aerobic fitness at baseline. See Figure XVII for a model of how aerobic exercise may influence dopaminergic structure and function through active and passive mechanisms and that fits with the pattern of results in Study III.

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Figure XVII. A model of aerobic exercise and its effect on the dopaminergic system in the basal ganglia including active and passive paths to system integrity and function. When performing aerobic exercise, active processes include calcium being released from bone tissue, entering the brain via the blood brain barrier. The increase in calcium leads to increased calcium/calmodulin dependent DA synthesis in the SN and VTA. IGF-1 is also produced during aerobic exercise and is involved in altering the expression of BDNF. BDNF has several functions, including involvement in synaptic plasticity and modulation of neurotransmitter release. Increased DA synthesis in the SN, coupled with downregulation of D2 autoreceptors, and internalization of DAT, increases the dopaminergic input to the striatum while reducing DA reuptake. Striatal D2R density is also likely to increase. Increased DA action in striatum hypothetically enhances the ability for striatal neural processes to influence cortical processing to improve behavioral function. Increased DA can explain why D2R availability was reduced and updating performance improved in Study III over six months. The resulting plastic changes will also be modulated by the specific exercise protocol used, as well as genotype. There are additional passive neuroprotective effects influencing survival of dopaminergic neurons in SN. BDNF reduces inflammation and protects against neurotoxicity. HIF1a and endothelial nitric oxide synthase (eNOS) both reduce oxidative stress in SN. In addition to increasing DA synthesis, calcium is also involved in reducing blood pressure. Higher D2R availability at baseline in Study III potentially depend on increased survival of dopaminergic neurons due to reduced oxidative stress and inflammation. In sum, active mechanisms involving structural plasticity and altered neurotransmission, and passive neuroprotective mechanisms promoting maintenance of dopaminergic system integrity may both influence function.

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Methodological considerations and limitations

Longitudinal imaging The use of longitudinal data to understand individual differences in brain structure and function is a powerful tool considering that contradictory conclusions could otherwise be drawn based on cross-sectional and longitudinal data 288–290. In both Study I and III we show how cross-sectional and longitudinal patterns differ. In Study I, this was observed for fitness-brain structure in both the hippocampus and the dlPFC, and in Study III with respect to D2R. The use of longitudinal data is not without challenges however.

In Study II, we performed a thorough investigation of factors able to influence the accuracy of longitudinal estimates of the PET signal for different reconstruction methods. Firstly, it is important to have a reliable method to reduce statistical noise. We found that all reconstruction methods were highly reliable, hence, the choice of reconstruction could be based on other qualities. Secondly, it is important to have a method reducing systematic variations due to factors other than the factor of interest. When using PET to image D2R in older adults over time there are two factors that could decrease the accuracy of longitudinal estimates of change, namely changes in volume and receptor densities. For the striatum, the OSEM+PSF algorithm provided the best result where the magnitude of the PVE would introduce a 0.08% reduction in signal for every 1% reduction in volume, compared to 1.18-1.42% for the other algorithms. Consequently, the reduction in D2R availability in Study III could not be explained by PVE as volume decreased by just above 1%, and BPND decreased by 1.2 – 3.1% for the aerobic and control groups respectively. Importantly, the bias that have been detected when BPND is low 237,238 did not influence the OSEM+PSF algorithm at BPND levels within the normal range in the striatum.

We did however observe that the magnitude of the PVE was not homogenous throughout striatum (Figure XIII). In particular the posterior caudate and anterior striatal voxels exhibited larger PVE. This effect is due to the dependency of the PVE on the shape of a region, and the uptake of surrounding voxels. Hence, if analyzing D2R in the hippocampus, the PVE would be different considering the long thin shape of the hippocampus. Moreover, the hippocampus has lower uptake than the striatum, and the bias reported for low-uptake regions when using high-resolution PET algorithms may also affect estimates in the hippocampus. The design of the PET phantom in Study II could easily be modified to target the hippocampus or any other brain region instead of the striatum. To conclude, the information gained from a study utilizing a similar protocol as Study II provides valuable information for interpreting data in longitudinal PET studies.

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Limitations

Control group As with any project there are certain limitations that should be addressed. One potentially severe limitation in PHIBRA is due to the active control group improving their aerobic fitness, and could hardly be considered a true control group. The aerobic group had larger improvements in aerobic fitness, yet, the control group also improved aerobic fitness significantly. To my knowledge, that has not been reported in other aerobic exercise interventions having active control groups. Furthermore, the active control circuit training involved resistance, coordination, and balance exercises. Research demonstrating both cognitive and neuroplastic effects of such exercises is now accumulating 66. Group comparisons may involve comparing two groups both exhibiting exercise-induced neural and cognitive changes. Consequently, the lack of group effects on brain indices should be interpreted with caution.

Freesurfer Another limitation concerns the use of cortical thickness to measure brain structure. Freesurfer is unspecific as for the underlying morphology. With VBM it is possible to separate gray and white matter in a voxel. DTI provides detailed information on the integrity of white matter without providing information on gray matter. However, thinning of the cortex with increasing age is an established fact. Cortical thickness is also sensitive to individual differences in cognitive functioning. In Study I, not only baseline measures, but also 6 months change in cortical thickness predicted cognitive change. It is not necessarily a limitation to adopt cortical thickness as a measure of brain health compared to VBM or other tools informing about tissue characteristics. Thickness could rather be viewed as a measure of the structural integrity of the cortical mantle.

[11C]raclopride The final limitation relates to the PET protocol adopted to image striatal D2R’s. BPND estimated using a single bolus protocol provides a measure of D2R availability. There is no way of knowing the number of actual receptors, i.e. density, and the amount of endogenous dopamine bound to receptors. A simple illustration of this problem is illustrated in Figure XVIII. Consequently, although results in Study III were in accordance with theory, a replication using a PET protocol able to estimate both D2R density and synaptic levels of DA is required. This can be done in several different ways. In the first approach, separate scans on the same day would be performed 291–293. E.g. the specific radioactivity of [11C]raclopride could be varied and one scan would have high specificity, and the other low. This approach has shown good test-retest reliability for estimating both D2R receptor density and affinity 292. Differences in affinity can then be used to infer synaptic levels of DA. The other approach also involves two scans, but

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these are on different days and involve pharmacologically depleting DA 136. Comparing the baseline to depleted state provides an estimate of DA levels. A problem with both approaches are the number of scans required, two at baseline, and two after the intervention. There are possible restrictions relating to the number of non-clinically motivated PET scans that an individual may perform. Four PET scans in a single sample is not uncommon however 136,292.

Figure XVIII. Binding of [11C]raclopride and DA to D2-receptors. Blue boxes on the neuronal surface depict D2R’s, located primarily on the post-synaptic neuron, but are located also on the pre-synaptic neuron where they function as autoreceptors. DA (red) and [11C]raclopride (purple) are freely moving in the synapse, or bound to D2R’s. (A) In this scenario there are many receptors, and little DA, consequently leading to high availability of D2R’s for [11C]raclopride binding. (B) In this scenario there are few receptors, and little DA, thereby leading to lower binding of [11C]raclopride than in (A). (C) There are again many receptors, but now there is also plenty of DA competing with [11C]raclopride for binding sites. Despite having more receptors than in (B), the measured availability of D2R is the same as under scenario (B), i.e. low.

Future directions Considerable effort has been devoted to understanding the structural and functional brain responses triggered by aerobic exercise 42,43,58,60,62,193,294. It is fairly certain that aerobic exercise increases growth factors involved in angiogenesis, neurogenesis, and synaptic plasticity 37,79,295,296. Yet, there are at least as many unanswered questions as there are resolved questions. I have identified certain issues particularly important to consider to understand inconsistencies in the literature, and for the neuroscience of physical exercise to progress.

A B C

High Availability Low Availability Low Availability

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Active vs. passive mechanisms Another prediction of the models proposed in Figure XVI-XVII concerns differences in the time course of active and passive factors influenced by aerobic exercise. Associations between aerobic fitness or physical activity and frontal, temporal, and parietal gray matter volumes or cortical thickness are commonly reported (Table I). Yet, only a few investigations replicate those findings over the course of an intervention 34,181. Conversely, exercise-induced alterations in neural processing and connectivity have been established in several studies, e.g. 67,197,199. One potential explanation is that active structural changes in the cortex are either slow, or involve primarily microscale changes to dendritic structure, synaptogenesis, altered levels of neurotransmitters and neurotransmission, as well as vascularization.

Aerobic exercise may also induce several factors involved in neuroprotection. By reducing inflammation, oxidative stress, or improve waste disposal, brain health could be relatively maintained over time. Hence, by being physically active and aerobically fit, the typical shrinkage of the cortical mantle or subcortical gray matter volumes expected with increasing age could be reduced and brain health relatively maintained. The value of including additional post-intervention data collections to understand the longevity of interventional effects has also been recognized by others 180,297. Additional time points may even be necessary to detect certain passive effects not detectable immediately following an intervention. To test the prediction that prefrontal structural differences will emerge over time due to neuroprotection, following the same individuals over several years would be necessary. It will be important to measure fitness, cognition, and brain indices repeatedly during that time, as well as other biological markers, e.g. those relating to inflammation. Such a study could preferably begin with a physical exercise intervention to produce initial individual variation. Critical to this type of design will be to carefully consider the methodological challenges involved in longitudinal brain imaging, as in Study II.

Aerobic exercise vs. other forms of exercise The first critical question concerns the differences and overlap between different kinds of physical exercise. For instance, 12 months of either aerobic or coordination exercise compared to controls influenced changes in the BOLD response during flanker task performance in frontal, temporal, and parietal areas 67. This suggests similar benefits, yet, differences in activation were also observed between the groups in frontal, temporal, and parietal areas, showing that the response to exercise may not be the same from different types of physical exercise. In that study, changes in VO2max explained differences in BOLD for all groups for example. On that note, both the hippocampal (Study I) and dopaminergic (Study III) effects of aerobic fitness in PHIBRA were also observed irrespective of group.

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Individual differences in oxygen uptake appear important for cognitive and brain function. Hence, to improve interpretability of interventional outcomes, aerobic fitness should be measured regardless of the specific exercise protocol utilized.

Other variables reflecting physical function, in addition to aerobic fitness, could also be measured to improve interpretability of physical exercise interventions. Of interest, in one study, only motor fitness, not aerobic fitness, was associated with larger hippocampal volume 183. Niemann et al. further detected an increase in hippocampal volume after 12 months of either aerobic or coordination training. In another study however, studying women diagnosed with mild cognitive impairment, only aerobic training, not resistance training, was associated with increased hippocampal volume 187. Whether coordination and/or resistance exercise may have similar influences on the brain as aerobic exercise is not yet clear based on the few existing studies. One thing is for certain nonetheless, by allowing an active control group to perform physical exercise, some effects of interest induced by aerobic exercise could be concealed due to the experimental and active control groups’ exhibiting overlapping plastic responses to exercise.

Concurrent cognitive stimulation If processing requirements influences the expression of plasticity-related genes 209, the functional utility of plastic resources elevated by exercise could also be modulated by other non-physical exercise lifestyle factors 194, e.g. the level of cognitive stimulation enjoyed by an individual during an intervention. This particular effect is akin to environmental enrichment in animals, with enrichment eliciting a stronger angiogenic response in the PFC compared to running without enrichment 254. Investigations combining physical exercise with cognitive interventions are key to understanding how exercise interacts with a cognitively stimulating lifestyle. Combined cognitive and physical exercise potentially produces added cognitive benefits compared to either exercise protocol alone 252. Hence, combining cognitive stimulation with physical exercise could be additive, like observations of increased survival of newly born neurons in the hippocampus when combining environmental enrichment with aerobic exercise 208.

The design of an experiment investigating whether combined physical and cognitive exercise have additive effects would naturally include at least three groups; one group performing combined physical and cognitive exercise, and two groups performing either physical exercise or cognitive exercise alone. A related question is whether specific types of cognitive training paradigms have dissociable effects depending on the pairing to specific physical exercise paradigms. In other words, are there specific types of training protocols particularly effective for improving a given cognitive function. In the latter case,

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at least four groups would be required. Two combined physical and cognitive exercise groups, and two groups performing the two corresponding cognitive exercise protocols alone.

Timing of optimal plasticity Another important issue concerns the timing of effects, which builds upon the previous sections. This argument refers to the time when active processes elicited by exercise are optimally effective for producing behavioral plasticity in combination with cognitive enrichment. Consider the following: long-term memory was improved if physical exercise was performed four hours, rather than immediately, after studying picture-location associations, possibly due to BDNF 298. The brain response during task also differed, with increased pattern similarity in the hippocampus during retention 48 hours later. Although the effect reported by van Dongen et al. was hippocampus-dependent, the increase in BDNF 0-2-6-24 hours following exercise were similar between hippocampus and cortex in mice 82, hence, plasticity of additional PFC dependent processes could similarly be sensitive to timing. Further, to speculate, DA levels in striatum are elevated during running in mice, and can remain elevated at least for 48 hours 122. The period with elevated DA could be a critical time window for optimizing learning and plasticity involving corticostriatal DA circuits. DA in the PFC has been suggested as a key modulator of plastic alterations from environmental enrichment in both cortical and subcortical regions 299. Understanding the time-course of optimal exercise-induced plastic resources could further potentiate behavioral benefits of physical exercise protocols in both older and younger individuals.

The design of an experiment answering questions regarding the timing of optimal plasticity also involves a combination of physical exercise and cognitive stimulation, and should be neurobiologically guided. Here the key would be to shift the timing of exercise. E.g. if physical exercise is performed at noon every second day, one group could perform cognitive exercise one hour prior to the physical exercise session, and one group one hour after. Critically, for the latter group, both BDNF 298 and dopamine levels 122 are be boosted compared to the former group’s. As the critical question is the timing of optimal plasticity, not specificity of cognitive transfer, initial studies could utilize multifactorial cognitive training protocols to boost transfer effects 300,301 despite loss of information on the specificity of cognitive training gains.

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Conclusion This thesis investigated the effects of aerobic exercise on cognition, brain structure, and dopamine in healthy older adults, and the findings are concluded as follows:

i. The cognitive benefits of performing aerobic exercise in old age should be seen as general rather than specific to any cognitive domain (Study I). This can explain why previous research has provided mixed results. Plasticity-related factors elevated from aerobic exercise likely interact with the environment to determine an individual’s specific functional outcome.

ii. Higher aerobic fitness was associated with larger dlPFC cortical thickness baseline, but not 6-month changes (Study I). The opposite pattern was observed for the hippocampus, exhibiting a linear increase only with fitness changes. Whereas increases in neurogenic and angiogenic factors influences the hippocampus, cortical thickness supposedly involves neuroprotective factors accumulating over time to maintain brain structure and function.

iii. In longitudinal PET imaging, both volumetric and receptor changes over time could bias data. The phantom design in Study II allowed testing the performance of potential image reconstruction algorithms, and estimating exactly how large the bias depending on size would be, i.e. the magnitude of the PVE. Results supported the use of high-resolution algorithms, which contrary to some reports did not introduce a bias depending on receptor levels at the normal range of D2R in the striatum. Further, as changes in the size of the striatum over 6 months in Study III were small, we could be certain that volume did not influence results through the PVE.

iv. Higher aerobic fitness was associated with higher D2R availability throughout the striatum at baseline (Study III). This provided the first evidence in healthy older adults that aerobic fitness is important for preserving D2R system integrity. Changes in aerobic fitness were associated with a reduction in D2R availability, putatively due to elevated levels of synaptic DA. Elevated DA from an intense period of exercise hypothetically to increased D2R expression in the long run, as was shown at baseline.

v. Finally, increased DA indirectly mediated the effect of improved aerobic fitness on improved working memory updating performance (Study III). Hence, data indicates that exercise-induced elevation of striatal DA to improve cognition is one neurocognitive mechanism of aerobic exercise.

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