Diffusion Tensor Imaging of Cerebral White Matter: A Pictorial Review of Physics, Fiber Tract...

Diffusion Tensor Imaging of Cerebral White Matter Integrity inCognitive Aging

David J. Maddena,b,*, Ilana J. Bennettc, Agnieszka Burzynskad, Guy G. Potterb, Nan-kueiChena,e, and Allen W. Songa,e

aBrain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710 USAbDepartment of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham,NC 27710 USAcCenter for Brain Health, School of Behavioral and Social Sciences, University of Texas at Dallas,Dallas, TX 75235 USAdMax Planck Institute for Human Development, Lentzeallee 94 D-14195, Berlin, GermanyeDepartment of Radiology, Duke University Medical Center, Durham, NC 27710 USA

AbstractIn this article we review recent research on diffusion tensor imaging (DTI) of white matter (WM)integrity and the implications for age-related differences in cognition. Neurobiologicalmechanisms defined from DTI analyses suggest that a primary dimension of age-related decline inWM is a decline in the structural integrity of myelin, particularly in brain regions that myelinatelater developmentally. Research integrating behavioral measures with DTI indicates that WMintegrity supports the communication among cortical networks, particularly those involvingexecutive function, perceptual speed, and memory (i.e., fluid cognition). In the absence ofsignificant disease, age shares a substantial portion of the variance associated with the relationbetween WM integrity and fluid cognition. Current data are consistent with one model in whichage-related decline in WM integrity contributes to a decreased efficiency of communicationamong networks for fluid cognitive abilities. Neurocognitive disorders for which older adults areat risk, such as depression, further modulate the relation between WM and cognition, in ways thatare not as yet entirely clear. Developments in DTI technology are providing new insight into boththe neurobiological mechanisms of aging WM and the potential contribution of DTI tounderstanding functional measures of brain activity.

KeywordsMagnetic resonance imaging; Brain; Behavior; Adult development; Neuroaxonal damage

© 2011 Elsevier B.V. All rights reserved.Correspondence to: David J. Madden, Ph.D., Brain Imaging and Analysis Center, Box 2737, Hock Plaza, Duke University MedicalCenter, Durham, NC 27710 USA, [email protected], Ph: (919) 681-9345, FAX: (919) 681-7033.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to ourcustomers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review ofthe resulting proof before it is published in its final citable form. Please note that during the production process errors may bediscovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

NIH Public AccessAuthor ManuscriptBiochim Biophys Acta. Author manuscript; available in PMC 2013 March 1.

Published in final edited form as:Biochim Biophys Acta. 2012 March ; 1822(3): 386–400. doi:10.1016/j.bbadis.2011.08.003.

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1. IntroductionThe recent decade has seen a rapidly emerging body of scientific investigation in magneticresonance imaging (MRI) of the brain and cognition, including the changes associated withhuman aging. The majority of cognitive and behavioral investigations have focused on eitherstructural MRI measures of brain volume and pathology [1–5] or functional MRI (fMRI)measures of the blood-oxygen-level-dependent (BOLD) changes in cortical gray matter(GM) [6–8]. Research with another form of MRI, diffusion tensor imaging (DTI), hasprovided a new and complementary perspective on age-related differences in brain structureand function, by allowing an in vivo characterization of the microstructural properties ofwhite matter (WM) [9–11]. Measures from DTI exhibit increased sensitivity to age-relateddecline, relative to volumetric measures [12–14], and DTI is beginning to show promise forthe detection of brain disorders such as Alzheimer’s disease and related forms of dementia(see Section 4, below, and Gold, this issue).

The success of this clinical translation, however, rests on the foundation provided by DTI tostudies of aging and WM in the absence of significant disease. Measures from DTI arepromising in this regard because they comprise several indices with a sufficient range ofvalues to permit detailed analyses of normative data. In addition, independently of clinicaltranslation, the empirical measures from DTI can be combined with behavioral measures ofcognitive performance, to further advance understanding of structure-function relations [15].Research with healthy adults has enabled the development of larger sample sizes andincreased statistical power, yielding more reliable estimates of the relations between WMintegrity and cognitive performance, and of age-related effects [16–18].

Most frequently, DTI research on aging is conducted with different groups of individuals atone point in time and thus yields information regarding age-related differences (i.e., cross-sectional differences). The vast majority of the research we consider here is cross-sectional,and thus the estimated effects of age may be influenced by a variety of individualdifferences between the age groups (cohort differences). Measurement of the sameindividuals over time is necessary to assess age-related changes within individuals (i.e.,longitudinal changes). Historically, longitudinal MRI studies have focused on measures ofbrain volume and pathology, such as WM lesions [19–22], but longitudinal studiesconducted with DTI are beginning to emerge [23–25]. Our goal in this article is to reviewthe application of DTI for understanding the changes in both WM integrity and cognitiveperformance associated with aging.

2. Microstructural properties of WM integrity2.1 Measurement of diffusivity in white matter

Measures from DTI allow inferences about WM microstructure in vivo by quantifying thedirectionality and rate of diffusion of water within tissue. At the level of the basic unit of theMRI image (i.e., voxel), this information is represented mathematically as a diffusionellipsoid. Diffusion along the major axis or eigenvector (λ1) of the ellipsoid is termed axialdiffusivity (AD), whereas the average of the second and third minor axes (λ2, λ3), termedradial diffusivity (RD), reflects diffusivity perpendicular to major axis of the tensor [26–29].

The most frequently used DTI summary measures are fractional anisotropy (FA) and meandiffusivity (MD). Fractional anisotropy represents the fraction of the tensor that can beassigned to anisotropic (directional) diffusion [26, 30–31]. Values for FA range between 0and 1, with higher values reflecting increased directionality of diffusion, independently ofthe rate of diffusion. The directionality of diffusion depends on the density of physicalobstructions such as membranes and the distribution of water molecules between different

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cellular compartments. Thus, FA is typically higher in WM, in which diffusion is restrictedby the myelin sheaths of axons, particularly in compact tracts with uniform fiber alignment,such as the corpus callosum, whereas diffusion in GM is less bounded and more isotropic.MD is a mean of all three axes of the diffusion ellipsoid and reflects the rate of waterdiffusion within a voxel, independently of the directionality.

Researchers have used several different methods of post-processing for DTI data andpresenting the results [9–11, 32]; and Figure 1 illustrates four approaches. In region ofinterest (ROI) analysis (panel A), data are extracted from regions that are placed directly onthe DTI image, typically in native (non-normalized) space, for each individual. Voxel-basedmorphometry (VBM; panel B) and tract-based spatial statistics (TBSS; panel C) both rely ongroup-normalized data to estimate either the tissue composition of individual voxels (VBM)or the “skeleton” of WM tracts (TBSS). Fiber tracking of WM pathways (tractography;panel D) is typically conducted in native space, although information from normalizationmay be incorporated into the procedure.

Fiber tracking uses seed and target ROIs to estimate the location of sets of spatially coherentWM fibers, and tractography is emerging as a widely used procedure for DTI analysis.Individual fiber tracts can be color-coded in terms of tract identity, as in Figure 1, butalternatively, color is often used to represent either the diffusion directionality or a relatedproperty of individual voxels within a tract. Figure 2 is an example of WM tractography inwhich seed and target ROIs (panel A) are placed on a DTI image as the basis for estimationof a WM pathway, in this case the genu of the corpus callosum (panel B). The estimatedfiber tract can be simplified by representing a “tube” of the mean values of the fibers withina tract (panel C). Color-coding the diffusion property of individual voxels (typically FA) canillustrate variation in WM integrity within the tract. In the case of the genu, for example, FAis higher in the center (blue-green values), where the fibers are most compact anddirectionally coherent.

The parameters of the ellipsoid reflect distinct aspects of WM microstructure. Animalmodels of nerve injury suggest that different types of WM injury lead to differential effectsin AD and RD. S. K. Song, Sun, and colleagues have reported that axonal damage withoutassociated myelin injury led to a decrease in AD without accompanying change in RD up tothree days after injury, whereas at 14 days following injury RD increased significantly,consistent with myelin damage and secondary (Wallerian) degeneration [33–36]. Theinterpretation of the neurobiological mechanisms of AD and RD, however, is complicatedby many variables (Section 2.4), for example, the local architecture of WM tracts withcrossing fibers, in which case the major axis of the tensor model may not be parallel tomyelinated axons [37–38]. Assaf and Pasternak [39] suggested that conjoint analysis of allmeasures derived from the diffusion tensor should yield a more comprehensive picture ofdifferent elements of WM microstructure. By analyzing FA and MD conjointly with RD andAD, it is possible to distinguish between diffusivity patterns with different neurobiologicalfoundations. In this approach, for example, Wallerian degeneration is characterized bydecreases in both FA and AD, combined with an RD increase and no net difference in MD[36–37, 40–42].

2.2 Adult age differences in WM diffusivity: Neurobiological mechanismsAcross many DTI investigations of aging, a general trend of the findings is a decrease in FAand an increase with measures of diffusivity, with increasing adult age, suggesting an age-related decline in the composition and integrity of WM [16–18]. These DTI findings arevalidated by neurobiological data. Post-mortem histological studies show that advanced agein the absence of neurological disease is linked to alterations of virtually all the WMcomponents. Some fraction of axons degenerate and swell, myelin becomes less compact,

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degenerates, or forms redundant wraps; glial cells accumulate cellular debris, form glialscars, increase in number or show activated morphology [43–47]. At the technical level, asnoted previously, the presence of crossing fibers within a voxel, as well as estimation errorin the primary eigenvector in the tensor model [37–38], lead to difficulties in directlycorrelating specific neurobiological mechanism with individual DTI parameters.

Variability in the pattern of age-related differences in DTI measures, across brain regions,also complicate direct interpretation of the DTI data but do help to provide a link toneurobiology. The first series of DTI studies of age-related effects, using an ROI approach,reported consistently that aging was associated with decreased FA in the genu of the corpuscallosum and associated frontal pericallosal regions, whereas age-related decline was lesspronounced (though detectable) in the splenium of the corpus callosum and parietal WM[48–50]. This anterior-posterior gradient was replicated in subsequent investigations [51–58].

Davis et al. [54] have demonstrated that this gradient is not isomorphic with the concept ofage-related decline in frontal lobe functioning [59–61]. Specifically, tracts traversing thefrontal lobe in an anterior-posterior direction (e.g., the uncinate fasciculus and cingulum)showed a monotonic decrease in the age-related decline in FA in an anterior-posteriordirection, and these age differences in FA did not change abruptly the boundary of thefrontal lobe. This pattern of age-related difference in WM integrity is in line withmylodegeneration as a potential neurobiological mechanism for regional variation in age-related differences in FA. That is, those brain regions that are the latest to myelinate fully,during development, may be those most vulnerable to the negative effects of adult aging.These include frontal regions but also more posterior sites (e.g., occipitotemporal boundary).The assumption is that oligodendrocytes of associative WM pathways are the mostmetabolically active cells and thus correspondingly vulnerable to the accumulation ofmetabolic damage. The anterior-posterior gradient is not an entirely comprehensiveframework, however, and Sullivan and colleagues have recently proposed that age-relateddecline in FA can also be characterized by a superior-inferior gradient, particularly withregard to fibers tracts in the internal capsule (e.g., centrum semiovale and corticospinal tract)[62–64]. Both the anterior-posterior and superior-inferior gradient theories share theassumption that damage within white matter pathways decreases the efficiency ofcommunication among the widely distributed neural systems comprising cognition, thusleading to a disconnection of cognitive networks [4, 65–70].

2.3 Axonal versus myelin degenerationAnalyses of the magnitude of age-related differences in AD and RD suggest that age-relatedRD differences(usually increases) are more prominent than AD differences [54–55, 71–72],which has been interpreted as support for a predominantly myelin-specific effect in aging.Age-related decreases as well as increases in AD, however, have been reported [12, 63–64],and thus differential age-related effects in axonal and myelin-specific variables are not clear.Two recent studies, by Bennett et al. [51] and Burzynska et al. [53] provided a morecomplete account by examining systematically the age-related differences in the diffusivitycomponents, in voxelwise analyses. Both of the studies used TBSS to analyze FA, AD, andRD concurrently, but Burzynska et al. also included MD as a variable.

From the whole-brain analyses of the diffusivity components, Bennett et al. [51] reportedthree main patterns of age-related difference, and Burzynska et al. [53] obtained five distinctpatterns. The two studies agree that the most spatially prominent pattern was a decrease inFA for older adults, relative to younger adults, accompanied by an increase in RD. In asubset of regions (e.g., genu of the corpus callosum), this pattern also included an age-related increase in MD and AD. Figure 3 (panels A and B) illustrates this result in the

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Burzynska et al. data. Table 1 provides a direct comparison of the two data sets as well asproposed neurobiological mechanisms for different patterns.

When age-related increases in both RD and AD are present, it is likely that pathology ofboth axon fibers and the surrounding myelin sheaths is involved. Cellular debris clearanceby phagocytic cells limits the restriction of water diffusion and contributes to an increase inisotropic diffusivity. In some regions (e.g., fornix, in Table 1), severe fiber degenerationmay lead to decreased tract volume, and, as a result, to a partial volume effect with thesurrounding cerebrospinal fluid (CSF), which magnifies the effect of the increase in theisotropic diffusivity. This pattern of diffusivity differences likely reflects higher extracellularvolume fraction and lower membrane density as a result of both myelin disruption and axonloss, a smaller volume fraction of axons, an increase in axonal spacing [31], as well as areduction in extracellular tortuosity [73]. A more specific disruption of myelin is impliedwhen an increase in RD occurs without an accompanying increase in AD (e.g., forcepsmajor, dorsal cingulum, in Table 1). Age-related increase in RD is a signature ofdemyelination is consistent with animal models, for example in the loss of myelin due toischemia [33, 35]. In human post-mortem data, from multiple sclerosis patients, RDcorrelated with demyelination severity [74–75].

Bennett et al. [51] and Burzynska et al. [53] both reported that some WM regions in olderadults (e.g., superior corona radiata, in Table 1) were characterized by a pattern in which thedecreased FA and increased RD were accompanied by a decreased AD, resulting in no netdifference in MD. This type of diffusivity pattern has been observed in secondary(Wallerian) degeneration, which is the degeneration, over time, of axon fibers distal to thepoint of transsection or injury [36–37, 40–42]. This process involves initial axonal beading,axolemma breakdown, organelle accumulation, and finally the breakdown of myelin andoligodendrocyte apoptosis. The cellular debris is cleared by activated microglia and glialscar is formed by astrocytes [76]. This glia infiltration, which restricts AD and decreasesMD [77] differentiates Wallerian degeneration from mere loss of fibers and myelin. Localischemic lesions, such as WM lesions typical for the healthy older population [1–2], may bethe primary lesions from which Wallerian degeneration originates in the aging brain [3],although decline in FA associated with healthy aging appears not to be dependent entirelyon WM lesions [53].

Finally, the Bennett et al. [51] and Burzynska et al. [53] studies reported that some WMregions of older adults showed a decrease in FA accompanied by a decrease in AD and MD,without a difference in RD (e.g., cerebral peduncles, in Table 1). Lowered FA driven bydecreased AD is considered a marker of acute and primary axonal damage and wasobserved, in human patients, in the early post-injury phase after corpus callosotomy [78] andin optic neuritis [79], as well as in various animal models of axonal injury [33, 80–85]. Theextent of acute axonal injury in normal aging is not clear, but its origin may be ischemic andrelated to development of WM lesions. Bennett et al. proposed that a decrease in FA and ADwith no significant change in RD and MD may be also a result of disrupted macrostructuralreorganization of the fibers, such as less coherent fiber alignment.

2.4 Limitations in interpreting axial and radial diffusivityAs noted previously (Section 2.1), it is difficult to distinguish components of themicrostructural pathology based on DTI indices alone. Although analyzing multiple DTIindices simultaneously provides the most information, the link to neurobiology remainsincomplete at this time. In the case of chronic WM lesions, for example, two post-mortemstudies of multiple sclerosis patients, combining DTI with histology, reported that myelincontent and axonal count are strongly correlated [74–75]. Specifically, lesions in thesepatients were characterized by lower myelin content, axonal loss, and gliosis, expressed by

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lower FA and higher MD, RD and AD values. Furthermore, in multiple regression analysesconducted on each DTI index, both myelin content an axonal count were concurrentpredictors of all indices except AD. This latter variable was related to myelin content but notto axonal count [74]. Thus, RD is sensitive but not specific to demyelination and may bemore appropriately considered as a marker of overall tissue integrity [74].

Recent findings suggest that partial volume effects (i.e., incorrect assignment of a voxel asWM) may have a significant effect on the estimation of age-related effects. Vos et al. [86]found that estimated diffusivity parameters are differentially affected depending on whetherpartial volume represented CSF, WM with different principal eigenvector orientation, orGM. In addition, the partial volume effect varied as a function of fiber bundle volume,orientation, and curvature. In view of the WM structural changes associated with aging, Voset al. recommend including these structural variables as covariates in analyses of age-relatedeffects. Similarly, Miller et al. [87] compared ultra-high resolution DTI images (0.73 mmisotropic voxels), acquired post-mortem, to data blurred to resemble the more typical scanresolution of 2–3.5 mm3, and found that the lower resolution images led WM tracts toappear thinner than they actually were, due to partial volume effects. Other investigations,however, that have included post-processing methods to specifically address partial volumeeffects, have reported that age-related differences in DTI measures remain detectablefollowing consideration of partial volume effects [71, 88]. Thus, although partial volumingrelated to age-related atrophy clearly affects thin tracts such as fornix (Table 1) [3, 53], age-related differences in diffusivity in other WM structures likely reflect true effects of age onmicrostructure.

3. Cognitive performance and WM integrity in agingDuring healthy aging, behavioral measures of fluid cognitive abilities (those relying onelementary perceptual speed and sensory/motor functioning) tend to decline, whereasmeasures of crystallized cognitive abilities (those relating to knowledge and expertise)exhibit little or no decline until very late in life [89–92]. Imaging studies of WM volume andpathology, particularly studies of the lesions that appear as WM hyperintensities, have longindicated a relation between WM integrity and fluid cognition, particularly in samples ofolder adults [19–20, 93–98]. Building on initial reports by O’Sullivan et al. [69] andSullivan et al. [49], evidence from DTI is rapidly accumulating to indicate thatmicrostructural integrity of normal appearing WM has some role in age-related decline incognitive functioning in healthy adults [16–18].

In a previous review of DTI studies of cognitive aging [16], we discussed severalinterpretive issues that currently limit the characterization of the specific relation betweenWM integrity and cognitive aging. Among these were the focus, in previous research, on theoverall variance in outcome measures rather than the age-related variance. That is, fewstudies have either a) investigated the degree to which individual differences in WMintegrity share age-related variance in cognitive performance; or b) directly compareddifferent age groups in the correlation between WM integrity and cognition. Salthouse [99]has reviewed these and related issues, particularly the failure of previous studies to evaluatecompeting models of the relations among neuroanatomy, age, and cognition. Salthouseproposed that, at the present time, the evidence for concluding that aspects of brain structureare neuroanatomical substrates of age-related cognitive decline is weak. For example, if therelation between a neuroanatomical variable and cognition is reduced when the effect of ageis controlled statistically, different interpretations are possible. This pattern may occurbecause age has a mediating role in the relation between the brain and cognition [100].Alternatively, the neuroanatomical variable and cognition may actually be independent andonly apparently related to each other by virtue of their relations to age.

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Recent empirical investigations have taken disparate approaches to characterizing therelation between WM integrity and cognition in aging. In Table 2, we summarize the resultsof 29 published DTI studies addressing this relation; this summary adds 11 published reportsto a corresponding table in our previous review [16]. We identified these studies fromPubMed searches using the terms “white matter integrity AND aging” and “diffusion tensorimaging AND aging AND cognitive.” From this output, we selected articles that wereempirical investigations of WM integrity-cognition effects in healthy older adults. Articlesthat used global rather than regional DTI measures [101–104] or used a measure of WMintegrity other than FA [65, 105] were excluded. In this version of the table, we focusspecifically on the three cognitive domains of executive function (e.g., maintaining arelevant task set and inhibiting distraction), processing speed, and memory, which is adivision that is often used in studies of WM and aging [103–104, 106–109].

Table 2 presents mean effect sizes [110] for relationships between FA and cognitiveperformance across nine brain regions. For each of the cognitive domains, results arepresented separately for studies in which FA-cognition relationships were reported for bothyounger and older adults, or in older adults alone (i.e., when only older adults wereexamined or were reported separately from younger adults). Bold font indicates effect sizesthat are moderate or larger (≥ 0.30). The reported data primarily suggest a stronger relationbetween FA and cognition for executive function and processing speed than for memory.This pattern has been noted previously [109, 111], but few direct comparisons amongcognitive domains have been conducted. The cognitive domains in Table 2 are complex andlikely to rely on multiple brain regions; at the present time, the WM-cognition relation doesnot exhibit a high degree of regional specificity [16, 108]. Within individual studies,however, reliable differentiation of specific information processing components has beenobserved. Madden et al. [55], for example, reported that FA within the genu and spleniumwas correlated with the efficiency of semantic memory retrieval but not with perceptual-motor speed. Bennett et al. [112] found that frontoparietal FA correlated with visual searchspeed, independently of perceptual-motor speed. The anterior-posterior gradient of age-related decline in FA also appears to correlate differentially with tests of speed andexecutive function (anterior) versus memory (posterior) [54, 108].

A new feature of Table 2 is that mean effect sizes are presented separately for: a) the rawbivariate correlations between FA and cognition, without age included in the model; and b)analyses that controlled statistically for the effect of age or age group (i.e., age partialedvalues). Thus, by comparing the raw bivariate and age-partialed values, we can estimate thedegree to which WM integrity and cognition share age-related variance. That is, some formof age dependence is implied when the effect size for the WM-cognition relation decreasesfrom the raw bivariate to the age-partialed version.

The current selection of published research yielded 28 raw bivariate correlations betweenFA and cognition that were both ≥ 0.30 (in bold font) and were accompanied by an age-partialed analysis. For 23 of those correlations, the raw correlation decreased to below 0.30when age was partialed, which was less than half the size of the original raw value for 18 ofthe correlations. That is, in nearly two-thirds of cases with an FA-cognition effect ofmoderate or greater size, age shared more than 50% of the variance in cognition that wasrelated to FA.

Independently of those 28 correlations with both raw and age-partialed values, Table 2 alsocontains 15 correlations from studies that only reported the age-partialed effects. Of these15, eight were of at least moderate size (≥ 0.30) and seven were smaller in magnitude. Thus,studies that reported only age-partialed effects yielded approximately equal numbers ofsmaller and moderate effect sizes for the WM-cognition relations. From this finding, as well

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as from the previously discussed comparison of raw bivariate and age-partialed values, weconclude that age shares a substantial portion of the of the relation between WM integrityand cognition. As Salthouse [99] has noted, however, from this pattern alone we cannotdetermine whether age is directly influencing a relation between WM and cognition. The FAand cognitive variables may be independent but both related to age. In addition, bypartialing age, the contribution of other variables that are associated with age (e.g., healthstatus variables such as aerobic capacity) is also removed [113–115].

Whereas Table 2 illustrates the effect of age in the relation between WM integrity andcognition, several investigations have taken a different approach, using multiple regressionand related techniques to identify the role of WM integrity in the relation between age andcognition. Following the addition of FA to models of the age-cognition relation, theproportion age-related variance is substantially reduced, yielding evidence for WM integrity(particularly within frontoparietal regions) as a mediating variable in age-related decline inthe efficiency of semantic categorization [55], shifting attentional sets [116–117], andimplicit learning [118]. Zahr et al. [64], however, compared these two approaches within thesame data set and reported that the attenuation of the relation between FA and workingmemory, by age, was at least as great in magnitude as the attenuation of the age-relatedvariance by FA. Thus, WM integrity and working memory may have independent relationsto age, without age exhibiting a directly causal role in the WM integrity-working memoryrelation [99].

The identification of specific causal relations is possible in path-analytic and relatedtechniques (e.g., structural equation modeling; SEM) that investigate the direct and indirectpathways of covariation among multiple variables [119]. Charlton et al. [120] have exploredSEM models for the relations among age, cognition, and MD (as a measure of WMintegrity) in a sample of adults 50–90 years of age. The results indicated that WM integrity(from a relatively large ROI: centrum semiovale and periventricular WM) mediated the age-related decline in working memory performance. Burgmans et al. [12] described an SEMmodel in which AD from a large ROI (WM in the parietal, temporal, and occipital lobes)mediated the age-related decline in pattern and letter comparison speed. Voineskos et al.[121] used DTI tractography rather than ROIs and developed an SEM model in which tract-specific FA mediated the relation between age and various forms of cognitive performance.These more sophisticated statistical methods will undoubtedly be valuable for understandingthe role of WM integrity in cognitive aging. Their application, however, will need to betempered by awareness that modeling is more appropriate for comparing different,competing models than for describing or exploring data [122–123], and that measures ofWM from different tracts may not be independent but instead reflect shared variability inWM integrity across tracts [103, 124].

4. White matter integrity in geriatric neuropsychologyAlthough the main focus of this review is healthy aging, WM integrity plays an importantrole in many age-associated neurocognitive disorders, ranging from Alzheimer’s disease andother forms of dementia, to late-life depression, and the effects of stroke and traumatic braininjury [125–126]. Gold (this issue) provides a more detailed review of issues relating toAlzheimer’s disease and mild cognitive impairment. Measures from DTI, includingtractography, are beginning to offer insights into neurological disease diagnosis andtreatment planning [127– 129].

4.1 Alzheimer’s disease and other forms of dementiaA fundamental question is whether Alzheimer’s disease differs qualitatively from normalbrain aging. Several DTI studies suggest that Alzheimer’s-related WM changes are not

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simply an exaggeration of the anterior-posterior gradient of normal aging but instead involveother brain regions (e.g., temporal) [130–133]. Both decreased FA and increased MD in thehippocampus and temporal regions appear to be associated with the episodic memorydeficits that characterize the earliest stages of this disease [134–135]. Multivariate analysesof whole-brain WM have suggested that mean diffusivity maps perform slightly better thanFA maps in classification of individuals with Alzheimer’s disease versus normal elderly[136]. Interestingly, even asymptomatic individuals with a family history of Alzheimer’sdisease show reduced FA in regions typically affected by the disease, which suggests thatWM integrity may be sensitive to Alzheimer’s-related pathology years before disease onset[137]. Data from DTI have also assisted in characterizing the transition from a preclinical tomanifest disease [132, 138–139]. Research with DTI has also clarified some of the clinicaland neuropathological differences between Alzheimer’s disease and other dementias,including frontotemporal dementia [140], vascular dementia [141], and dementia associatedwith Parkinson’s disease [142–144].

4.2. Late-life depressionDepression is the most prevalent psychiatric disorder and is particularly problematic in olderadulthood because of its association with cognitive impairment and other adverse outcomes[145–146]. Region of interest based studies of late-life depression have found lower FA fordepressed individuals relative to controls, particularly in frontal brain regions [147–148];however, whole brain, voxel-based approaches have reported more widely distributeddecrements in WM integrity [149–150]. Some studies have also found higher MDthroughout the brain of older adults relative to controls, but most prominently in frontalregions [151]. The WM differences associated with late-life depression have also beenassociated with worse neuropsychological performance on tests of executive function(Stroop, Tower of Hanoi) and processing speed, although regional localization varies [150–152].

4.3 Plasticity of white matterThe prospect that WM integrity may increase in response to cognitive training has importantimplications for diagnosing and treating age-related cognitive disorders. Lovden et al. [106]compared DTI measures for younger and older adults before and after an intensive trainingregimen (101 1-hr sessions over 180 days), on tests of working memory, episodic memory,and perceptual speed. Relative to a no-training control group, individuals in the cognitivetraining group exhibited greater increase in FA and decrease in MD, in the genu of thecorpus callosum. Further, the magnitude of the training-related improvements in WMintegrity was as great for older adults as for younger adults. This type of plasticity of WMhas also been demonstrated following brain surgery in humans. Yogarajah et al. [153]investigated epilepsy patients before and after temporal lobe resection and noted bothpositive and negative changes in WM integrity following surgery. In particular, largerincreases in AD in the ventro-medial language network after the surgery were associatedwith smaller fall in post-operative verbal fluency, suggestive of structural reorganization.Sidaros et al.[154] and Bendlin et al. [155] examined the utility of DTI measures forassessing recovery following traumatic brain injury. Although the changes in WM integrityfollowing brain injury are not entirely consistent, and a complete model of adult brainreorganization associated with injury is not available, DTI will be valuable for furtheradvances on these issues. The concept of plasticity is particularly important in view of theresults from fMRI, in healthy older adults, which suggest that some patterns of functionalbrain activity represent a direct response to structural changes [153, 156–158].

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5. Technical developments in DTIAdvances in the technology of DTI can contribute significantly to both defining theneurobiological mechanisms of WM integrity in aging and identifying clinical translation.As discussed previously (Section 2), a difficulty in understanding age-related decline in FAis the presence of crossing fibers within a voxel, which may not always be captured at thelevel of spatial resolution available. Thus, a decline in the structural integrity of WM may bedifficult to distinguish from a change in the local coherence of fibers [37–38, 51]. Similarly,atrophy and related structural changes associated with aging may contribute to estimates ofage-related differences in DTI measures [86–87]. Several types of improvements in DTIresolution, and the integration of DTI and fMRI measures, promise to provide a moreaccurate characterization of age-related WM effects.

5.1. Correction for geometric distortions in single-shot-EPI-based DTI dataMost DTI data in investigations of aging and cognition are acquired with single-shot echo-planar imaging (EPI) pulse sequence, which has superior imaging speed and is relativelyinsensitive to intra-scan motion. However, it is well known that the spatial resolution andspatial accuracy are lower in EPI, as compared with other clinical MRI pulse sequences.Specifically, DTI data obtained with EPI are geometrically distorted, resulting from bothsubject-dependent susceptibility field gradients and subject-independent eddy currents. Oneapproach to reducing distortion is to use a twice-refocused DTI sequence [159], but a moreeffective and complete correction for both susceptibility and eddy current related distortionsrelies on extra reference scans, in the form of field inhomogeneity maps that correspond tothe various diffusion weighting directions [160]. Using this latter approach, Truong et al.[161] demonstrated that, by adding sensitivity encoding (SENSE) DTI acquisition and areconstruction algorithms with higher-order polynomial correction, significantly improvedspatial accuracy and signal-to-noise ratio can be obtained.

Recently, Truong et al. [162] further demonstrated that eddy currents-induced distortionshave a complex time-domain pattern. Truong et al. proposed an alternative dynamic B0mapping and off-resonance correction method that measures the exact spatial, temporal, anddiffusion-weighting direction dependence of the susceptibility- and eddy current-inducedmagnetic fields. As a result, both susceptibility effects and time-varying eddy currentartifacts can be effectively corrected, yielding DTI data with the highest possible spatialfidelity and accuracy. Figure 4 shows color-coded FA maps in three representative sliceswith no correction (A), static correction (B), and dynamic correction (C). The uncorrectedFA maps in three representative slices show severe susceptibility-induced distortions,particularly near the genu of the corpus callosum (Figure 4A, dashed lines), as well as eddycurrent-induced FA errors, most prominently at the anterior and posterior edges of the brain(arrows). The conventional static off-resonance correction can only correct for thesusceptibility-induced distortions, and for the eddy current-induced FA errors at theposterior edge of the brain, but not at the anterior edge (Figure 4B, arrows). In contrast, thedynamic off-resonance correction can more effectively correct for all artifacts (Figure 4C).

5.2. Beyond single-shot EPI acquisition: Alternative imaging pulse sequences for DTI ofhigh spatial-resolution

Several studies have shown that alternative imaging pulse sequences can be used to acquireDTI data with significantly improved spatial-resolution, relative to single-shot EPI basedDTI. First, high-resolution DTI with significantly reduced geometric distortion and motion-related artifact can be achieved through acquiring segmented k-space data along the readoutdirection (i.e., readout-segmented EPI or RS-EPI), in contrast to conventional multi-shot EPIwith k-space segmentation along the phase-encoding direction [163–164]. Second, the self-

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navigated interleaved spiral (SNAILS) sequence for high-resolution DTI, developed by Liuet al. [165] improves spatial resolution by using a multi-shot, fat-saturated diffusion-weighted spin echo sequence that oversamples the center of k-space, providing an inherentmotion compensation capability. Third, Pipe et al. [166] showed that high-resolution andhigh-quality DTI data can be obtained by integrating two sequences: fast spin echo imaging(which reduces distortion), and the periodically rotated overlapping parallel lines withenhanced reconstruction (i.e., PROPELLER), which increases tolerance to intra-scanmotion. It is also feasible to integrate a multi-shot EPI sequence and PROPELLER motion-artifact reduction, producing high-resolution DTI with reduced artifact [167].

5.3. Beyond single- tensor model: Diffusion measurement in q-space and at higherangular-resolution

Within the DTI framework, significant advances in spatial resolution have been achieved byusing extended scanning periods with post-mortem brains [87], and by using the long-rangeinformation contained in the fibers tracts, at higher MRI field strengths [168–169]. Thediffusion tensor model, however, relies on the assumption of Gaussian diffusion [170], witha single diffusion tensor within a voxel, which may not always be valid. To address this,researchers have been using different mathematical frameworks for measuring diffusion toincorporate non-Gaussian diffusion and as a result can better resolve multiple diffusiontensors within the same voxel (angular resolution).

The initial methods for non-Gaussian diffusion measurement include q-space imaging, inwhich the probability density function for molecular diffusion is estimated without theassumption of a Gaussian distribution [171–172]. More recent approaches include diffusionspectrum imaging (DSI) [173–175], high-angular-resolution diffusion imaging [176–177],q-ball imaging [178], and generalized diffusion tensor imaging (GDTI) methods [179],among others. These methods can more accurately resolve crossing fibers, as compared withthe conventional DTI and in some cases can resolve multiple axon directions within a singlevoxel. For example, Figure 5 shows that more fibers can be correctly identified using DSI,in comparison to the conventional DTI model [175]. The patterns of crossing fibersobserved from these advanced imaging methods correspond to those obtained, in other datasets, from autoradiography [174], although validation within the same brain is needed [180].In addition, these new methods tend to require extensive scanning time, and thus moreefficient data acquisition techniques will be needed for applications in vivo.

5.4. Toward tissue specificity: Axon- and myelin-specific DTI measuresAge-related decline in WM integrity tends to be more pronounced in RD than in AD [54–55,71], consistent with a prominent role for decreased myelin integrity in aging [65–66].Multiple DTI indices concomitantly vary with adult age (Table 1), however, and assigningspecific neurobiological interpretation to DTI parameters is problematic (Sections 2.1, 2.4)[37–38].

The MRI pulse sequences may be modified in a way that the derived tensor measuresspecifically reflect physiological information of myelin. A recent study by Avram et al.[181] described a magnetization transfer (MT) prepared stimulated-echo DTI technique thatis sensitive to the microanatomy of myelin tissue. The short echo time enabled by thestimulated-echo acquisition preserves significant signal weighting from myelin water ofshort T2 time constant, and the MT preparation further provides differentiating sensitizationto this signal. This myelin water weighted DTI has the potential to lead to improvedassessment of myelin pathology and detection of degradation in myelin microanatomy.

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One recently developed method, the AxCaliber technique [182–183], will be valuable incharacterizing changes of white-matter microstructure related to aging, particularly withrespect to axonal populations with differing diameters. The AxCaliber technique provides anoninvasive estimate of diameter distribution within each voxel, based on the differences inrestricted diffusion, across populations of axons, at different diffusion times. Therefore, bychanging the diffusion time from short to long one can probe the relative contributions ofdifferent subpopulations, reflecting axonal diameter distribution, within each voxel. Largerdiameter axons generally occur in pathways supporting rapid neural transmission, such asthe pyramidal (corticospinal) tract. Single-cell recording of pyramidal tract neurons in thecat has demonstrated significant reduction in conduction velocity with age [184]. Humanpost-mortem studies, however, are divided as to whether age-related volumetric decline ismore pronounced for larger myelinated axons [45] or more thinly myelinated ones [44, 47,185]. The integrity of smaller, later myelinating fibers within association (e.g., prefrontal)cortex is correlated with maximum motor speed [105]. In view of the central role ofprocessing speed in theories of cognitive aging [186–187], and the relation between FA andspeed (Table 2), AxCaliber is a particularly promising method for investigations of agingand cognition.

5.5. Toward brain activations: Functional DTIIn contrast to functional MRI with BOLD contrast, which measures hemodynamic responsesassociated with neuronal activity, DTI is a structural imaging modality that assesses theintegrity of WM. Although neuroimaging research typically assumes that sensory andperceptual experience derives from neuronal activity, recent evidence suggests thatdetectable changes in WM occur in response to sensory stimulation. Mandl et al.[188]reported that changes occur in FA occur selectively in relevant WM tracts following sensorystimulation, when analyzed as a 200 sec time course. During and following theadministration of a 1 min tactile or visual stimulus, changes occurred in the thalamocorticaltracts for the tactile stimulus and in the splenium and optic radiations for the visual stimulus.The FA changes were relatively small in magnitude (~1%) and comprised both positive andnegative effects, but Mandl et al. proposed that the positive changes (stimulation-relatedincreases in FA) represent local fiber activity, possibly glial cell swelling, although thespecific biophysical mechanisms remain unknown.

5.6. Toward constructing a Brain Connectome: Graph theory representation of DTI basedconnectivity maps

The quantitative maps (e.g., FA maps) derived from DTI data include valuable physiologicalinformation for different brain regions. These data are derived either from an a prioriselection of relevant regions (e.g. tractography) or from an unbiased, voxelwise analysisacross the whole brain (e.g., TBSS). However, the DTI data alone do not define the higher-order structure among WM tracts.

Recently, researchers have been using new mathematical approaches, particularly graph-theory analysis [189–190], to assess the overall topographical network properties (e.g., theglobal efficiency) of FA maps and to estimate the relative strength of the connections amongtracts. Gong et al. [191], for example, analyzed DTI data from 80 healthy young adults andfound that the DTI derived topographical measures of connectivity networks resemble thatof a small-world architecture, which is characterized by greater local interconnectivityrelative to a random network. Iturria-Medina et al. [192] used DTI based graph-theoryanalysis to measure the overall network properties of the right and left hemispheres. Theirresults indicate that the right hemisphere is more efficient and interconnected, whereas theleft hemisphere comprises regions that are more central or indispensable (e.g., language) forthe whole-brain structural network. In a graph-theory study of individual differences in DTI

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tractography data, Li et al. [193] found that individuals with higher intelligence scoresexhibited WM networks with a shorter characteristic path length and a higher globalefficiency, indicating a more efficient parallel information transfer in the brain. The changesin WM integrity associated with normal aging, however, have not as yet been explored froma graph-theory perspective.

Researchers in the area of BOLD resting-state activity have demonstrated reliabledifferences in resting state functional networks associated with aging in healthy adults [194–197]. These differences, predominantly a reduction in connectivity in the default modenetwork of regions, centered in medial prefrontal and posterior cingulate cortical regions,reflect age-related differences in the resting state activity of the brain. Structural WMconnectivity, as assessed from DTI, appears to be an anatomical constraint on this functionalconnectivity [198–200]. In addition, DTI data suggest that age-related changes in cognitionrepresent the combined influences of WM integrity and resting state functional connectivity,not only within the default mode network but also within networks involving prefrontal andsubcortical regions [107, 109, 201]. Further studies that integrate DTI and fMRI measureswill be valuable in characterizing the structure-function relations within the human brainconnectome.

6. ConclusionInformation from DTI provides a unique perspective on cognition in aging by contributingseveral indices of the microstructural integrity of cerebral WM. To date, a wide range ofresearch investigations suggests that, even in the absence of significant disease, individualdifferences in WM integrity share a substantial portion of age-related variance in cognitiveabilities, particularly those related to elementary perceptual speed and executive function.Further, age-related differences in WM integrity vary across brain regions, with older adultsexhibiting relatively greater decline (lower FA, higher diffusivity) in more anterior andsuperior brain regions, which may reflect the later development of myelination in theseregions. Although preliminary findings suggest that the age-related effects derive moredirectly from changes in myelin structure than in axonal integrity, additional technicaldevelopments in DTI resolution and tissue specificity are needed to define theneurobiological mechanisms of WM changes associated with aging. At the interpretivelevel, it will be necessary to move beyond describing individual correlations amongvariables, to testing competing models of the relations among WM integrity, aging, andcognition. Initial evidence supports one model in which decline in cerebral WM integritydirectly contributes to the less efficient performance of fluid cognitive abilities that occursduring healthy aging.

Highlights

> We review diffusion tensor imaging (DTI) of white matter (WM) integrity inaging.

> Even without disease, age-related decline occurs in both cognition and WMintegrity.

> Age-related decline in WM integrity interrupts communication among brainnetworks.

> Disconnection of brain network may contribute to normal cognitive declinewith age.

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AcknowledgmentsPreparation of this article was supported by grants R01 AG011622, R21 NS065344 and R01 EB009483, F32AG038299, and K23MH087741 from the National Institutes of Health. We are grateful to Tim Salthouse for hiscomments on a previous version of this article.

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Figure 1.Methods for representing diffusion tensor imaging (DTI) data. A = regions of interest (incolor) placed directly on DTI image; B = voxel-based morphometry (VBM); C = mean“skeleton” of white matter tracts from tract-based spatial statistics (TBSS); D = fibertracking of white matter pathways. Image courtesy of Simon Davis, University ofCambridge.

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Figure 2.Fiber tracking methodology. A = seed and target regions placed on DTI raw image; B =estimated fibers of the genu of the corpus callosum passing through the seed and targetregions; C = “tube” of averaged fibers within the genu; D = voxels within averaged fiberscolor coded by age group differences in fractional anisotropy (FA), with the magnitude ofage-related decline in FA scaled from lower (blue) to higher (red) values [54]. Adapted withpermission from Neuroimage, Volume 46/Issue 2, S. W. Davis, N. A. Dennis, N. G.Buchler, L. E. White, D. J Madden, & R. Cabeza, Assessing the effects of age on long whitematter tracts using diffusion tensor tractography, pp. 530–541, Copyright 2009, withpermission from Elsevier.

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Figure 3.A = The spatial extent of five patterns of age-related difference in diffusivity, relative to thetotal volume of age-related decrease in FA values across regions (55,973 mm2), fromBurzynska et al. [53]; B = spatial localization of the main diffusivity pattern: age-relateddecrease in FA, increase in RD and MD (in red) and additionally an increase in AD (inorange); C = WMH volume accounts for age-related decrease only in a few WM regions.Age-related FA decrease (in yellow) is contrasted with age-related FA decrease afteraccounting for WMH volume (in red). FA = fractional anisotropy; RD = radial diffusivity;AD = axial diffusivity; MD = mean diffusivity; WMH = white matter Hyperintensity; ALIC= anterior limb of the internal capsule; BCC = body of the corpus callosum; EC = externalcapsule; FX = fornix; GCC = genu of the corpus callosum; MFG = middle frontal gyrus;SCC = splenium of the corpus callosum. Adapted with permission from Neuroimage,Volume 49/Issue 3, A.Z. Burzynska, C. Preuschhof, L. Backman, L. Nyberg, S. C. Li, U.Lindenberger, & H. R. Heekeren, Age-related differences in white matter microstructure:region-specific patterns of diffusivity, pp. 2104–2112, Copyright 2010, with permissionfrom Elsevier.

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Figure 4.Color-coded fractional anisotropy (FA) maps, with red = right–left, green = anterior–posterior, blue = superior–inferior. In three axial slices, the dashed lines and arrowshighlight regions with severe susceptibility-induced distortions and eddy current-induced FAerrors, respectively [162]. A = no correction. B = static correction; C = dynamic correction.Adapted with permission from Neuroimage, Volume 57/Issue 4, T.-K. Truong, N.-k. Chen,& A. W. Song, Dynamic correction of artifacts due to susceptibility effects and time-varyingeddy currents in diffusion tensor imaging, pp. 1343–1347. Copyright 2011, with permissionfrom Elsevier.

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Figure 5.A = Diffusion spectrum imaging (DSI) tractography of monkey cerebral cortex showingfibers within the white matter of the cortical gyrus, and radiate fibers within adjacent gyri ofthe cerebral cortex (arrows); B = DTI tractography of a corresponding region shows fewerfibers within the white matter of the gyrus and no radiate fibers within the cortex [175].Reprinted from Neuroimage, Volume 41/Issue 4, V.J. Wedeen, R.P. Wang, J.D.Schmahmann, T. Benner, W.Y.I. Tseng, G. Dai, D.N. Pandya, P. Hagmann, H. D'Arceuil, &A.J. de Crespigny, Diffusion spectrum magnetic resonance imaging (DSI) tractography ofcrossing fibers, pp. 1267–1277, Copyright 2008, with permission from Elsevier.

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Table 1

Patterns of adult age differences in white matter diffusivity properties

Anatomical location Pattern for older adults,relative to younger adults

Biologicalinterpretation

Genu of the corpus callosum FA ↓ RD↑ AD↑ (MD↑)* Loss/disruption of both axons and myelin

Body of the corpus callosum (FA ↓ RD↑ AD↑ MD↑)*

External capsule FA ↓ RD↑ AD↑ (MD↑)*

Anterior dorsal cingulum (FA ↓ RD↑ AD↑ MD↑)*

Fornix FA ↓ RD↑ AD↑ (MD↑)* Loss/disruption of both axons and myelin; partial volume with CSF

Anterior limb of internal capsule (FA↓ RD↑ MD↑)* Predominant myelin disruption or loss

Middle frontal gyrus (SLF) FA ↓ RD↑ (AD↑ MD↑)*

WM of the straight gyrus FA↓ RD↑

Forceps major FA↓ RD↑

Sagittal stratum FA↓ RD↑ (MD↑)*

Posterior dorsal cingulum (FA ↓ RD↑)*

Superior corona radiata FA↓ RD↑ AD↓ Fiber loss with glial infiltration

Forceps minor FA↓ RD↑ AD↓

Retrolenticular internal capsule FA↓ RD↑ AD↓

Parahippocampal WM FA↓ RD↑ (AD↓)*

Posterior limb of internal capsule (FA↓ AD↓ MD↓)* Gliosis or early axonal injury/Less coherent tract organization

Cerebral peduncles FA↓ AD↓

Note. The patterns of age differences (second column) are expressed as the direction of significant diffusivity effects, for older adults, relative toyounger adults, reported by Bennett et al. [51] and Burzynska et al. [53]. FA = fractional anisotropy; AD = axial diffusivity; RD = radialdiffusivity; MD = mean diffusivity; CSF = cerebrospinal fluid; SLF = superior longitudinal fasciculus; WM = white matter; ↓ = decrease; ↑ =increase.

*These effects (within parentheses) were specific to Burzynska et al. [53]. The remaining effects were common to both Bennett et al. (2010) and

Burzynska et al. [53]. None of the effects was specific to Bennett et al. [51].


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Madden et al.

Tabl

e 2

Sum

mar

y of

agi

ng st

udie

s exa

min

ing

rela

tions

hips

bet

wee

n fr

actio

nal a

niso

tropy

(FA

) and

cog

nitiv

e pe

rfor

man

ce

Cog

nitiv

e D

omai

nB

rain

Reg

ion

Fron

tal

CC

gIn

f Fro

ntIC

Mid

Sup

CC

sPa

rO

cc

Exec

utiv

e Fu

nctio

n

You

nger

and

Old

er

C

ompo

site

mea

sure

s [63

–64,

121

]

Raw

biv

aria

te—

0.62

0.34

0.53

0.65

0.28

0.24

——

Age

par

tiale

d—

0.06

——

0.06

——

——

S

et sh

ift/ta

sk sw

itch

[108

, 116

–117

, 202

]

Raw

biv

aria

te0.

400.

650.

61—

0.22

0.45

—0.

400.

26

Age

par

tiale

d0.

25—

0.52

—0.

230.

46—

0.29

0.23

W

orki

ng m

emor

y [1

08, 2

02]

Raw

biv

aria

te0.

420.

30—

—0.

18—

—0.

470.

04

Age

par

tiale

d0.

14—

——

−0.04

——

0.18

0.03

M

emor

y sp

an [1

08, 2

02]

Raw

biv

aria

te0.

130.

28—

—0.

02—

—0.

060.

02

Age

par

tiale

d−0.02

——

—0.

00—

—0.

020.

06

Old

er o

nly

C

ompo

site

mea

sure

s [20

3–20

6]

Raw

biv

aria

te0.

380.

30—

—0.

330.

29—

0.30

—

Age

par

tiale

d0.

260.

020.

15—

0.07

0.20

—0.

05—

S

et sh

ift/ta

sk sw

itch

[54,

69,

116

]

Raw

biv

aria

te0.

41—

0.63

—0.

090.

47—

0.32

—

Age

par

tiale

d0.

09—

——

−0.34

——

−0.04

—

W

orki

ng m

emor

y [5

4, 2

07]

Raw

biv

aria

te0.

09—

0.57

——

0.41

——

0.28

Age

par

tiale

d—

——

——

——

——

M

emor

y sp

an [5

4]

Raw

biv

aria

te—

0.57

——

——

——

—

Age

par

tiale

d—

——

——

——

——


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Madden et al.

Cog

nitiv

e D

omai

nB

rain

Reg

ion

Fron

tal

CC

gIn

f Fro

ntIC

Mid

Sup

CC

sPa

rO

cc

F

luen

cy [6

9, 2

07–2

09]

Raw

biv

aria

te0.

09—

——

0.69

0.20

—0.

370.

20

Age

par

tiale

d0.

04—

——

0.61

——

0.28

—

R

easo

ning

[207

–209

]

Raw

biv

aria

te0.

13—

——

—0.

16—

0.23

0.10

Age

par

tiale

d—

——

——

——

——

S

troop

Inte

rfer

ence

[210

]

Raw

biv

aria

te—

——

—0.

64—

——

—

Age

par

tiale

d—

——

——

——

——

Proc

essi

ng S

peed

You

nger

and

Old

er

C

ompo

site

mea

sure

s [63

–64,

121

]

Raw

biv

aria

te—

0.64

0.60

0.54

0.66

0.42

0.35

——

Age

par

tiale

d—

0.13

0.34

—0.

19—

——

—

D

igit

Sym

bol [

62]

Raw

biv

aria

te0.

300.

19−0.01

0.14

0.15

0.17

0.24

—0.

25

Age

par

tiale

d—

——

——

——

——

S

impl

e/ch

oice

reac

tion

time

[57,

202

, 211

]

Raw

biv

aria

te0.

350.

51—

0.31

0.16

—0.

510.

350.

05

Age

par

tiale

d0.

17—

——

0.05

——

0.19

0.05

F

inge

r tap

ping

/mov

emen

ts [4

9, 6

2, 2

02]

Raw

biv

aria

te0.

22—

——

0.42

——

0.41

0.18

Age

par

tiale

d0.

190.

100.

030.

350.

110.

120.

090.

170.

20

V

isua

l sea

rch

[112

]

Raw

biv

aria

te—

0.68

0.41

——

0.40

0.38

——

Age

par

tiale

d—

——

——

——

——

Old

er o

nly

C

ompo

site

mea

sure

s [20

3]

Raw

biv

aria

te0.

32—

——

0.42

——

0.34

—


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Madden et al.

Cog

nitiv

e D

omai

nB

rain

Reg

ion

Fron

tal

CC

gIn

f Fro

ntIC

Mid

Sup

CC

sPa

rO

cc

Age

par

tiale

d0.

01—

——

0.02

——

−0.06

—

D

igit

sym

bol [

207]

Raw

biv

aria

te0.

14—

——

—0.

36—

—0.

10

Age

par

tiale

d—

——

——

——

——

S

impl

e/ch

oice

reac

tion

time

[57,

207

, 212

]

Raw

biv

aria

te0.

200.

63—

0.55

—0.

43—

—0.

01

Age

par

tiale

d—

——

——

——

——

F

inge

r tap

ping

/mov

emen

ts [2

13]

Raw

biv

aria

te—

——

——

—0.

71—

—

Age

par

tiale

d—

——

——

——

——

Mem

ory

You

nger

and

Old

er

F

ree

reca

ll [1

08]

Raw

biv

aria

te—

——

——

——

——

Age

par

tiale

d—

——

0.36

0.30

——

——

P

aire

d-as

soci

ate

lear

ning

[108

]

Raw

biv

aria

te—

——

——

——

——

Age

par

tiale

d—

——

0.43

——

——

—

Im

plic

it le

arni

ng [1

18]

Raw

biv

aria

te0.

46—

——

——

——

—

Age

par

tiale

d—

——

——

——

——

S

eman

tic c

ateg

oriz

atio

n [1

6]

Raw

biv

aria

te—

——

——

——

——

Age

par

tiale

d—

0.46

——

——

0.52

——

Old

er o

nly

F

ree

reca

ll [2

06, 2

08–2

09, 2

14]

Raw

biv

aria

te0.

14—

——

0.35

0.12

—0.

46−0.08

Age

par

tiale

d—

−0.01

0.26

—0.

090.

18—

0.58

—

R

ecog

nitio

n [5

2, 5

4]


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Madden et al.

Cog

nitiv

e D

omai

nB

rain

Reg

ion

Fron

tal

CC

gIn

f Fro

ntIC

Mid

Sup

CC

sPa

rO

cc

Raw

biv

aria

te—

—0.

27—

—0.

020.

29—

—

Age

par

tiale

d0.

490.

49—

——

——

——

P

aire

d-as

soci

ate

lear

ning

[54]

Raw

biv

aria

te—

—0.

43—

—0.

47—

——

Age

par

tiale

d—

——

——

——

——

Not

e. V

alue

s are

mea

n ef

fect

size

s (Pe

arso

n r)

for e

ffec

ts re

porte

d in

the

refe

renc

ed st

udie

s in

the

cogn

itive

dom

ains

of E

xecu

tive

Func

tion,

Pro

cess

ing

Spee

d, a

nd M

emor

y. R

esul

ts a

re se

para

ted

acco

rdin

gto

whe

ther

the

FA-c

ogni

tion

rela

tions

hips

wer

e ex

amin

ed a

cros

s bot

h yo

unge

r and

old

er a

dults

com

bine

d or

onl

y am

ong

olde

r adu

lts (d

ata

are

not p

rese

nted

from

the

few

stud

ies t

hat a

lso

exam

ined

the

FA-

cogn

ition

rela

tions

hips

for y

oung

er a

dults

sepa

rate

ly).

The

mea

n ef

fect

size

s obt

aine

d w

ith a

ge-r

elat

ed v

aria

nce

incl

uded

in th

e m

odel

(i.e

., ra

w b

ivar

iate

val

ues)

are

pre

sent

ed se

para

tely

from

thos

e th

atw

ere

parti

aled

for t

he e

ffec

t of a

ge. M

oder

ate

effe

ct si

zes a

nd la

rger

(> 0

.30)

are

list

ed in

bol

d fo

nt.

Mea

n ef

fect

size

s wer

e co

mpu

ted

follo

win

g th

e m

etho

d of

Ros

enth

al a

nd D

imat

teo

[110

]. Pe

arso

n r v

alue

s wer

e ob

tain

ed fr

om F

and

Spe

arm

an p

stat

istic

s (in

the

latte

r cas

e vi

a co

nver

sion

to st

anda

rdno

rmal

dev

iate

Z) w

hen

nece

ssar

y, a

nd r

valu

es w

ere

aver

aged

afte

r tra

nsfo

rmin

g th

em to

wei

ghte

d Fi

sher

z sc

ores

. In

som

e ca

ses,

r val

ues w

ere

adju

sted

such

that

pos

itive

rela

tions

hips

indi

cate

whe

rebe

tter p

erfo

rman

ce is

ass

ocia

ted

with

hig

her F

A.

Fron

t = fr

onta

l whi

te m

atte

r (in

clud

ing

forc

eps m

inor

, ant

erio

r per

ical

losa

l whi

te m

atte

r, an

d fr

onto

-sub

corti

cal t

ract

s); C

Cg

= ge

nu o

f the

cor

pus c

allo

sum

(inc

ludi

ng to

tal c

orpu

s cal

losu

m);

Inf F

ront

=in

ferio

r fro

ntal

whi

te m

atte

r (in

clud

ing

infe

rior l

ongi

tudi

nal f

asci

culu

s, in

ferio

r fro

nto-

occi

pita

l fas

cicu

lus,

and

unci

nate

fasc

icul

us);

IC =

inte

rnal

cap

sule

(inc

ludi

ng a

nter

ior,

genu

, and

pos

terio

rsu

breg

ions

); M

id =

mid

dle

whi

te m

atte

r (in

clud

ing

the

body

of t

he c

orpu

s cal

losu

m, f

orni

x, e

xter

nal c

apsu

le, c

ortic

ospi

nal t

ract

, and

tem

pora

l whi

te m

atte

r); S

up =

supe

rior w

hite

mat

ter (

incl

udin

gci

ngul

um b

undl

e, su

perio

r lon

gitu

dina

l fas

cicu

lus,

and

cent

rum

sem

iova

le);

CC

s = sp

leni

um o

f the

cor

pus c

allo

sum

(inc

ludi

ng te

mpo

ral s

ubre

gion

s of t

he c

orpu

s cal

losu

m);

Par =

par

ieta

l whi

te m

atte

r(in

clud

ing

post

erio

r per

ical

losa

l whi

te m

atte

r); a

nd O

cc =

occ

ipita

l whi

te m

atte

r (in

clud

ing

forc

eps m

ajor

).


Diffusion Tensor Imaging of Cerebral White Matter: A Pictorial Review of Physics, Fiber Tract...

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Transcript of Diffusion Tensor Imaging of Cerebral White Matter: A Pictorial Review of Physics, Fiber Tract...