Post on 05-Mar-2023
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logical Amalgamation of Functional Magnetic Resonance Imaging and
Cognitive Neuroscience
A S Chaudhuri
Introduction
The recent field called neuromarketing (Singer, Tania and Ernst Fehr, 2005) applies the tools
of neuroscience to determine why we like some products over others. Neuroscience explains how
raw brain data is helping researchers open the mysteries of consumer choice. Input concepts
contain:
•Neuroscientists when tracking brain functions (Ramachandran, Vilayanur ,2004) generally
use either electroencephalography (EEG) or functional magnetic resonance imaging (fMRI)
technology. Fluctuations in the electrical activity directly below the scalp is measured by EEG ,
while blood flow throughout the brain is tracked by fMRI.
• Studies have shown activity in that brain (Soldow, Gary F. and Gloria P. Thomas ,1984),
(Sujan, Harish ,1999) and (Marketing Week, London ,2005) area can predict the future
popularity of an experience or a product.
• For businesses planning to outsource neuromarketing services, marketing researchers often
advise (Karmarkar, Uma R.,2012; Karmarkar, Uma R., and Zakary L. Tormala,2012)
seeking out a firm that was founded by a operational scientist, or one that has a strong science
advisory board. This research shows the effect of source certainty that is the level of certainty
expressed by a message source-on arguments. In experiments, consumers receive persuasive
messages from sources of varying expertise and certainty. Across studies, low expertise sources
violate expectancies, stimulate involvement, and promote persuasion when they express
certainty, whereas high expertise sources violate expectancies, stimulate involvement, and
promote persuasion when they express uncertainty.
In the early 1950s, two scientists at McGill University James Olds and Peter Milner(Olds, J.,
and P. Milner, 1954; Olds, J. 1977) discovered the reward centre of the brain with . James
Olds was a postdoctoral fellow at McGill University in 1954. Olds was considered to be
important founders of modern neuroscience. These two researchers inadvertently discovered an
area of the rodent brain dubbed "the pleasure centre," located deep in the nucleus accumbency
systems. When a group of lab rats had the opportunity to stimulate their own pleasure centres via
a electrical current activated by levers, they pressed the lever again and again, hundreds of times
per hour, foregoing food or sleep, until many of them dropped dead from overtiredness. Further
research found pleasure centres exist in human brains, too. James Olds was one of the most
important psychologists of the twentieth century. Indeed, many feel that his discovery of the
"reward" system in the brain is the most important single discovery yet made in the field
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concerned with brain substrates of behavior(Olds, J., Disterhoft, J. F., Segal, M., Kornblith, C.
L., and Hirsh, R.1972). In retrospect, this discovery led to a much-increased understanding of
the brain bases and mechanisms of substance abuse and addict
It is obvious that people are fairly good at expressing what they need, what they desire, or even
how much they will pay for purchase an item. But they aren't very good at understanding where
that value comes from, or how and when it is added by factors like store displays or brands.
Humans are more complicated than rats. But they are largely interested by what makes them feel
good, basically when it comes to their decisions for product purchasing . Consequently, many
major corporations have begun to take special interest in how understanding the human brain can
help them better understand the mindset of consumers. Thus a promising but fast-growing field
called neuromarketing which uses brain-tracking tools to determine why we prefer some
products over others has come into vigorous analysis (Morgan, Robert M. and Shelby D. Hunt
,1994).
People behave fairly accurately while expressing what they want, what they desire, or even how
much they may have to pay for an item But sometimes they appear to be not so good to
understand where that value comes from, or how and when it is subjective by factors like store
displays or brands. It has been researched that neuroscience can help us understand those
clamped elements of the decision process (Karmarkar, Uma R.., 2012). However, there is a clear
difference between the goals of researcher from academia and the goals of a corporation in
utilizing neuroscience.
For marketing researchers from academia work plummets into the category of decision
neuroscience, which is the study of what our brains function when we make choices. Researchers
attempt to understand that process and its implications for behaviour, and draw on concepts and
techniques from neuroscience to conform their research in marketing.
For corporations, on the other hand, the science is a means to an end goal of selling more stuff.
But the tools, once restricted to biomedical research, are largely the same. And Karmarkar et al.
(Karmarkar, Uma R., and Zakary L. Tormala , 2010) expect brain data to play a key role in
future research on consumer choice.
Brain Functions and Neuroscience
Neuroscientists when tracking brain functions generally use either electroencephalography
(EEG) or functional magnetic resonance imaging (fMRI) technology. EEG measures directly
fluctuations in the electrical activity below the scalp, occurring s as a result of neural activity.
Researchers can track the intensity of visceral responses such as anger, lust, disgust, and
excitement by attaching electrodes to subjects' heads and evaluating the electrical patterns of
their brain waves (Nunez PL, Srinivasan R ,1981; Niedermeyer E. and da Silva F.L. ,2004).
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EEG refers to the recording of the brain's spontaneous electrical activity over a short period of
time, usually 20–40 minutes, as recorded from multiple electrodes placed on the scalp.
Diagnostic applications generally focus on the spectral content of EEG, that is, the type of neural
oscillations that can be observed in EEG signals. Despite limited spatial resolution, EEG
continues to be a valuable tool for research and diagnosis, especially when millisecond-range
temporal resolution is required. Derivatives of the EEG technique include evoked
potentials (EP), which involves averaging the EEG activity time-locked to the presentation of a
stimulus of some sort (visual, somatosensory, or auditory). Event-related potentials (ERPs) refer
to averaged EEG responses that are time-locked to more complex processing of stimuli; this
technique is used in cognitive science, cognitive psychology, and psychophysiological research
(Hamalainen, M., Riitta, H., Ilmoniemi, R., Knuutila, J., & Lounasmaa, O. ,1993).
Karmarkar Uma R. (Carmen Nobel, 2012) gives the example of junk-food giant Frito-Lay,
which in 2008 hired a neuromarketing science-based consumer-research firm NeuroFocus, a
Berkeley, California-based company wholly owned by Nielsen Holdings N.V. that claims to
have the tools to tap into your brain to plumb the depths of our minds to look into how
consumers respond to Cheetos, the top-selling brand of cheese puffs in the United States.
Cheetos is a brand of cheese-flavored cornmeal snack made by Frito-Lay, a subsidiary
of PepsiCo. Fritos creator Charles Elmer Doolin invented Cheetos in 1948, and began national
distribution in the U.S. The initial success of Cheetos was a contributing factor to the merger
between The Frito Company and H.W. Lay & Company in 1961 to form Frito-Lay. In 1965
Frito-Lay became a subsidiary of The Pepsi-Cola Company, forming PepsiCo the current owner
of the Cheetos brand.
In 2010, Cheetos was ranked as the top selling brand of cheese puffs in its primary market of the
United States; worldwide the annual retail sales totaled approximately $4 billion. The
original Crunchy Cheetos are still in production but the product line has since expanded to
include 21 different types of Cheetos in North America alone. As Cheetos are sold in more than
36 countries, the flavor and composition is often varied to match regional taste and cultural
preferences--such as Savory American Cream in China, and Strawberry Cheetos in Japan.
Dr Anantha Krishnan Pradeep, CEO of NeuroFocus, presented at the 75th Advertising Research
Foundation (2008) conference the latest innovation: a product called of his company,
NeuroFocus, the Mynd, the world's first portable, wireless electroencephalogram (EEG) scanner.
The skullcap-size device sports dozens of sensors that rest on a subject's head like a crown of
thorns. It covers the entire area of the brain, he explains, so it can comprehensively capture
synaptic waves; but unlike previous models, it doesn't require messy gel. What's more, users can
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capture, amplify, and instantaneously dispatch a subject's brain waves in real time, via Bluetooth,
to another device--a remote laptop, say, an iPhone, or that much-beloved iPad. Over the coming
months, Neuro-Focus plans to give away Mynds to home panelists across the country.
Consumers will be paid to wear them while they watch TV, head to movie theaters, or shop at
the mall. The firm will collect the resulting streams of data and use them to analyze the
participants' deep subconscious responses to the commercials, products, brands, and messages of
its clients. NeuroFocus data crunchers can then identify the products and brands that are the most
appealing (Adam L. Penenberg,2011)
Using EEG technology on a group of willing subjects, the firm determined that consumers
respond strongly to the fact that eating Cheetos turns their fingers orange with residual cheese
dust. An article in the August 2011 issue of Fast Company (already cited), which describes how
the EEG patterns indicated a sense of capricious insurrection that consumers enjoy over the
scruffiness of the product. With data in hand, Frito-Lay moved ahead with an ad campaign called
"The Orange Underground," featuring a series of 30-second TV spots in which the Cheetos
mascot, Chester Cheetah, encourages consumers to commit subversive acts with Cheetos. The
campaign garnered Frito-Lay a 2009 Grand Ogilvy Award from the Advertising Research
Foundation.
The Self-Organized Mapping of EEG
The self-organized mapping of EEG encompasses the real-life picture of human perception.The
stages in our perception of the world have a delicate but powerful influence on later thought
processes; they provide the appropriate links within which our thoughts are framed and they
adapt to many different environments throughout our lives. Understanding the changes in these
links is vital to understanding how our perceptual ability extends, but these changes are often
difficult to quantify in sufficiently complex tasks where objective measures of development are
available. The perceptual learning can be incorporated in neural networks and demonstrate
fundamental changes in these links as a function of decision making skill. These signals are
cognitively grouped together to form perceptual maps that enable rapid picturous categorisation
of complex decision process (Michael Harré, Terry Bossomaier & Allan Snyder, et al.,
2012). Such categories reduce the computational load on our capacity limited thought processes,
they inform our higher cognitive processes and they suggest a framework of perceptual pre-
processing of the compressed representations of sensory perceptions such as Self-Organizing
Maps of EEG that captures the central role of perception in expertise.
Thus we find and compare the structured information, in the form of contextual signals, that is
available to experts and non-experts to assign definite marketing importance. It is argued that
this information is used during implicit learning and subsequent early perceptual processing of
information within a given domain of expertise to aid in fast and accurate categorisation and
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decision-making in complex environments. In particular, these processes enable the reduction of
the dense information perceived in a complex natural environment using the available structured
regularities in EEG maps (Kahneman, D. A, 2003; Turk-Browne, N., Scholl, B., Chun,
M. & Johnson, M., 2009). Furthermore, the integration of these prompts into a cognitive whole
leads to the notion of perceptual networks, the aggregate, sparse representations of the salient
features of the task environment that enables many of the remarkable feats reported in studies of
domain-specific expertise in consumer behavior in retail marketing.
The theory of self-organizing maps (SOM) of EEGs are a direct result of consideration of the
role of perception (Ericsson, K. et al., 1993, 1996) in problem solving, particularly the first
seconds of considering a complex problem. SOM Theory addresses the primacy of perception
and pattern recognition in tasks that previously had been thought to be the domain of conscious
thought processes involving logical reasoning such as search, planning and evaluation. Such
conscious reasoning is characterised as slow, serial and capacity constrained whereas the
perceptual processes obtained from SOM of EEG (Kahneman, D., 2003) considered are fast,
parallel and unconstrained in capacity. Recent work in this area has shown
that conscious perceptual learning can occur in domains as complex as visuals, speech and
mathematics (Kellman, P. & Garrigan, P. , 2009). The perceptual processes can adapt and learn
the complex relationships between visual elements, effectively acting as a pre-processing step
that influences the later stages of SOM of EEG cognition induced in sensory regions of the brain
by extensive mapping.
The Neural Circuitry and the Brain Imaging Techniques
In the past few decades, researchers have learned much about the fundamental workings of the
brain, with tremendous gains in knowledge about the molecules that make it run. Scientists
identified genes for receptor proteins that detect smell and taste. They determined that the stuff
of memories is, literally, a cascade of biochemical changes at the connections, or synapses,
between neurons (Spitzer, N.C. (2012). The voltage-dependent ion channels and
neurotransmitter receptors the mechanisms by which neurons differentiate to achieve the
spectacular complexity of the brain . The ion channel activity participates in signal transduction
that directs subsequent steps of development. The spontaneous transient elevations of
intracellular calcium, generated by ion channels and receptors, control several aspects of
differentiation. The work (Spitzer, N.C. ,2006) is aimed at understanding the roles of electrical
activity in assembly of the nervous system, by analyzing the effects of calcium transients on
neuronal differentiation and determining the molecular mechanisms by which they exert these
effects. Specification of neurotransmitters and selection of transmitter receptors are processes
that depend on patterned spontaneous calcium-dependent electrical activity which has broad
impact on cognitive states and on behavior revealing a partnership of electrical activity and
genetic programs in the assembly of the nervous system (Arroyo, S., Lesser, R.P., Gordon, B.,
Uematsu, S., Jackson, D., Webber, R., 1993).
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Now armed with the human genome and a combination of cutting-edge genetic methods and
brain imaging techniques, lab scientists are exploring the neural circuitry of living animals in
ways they could likely have never dreamed of 20 years ago (Borodinsky, L.N. and Spitzer,
N.C. ,2007). Rather than scrutinizing one or two neurons at a time, they aim to study how
networks or systems of the cells function to influence behavior. Such efforts promise to bridge
the gap between studies of the cognitive powers of the mind, traditionally the turf of
psychologists and linguists and investigations of the physical brain by neurobiologists (Spitzer,
N.C. ,2012) . "We're at the point now... where we can put together these two disciplines and
understand the mind in terms of the operations of the nerve cells in the brain,” says Nicholas
Spitzer, co-director of the Kavli Institute of Brain and Mind at the University of California, San
Diego.
In order to fathoming how the whole nervous system functions will require building powerful
computer simulations that can predict the behavior of millions to billions of neurons working
together. The nascent subspecialty of computational neurobiology is thus “a hugely important
domain for the future," says David Van Essen, president of the Society for Neuroscience and a
researcher at Washington University in St. Louis, Missouri(Yarkoni T, Poldrack RA, Van
Essen DC, Wager TD, 2010). The Van Essen lab uses neuroimaging approaches combined with
novel methods of computerized brain mapping and neuroinformatics to explore the functional
organization, connectivity, development. The Human Connectome Project (HCP;
http://www.humanconnectome.org/) involves a large-scale collaborative effort to chart long-
distance connectivity and its variability in healthy adult humans. Van Essen et al. contribution
to the HCP includes the development and application of analysis methods for characterizing
brain connectivity, and the development of a user-friendly platform for data mining of the HCP
datasets that will be made freely available to the neuroscience community (Wager TD, et al.,
2007). In cognitive science of EEG neuroimaging important scientific advances result from the
synthesis and modeling of existing data, in addition to the collection of new data. The overall
behavior of a system as complex cannot readily be inferred from isolated analyses of a few
variables as the human brain. In recognition of these basic principles, a trend has emerged across
disciplines towards the synthesis of data and modeling of the overall behavior of highly
multivariate systems (Poldrack RA., 2006). These approaches build on accumulated evidence
from hundreds or thousands of individual experiments, and provide a ‘bird's eye view’ that
complements the traditional experimental approach.
The explosion of information in the neurosciences demands fresh approaches to data sharing and
data mining. To this end, Van Essen et al. have established the Sums DB database
(http://sumsdb.wustl.edu/sums/) as a repository for many types of neuroimaging data. This
includes a large and freely accessible library representing summary results from thousands of
fMRI, EEG, and structural imaging studies (Van Essen D., 2002).
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Researchers will no doubt be busy for years to come before they can pull together a unifying
theory that explains the miracle of the brain. But step-by-step, they are making headway in many
areas, including these developments on several fascinating fronts. Neuroscientists need a
diagram of the brain’s internal wiring, but mapping neural circuits isn’t easy. The human brain
houses dozens of types of neurons, all intimately intertwined. Each nerve cell is like a tree, with
a head of fine branches known as dendrites that receive messages from several hundred to
thousands of neighbours, and with a complex array of roots that pass the signals on to other cells
across synapses. Small wonder that Santiago Ramon y Cajal famously described the cerebral
cortex as an "impenetrable jungle.” But modern-day researchers can finally see how to survey
that wilderness with some nifty genetic tools.
In the nervous system, a synapse is a structure that permits a neuron to pass an electrical or
chemical signal to another cell (neural or otherwise). Santiago Ramón y Cajal (The Nobel Prize
in Physiology or Medicine 1906) proposed that neurons are not continuous throughout the body,
yet still communicate with each other, an idea known as the neuron doctrine (Ramón y Cajal,
Santiago,1899). Synapses are essential to neuronal function: neurons are cells that are
specialized to pass signals to individual target cells, and synapses are the means by which they
do so. At a synapse, the plasma membrane of the signal-passing neuron comes into close position
with the membrane of the target cell. The cells contain extensive arrays of molecular
machinery that link the two membranes together and carry out the signaling process (Elias, L. J,
& Saucier, D. M.,2005).
Cajal's opus " Histology of the Nervous System of Man and Vertebrates, 2 vols. " (1894-1904),
was made available to the international scientific community in its English translation, by N. and
L.W. Swanson, was published in 1994 by Oxford University Press). Cajal's opus provided the
foundation of modern neuroanatomy, with a detailed description of nerve cell organization in the
central and peripheral nervous system of many different animal species, and was illustrated by
Cajal's renowned drawings, which for decades (and even nowadays) have been reproduced in
neuroscience textbooks (Bentivoglio, M. 1998). .
In addition, Cajal defined "the law of dynamic polarization," stating that the nerve cells are
polarized, receiving information on their cell bodies and dendrites, and conducting information
to distant locations through axons, which turned out to be a basic principle of the functioning of
neural connections. Cajal also made fundamental observations on the development of the
nervous system and its reaction to injuries (his volume "Degeneration and Regeneration of the
Nervous System" translated and edited by R. M. May, London, Oxford University Press, 1928,
has been re-edited by J. DeFelipe and E.G. Jones, Oxford University Press, 1991.
Edward M. Callaway's research is aimed at understanding how neural circuits give rise to
perception and behavior and focuses primarily on the organization and function of neural circuits
in the visual cortex. Relating neural circuits to function in the visual system, where correlations
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between neural activity and perception can be directly tested, provides fundamental insight into
the basic mechanisms by which cortical circuits mediate perception (Kazunari Miyamichi et
al., 2011).. “our brain performs millions of complex computations every second. We are
studying the organization and function of neural circuits in the visual cortex to better understand
how specific neural components contribute to the computations that give rise to visual perception
and to elucidate the basic neural mechanisms that underlie cortical function.” Says Callaway.
Neuroscientists have identified dozens of different neuronal cell types in the brain that work
together in distinct networks. But the circuits are intermingled, and even neighboring neurons of
the same type differ in connectivity and function. Without access to a “wiring diagram” a map of
the neuronal Connections attempting to grasp how the brain lets us understand language,
recognize faces, and schedule our day is akin to trying to discern how a computer chip works
simply by looking at it.
“We still have to hack through some vines here and there, but we have sharper machetes now,”
says neuroscientist Edward Callaway of the Salk Institute for Biological Studies in La Jolla,
California. He and colleagues have invented a method that should make it possible for the first
time to pick any cell in the cortex and then label “every single neuron in the brain that connects
to exactly that one cell,” he says.In addition, neuroscientists are mastering the art of turning
neurons on and off, which will also help with tracing circuits. The standard means of activating
nerve cells is to gently zap them with an electrode, but that stimulates all cells in the area.
Research labs have devised a number of ingenious ways of genetically introducing molecular
switches into neurons that can control their activity more precisely. Lately, neuroscience circles
have been abuzz over one new breakthrough technique in particular: photo-sensitive proteins that
can trigger neurons to respectively fire or shut down within milliseconds when exposed to light.
EEG oscillations reflect repeated variations in the neuronal excitability, with particular frequency
bands reflecting differing spatial scales of brain operation. However, despite decades of clinical
and scientific investigation, there is no unifying theory of EEG organization, and the role of
ongoing activity in sensory processing remains often undecided .The study of Peter Lakatos et
al. (Peter Lakatos et al, 2005) analyzed laminar profiles of synaptic activity of current source
density and multiunit activity, both spontaneous and stimulus-driven, in primary auditory cortex.
The results reveal that the EEG is hierarchically organized (Ulbert I, Halgren E, Heit G, and
Karmos G. , 2001). This oscillatory hierarchy controls baseline excitability and thus stimulus-
related responses in a neuronal ensemble (Freeman WJ and Rogers LJ., 2002). It is proposed
that the hierarchical organization of ambient oscillatory activity allows auditory cortex to
structure its temporal activity pattern so as to optimize the processing of rhythmic inputs
(Buzsaki G and Draguhn A., 2004).
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Stanford University investigators and their collaborators created transgenic mice that produce
channel for perception of light hroughout their brains, without ill effect. The researchers quickly
scanned with blue light over huge regions of an anesthetized rodent’s exposed brain; then, using
electrodes, they monitored responses triggered in other areas. With this strategy, says Stanford
bioengineer and psychiatrist Karl Deisseroth (Deisseroth K, 2012) scientists “can start to map
circuitry much faster than you could before.” In this case, they examined a key neural pathway
involved in processing smells (Deisseroth K., 2011; Deisseroth K, Feng G, Majewska A, et al.
2006; Yizhar O, et al. 2011).
Neuroscientist Karel Svoboda and colleagues at Cold Spring Harbor Laboratory in New York,
and the Howard Hughes Medical Institute’s Janelia Farm campus in Ashburn, Virginia, have
used channel rhodopsin to trace the long neurons that link the two sides of the mammalian brain
through the structure known as the corpus callosum (Huber, D., Gutnisky, D.A., Peron, S.,
O'Connor, D.H., Wiegert, J.S., Tian, L., Oertner, T.G., Looger, L.L., Svoboda, K. , 2012).
By turning on or off parts of a neural loop and watching what happens, researchers hope to learn
how specific complex circuits influence an animal’s behavior. The light-activated methods,
Svoboda says (Svoboda, K. ,2011), “will make a new kind of neurobiology possible.”
Research on natural vision focuses on the acquisition of structured visual information and the
conversion of this information into sophisticated internal representations for controlling behavior
(Fiser, J., Chiu, C., & Weliky, M. , 2004). An integrated approach with three main
components, human visual and learning experiments, computational modeling of learning, and
multi-electrode recording from behaving humans. The recurrent theme of our work is the pursuit
of a statistically based and biologically sound framework to link low-level visual mechanisms
(e.g., adaptation) with the development and learning of higher level complex features and
constancies for efficient visual representations of objects and scenes.Humans learn to understand
their visual environment based on their sensory experience. Despite decades of research, it is still
not clear what representations the brain uses in this process and how it acquires them.
The basic ability EEGs are key in the formation of visual representations from the simplest
levels of luminance changes to the level of conscious memory traces, rules and abstract
knowledge investigating the interaction between learning ability and various perceptual
constraints due to eye movements, clutter, occlusion and other presumably more hardwired
constraints, and the consolidation effect to investigate what visual features humans use for object
recognition (Fiser, J., Bex, P.J., & Makous, W.L., 2003).
The computational modeling work interprets experimental data in a Bayesian framework.
Specifically generative statistical model selection learning can better capture human behavior
observed in the experiments than simple associative learning can (Berkes P, Orbán G, Lengyel
M, Fiser J. , 2011). This suggests that humans interpret their sensory input through an
"unconscious inference" process that follows precisely the statistical structure of the environment
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but aims at the simplest possible internal description of the input which gives framework for
statistically based interpretation of empirical rules, decision making, attention as well as provides
a tightly coupled explanation for visual recognition and visual learning. The implementation of
the scheme cognizance in the brain requires a continuous reciprocal interaction between groups
of elements at different levels of the hierarchical representation encoded in the cortex cortical
bone forms the cortex, or outer shell, of most bones.. This dynamic collective coding is in
contrast with the traditional feed forward view of how visual information is processed in the
cortex. The level of primary visual cortex and at higher areas the representation of visual
information is best described as the activity pattern of cell assemblies rather than a set of
individual feature detectors. The correspondence between evoked neural activity and the
structure of the input signal systematically improved with age. This improvement was linked to a
shift in the dynamics of spontaneous activity. At all ages including the mature human,
correlations in spontaneous neural firing were only slightly modified by visual stimulation,
irrespective of the sensory input. These results suggest that in both the developing and mature
visual cortex, sensory evoked neural activity represents the modulation and triggering of ongoing
circuit dynamics by input signals, rather than directly reflecting the structure of the input signal
itself.
Tracing the Deep History of the Brain
The EEG has been widely used for over 75 yr as a measure of human brain function . However,
because of the dynamic complexity of the EEG, our understanding of its control and functional
significance remains elementary. Modern studies have begun to link specific brain operations to
specific components of the EEG, including “gamma” (Bertrand and Tallon-Baudry 2000;
Engel et al.2001; Fries et al. 2001; Singer and Gray 1995), “theta” (Buzsaki and Draguhn
2004; Chrobak et al. 2000; Kahana et al. 2001), and “alpha” (Makeig et al. 2004; Worden et
al. 2000).
EEG oscillations reflect repeated variations in the neuronal excitability, with particular frequency
bands reflecting differing spatial scales of brain operation. However, despite decades of clinical
and scientific investigation, there is no unifying theory of EEG organization, and the role of
ongoing activity in sensory processing remains often undecided .The study of Peter Lakatos et
al. (Peter Lakatos et al, 2005) analyzed laminar profiles of synaptic activity of current source
density and multiunit activity, both spontaneous and stimulus-driven, in primary auditory cortex.
The results reveal that the EEG is hierarchically organized. This oscillatory hierarchy controls
baseline excitability and thus stimulus-related responses in a neuronal ensemble. It is proposed
that the hierarchical organization of ambient oscillatory activity allows auditory cortex to
structure its temporal activity pattern so as to optimize the processing of rhythmic inputs
(Buzsaki G and Draguhn A., 2004).
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Approximately two million years ago, the brain capacity of our ancient forebears began greatly
increasing, eventually culminating in a brain that today is roughly three times larger than that of
our closest evolutionary cousin. How that transformation happened, and how we acquired our
impressive cognitive abilities, is a mystery that touches on the core of what made us human.
Lately, scientists have been gleaning fresh clues from studying genetic data troves and the
anatomy of human brains at the cellular, molecular and genetic levels. Using computer
algorithms, researchers have compared the whole genomes of humans and identified several
hundred regions containing DNA differences that may have played a role in human evolution.
Because periodically arising genetic changes are what drive evolution, our DNA is a historical
record of deep ancestral secrets.
For instance, biologists at the University of California, Santa Cruz have identified 202 human
DNA segments that underwent rapid changes in the 6 to 7 million years (Kingston, R E and
Tomkin J W, 2006). Most of those regions aren't genes that code for proteins; instead, they are
sequences that appear to regulate when or where certain genes turn on in the body – and some of
those genes may be involved in neuro-development.
In lab experiments, the research team found that one DNA region, named Human accelerated
regions (HARs) (Pollard KS, Salama SR, Lambert N, Lambot MA, Coppens S, Pedersen JS,
Katzman S, King B, Onodera C, Siepel A, Kern AD, Dehay C, Igel H, Ares M Jr,
Vanderhaeghen P, Haussler D ,2006) is active in neurons, which organize the initial formation
of the neocortex. Although that discovery is exciting, the scientists have much to learn about
Human accelerated regions (HARs) 's function in the brain before reaching any conclusion about
its potential role in human evolution, says computational biologist Katherine Pollard (K.S.
Pollard , 2009 and K.S. Pollard, S. Dudoit, M.J. van der Laan 2005),who now works at the
University of California, Davis.
Indeed, understanding the early development of the cerebral cortex is another important source
of information for deciphering possible mechanisms of how evolution built a bigger and more
intricate brain (Imamura F, Ayoub AE, Rakic P, Greer CA.2011, Dominguez MH, Rakic
P.2009). “We want to figure out how it was done, and the secret is in individual cells, in how
they behave during embryonic development,” says Pasko Rakic, director of the Kavli Institute
for Neuroscience at Yale University. Working at the molecular and genetic level, his lab has
been studying how neurons born deep inside the brain know exactly where to go as they migrate
upward to form the six layers of the neocortex.
An early proposition (Bishop 1933) suggested that impulsive EEG reflects recurring variation of
cortical excitability. Although the relationship of the EEG to neuronal activity was relatively
neglected over the prevailing years, recent studies have rekindled interest in this topic.
Intracellular recordings in EEG provide a striking demonstration of neuronal
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membranepotentials undergoing slow rhythmic shifts between depolarized and hyperpolarized
states (Sanchez-Vives and McCormick 2000). Other recent findings have pointed to an
underlying structure to the EEG spectrum. There is gathering evidence that ongoing cortical
activity has an effect on sensory processing (Fiser et al. 2004; Massimini et al. 2003).
This study (Massimini et al. 2003) provides a way to organize these important findings. First,
there is a hierarchical structure to the EEG, by the phase of a lower frequency oscillation at each
oscillatory frequency the amplitude is modulated. This structure seems to extend from slow
waves up through the gamma frequencies, although technical constraints in this study precluded
quantitative assessment of the interrelationship of delta and very slow oscillations. The
intracellular recordings in vitro suggest that layer 5 pyramidal cells play a key role in organizing
and promoting slow oscillations in cortical neurons (Sanchez-Vives and McCormick 2000).
A second key aspect of findings (Peter Lakatos, Ankoor S. Shah, Kevin H. Knuth, Istvan
Ulbert, George Karmos,and Charles E. Schroeder, 2005) is that like the slow oscillation, the
higher frequency oscillations reflect determined excitability variations in cortical ensembles.
This is reflected in local neuronal firing, which is clearly related to the phase of delta, theta, and
gamma oscillations.
Finally, the ambient oscillatory activity has significant effects on stimulus processing (i.e.,
stimulus-related activity) during which stimulus response is enhanced or suppressed. The facts
that impulsive and event-related oscillations occur in the same frequency bands are both in phase
and have similar laminar distributions implies that the same neural circuitry is used.
However, whether the oscillatory hierarchy present in spontaneous activity is preserved in
stimulus-related activity remains an important question for future studies in neurological
sensitivity modeling as may be required in neuro decision making. The above findings have
important implications for cortical processing of natural acoustic and visual stimuli. While
stimulus processing clearly is structured by the ambient context (Arieli et al. 1996), the onset of
a sound and vision can instantly reset the phase of the ambient delta oscillation, which
effectively phase-locks the entire hierarchical structure of oscillatory activity to the stimulus.
Thus on cortical processing effects of ambient activity should be dramatic for complex rhythmic
inputs that are typical of a natural environment. The brain is described as a system of
chronological stations leading from an object that has a fixed internal representation to the
behavioral response. According to this conventional view of feature detection, each element can
be characterized to a specific stimulus by a fixed response (Arieli A ,1992). Even during well-
defined cognitive tasks, successive brain responses to repeated identical stimulations are highly
variable due to the ongoing cortical activity: even in the absence of external sensory input,
cortical activity exhibits highly structured, internally driven ongoing (spontaneous) waves of
13
activity (including in the sensory areas) (Kenet T, Bibitchkov D, Tsodyks M, Grinvald A,
ArieliA, 2003). For example, resetting of the ambient oscillatory hierarchy should be
enormously useful in processing sounds and vision that occur with rhythmic components. It so
happens that for humans, the temporal structure of numerous biologically relevant stimuli (Singh
and Theunissen, 2003) fit this pattern remarkably well.
From Molecules to Memory
One of the strangest and most wondrous things in the universe is the wrinkled lump in every
person’s head: the human brain. Weighing about three pounds for the average adult, within the
brain are 100 billion neurons that give us the ability to see, smell and move, as well as think,
weep, talk and read. Furthermore, all we experience and remember – in essence, every little thing
that makes us who we are – is rooted in the neocortex, the seat of the "thinking" brain.
Understanding how such a miracle is possible is the vast mission of the relatively young field of
neuroscience and the nascent field of neuromarketing .
In the past few decades, researchers have learned much about the fundamental workings of the
brain, with tremendous gains in knowledge about the molecules that make it run. Scientists
identified genes for receptor proteins that detect smell and taste. They determined that the stuff
of memories is, literally, a cascade of biochemical changes at the connections, or synapses,
between neurons. And belying an old view that the nervous system is hardwired from birth,
experts found that its cells retain some capacity to adapt and reorganize in response to
experience.
The main action happens at the connections between neurons. In the first hour of memory
formation, neurotransmitters are released, receptors congregate and the signals that cross the
synapse are boosted. Most scientists believe that ultimately, it is an overall persistent
strengthening of synaptic activity that lays down a long-term memory.
Without the brain's knack for remembering, you would have no learning and no autobiography,
crafting who you become. Our memories are what make us each unique, says neurobiologist
Roger Nicoll of the University of California, San Francisco, winner of the 2012 Edward M.
Scolnick Prize in Neuroscience. “What identifies you is nothing other than storage of events and
places and people." Roger Nicoll is interested in elucidating the cellular and molecular
mechanisms underlying learning and memory in the human brain. Long-term potentiation (LTP),
a phenomenon in which brief repetitive activity causes a long lasting (many weeks) enhancement
in the strength of synaptic transmission, is generally accepted to be a key cellular substrate for
learning and memory. His lab uses a combination of electrophysiological and molecular
techniques to elucidate the molecular basis of LTP. It has been have found that LTP involves the
rapid activity-dependent trafficking of glutamate receptors to the synapse through
aminomethylphosphonic acid (AMPA) which intervene fast synaptic transmission in the central
14
nervous system (CNS) (Tzingounis AV, Nicoll RA., 2006; Nicoll RA, Alger BE, 2004; Wilson
RI, Nicoll RA., 2002; Anders S. Kristensen1 & Stephen F. Traynelis, 2005) . AMPA
receptors that are cationic channels allowing the passage of Na+ and K+ and therefore have
an equilibrium potential near 0 mV. Nicoll’s main research goal for almost three decades has
been to understand, at the cellular and molecular level, how electrical activity reshapes the
brain’s connections. Much of his work has involved a form of synaptic plasticity known as long-
term potentiation (LTP), an experimental procedure by which a burst of high-frequency electrical
stimulation can induce a lasting increase in synaptic strength. LTP is most commonly studied
within the hippocampus, a brain structure that is crucial for memory. Since its discovery in
1966, a growing body of evidence has indicated that the LTP reflects the natural mechanism by
which experience leads to the formation and storage of new memories. Neuroscientists have
detailed the basic, initial biochemical steps that convert perceptions of the world into permanent
recollections of facts and occurrences. “It’s absolutely incredible how far we’ve come,” says
Nicoll (Milstein AD, Nicoll RA., 2009). Support for that idea comes from a decades-old
observation that, when hippocampal cells are rapidly bombarded with electrical zaps, neurons on
the receiving end of the stimulated cells’ synapses respond with a long-lasting jump in firing
activity. But the theory that this so-called long-term potentiation (LTP) underlies real-life
memory encoding has been tough to prove.
Todd Sacktor and many scientists are now focusing on later stages of memory formation
(Pastalkova, E.,Serrano, P., Pinkhasova, D.,Wallace, E., Fenton, A. A., and Sacktor, T. C.,
2006). In people, as years pass, the hippocampus is apparently no longer needed to sustain a
recollection, which instead becomes embedded in neurons distributed across the neocortex.
Scientists know little about this consolidation process.
On another front, neurobiologists are unraveling the molecular underpinnings of working
memory, the mental scratchpad that makes it possible to retain a phone number long enough to
dial it. Working memory depends on a network of cells, housed in the brain’s prefrontal cortex,
that all trigger each other to fire persistently to hold onto that number. Recent research has
shown that certain molecules, called Hydrogen cyanide channels, control whether this neural
network is functioning. The channels are like tiny gates in a neuron’s cell membrane that let
charged molecules flow through. When the channels are open, they weaken the ability of a
neuron to receive information from other cells, and thus disconnect the circuit, says Amy
Arnsten, a neurobiologist at the Kavli Institute of Neuroscience at Yale (Arnsten, A.F.T., 2004 ;
Southwick, S., Rasmusson, A., Barron, X., Arnsten, A.F.T, 2005).
The Inexplicable riddle of consciousness
Some of life’s secrets seem so formless and inconceivable as to disregard any attempt at inquiry.
Such is the great riddle of consciousness (Gerald Maurice Edelman,1990). Where does it come
from? How can electrical buzzing of physical brain cells produce nonphysical sensations of pain
15
or the emotion of savoring the redness of a rose? What accounts for the conscious and the
essentially private state of being you? Although it is obvious to researchers that consciousness
arises from the brain in the 1990s, however, Nobel laureates Gerald Edelman (The Nobel Prize
in Physiology or Medicine 1972) began pushing for serious biological investigations (Gerald
Maurice Edelman, 1990, 1993, 2004, 2006, Gerald Maurice Edelman, Giulio Tononi, 2000).
Gerald Maurice Edelman's theories are entrenched in neurology. In fact, he insists that this is the
only foundation for a successful theory of consciousness: the answers are not to be found in
quantum physics, philosophical speculation, or computer programming.
For Edelman the structure of the brain is a key factor. The neurons in the brain wire themselves
up in complex and distinctive patterns during growth. No two people are wired the same way.
The neurons do come to compose a number of structures, however. They form groups which
tend to fire together, and for Edelman these groups are the basic operating unit of the brain. The
other main structures are maps and mapping not just sensory inputs, but each other and other
kinds of neuronal activity. The whole system is bound together by re-entrant connections
The principle which makes this structure work is Neuronal Group Selection, or Neural
Darwinism. Some patterns are reinforced by experience, while many others are eliminated in a
selective process which resembles evolution. Edelman draws an analogy with the immune
system, which produces a huge variety of random antibodies: those which link successfully to a
foreign substance reproduce rapidly. This explains how the body can quickly produce antibodies
for substances it has never encountered before (and indeed for substances which never existed in
the previous history of the planet): and in an analogous way the Theory of Neuronal Group
Selection (TNGS) explains how the brain can recognise objects in the world without having a
huge inherited catalogue of patterns, and without a scale model to do the recognising for it.
The re-entrant connections between neuronal groups in different parts of the brain co-
ordinate impressions from the different senses to provide a consistent continuous experience; but
re-entry is also the basic mechanism of recategorisation, the fundamental process by which the
brain carves up the world into different things and recognises those it has encountered before.
Edelman is noted for his theory of consciousness, which he has documented in a trilogy of
technical books, and in several subsequent books written for a general audience including Bright
Air, Brilliant Fire (1992), A Universe of Consciousness (2001, with Giulio Tononi), Wider than
the Sky (2004) and Second Nature: Brain Science and Human Knowledge (2007).
In Second Nature Edelman defines human consciousness as being:
"... what you lose on entering a dreamless deep sleep ... deep anesthesia or coma ... what you
regain after emerging from these states. [The] experience of a unitary scene composed variably
of sensory responses ... memories ... situatedness ... "
16
The first of Edelman's technical books, Neural Darwinism (1987) explores his theory
of memory that is built around the idea of plasticity in the neural network in response to the
environment. The second book, Topobiology (1988), proposes a theory of how the original
neuronal network of a newborn's brain is established during development of the embryo. The
Remembered Present (1990) contains an extended exposition of his theory of consciousness.
Edelman proposes a biological theory of consciousness, based on his studies of the immune
system. He explicitly locates his theory within Charles Darwin's Theory of Natural Selection,
citing the key tenets of Darwin's population theory, which postulates that individual variation
within species provides the basis for the natural selection that eventually leads to the evolution of
new species. He rejects dualism and also dismisses newer hypotheses such as the so-called
'computational' model of consciousness, which liken the brain's functions to the operations of a
computer.
Edelman argues that the mind and consciousness are wholly material and purely biological
phenomena, arising from highly complex cellular processes within the brain, and that the
development of consciousness and intelligence can be satisfactorily explained by Darwinian
theory. In Edelman's view, human consciousness depends on and arises from the uniquely
complex physiology of the human brain:the vast number of neurons and associated cells in the
brain almost infinitely complex physiological variations in neurons (even of the same general
type) and in their connections with other cells the massive multiple parallel reentrant connections
between individual cells, and between larger neuronal groups, and so on, up to entire functional
regions and beyond. Edelman's theory of neuronal group selection, also known as Neural
Darwinism, has three basic tenets; Developmental Selection, Experiential Selection and
Reentry.
In Developmental selection the formation of the gross anatomy of the brain is controlled by
genetic factors, but in any individual the connectivity between neurons at the synaptic level and
their organisation into functional neuronal groups is determined by somatic selection during
growth and development. This process generates tremendous variability in the neural circuitry
like the fingerprint or the iris, no two people will have precisely the same synaptic structures in
any comparable area of brain tissue. Their high degree of functional plasticity and the
extraordinary density of their interconnections enables neuronal groups to self-organise into
many complex and adaptable "modules". These are made up of many different types of neurons
which are typically more closely and densely connected to each other than they are to neurons in
other groups.
In Experimental selection overlapping the initial growth and development of the brain, and
extending throughout an individual's life, a continuous process of synaptic selection occurs
within the diverse repertoires of neuronal groups. This process may strengthen or weaken the
connections between groups of neurons and it is constrained by value signals that arise from the
17
activity of the ascending systems of the brain, which are continually modified by successful
output. Experiential selection generates dynamic systems that can 'map' complex spatio-temporal
events from the sensory organs, body systems and other neuronal groups in the brain onto other
selected neuronal groups. Edelman argues that this dynamic selective process is directly
analogous to the processes of selection that act on populations of individuals in species, and he
also points out that this functional plasticity is imperative, since not even the vast coding
capability of entire human genome is sufficient to explicitly specify the astronomically complex
synaptic structures of the developing brain.[17]
Reentry the third principle of Edelman's thesis is the concept of reentrant signaling between
neuronal groups , the neural circuitry. He defines reentry as the ongoing recursive dynamic
interchange of signals that occurs in parallel between brain maps, and which continuously
interrelates these maps to each other in time and space. Edelman demonstrates spontaneous
group formation among neurons with re-entrant connections. Reentry depends for its operations
on the intricate networks of massively parallel reciprocal connections within and between
neuronal groups, which arise through the processes of developmental and experiential selection
outlined above. Edelman describes reentry as "a form of ongoing higher-order selection ... that
appears to be unique to animal brains" and that "there is no other object in the known universe so
completely distinguished by reentrant circuitry as the human brain".
Crick and Caltech neuroscientist Christof Koch argued the problem could be tackled by breaking
it down into smaller research questions (Crick F, Koch C 1990, 1995a, 1995b , 1995c and
Koch C. and Hepp K. 2006). One fascinating approach has been to ask how the mind becomes
conscious of certain information while apparently ignoring other stimuli that shower the senses.
For instance, it’s well known that if one of your eyes is presented with a photo of, say, a house
while the other eye sees a photo of a face, the two images do not blend. You alternately perceive
only either picture for a few seconds each even as each retina “sees” the same image all the
while. A similar effect happens while gazing with both eyes at an outline of a 3-D cube, which
flips between facing leftward and rightward. Such optical illusions are called bistable visual
patterns. In bistable vision perception oscillates automatically between two mutually exclusive
states (Sabine Windmann, , Michaela Wehrmann, 2006).
The prefrontal cortex might influence this process either by maintaining the dominant pattern
while protecting it against the competing representation, or by facilitating perceptual switches
between the two competing representations (Mitchell, J. F., Stoner, G. R., & Reynolds, J. H.
2004 ; Moore, T., & Armstrong, K. M. 2003).
In numerous experiments scientists have monitored the brain’s responses to a bistable image.
Neural areas that initially process visual data fire constantly, showing no differences when
conscious perception shifts from one image to the other. But something interesting happens in
18
the higher visual processing regions (Kanwisher, N., & Wojciulik, E. (2000; Leopold, D. A.,
Wilke, M., Maier, A., & Logothetis, N. K. 2002). Such findings supported the idea that a
subset of brain cells which Crick and Koch called “neuronal correlates of consciousness” (Crick
F, Koch C ,1990) are specialized to transmit selective visual signals to the mind’s consciousness
(Rees G. and Frith C. 2007).
Using t magnetic stimulation, researchers can apply magnetic pulses to the brain and then use
EEG recordings to monitor electrical activity across the cerebral cortex. The studies are helping
Tononi fine-tune a theory that views consciousness as an integrated system of information, with
parts of the cortex and underlying thalamus ideally suited for managing the integration (Laurey,
S.and Tononi, G. , 2009). Although overall progress in the field of consciousness is slow, he
says, a growing number of scientists are now using the best neuroscientific tools to inquire
questions about consciousness
Observational Control, Object Perception in conscious Processes using fMRI
The primary source of observational control in object perception is the prefrontal cortex. This
region is involved in the maintenance of goal-related information as well as in observational
selection and set shifting. Recent analyses (Windmann Sabine et al., 2006) have highlighted the
role of top-down processes during elementary visual processes as illustrated in bistable vision
where perception automatically oscillates between two mutually limited states. This influence the
process either by maintaining the leading pattern while protecting it against the competing
representation using perceptual switches between the two competing representations. Humans
are able to control perceptual switches in the hold condition. These results suggest that the
presentation is necessary to bias the selection of visual representations in accord with current
goals for maintaining selected information active that is continuously available in the
environment.
Cognitive neuroscience aims to map mental processes onto brain function, which attempts to
answer the question of what “mental processes” exist and how they relate to the tasks and
objects that are used to influence and measure them. The increasing progress in cognitive
neuroscience requires a more systematic approach to represent the mental entities that are being
mapped to brain function and the tasks used to control and determine mental processes.
(Poldrack RA,et al. , 2011) describe a new open collaborative project that aims to provide a
knowledge base for cognitive neuroscience, called the Cognitive Atlas (accessible online at
http://www.cognitiveatlas.org), and outline how this project has the prospective to drive novel
discoveries about both mind and brain in matters of decision and perception.
The real life reasoning is fundamental to science, human culture, and the solution of problems in
daily life. It starts with domains and yields a logically necessary conclusion that is not
unambiguous in the premises. Fangmeier Thomas et al. 2006 investigated the neurocognitive
19
processes underlying coherent thinking with event-related functional magnetic resonance
imaging. The researchers specifically focused on three temporally separable phases: (1) the
premise processing phase, (2) the premise integration phase, and (3) the validation phase in
which humans decide whether a conclusion logically follows from the premises. The distinct
patterns of cortical activity during these phases with initial activation shifting to the prefrontal
cortex was found along with the reasoning process. Activity in these latter regions was specific
to reasoning. The phenomenon of stabilized retinal stimuli to fade and become replaced by their
backgroundis a good example of central brain mechanisms that can selectively add or delete
information to/from the retinal input. Importantly, such cortical mechanisms may overlap with
those that are used more generally in visual perception. In order to identify cortical areas that
contribute to the perception, researchers (Mendola J. D., et al., 2006) used functional magnetic
resonance imaging to image activity in the visual cortex while subjects experienced perception.
The results of investigations lead to propose that perceptual filling-in suggests high-level control
mechanisms to reconcile competing percepts, and alters the normal image-related signals at the
first stages of cortical processing. The overall pattern of activation in resembles is seen as of
perceptually bistable stimuli, including binocular rivalry, indicating common control
mechanisms.
It is necessary in everyday life to selectively adapt our behavior to different situations and tasks.
in cognitive psychology, Such adaptive behavior in cognitive psychology can be investigated
with the task-switching model. The functional magnetic resonance imaging (fMRI) study may be
set out to investigate processes that are relevant when participants can decide by their own which
task to perform and chose a better objective (Matthew F S Rushworth, Timothy E J Behrens
,2008). It may be expected to find prolonged reaction times as well as higher activations within
the cortex for the choice conditions compared to the no-choice condition (Susanne Karch, et al.
2010). The fMRI results revealed a significant activation difference for the choice conditions
versus the no-choice condition. These activations revealed no selection-specific difference
between three and two choices. The analysis (Forstmann Birte U., 2006) showed that the
activation is associated with higher task-dependent response when participants can select a task
and objective.
Evidence Derived from fMRI and Dynamic Causal Modeling (Thorsten Plewan, etal. , 2012)
the human visual system converts identically sized retinal stimuli into different-sized
perceptions. The strength of this perception can be expressed as the difference between physical
and erceived reality. Accordingly, imaginative strength reflects how strong a representation is
transformed along its track from a retinal image up to a conscious perception. It has been
investigated that changes of effective connectivity between brain areas supporting these
transformation processes to further illustrate the neural underpinnings of imaginations. Dynamic
causal modeling was employed to investigate cognitive interactions between visual streams to
model bidirectional connections at areas most accurately modeled for the underlying network
20
dynamics. directly related to size transformation activation processing task-related supervisory
functions. Over the last 20 years, fMRI has revolutionized cognitive neuroscience. It is hoped
that fMRI in cognitive neuroscience might include increased methodological rigor, an increasing
focus on connectivity and pattern analysis with greater focus on selective inference powered by
open databases, and increased use of computational models to describe underlying processes.
The eye-tracking approach of an fMRI is used to examine the mechanisms involved in learning
to understand an object in a scene. This has suggested a role for effective visual sampling and
prior experience in the development of mature object perception. integrating across variable
sampled experiences to persuade perceptual change. It has been found with fMRI that relative to
the Control condition, participants in the training condition were significantly more likely to
change their percept from “disconnected” to “connected,” an as indexed by pre-training and post-
training test performance. This pattern was not restricted to participants who changed their
initial “disconnected” object perception. Neuroimaging data (Lauren L. Emberson and Dima
Amso, 2012)suggest an involvement of the ongoing regular experience to enable changes in a
modal completion.
Some brain areas preferentially process information from a particular sensory process.
Awareness to perceptible stimuli depends on the temporal frequency of stimulation as observed
in images of fMRI (Per F. Nordmark, et al., 2012).Whole-brain analysis revealed an effect of
motivation frequency in the visual cortex. The blood oxygen level dependent fMRI (BOLD) response
in the auditory cortex was stronger during stimulation at hearable frequencies (20 and 100 Hz)
whereas the response in the visual cortex was suppressed at infrasonic frequencies (3 Hz).
Regardless of which hand was stimulated, the frequency-dependent effects were lateralized
to the left auditory cortex and the right visual cortex.Furthermore, the frequency-dependent
effects in both areas were removed when the participants performed a visual task while receiving
identical tangible stimulation as in the perceptible threshold-tracking task. As such the brain
areas contribute to sensory processing by performing specific computations regardless of input
in physical phenomenon.
Magnetic resonance imaging (MRI) has fast become an important tool in clinical medicine and
biological research. Its functional variant (functional magnetic resonance imaging; fMRI) is
widely used method for studying the neural basis of human cognition and brain mapping with
sufficient knowledge of the physiological foundation of the fMRI signal to interpret the data with
respect to neural activity. This paper reviews the basic principles of The blood-oxygen-level-
dependent (BOLD) fMRI signal elicits the neural activity during sensory stimulation
(Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oelter-mann, A. 2001).
Depending on the temporal characteristics of the stimulus of BOLD responses a strong corre-
lation was found between the neural activity measured with microelectrodes and the BOLD
signal averaged over a small area around the microelectrode tips. The BOLD signal has higher
21
significant with the neural activity, indicating that human fMRI combined with traditional
statistical methods successfully provides the reliability of the neuronal activity. To understand
the contribution of spatio-temporal fMRI responses the statistical analysis of signal has been
observed with tools of systems analysis to predict the fMRI responses. These findings, together
with an analysis of the neural signals, indicate that the BOLD signal primarily measures the input
and processing of neuronal information within a region and not the output signal transmitted to
other brain regions (Nikos K. Logothetis, 2002).
The MRI has optimized diagnostics and enabled us to monitor therapeutics, providing not only
clinically essential information but also insight into the basic mechanisms of brain function and
malfunction. Its recently developed functional variant, fMRI has had an similar impact in a
number of different research disciplines ranging from developmental biology to cognitive
psychology.In the neurosciences, imaging techniques are indispensable. Understanding how the
brain functions requires not only a comprehension of the physiological workings of its individual
elements, that is its neurons, but also demands a detailed map of its functional architecture and a
description of the connections between populations of neurons, the networks that underlying
behaviour. The neural origin of the BOLD contrast mechanism of fMRI will concentrate on the
application of MRI to the study on the emphasis to be placed on fMRI at high spatio-temporal
resolution and its combination with electrophysiological measurements (Heeger, D. J. & Ress,
D. 2002).
Frequency modulation (FM) is an acoustic feature of nearly all complex sounds. Directional FM
sweeps are especially pervasive in speech, music, animal vocalizations, and other natural sounds.
Although the existence of FM-selective cells in the auditory cortex of humans Using fMRI and
Multivariate Pattern Classification has been documented (Hui Hsieh.,2012). Multivariate
pattern analysis may be used to identify cortical selectivity for direction of a multitone FM
sweep. This method distinguishes one pattern of neural activity from another within the same
FM, even when overall level of activity is similar, allowing for direct identification of FM-
specialized networks. Standard contrast analysis showed that despite robust activity in auditory
cortex, no clusters of activity were associated with up versus down sweeps. Multivariate pattern
analysis classification, however, identified two brain regions as selective for FM direction, the
right primary auditory cortex on the supra-temporal plane and the left anterior region of the
superior temporal plane. directly demonstrating the existence of FM directional selectivity in the
human auditory cortex.
Stimulus repetition often leads to facilitated processing, resulting in neural decreases and faster
repetition. Such repetition-related effects have been accredited to the facilitation of repeated
cognitive processes and/or the retrieval of previously encoded stimulus–response bindings. The
spatial and temporal resolutions of fMRI and EEG has been respectively utilized to examine a
22
long-lag classification priming paradigm that required response repetitions or reversals at
multiple levels of response representation. A repetition effect has been observed in
occipital/temporal cortex (fMRI) where stimulus onset (EEG) was time-locked and strong to
switches in response, together with a repetition effect in (fMRI) ( Aidan J. Horner et al. , 2012).
The response-sensitive effect occurred even when changing from object names to object pictures
between repetitions, suggesting that stimulus–response bindings can code abstract
representations of stimuli. Most importantly, we found evidence for Interference effects of
response-sensitive bindings were retrieved with increased neural activity.
Participants in two fMRI experiments named pictures with superimposed disturbances that were
high or low in frequency or varied in terms of age of acquisition (Greig I. de Zubicar, 2012).
Pictures superimposed with low-frequency words were named more slowly by participants than
those superimposed with high-frequency words, and late-acquired words interfered with picture
naming to a greater extent than early-acquired words with self-monitoring system in picture–
word interference.
The fMRI studies have often used confidence ratings as an index of memory strength to
investigate potentially recognition memory responses. Confidence ratings correlated with
memory strength reflect sources of changeability in terms of applying decision criteria including
task-irrelevant item effects. The fMRI analyses of correct old responses on the basis of
subjective confidence ratings or estimates from single- versus dual-process recognition memory
models have been conducted. The effect of highlighting attention on spaced repetitions at study
has proven as enhanced recognition memory performance. The patterns of activity indicates that
fMRI signals associated with subjective confidence ratings reflect additional sources of
variability. The results are reliable with predictions of single-process models of identification
memory (Greig I. de Zubicaray et al., 2011).
Self-projection, the capacity to re-experience the personal past and to mentally infer another
person's perspective has been linked to be associated with inferences about one's own self. In the
fMRI studies it has been examined that self-projection using a novel camera technology, which
employs a sensor and timer to automatically take hundreds of photographs when worn, in order
to create dynamic visuo-spatial signals taken from a first-person perspective (Peggy L. St.
Jacques et al., 2011).This allowed to ask participants to self-project into the personal past or into
the life of another person. We predicted that self-projection to the personal past would elicit
greater activity in self-projection revealed task-related functional connectivity analysis to
contributed to the network linked to memory processes.
The functional magnetic resonance imaging studies have identified brain regions associated with
different forms of memory(Brass, M., Derrfuss,et al. 2005). Working memory has been
associated primarily with the bilateral prefrontal and parietal regions; semantic memory with the
23
left prefrontal and temporal regions; episodic memory encoding with the left prefrontal and
medial temporal regions; episodic memory retrieval with the right prefrontal, posterior midline
and medial temporal regions; and skill learning with the motor, parietal, and subcortical regions.
Recent studies (Cabeza, R.,et al. , 2000) have provided higher specificity, by dissociating the
neural correlates of different subcomponents of complex memory tasks, and the cognitive roles
of different subregions of larger brain areas.
The fMRI neuroimaging is obtained introspectively through memory recall. Consequently,
several confounding factors may guide the accuracy of fMRI reports including forgetting,
reconstruction mechanisms, verbal description of difficulties. The researchers must be well
aware of these limitations and should minimize them by using suitable strategies when collecting
or analyzing fMRI data. The modern brain imaging techniques have emerged as major tools to
better understand the neural mechanisms of cognitive consciousness, perceptual and emotional
characteristics with valuable and unique information about brain functions. The recent
neuroimaging studies, in particular functional MRI studies showed that it is now possible to
capture more transient, dynamic changes of brain activity with a high anatomical
resolution(Corbetta, M., & Shulman, G. L. 2002). While these advanced imaging methods will
undoubtedly contribute to redefining the links between brain processes and the varieties of
cognition experiences. Another important challenge for future studies will be to systematically
investigate changes in brain activity and mental content across all audio-visual cognitive states
and achieve a detailed characterization of the neural constraints affecting the daily oscillations of
human conscious experiences. Such an integrated framework for the study of human cognitive
process is necessary to accommodate the diversity and increasing sophistication of modern
neuroimaging research and to improve our understanding of the neuroanatomy and functions of
consciousness.
High-resolution maps of genome-wide gene expression have been available for mice for a few
years, but only relatively coarse equivalents have been published for the human brain because of
the challenges presented by the 1,000-fold increase in size and the limited availability and
quality of postmortem tissue. Now Michael Hawrylycz and colleagues at the Allen Institute for
Brain Science in Seattle, Washington, have used laser microdissection and microarrays to assess
900 precise subdivisions in brains from two healthy men with 60,000 gene-expression probes.
The resulting atlas, freely available at www.brain-map.org, allows comparisons between humans
and other animals, and will facilitate studies of human neurological and psychiatric behaviours.
Neuromarketing a subset of Neuroeconomics is a new highly promising approach to
understanding the neurobiology of decision making and how it affects cognitive social
interactions between humans and societies/economies. This book(Paul W. Glimcher et al,
2008)is the first edited reference to examine the science behind neuroeconomics/neuromarketing,
24
including how it influences human behavior and societal decision making from a behavioral
economics point of view. Presenting a truly interdisciplinary approach, the book presents
research from neuroscience, psychology , and behavioral economics, and includes chapters by all
the major figures in the field, including two Economics nobel laureates. Carefully edited for a
cohesive presentation of the material, the book is also a great textbook to be used in the many
newly emerging graduate courses on Neuroeconomics in Neuroscience, Psychology, and
Economics graduate schools This groundbreaking work is sure to become the standard reference
source for this growing area of research.
An fMRI Study of the Cognitive Regulation of Emotions, Thinking and Feelings
For mental and physical health the ability to cognitively regulate emotional responses to events is
important The functional magnetic resonance imaging is employed to examine the neural
systems used review scenes in unemotional terms with subjective experience. Neural correlates
of appraisal are increased activation of the lateral and medial prefrontal regions and decreased
activation of the amygdala which is a groups of nuclei located deep within the medial temporal
lobes of the brain in complex human vertebrates and medial orbito-frontal cortex (Jackson, D.
C.,et al. 2000). The prefrontal cortex is involved in constructing reappraisal strategies that can
modulate activity in multiple emotion processing systems.
Taking help of a vast array of coping skills humans are extraordinarily adaptable creatures who ,
can successfully manage situation in even the most trying of circumstances. Shakespeare’s
(1998/1623, p. 216) Hamlet observed, ‘‘there is nothing either good or bad, but thinking makes
it so’’ is one of the most remarkable of these skills. Hamlet’s message is clear. By changing the
way we think we can change the way we feel thereby lessening the emotional consequences of
otherwise worrying experience. The cognitive transformation of emotional experience is an
unpleasant stimulus in unemotional terms which reduces negative affect with few of the
physiological, cognitive, or social costs associated with other emotion-regulatory strategies,such
as the inhibition of emotion-expressive behavior (Jackson, D. C.,et al. 2000; Gross, J. J., 1998,
2002).The functional magnetic resonance imaging (fMRI) elucidates the neural bases of
reappraisal. The neural systems involved in the cognitive control of emotion would involve
processing dynamics similar to those in implicated in other forms of cognitive control.
Reappraisal involves reinterpreting the meaning of an emotional event; for example, creating an
alternative scenario or adopting a different attitude (Gross, 2002; Ochsner et al., 2004). It is the
basis of cognitive rehabilitation (Frewen et al., 2008), has been found to be more beneficial than
suppressing emotions (Ochsner et al., 2002) can be instructed or and varies widely across
individuals (Gross and John, 2003).Emotional responses are often quick adaptive responses
that help us successfully address challenges that arise in our environment . However, in some
context , otherwise adaptive emotional responses may be inappropriate because they are either
ill-timed or are of intensity for the particular situation at hand. Healthy adaption therefore
25
requires the ability to regulate our emotion. fMRI can assist in creating an emotion regulatory
networks (Kevin N. Ochsner et al., 2002)
The Neural Groundworks of Perception: The Real World and the fMRI Scanner
Our knowledge of the neural groundworks of perception is largely built upon studies employing
2-dimensional (2D) planar images.An effect commonly observed using 2D images having slow
event-related functional imaging in humans may be examined to show whether neural
populations have a characteristic repetition-related change in haemodynamic response for real-
world 3-dimensional (3D) objects. Surprisingly, however, repetition effects were weak, if not
absent on trials involving the 3D objects. These results suggest that The neural mechanisms
involved in processing real objects are distinct from those that arise when a2D representation of
the same items is met. These introductory results suggest the need for research to widen our
understanding of the neural mechanisms underlying human vision with ecologically valid stimuli
in the imaging designs (Jacqueline C. Snow,et al, 2011).
By almost all functional magnetic resonance imaging (fMRI) studies the 2-dimensional (2D)
pictures of objects of human neural substrates have been examined. We interact with real 3-
dimensional (3D) objects far more often than 2D representations even pictures are same in
everyday life since we have little difficulty in distinguishing between the two. By almost all
functional magnetic resonance imaging (fMRI) studies the 2-dimensional (2D) pictures of
objects of human neural substrates have been examined. We interact with real 3-dimensional
(3D) objects far more often than 2D representations even pictures are same in everyday life
since we have little difficulty in distinguishing between the two. Investigation with fMRI help us
examine whether real-world objects involving the large body of evidence pertaining to human
neural processing of pictorial stimuli. The processing of object shape in the brains of humans is
broadly distributed across a number of cortical areas spanning both the dorsal and ventral visual
pathways known as lateral occipital complex (LOC) (Kanwisher N. G. et al., 1996; Malach
R., et al. 1995).
By using the technique of functional magnetic resonance imaging. the stages of integration
leading from local feature analysis to object recognition were explored in human visual cortex as
evidence for object-related activation. Compared to a wide range of texture patterns LOC shows
preferential activation to images of objects. By using the technique of functional magnetic
resonance imaging. the stages of integration leading from local feature analysis to object
recognition were explored in human visual cortex as evidence for object-related activation.
Compared to a wide range of texture patterns LOC shows preferential activation to images of
objects. This activation was not caused by a global difference in The Fourier spatial frequency
content of objects versus texture images produce enhanced LOC activation compared to textures
matched in power spectra. A conspicuous demonstration that activity in LOC is uniquely
26
correlated to object detect ability in which digitized objects increase their recognizability
leading to significant enhancement of LOC activation (Konen C. S. & Kastner S, 2008). Thus,
objects varying extensively are activated y in their recognizability (e.g., famous faces, common
objects, and unfamiliar three-dimensional abstract sculptures) to a similar degree. These lead to
object recognition in human visual cortex resulting in showing s evidence for an intermediate
link in the chain of processing stages.
Neural coding within object-selective cortex beyond simple fMRI subtraction designs has been
investigated using comparisons between repeated vs. unrepeated objects ( Squire L. R., et
al.. 1992; Stern C. E., et al.. 1996; Buckner R. L., et al..1998: Wiggs C. L. & Martin
A.,1998). The characteristic reduction in haemodynamic response with stimulus repetition has
been variously referred is a robust effect in which neurons within infero-temporal cortex show
reduced firing rates as a result of stimulus repetition. Repetition designs have become a popular
methodological approach that contrast with standard mapping techniques in their ability to probe
neural selectivity in higher-order visual areas with traditional fMRI designs (Krekelberg B.,et
al., 2006). In the field of object perception, repetition designs have perhaps most commonly
been used to determine whether object-selective neural populations are response invariant to
image transformations such as changes in viewpoint size or elucidation (Grill-Spector K., et
al..1999).
An fMRI Trial Sequence f Stimulus Items
The choice of 2D stimuli to study object recognition has been largely one of expedient and
experimental control. A flat screen presentation of 2D images basically requires projection of the
images through a mirror while the participant can lie comfortably in the favourable position. The
control of image parameters (e.g., size, depth, timing) is straightforward Many additional
challenges arising in the presentation of real world 3D stimuli have been solved in 2D fMRI
research on grasping and reaching where 3D objects are required to bring forth normal object-
directed actions(Cavina-Pratesi C., et al.. 2010).
An fMR repetition pattern may be used to examine both the overall level of activation and
repetition-based effects in the framework of real-world 3D objects compared to 2D pictures. It is
expected to have clear activation and repetition effects within the areas identified across prior
studies for both motivated classes. However, the main question was how similar these effects
would be for 3D objects. It has been found that the overall level of activation as well as the
strength of repetition effects for the richer, real-world 3D objects are at least equal to, if not
greater than, those for 2D pictures (Ishai A., Ungerleider L. G., Martin A., Schouten J. L. &
Haxby J. V. , 1999). Neurophysiology research has characterized several areas with 3D object-
selective responses(Verhoef B. E., Vogels R. & Janssen P. , 2010) for which human
homologues have been proposed (Culham J. C. & Valyear K. F. ,2006). These areas are
postulated to be involved in the extraction of shape for visual transformations associated with
27
the control of action. The human anatomical structural areas show fMR-adaptation for studying
the functional properties of human cortical neurons (Grill-Spector K, Malach R 2001). Such
areas may be expected to show larger responses and stronger repetition effects in the context of
real-world objects.
An adaptation index (AI) which estimates response difference between Repeat and Different
conditions relative to the overall fMRI response to a given stimulus (Konen C. S. & Kastner S.,
2008) shows consistency of observation in fMRI BOLD response on each stimulus type and
region of interest (ROI). The slow event-related fMRI has been to show contrast repetition-
related changes in fMRI responses to 2D pictures of objects with real-world 3D exemplars.
Whereas presentation of 2D pictures elicited strong repetition-related changes in the BOLD
response (Schacter D. L., et al.. 1995)and within this area there has been marked variability
across participants in the relative magnitude of the BOLD response. In the ROI analyses
significant 2D repetition effects are observed and BOLD response patterns are highly consistent
across observers. Accordingly, whole-brain analyses revealed robust repetition effects for 2D
objects.
Neurophysiological studies have identified neurons that are sensitive to shapes defined by
binocular disparity within early visual area. In fMRI studies for stereo displays involving planar
shapes to show that responses within LOC are identical despite changes in the stereoscopic depth
of the shape showing equivalent BOLD responses depicting identical silhouette shapes where a
2D silhouette has followed by a stereo silhouette image (so that the shape appeared to lie in front
of the fixation plane). These findings imply that object shape is processed similarly within LOC,
whether the shape is depicted in a purely 2D format or with additional stereo cues (Mur M., et
al., 2010). Face recognition that requires distinguishable neuronal representations of individual
faces is a complex cognitive process. The functional magnetic resonance imaging (fMRI) studies
performed blood-oxygen level--dependent (BOLD) fMRI measurements using the ‘‘fMRI-
adaptation’’ technique has suggested the existence of face-identity representations in face-
selective regions. These results remind us that fMRI stimulus-change effects can have a range of
causes and do not provide conclusive evidence for a neuronal representation of the changed
stimulus property.
The stimulus objects in these studies simply have defined figure from ground and provided
information about the outer contours of the shape (i.e., first-order stereo). Unlike real objects,
they contained no information about inherent curvature or shape (i.e., second-order stereo).
Binocular perceptions of picture indicate that it is completely flat whereas monocular perceptions
such as shading, texture gradients, occlusion, spherical highlights, and other pictorial perceptions
signify a 3D representation. It is possible that classical repetition and release effects typically
observed in picture viewing may be attributable to processes associated with resolving such
depth perceptions conflict.
28
An additional processing is required to decipher object identity from 2D pictures. Results from
fMRI raise the stimulating suggestion that the presence of real-world objects invokes
qualitatively different computations to those illustrated by 2D images. Researchers in the field of
behavioral psychophysics have expressed long-standing concern about the extent to which
pictures of objects capture the properties of their real-world ecological validity as to their
appropriateness as stimuli with which to examine the nature of human object perception (Marr,
D., 1982). The images consist merely of patterns of light arising from a 2D projection surface of
real objects of physical substances with a definite texture, reflectance, colour and shape. An
object placed within arm's length affords reaching, grasping, and manipulation. Indeed, fMRI
studies demonstrate that information is critical for the visual control of grasping and
manipulation (Gallivan, J. P.et al., 2009).
The Voxel Analysis of Functional Magnetic Resonance Imaging (fMRI)
The analysis of functional magnetic resonance imaging (fMRI) data is a difficult procedure. The
large data sets are computationally difficult to control and special modeling techniques are
necessary to deal with sequential correlation. The statistical models used to analyze fMRI data
require many steps starting with raw data and ending with achievable results for evaluation
(Jeanette A. Mumford and Russell A. Poldrack, 2007) . Fortunately there are easy-to-use
software packages that allow users to input their data and choose certain modeling options
toconduct data analyses.The disadvantage of the data analysis ‘black box’ is that users are often
not aware why certain types of models are used and the purpose of different modeling options to
describe the model used to analyze group fMRI data.
The proper model for group fMRI data is a mixed model of the two-stage summary statistics
approach. A mixed model is necessary to extrapolate results beyond the study sample. The two-
stage summary statistics approach of this model reduces the computational burden of analyzing
the large volumes of data collected in fMRI studies. The different models are necessary under
each of these data collection topics. In order to understand the models and how they differ, it is
necessary to understand the two different effects that can be specified in a model: fixed and
random. When defining effects as being fixed or random one must consider how the data were
collected, what inferences are of interest and to which population inferences will be applied.
Under the incorrect data collection description, a fixed effects model is used to carry out
inference on the overall mean opinion change fixed effect and gives a mean estimate In the case
of group fMRI data, the data for a single volumetric pixel (voxel) consist of time series from
multiple subjects, where each time series is a group of data specific to a particular subject. Each
point in an fMRI time series is not randomly selected from a random subject, but an entire time
series is selected from random subjects. The distributions of fMRI time series between subjects
can be very different, with some subjects activating more and/or having more variability in their
29
signal than others. Since the goal of most fMRI studies is to apply the inferences beyond the
study sample, a mixed effects model accounting for between- and within-subject variability, is
the appropriate model used on group fMRI data having small estimated variances. The
characteristic mixed model used by statisticians to analyze multiple time series from multiple
subjects is a one-stage all-in-one approach that includes all subjects’ data concurrently (Verbeke
and Molenberghs, 2000). This type of model is computationally difficult to use on fMRI data,
which consist of time series in excess of 100 time points for each of 100 000 or more voxels. To
apply the mixed model to a voxel of data, the all-in-one model is broken up into two stages of
modeling known as the two-stage summary statistics model (Holmes and Friston, 1998).
In the case of fMRI data it is attempted to obtain subject specific signal size parameters and
within-subject variance. fMRI time series are very noisy, with noise contributions from the
subject (cardiac, respiratory noise, head motion, etc.) as well as the scanner.The objective is to
create a model that captures both the noise structure and the fMRI signal. fMRI data analysis
software offers many options to deal with these complications including highpass filters, lowpass
filters and correlation estimation (or whitening) to model or reduce the noise and hemodynamic
response function convolution to improve the model of the fMRI signal. Classical hemodynamic
monitoring is based on the invasive measurement of systemic, pulmonary arterial and venous
pressures, and of cardiac output. In reality, since the fMRI signal is a measurement of
hemodynamic change, there is a delay and the response to an event and the model must reflect
this. The noise of the time series starts with the low frequency drift which appears in the time
series as a downward trend over time. The highpass filter is designed to reduce this type of noise
by passing the high frequency noise and reducing the low frequency noise. Software packages
handle this issue different ways.Specific group fMRI modeling assumptions of different software
packages and how they differ are available (Mumford and Nichols ,2006). Since fMRI data
consist of over 100 000 time series that can each be at least 100 time points long, data are
analyzed in a voxel-wise fashion and the mixed model is broken into two stages, where single
subjects are analyzed at the first level and group analyses are carried out at the second level.
Convolution and highpass filtering tend to improve the fit of the model. Modeling the positive
correlation of fMRI data reduces bias in the variance estimates (Nichols and Hayasaka ,2003).
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