Imaging the Neural Systems for Motivated Behavior and ... · motivated behavior. The combined neural systems that produce this directed behavior constitute the neural basis of what
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Imaging the Neural Systems for Motivated Behavior and their Dysfunction in
Neuropsychiatric Illness
Hans C. Breiter1,2,3, Gregory P. Gasic1,2,3, and Nicholas Makris2,4
1Motivation and Emotion Neuroscience Collaboration, Department of Radiology,
Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA 2Athinoula Martinos Center for Biomedical Imaging, Massachusetts General Hospital,
Massachusetts Institute of Technology, and Harvard Medical School, Boston, MA, USA 3Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School,
Boston, MA, USA 4Center for Morphometric Analysis, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School, Boston, MA, USA
cingulate gyrus), pCG (posterior cingulate gyrus), INS (insula), pHip (parahippocampus),
and TP (temporal pole). The colored symbols on the brain slices show reported activation
surveyed from 26 studies of reward function in healthy controls (Aharon et al., 2001;
Bartels and Zeki, 2000; Berns et al., 1997; Berns et al., 2001; Blood et al., 1999; Blood
and Zatorre, 2001; Breiter and Rosen, 1999; Breiter et al., 2001; Bush et al., 2002;
Delgado et al., 2000; Drevets et al., 2001; Elliott et al., 2000; Elliott et al., 2003; Kahn et
al., 2002; Kampe et al., 2001; Ketter et al., 1996 Knutson et al., 2001; Knutson et al.,
2003; Liu et al., 2000; O’Doherty et al., 2001a; O’Doherty et al., 2001b; O’Doherty et al.,
2002; Small et al., 2001; Thut et al., 1997; Volkow et al., 1995; Volkow et al., 1996;
Volkow et al., 1997b). These include ten studies with monetary reward (five with a
guessing paradigm determining compensation, four with a performance task determining
compensation, and one with a prospect theory based game of chance). Four studies
focused on appetitive reward with fruit juice, chocolate, or pleasant tastes, while five
studies focused on some aspect of social reward (two with beautiful faces, one with
passive viewing of a loved face, and two with music stimuli). Five studies involved
amphetamine or procaine reward, and two studies focused on a probabilistic paradigm.
Figure 12: The same structural scans shown in Figure 11 are displayed here, grouped
two-by-two, and numbered to correspond with the anterior-to-posterior orientation. The
three groupings of brain slices at the bottom of the figure display changes in the structure,
function, or morphology of subcortical gray matter and paralimbic cortices for the
following three groupings of studies. Studies grouped as “putative endophenotype
variation 1” were focused on recurrent depression with strong familiality (i.e., sometimes
referred to as familial pure depressive disorder). Studies grouped as “putative
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endophenotype variation 2” were focused on primary depression with and without
obsessive-compulsive features and without manifested familial connections. Studies
grouped as “putative endophenotype variation 3” were focused on primary and secondary
depression in older subjects who were studied post-mortem (see text for references).
Regions with differences in resting brain metabolism from healthy baselines are noted
with an “O” symbol, while regions with differences in regional morphology or volume
from healthy baselines are noted with a diamond. Most of these studies were not
performed with a family segregation design, yet they do suggest the potential for
circuitry-based endophenotypes for major depressive disorder. Aggregation of such data
across studies, as done for the four slices shown at the top of the figure, point to a strong
focus on the generalized reward/aversion system for circuitry-based alterations
characterizing major depressive disorder. Such circuitry-based sub-types may aid
treatment planning in the future.
Figure 13: The groupings of structural images in gray tone are the same as in Figure 12.
Groupings (1) – (5) are placed like the spokes of a wheel around a central sagittal slice
showing the approximate location of each coronal slice relative to a yellow rectangle
around brain regions containing the subcortical gray matter and paralimbic cortices
hypothesized to produce reward/aversion functions. Each grouping represents a partial
consolidation of findings from the neuroimaging literature comparing patient groups to
healthy controls on the basis of (a) resting metabolism, blood flow, or receptor binding, (b)
blood flow or metabolic responses to normative stimuli (i.e., pictures of emotional faces
that are rapidly masked in an effort to present them subconsciously to subjects with post-
traumatic stress disorder), (c) structural differences, or (d) magnetic resonance
spectroscopy measures (see text for references). As in Figure 12, regions with functional
differences (a & b above) between subjects and healthy controls are noted with an “O”
symbol, while regions with differences in regional morphology, volume, or spectroscopy
signal from healthy baselines (c & d above) are noted with a diamond. Regions with an
asterisk are noted when a set of studies implicate a difference between patients and healthy
controls for a large region, and a more recent study with significantly better spatial
resolution in healthy controls notes an effect to the same experimental paradigm (i.e.,
37
amphetamine infusions) localized to a specific subregion (i.e., the NAc vs. the basal
ganglia). The clinical groupings used for (1) – (5) are listed above each set of slices and
described in detail in the text. This schema supports the hypothesis that neuropsychiatric
illness may lend itself to objective diagnosis by use of circuitry-based neuroimaging
measures.
Figure 14: (a) This diagram emphasizes the tripartite division of influences that shape an
organism, namely the genome, epigenome, and environment. The set of all possible
behaviors for an organism (i.e., communication) is determined by these three influences,
although the specific sequence of output is not. (b) The internal environment produced by
the genome/epigenome produces the putative spatiotemporal scales of brain function. In
this case, activity at the level of distributed groups of cells, local networks or groups of
cells, and individual neurons modulate the function of the genome/epigenome, and
activity at the level of the genome/epigenome significantly modulates the function of
each of the spatiotemporal scales of function that embed it. The linked spatiotemporal
scales of brain function are again distinct from observed behavior in the outside world
(i.e., exophenotype) and will have a stronger connection, as endophenotypes observable
with neuroimaging and other measurement systems of brain function, with the
genome/epigenome. The scale of distributed groups of cells produces behavior, and
accordingly serves as an interface between the environment and genome/epigenome.
Figure 15: As a rough approximation, brain processes can be analogized to a set of
nested scales of function. The genome/epigenome is nested in cells (neural and glial),
which in turn are nested in neural groups as local circuits, which in turn are nested in sets
of inter-connected groups that are distributed across the brain and modulated by
monoaminergic and hormonal systems. The scale of distributed neural groups, which
produces systems biology, can be sampled using a number of distinct technologies,
including tomographic imaging modalities such as fMRI and PET. Local circuits or
neural groups, comprised of excitatory and inhibitory synapses, axonal and dendro-
dendritic circuits, can be sampled by multicellular recording techniques. The individual
38
cell, with its intracellular signaling and surface receptors, can also be characterized by
measures of local field potential and sequences of action potentials. From the scale of
molecular genetics to that of distributed neural groups, reductionistic explanation of
empirical observation using linkage of measures across scale has to occur both from “top-
down” and “bottom-up” to be self-sufficient. Given the nesting of scales, and the
measurable relationship of information processing at one scale to another, dense sampling
of one scale of brain function will reflect processes at the other scales (see Figure 10).
Figure 16: This schematic illustrates one potential “top-down” approach to integrative
neuroscience, such as might to used for identifying the genes associated with a
susceptibility or resistance to addiction. Overlapping sampling of circuitry processing
reward/aversion input (cartoon in top-left) from families with addiction, could be used to
produce a systems biology map (cartoon top right) that identifies quantitative traits with a
demonstrated familiality, and little alteration with disease progression. These
endophenotypes could then be used in a multipoint genetic linkage analysis to
chromosomal loci (schematic in center/bottom). This “top-down” approach would define
disease susceptibility by continuous quantitative traits measured from systems biology (as
via neuroimaging), and might perform a total genome scan and a multipoint linkage
analysis using a variance component approach (for quantitative and potential qualitative
traits). Analysis of microsatellite repeats and SNP markers could then drive gene
identification.
Figure 17: (a) Attempts at a “top-down” approach to integrative neuroscience have
frequently started from the delineation of behaviorally defined exophenotypes, which are
theoretically related to circuitry-based phenotypes (endophenotypes). Illness category can
stand in for any number of American Psychiatric Associations Diagnostic Statistical
Manual Axis I neuropsychiatric disorders, such as subtypes of major depressive disorder,
or cocaine abuse and dependence. Altered function in a distributed set of neural groups
(referred to in the figure as “circuits”) is symbolized by an asterisk after the circuit
number. This altered function may include diminished or increased circuitry activity, or
substitution of an alternative circuitry to fulfill a functional deficit. There may be a
39
number of altered functions or metric traits, determined by altered circuitry performance,
which determine a particular neuropsychiatric disorder. This is highly likely given the use
of multiple signs and symptoms currently used to define neuropsychiatric exophenotypes
using the American Psychiatric Associations Diagnostic Statistical Manual (APA, 1994).
Given the embedding of scales of brain function, “top-down” approaches starting from
continuous quantitative measures of systems biology, would, with the appropriate subject
sample size, have the potential to identify all polymorphic traits and temporal adaptations
for a behavioral varient. (b) “Bottom-up” approaches evaluate one genetic polymorphism
at a time to determine how it leads to an altered profile of circuitry function.
40
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