Download The Neural Architecture Underlying Habit Learning: An Evolving

The Neural Architecture Underlying Habit Learning: An Evolving View
Ann Martin Graybiel
Massachusetts Institute of Technology
I hope to tell this story of our work as an adventure story, for that is what it is and has been. I never
would have predicted at the start that we would now be working on how we form habits and rituals
— and how these turn out to be related to disorders in neurology and psychiatry. But I have
become fascinated with habits and rituals — and with trying to understand the neurobiology that
underlies these behaviors of ours. Our habits are so familiar to us, so common in our lives, that for
many of the little habits and mannerisms that we have, we almost are unaware that we are doing
them — from morning routines to evening routines. These, of course, are individual habits; but we
all share in rituals and habits that are social and even societal. These rituals are like threads
running through the history of mankind (Fig. 1); once shared as cultural habits, they can have great
power.
As I began this work, and decided to do
particular experiments, I was delighted by
unexpected findings; and we — for it became a
small band of adventurers — had to find new
methods in order to follow the implications of
our findings. At each step, there was a sort of
leap of faith that if we tried in this new way, or
that, we might truly learn something deeply
important about the brain. Our early work on
the striatum started with discovering the
chemical architecture of the striatum in the
1. Statuary from ancient Crete, depicting rituals in
human brain (striosomes and surrounding Figure
the Minoan civilization, about 2000 BC.
matrix)
and
then
the
more
global
compartmental architecture (matrisomes as well as striosomes) that we found in laboratory animal
experiments. Our findings made me think that the striatum might have a learning architecture. And
so we needed to — and did — begin to record striatal activity day by day as animals learned tasks
to the point of their becoming habitual. We discovered learning-related patterns of striatal activity
in freely moving rodents that seemed to bracket behaviors that were becoming habitual, and then
in primates we found similar patterns: it was as though with the transition from deliberative
behavior to habitual behavior, the entire circuit-level activity of the striatum changed so as to
bracket the habitual behavior. This all has all led to our current work, in which we are trying to
manipulate these habit-related patterns to probe the underlying circuits and also to develop
therapeutic strategies. I wanted at the same time to push toward understanding the molecular
bases of these behaviors; and, from using early-gene assays, to discovering striatum-enriched
genes, to analyzing the effects of their deletion, the idea is emerging that some behavioral problems
related to repetitive, over-focused behaviors could result in part from imbalances between
striosome and matrix processing. And through this all, despite the still limited methods for seeing
into the depth of the functioning brain at even 1 mm levels of resolution, we have tested as directly
Ann Martin Graybiel
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as possible for how the chemical architecture might be important in neurologic and
neuropsychiatric disorders. What follows is, as a consequence, full of jumps across different
methodologies and strategies, but the big jumps were necessary. The hope is that the findings are
coalescing to point toward the beauty and intricacy of deep-brain functions. These functions seem
somehow to lie very close to the core of ourselves as humans.
The Neocortex
When I began to study the brain, as a student in the late 1960's, there was enormous excitement
about work on the neocortex. Surely this was the organ of thought and creativity, the organ
underlying our ability to see and hear and feel, our ability to act deliberatively, to do mathematics.
And, building on the great discoveries about the anatomy of the neocortex, with its finely organized
layers of neurons, many with apical dendrites oriented at right angles to these, it seemed
reasonable that this elaborate biological structure underpinned such complex functions. The
excitement came with the new findings from microelectrode recordings made by Mountcastle and
Hubel and Wiesel and others — recordings that showed that the functional organization of the
neocortex was related to its anatomical architecture, and that the representations of the world
were somehow built up step by step by groupings of neurons, in cortical columns and microcircuits (Mountcastle, 1957; Hubel and Wiesel, 1962). This all had a big influence on me. I was being
trained to use anatomical tract-tracing methods in the laboratory of W.J. H. Nauta, and I was lucky
that he let me work not on his main theme, but on the outlying regions of the neocortex then known
as the association cortex, which were considered also to have sensory functions but presumably
'higher' ones — association regions innervated by the pulvinar.
There seemed to be a logic in the connectivity patterns I found. This is still a deep value of
working on what is now called the wiring diagram of the brain. As I studied, I added more and more
work related to the motor system. There were terrible limitations in the methods then available.
The anatomical tracing methods were based on silver stains to track degenerating fibers induced to
degenerate by an earlier experimental lesion. This meant that there were confounds from damage
to fibers just passing through the region of the lesions. And there was no good way to look at a
connection 'backwards’, to identify neurons projecting to a given site in the brain. When two new
methods were introduced to get around these problems, I immediately turned to the long-neglected
brainstem — where it had been almost impossible to work before because of the thicket of passing
fibers lacing through the region. I reveled in being able to study the brain's wiring diagram for
regions that earlier workers had been unable to chart, and roamed across many regions
documenting ‘new’ pathways related to vision and oculomotor control. (The most amusing was the
finding that one nucleus known to be related to control of the tongue and so named for that
function — the perihypoglossal nucleus — actually was a pre-oculomotor nucleus with direct
projections to motor neurons controlling eye movements!)
Chemical Architecture of the Striatum: Striosomes
I was increasingly questioning whether to continue in anatomy, because I wanted to return to
functional issues that had attracted me to work on the brain in the first place. As a first step, I
wanted to find a way to link the experimental studies to work on the human brain, to which I had
been brilliantly exposed by Hans-Lukas Teuber and others at MIT. This was before fMRI was
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developed, when even PET images were new. Having become interested in histochemistry, and
being originally trained in chemistry, I decided to try to find histochemical stains that would work
both on human brain (post-mortem) and on the brains of the laboratory animals in which I had
traced anatomical connections.
I decided to use enzyme stains for the cholinergic enzyme, acetylcholinesterase, and after
testing several methods began to stain the experimental brains. I was astonished. I could identify
many patterns that I had seen in the connectivity studies. But how to look at the human brain? With
some trepidation, I found my way to the Massachusetts General Hospital, where I met its wonderful
diener. I explained that I wanted to take a brain to MIT to stain it. This began the period in which I
would receive these contributions and take them in a carefully covered bucket back to MIT, only
one subway stop away! I learned how to fix and handle the brains, and how to stain carefully cut
sections, and our small lab began to work in parallel on the human and animal material.
The striatum, part of the basal ganglia, was well known to be highly enriched in molecules
related to cholinergic function, and the striatum was strongly stained for acetylcholinesterase
activity, that was for sure. But what we saw attracted my interest. The striatum was known as a
primitive part of the forebrain — a vast ball of neurons jammed underneath the elegant neocortex.
So it was surprising that when Cliff Ragsdale, an MIT undergraduate, and Henry Hall and I did the
staining, the stains did not look homogeneous; there were small zones scattered through the tissue
in which the staining was weak (Graybiel and Ragsdale, 1978) . We were just learning how to
incubate the sections in the staining solutions, so we stayed up all night testing, and it was about 4
AM when we saw these. Doubtful, we checked again after
some sleep, and they were still there (Fig. 2).
What was so tantalizing was that these little
zones had about the same dimensions as cortical
columns. We had no hint that connections might also
form column-like structures in the striatum; after all, the
striatum was known as an anatomically primitive part of
the forebrain. But we looked, and soon we found that
indeed, the cholinesterase-poor zones corresponded to
striatal zones with special input and output connections
that we could mark in the experimental animals. I
reconstructed these in serial sections (the old way, on
large plastic plates onto which I projected images of the
sections — done nights, at home, with the understanding
of my kind and patient husband).
This meant that there were three-dimensional
labyrinths running through the striatum — for all the
world as though there was a subcortical column or layer
system in the striatum. We called these zones Figure 2. Photomicrograph of section from
human brain, stained for acetylcholinesterase,
striosomes, for striatal bodies, a name I came up with showing pockets of low enzyme activity. These
after a delightful time pouring over Latin and Greek are the histochemically identified striosomes.
From Graybiel and Ragsdale, 1978.
dictionaries.
Ann Martin Graybiel
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To test the layer and column idea, I managed to borrow some brains that had been
experimentally labeled with 3H-thymidine for neuronal birth-dating studies from Terry Hickey, a
friend who worked on the visual system. Striosomal neurons were born in a relatively narrow timewindow, just like a cortical layer — in fact, like layer 6 of the neocortex (Fig. 3) (Graybiel and
Hickey, 1982). This finding strengthened our thought that the striatum, far from being primitive,
had a hidden neurochemical architecture that in some ways was comparable to that of the
neocortex, even though there were not strictly aligned layers and columns.
Striosomes Correspond to Regions in the Embryonic Brain Receiving Early-Arriving
Dopamine Innervation and Having Early Cholinergic Receptor Expression
I had learned that Dahlstrom
and Fuxe had discovered in
very young brains so-called
'dopamine islands', zones of
intense fluorescence marking
the developing nigrostriatal
innervation. We made direct
comparisons of these to the
'young' striosomes, and I was
excited that the 3H-thymidine
Figure 3. Evidence that neurons and striosomes share birth-dates. Matched adjoining
birth-dating would give a way transverse sections through the striatum of an adult cat illustrating clusters of striatal
to find out whether the neurons born on embryonic day 26 (A) and acetylcholinesterase-poor striosomes (B).
Scale bar: 1 mm. From Graybiel and Hickey, 1982.
striosomes that we found at
adulthood corresponded to the dopamine islands of the
developing brain. To make the comparisons at adulthood,
we needed the marker to bridge across time. It turned out
that there was a precise correspondence (Graybiel et al.,
1981; Graybiel, 1984) (Fig. 3). Moreover, I was able to
obtain fetal brain material from the Children's Hospital,
again with trips back and forth from hospital to lab. We
soon found the striosomal compartments were visible early
in the human striatum, and remarkably, that through the
course of embryonic development and early postnatal
months, the zones switched from being cholinesterase-rich
to being cholinesterase-poor (Graybiel and Ragsdale,
1980). We later found that many neurotransmitter-related
substances — including dopamine — make these switches.
Fu-Chin Liu and I spent many hours at the microscope
together looking at developing brains, prepared with great
Figure 4. A cross section through the brain of
skill by Diane Major.
a human fetus at 22 weeks of gestation,
But the critical point was that now we knew that illustrating the distribution of muscarinic
binding sites, regions that
there was a close connection of the striosomal system to cholinergic
correspond to developing striosomes. Scale
the two key neurotransmitters famously important for bar: 1 mm. From Nastuk and Graybiel, 1985.
Ann Martin Graybiel
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disorders like Parkinson's disease through the concept of a cholinergic-dopaminergic balance in the
striatum. This development was vividly shown in ligand binding for the M1 acetylcholine receptor,
work that Mary Nastuk and I did for human and laboratory animal brains (Fig. 4) (Nastuk and
Graybiel, 1985, 1988). This cholinergic-dopaminergic linkage is again a focal point for our work
now.
Striosome-Matrix Compartmental Organization Holds for Most Neurotransmitter-Related
Molecules in the Striatum
During these years we and many other people stained and labeled for tens and tens of
neurotransmitter-related substances, and, with a series of wonderfully gifted students, postdocs,
and occasional visitors, we all found that the substances tended to be enriched either in striosomes
or in the extrastriosomal matrix (which we called matrix for short). In other words, there was an
elaborate, developmentally regulated molecular compartmentation of the striatum that had been
totally unseen in the standard classic stains (Graybiel, 1990).
We were already finding that the striosomes received inputs from parts of the neocortex
related to the limbic system, thought to regulate emotion, and that striosomes projected either to
the dopamine-containing substantia nigra pars compacta, or close to them. This meant that the
striosomes might be like outlying limbic parts of the striatum, embedded in the large matrix region,
which we and others found to be more related to sensorimotor and associative cortex. But this was
all vague, functionally, and besides, why would there be only one set of compartments not a whole
series, if the compartments were like layers or columns?
Global Compartmentation of the Striatum: Striosomes and Matrisomes
We clearly needed physiology. Fortunately, Carl Olson came to the lab, and though we did not work
together on the striatum, I learned from him about mapping the somatosensory representations of
the brain. I learned about sleep deprivation, too: some of these experiments lasted for up to three
days straight! Rafi Malach and I then decided to try to use the cortical maps as a way to learn more
about the functional organization of the striatum. We carefully mapped the primary sensory cortex
and adjoining area 3a in acute recording experiments, and then traced corticostriatal connections
from small, identified parts of the somatotopic
representation, using the improved anatomical
tracing methods. What we found was, to us,
remarkable (Malach and Graybiel, 1986): if we
injected tracer into the cortex representing one
small body part, say the hindpaw, we found
multiple, relatively confined zones of labeling —
about the size of striosomes — but these were all in
the matrix compartment. These dispersed zones
formed a map globally, but they had interesting local
order as well, for example, interdigitation patterns Figure 5. Diagram illustrating mosaic of striosomes
suggesting the possibility of local computation and matrisomes that in the aggregate form a global
compartmentation in the striatum. IN-striosome; OUTacross compartments related to similar functions extrastriosomal matrix. From Malach and Graybiel,
1986.
(Fig. 5).
Ann Martin Graybiel
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Alice Flaherty and I then worked on the somatosensory maps in the squirrel monkey,
applying tracers in different combinations, with kind advice from Mriganka Sur, who had done the
early mapping of this system and to our good fortune had a lab next door to ours. Alice and I
confirmed the Malach results but then studied the mapping in detail, eventually also combining
different tracers to see how the corticostriatal mappings were related to the output organization of
the striatum mapped by using retrograde labeling of the output cells (Flaherty and Graybiel, 1991).
The output neurons, we found with Alice and Juan Jimenez-Castellanos and Jose Giminez-Amaya,
were also in dispersed clusters about the size of striosomes! We made injections of retrograde
tracers at many different sites in different experiments (Jiménez-Castellanos and Graybiel, 1989;
Giménez-Amaya and Graybiel, 1990;
Flaherty and Graybiel, 1993), and we
found labeling of either neurons in
striosomes or neurons in the matrix,
depending on the pallidal or nigral site
that we injected. We named the zones in
the matrix 'matrisomes' (for matrix
bodies).
In later experiments, Hemai
Parthasarathy and I found, with the help of
Jeff Schall, that the oculomotor regions of
the cortex also sent distributed clustered
projections to the striatum, and moreover,
Figure 6. Correspondence of ‘input matrisomes’ labeled by an
that these two corticostriatal input anterograde tracer injection in the foot region of the motor cortex
systems converged in the input (A) and ‘output matrisomes’ labeled by a retrograde tracer injection
in the pallidum (B) in matched adjoining sections. Scale bar: 1 mm.
matrisomes, just as the somatotopically From Flaherty and Graybiel, 1994.
matched sensory and motor maps had in
the Flaherty experiments (Parthasarathy et al., 1992). We gradually reached the conclusion that the
entire matrix compartment was tiled (three-dimensionally) with zones that were not visible with
current staining methods but that nonetheless were highly organized and visible by looking at the
connectivity of the striatum. This surely meant that compartmentation was as crucial to striatal
function as the columns and layers of the neocortex.
Potential Learning Architecture of Striatal Networks
In a subset of our animals, Alice and I had found that the input matrisomes labeled from the
sensorimotor cortex were precisely aligned with output matrisomes labeled in the same monkeys
by pallidal injections (Fig. 6) (Flaherty and Graybiel, 1994). I had seen the computational mixtureof-experts learning architecture being developed by Michael Jordan, and the divergentreconvergent patterns that Alice and I were seeing were highly reminiscent of this architecture. We
suggested that the striatum, by redistributing incoming sensory information as though giving the
information to little expert sub-networks, and then sending it on within a re-convergent mapping
framework to the output structures, could function to promote neuroplasticity of the cortico-basal
ganglia system. If so, the striatum could be the learning machine of the basal ganglia (Fig. 7).
These results were emerging in our lab just
as Schultz and Romo were first reporting their
stunning finding that the dopamine-containing
neurons of the substantia nigra pars compacta
responded in relation to primary rewards and then
acquired responses to stimuli that predicted those
rewards (Romo and Schultz, 1990). The two sets of
findings seemed fully complementary (Fig. 8). The
relationship between these was bolstered when
Minoru Kimura and I began to collaborate and,
with his coworker, Toshihiko Aosaki, found that the
so-called tonically active neurons (then thought
and now known to be the famous cholinergic
interneurons of the striatum) also had responses to
primary rewards and acquired responses to
conditioning stimuli predicting reward, and that
these acquired responses depended on dopamine
in the striatum (Aosaki et al., 1994). We mapped
them in acute recording and marking experiments
and
found
that
these
neurons
were
disproportionately adjoining striosomes (Graybiel
et al., 1994; Aosaki et al., 1995). These experiments
were very encouraging to me, and I finally decided
to try to set up chronic physiological recording in
the lab to follow up on our mapping studies.
Ann Martin Graybiel
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Figure 7. Diagram illustrating the analogy of the inputoutput architecture of the striatum to a mixture of
experts computational architecture introduced by Jordan
and colleagues. Adapted from Graybiel, 1998.
Figure 8. Diagram illustrating concept of cortico-basal
ganglia loops as helping in the selection of actions under
the guidance of reinforcement signals from the midbrain
dopamine system (and related neural modulatory
influences).
Reorganization of Ensemble Spike Activity in
the Sensorimotor Striatum during Learning
Setting up chronic recording suitable for long-term recording during learning was quite a challenge,
given my background, and it seemed smart to start in rats, for which ensemble recording was being
done in the hippocampus already. Mandar Jog, Chris Connolly and Yasuo Kubota, and I, with initial
technical advice from Matt Wilson's lab, then initiated a series of experiments in which we inserted
multiple tetrodes into the striatum. Experiments of this type still are ongoing on in our lab.
We began with a simple conditional maze task, influenced by the success psychologists had
had in doing behavioral experiments. We first made peri-event histograms of firing at different
parts of the maze runs, and found that there were many neurons in the sensorimotor striatum that
were active at different times during the runs. We noted this array of responses, but what piqued
our interest was that as the rats acquired the task behaviorally, we began to see changes in the
global patterns of ensemble firing of the striatal neurons. We developed analyses to look at the
entire time that the rats were performing the maze runs — not just small peri-event times. This
strategy of looking at the entire behavioral sequences turned out to be critical. What we found was
that, instead of the ensemble activity being strong throughout the maze runs, the activity gradually
Ann Martin Graybiel
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became stronger toward the beginning and the end of the runs (Fig. 9). Fewer neurons, on average,
had high activity during the middle of the runs (Jog et al., 1999).
Figure 9. Dynamic reorganization of neural activity in the striatum during habit learning. Schematic activity diagrams
illustrating the changes in proportions of task-responsive neurons (A, top), spikes proportions (A, bottom), and
ensemble activity (B) that occur in the sensorimotor striatum as rats learn to perform a conditional T-maze task. The
pseudocolor scale indicates the population spike activity (red: high activity). The ensemble activity patterns gradually
shift toward emphasizing the beginning and end of the maze-runs. A: from Jog et al., 1999. B: from Barnes et al., 2005.
The Idea of Chunking of Action Repertoires as a Function of Corticostriatal Circuits
This learning-related pattern suggested that as the animals acquired the maze runs as habits, the
activity in the sensorimotor striatum was reconfigured so as to bracket the entire learned behavior.
This was an idea that we began to explore in further experiments. With Terra Barnes, we found that
this pattern could be seen in firing rate plots as well as cell count plots, and strikingly, that if we
took away the rewards, in extinction trials, the pattern faded, but if we put back the rewards, the
pattern reappeared almost immediately (Barnes et al., 2005). This meant that as the animals
learned, patterns of spike firing were laid down in the striatum in such a way that they could be
covert or overt, depending on the situation. I
likened this to the notion of chunking that had
been introduced by George Miller as a way to
make memories manageable: maybe when we
learn behaviors, the sensorimotor striatum
brackets the ones that have the most positive
and least negative value (Graybiel, 1998). And
at the same time, 'expert neurons' developed,
freeing many other striatal neurons from fulltime duty in driving the habitual behavior. Figure 10. Diagram illustrating the idea of chunking of
Thus through the stream of behavior, episodes action repertoires, whereby entire sequences of behavior,
could be marked as scripts ready to be called accepted by cortico-basal ganglia circuits as valuable, are
marked by activity bracketing the entire sequence.
up (Fig. 10).
Action Circuits and Permissive Circuits: An Idea about Corticostriatal Circuit Dynamics
Katy Thorn and I decided to ask whether similar patterns would form in the 'associative' part of the
striatum, the part receiving inputs form the association areas of the neocortex. The answer was no.
In fact, in the medial part of the stratum, the activity patterns were almost the reverse of those in
the sensorimotor striatum (Thorn et al., 2010). The activity was strongest mid-run! We developed
the idea that the activity in the associative part of the stratum had to die down before the
Ann Martin Graybiel
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beginning-and-end pattern in the sensorimotor stratum could actually take over control of the
behavior (Thorn et al., 2010). This was a proposal for a new dynamic: one in which one set of
cortico-basal ganglia loops could control another set during the course of learning.
We had not yet proved that the maze runs in the over-trained rats actually were habits. Only
much more recently, when Kyle Smith joined the lab, could we do this. Kyle introduced to our lab
the devaluation protocol that psychologists use to test for the presence of habitual behavior. With
this protocol, our over-trained rats continued to do the same behaviors over and over again even
when the former reward had lost its positive value (Smith et al., 2012). The maze runs in the highly
trained animals passed the test for being habits!
The idea of an executive network overseeing habitual behavior is an idea that psychologists
had formulated from lesion studies of the cortex. In new experiments, Kyle and I decided to record
simultaneously in the medial prefrontal cortex and the sensory motor striatum — the two regions
singled out as 'habit-related' in prior lesion work — as our rats learned maze tasks. We found that
the familiar beginning-and-end pattern formed not only in the sensorimotor striatum, but also in
the medial prefrontal cortex. But, unlike the striatal pattern, the prefrontal pattern only emerged as
the habitual behavior became deeply ingrained during the over-training period. Moreover, when
the animals were then given devaluation exposure to degrade the reward value of one of the maze
rewards, the sensorimotor striatal task-bracketing pattern was rock-steady, but the prefrontal
cortical bracketing pattern became obscured. It was as though once a habit is engrained, its
bracketing representation in the sensorimotor striatum survives changes in reward value, but the
neocortical bracketing pattern remains sensitive to reward value and quickly weakens. These
findings are fresh and only now being submitted for publication.
This result has led us to test for the effects of inhibiting this region of the medial prefrontal
cortex by using on-line temporally controlled optogenetic inhibition with virally delivered opsin
contributed by Karl Deisseroth. We not only could block the habitual behavior, as predicted from
earlier lesion work, but also turn the habit back on later with a reapplication of the optogenetic
silencing to the same cortical site! We now can, in effect, toggle the habits on and off. This work
suggests that Pavlov was quite right when he suggested that we never forget a habit, we just inhibit
it. We are hard at work on further experiments now.
Cost-Benefit Coding in the Primate Striatum and Neocortex
Once we knew that we could record in rodents, I, with some trepidation, decided to set up
recordings in behaving primates. Luckily, Jun Kojima, Naotaka Fujii and Pablo Blazquez joined the
lab. The beginnings were almost amusing. We had one totally empty room. Jun came first, from
Japan, where he had learned monkey neurophysiology, and he was still learning English. I myself
knew English, all right, but I had never set up a monkey lab. But eventually, it all worked. Pablo and
I found, by using airpuffs for negative reinforcement, that many of the tonically active neurons
responded to the unrewarding air puffs as well as to rewarding juice treats (Blazquez et al., 2002).
That is, both cost and punishment could modulate their firing rates. Moreover, the development of
these responses beautifully matched the development of EMG activity in the orbicularis oculi
muscle, which controls eye blinks. Having had exposure early on to the invertebrate field, I was
thrilled that we could actually, in non-human primates, record from a single identified neuronal cell
type and record activity in a single muscle and find a highly systematic relation between the two
Ann Martin Graybiel
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(Fig. 11). In fact, with the activity of about 150 of the tonically active neurons we could predict the
occurrence of a blink with a 90% probability of being accurate!
Figure 11. Responses of tonically active neurons are tightly related with the conditioned eye blink behavior. (A) Proportions
of conditioned responses (red) and neuronal responses (blue) during conditioning (shaded) and extinction (non-shaded)
training. (B) Significant correlations between behavioral and neuronal responses. (C) Neuronal responses predict the
occurrence of behavioral responses. From Blazquez et al., 2002.
This contrasting reward and punishment
sensitivity has become important in current studies of
the cortico-basal ganglia system, and became important
for experiments that Ken-ichi Amemori and I have done
using approach-avoidance conflict paradigms to study
neural responses in the cortex and striatum.
Action Boundary Coding by Primate Corticostriatal
Ensembles
As we were working on the early maze experiments in
the rats, Naotaka Fujii and I found responses in the
prefrontal cortex of monkeys that, as in the rodent
experiments, formed a task-bracketing pattern around a
well-learned behavior — saccadic eye movement
sequences that the monkeys made in response to visual
cues (Fujii and Graybiel, 2003). It was as though actionboundaries formed in the prefrontal cortical ensembles,
and also in neuronal ensembles in the oculomotor part of
the stratum. We found that at the end of a given
sequence, many neurons produce a single burst of
activity — an 'extra peak' we called it — as though
literally marking the end (Fig. 12). This activity didn't
appear to be related to reward delivery — instead, it
seemed to be related to ending the sequence. Theresa
Desrochers now is studying the evolution of such
bracketing responses during untutored habit formation
Figure 12. Accentuated activity of neurons in
the prefrontal cortex at the start and end of
sequences of saccadic eye movements. (A)
Task cartoon. (B) Raster plot (above) and peristimulus time histogram (below) illustrating
the spike activity of a single prefrontal neuron.
The monkey made four saccades. Note the
fifth, ‘extra’, peak of activity. From Fujii and
Graybiel, 2003.
in primates. This activity is of much interest to us
now as we explore oscillatory patterns of activity
occurring in striatal and cortical networks during
learning.
Dezhe Jin, a physicist, helped us look at the
fine timing of the responses we found in the
prefrontal cortex and oculomotor striatum as the
monkeys performed the saccade sequences. What
we found was that time is embedded in these
representations. With perceptron models, we could
map task-time with a resolution of at least 50
milliseconds for the prefrontal and striatal
ensembles (Fig. 13) (Jin et al., 2009). This was a
critical finding; in the maze experiments, we could
not separate navigational distance and navigational
time. We suggested that these responses might
provide the time-stamp representations predicted
by spectral timing theory and by reinforcement
learning models.
Ann Martin Graybiel
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Figure 13. Evidence for a neural representation of time
in cortico-basal ganglia circuits illustrated in the output
of perceptrons driven by prefrontal (A and B) or by
striatal (C and D) inputs. Maximum margins for the
perceptrons with prefrontal and striatal inputs
discriminate single time points from all others at least 50
msec apart. Gray trace indicates the noise level. B and D
illustrate weighted sums of inputs for three perceptrons.
Red dots indicate the times decoded. From Jin et al.,
2009.
Multiple Patterns of Plasticity Emerge
Simultaneously in Striatal Circuits as Learning
Occurs
Altogether, these experiments in both rats and mice and in monkeys suggest that learning-related
representations are developed in the striatum as habits form, that such representations form in the
neocortex also, and that, among these, task-bracketing and end-task activation patterns occur as a
result. From the devaluation work, we are finding that the action-boundary representations in the
sensorimotor striatum are stable to changes in value, but that such patterns are not stable in the
medial prefrontal cortical region. Activity in this region, as our optogenetic experiments show, is
necessary for the performance of the maze behavior as a habit rather than as a goal-directed
behavior (Smith et al., 2012). This last finding is curious, as it implies that even nearly automatic
behaviors are, despite their apparent automaticity, monitored on a moment-to-moment basis by a
neural system that must give permission for the automaticity.
The fact that habitual behavior has some fixed representations and some representations
that are malleable and responsive to current reinforcement conditions suggests that network-level
neural activity must be coordinated to ensure smooth transitions in behavior. By now, we have
found that multiple learning-related patterns of activity develop simultaneously in different parts of
the striatum during learning. Task-bracketing patterns form in the sensorimotor striatum,
complementary decision-period activity in the medial striatum, and cue-plus-reward-related
activity develops in the ventral striatum, activity that is quite similar to that reported for the
dopamine-containing neurons of the midbrain in cue-plus-reward conditioning contexts (Atallah et
al., in prep.). Others in the field are finding these patterns as well. This diversity means that
Ann Martin Graybiel
12
different cortico-basal ganglia loops are simultaneously active and developing contrasting patterns
as habits are acquired. How do they become integrated?
Oscillatory Local Field Potential Activity in the Striatum Changes during Learning and Is
Reorganized in Relation to Local Spiking Activity
Almost certainly, oscillatory activity indicative of synchrony in networks of neurons is associated
with this plasticity in spike patterning. We began to look at the frequency domain in experiments
with Richard Courtemanche. We found little zones in which striatal beta activity popped out of
synchrony with the beta activity recorded around them when task-related spike activity in the little
zones increased (Courtemanche et al., 2003). We think that these might be matrisomes. These
experiments alerted us: there was much beta activity in the striatum even in normal animals, and it
was modulated according to on-going behavior.
Having Richard in the lab helped as we began to look at oscillatory activity in the rodent
maze experiments. With Bill DeCoteau and others in our group, we found that coordination
between theta activity in the stratum and theta activity in the hippocampus, peaking during the
decision-period of the task, predicted whether individual rats would learn the task successfully
(DeCoteau et al., 2007). Mark Howe, Hisham Atallah, Dan Gibson and I have found that in the
ventral striatum, the spike firing of the striatal neurons occurring at the end of the maze runs
becomes temporally tuned to fleeting bursts of oscillatory activity that are mainly at one frequency
early in learning (a gamma frequency) but then shift to a lower (beta) frequency late in learning
(Howe et al., 2011). We interpret these experiments as suggesting that the global organization of
the ventral striatal networks changes during learning — likely reflecting more widespread
reorganization occurring in larger networks. And the detailed patterns suggest that this process has
to do with reorganizing the networks to mark the end of the task and to adjust the network
plasticity state as a function of learning, as suggested by the 'extra peak' findings in our primate
work (Fig. 14). Moreover, with Joey
Feingold and Dan Gibson, working
with monkeys making series of
voluntary joystick movements to
visual targets, we are also finding a
remarkable end-of-task oscillatory
bursting, with fleeting, coordinated
beta bursts in the striatum and frontal
cortex. The field of brain dynamics is
opening up, and we are excited to see
these inklings of how powerfully the
dynamics in corticostriatal circuits
are influenced by learning.
One huge missing piece in this
work is any idea of how the
striosome-matrix story fits in, let Figure 14. Illustration demonstrating oscillatory activity recorded in the
ventral striatum early during T-maze learning. Power in the gamma range
alone
the
striosome-matrisome (A and C) decreases with learning, whereas power in the beta range (B
patterns in detail. This has been a and D) increases with learning. From Howe et al., 2011.
Ann Martin Graybiel
13
formidable technical challenge. We are working full tilt trying to fill this gap, with Alexander
Friedman and Leif Gibb leading the effort in the rodents and with Ken-ichi Amemori, Satoko
Amemori, Hideki Shimazu, Patrick Tierney and, most recently, Simon Hong in primates. We have an
excited, pioneering feeling about all this — and as I will return to below, there is real hope that we
can solve at least part of the puzzle.
Another Approach: Circuit-Level Reorganization of Gene Expression in the Striatum
During these years of setting up physiology, I continued to wish that we could 'find' striosomes
functionally. The excitation of immediate early genes was just being introduced as a way to mark
active neurons (I learned this from Steve Hunt, who was working on spinal cord pain pathways in
England), and I immediately decided to give rats a dose of a dopamine agonist drug — the habit
forming drug amphetamine — to try to activate striosomes. I cut the first brains too excitedly, so
rapidly that there were knife marks on the sections, but it was clear what the stain showed: in the
rostral part of the striatum, striosomes were differentially labeled with the early-gene protein
stains. This was the first 'functional' view that we had had of the striosomes (Graybiel et al., 1990).
Rosario Moratalla came to the lab at nearly the same time, and I had met Harry Robinson, who was
working with the gene method that he, too, had learned in England, and he joined in those early
experiments. I have never gotten over the feeling of amazement that one dose of a drug such as
amphetamine could so powerfully and immediately influence the expression of genes in the brain.
Epigenetics is now a major field of study, but for anyone thinking about taking a drug, this response
should be thought provoking.
Rosario Moratalla and I then found that if we gave the drug repeatedly, the striosome
predominance of the patterns became even stronger (Moratalla et al., 1996). When Juan Canales
joined the lab, he brought his trained eyes to the experiments and taught us how to score the
repetitive behaviors that developed in the animals given the repeated dosing. The animals
developed stereotypical behaviors, and we found that the
strength of these repetitive behaviors was highly correlated
with the levels of striosome- predominant gene expression
in the same animals (Fig. 15) (Canales and Graybiel, 2000).
Bulent Elibol and then Essen Saka, neurologists from
Turkey, joined the lab, and Essen and I teamed up with the
New England Primate Research Center's Bertha Madras to
do such experiments in monkeys. There, too, chronic dosing
led to increased striosome-predominant expression of early
response genes, and this expression was highly correlated
with the emergence of stereotypical behaviors (Saka et al.,
2004). But correlations, enticing as they are, do not provide
causal evidence.
Figure 15. Illustration of the high correlation
I had become determined, as we began the between levels of stereotypical behavior
elicited by repeated treatments with direct
physiology, to try to find striatum-enriched genes so that we and indirect dopamine receptor agonists (y
could manipulate the system. I learned how to use a PCR axis) and the degree to which the drug
induced differential labeling of
machine and to do simple procedures, especially from Moses treatments
striosomes after the final challenge dose (x
Chow, and then set up a tiny bit of equipment for doing gene axis). From Canales and Graybiel, 2000.
Ann Martin Graybiel
14
and protein work. I was lucky to enlist the collaboration of Brent Cochran and later David Housman,
and lucky that Hiroaki Kawasaki came to the lab. We worked together and eventually discovered a
novel class of genes that had at one end second-messenger binding motifs, and at the other end
motifs for guanine nucleotide exchange factors that targeted members of the ras superfamily. One
pair bound cAMP (Kawasaki et al., 1998a), the other pair calcium and diacylglycerol (Kawasaki et
al., 1998b). We called them the cAMP-GEFs and the CalDAG-GEFs. We were lucky. One of the
CalDAG-GEFs was highly enriched in striosomes, the other in the matrix. And the matrix-enriched
gene, CalDAG-GEFI, was expressed in much lower levels elsewhere. They acted on the MAP
kinase/ERK pathways, we found in cell assays.
Jill Crittenden came to the lab and made knockouts of the CalDAG-GEFs, and later the other
genes, and we have worked together for some years to characterize the behavioral and signaling
functions of the genes thanks to her engineering of knockout mice (Crittenden et al., 2009;
Crittenden et al., 2010; Crittenden and Graybiel, 2011). Deletion of the matrix-enriched CalDAGGEFI produces a tendency toward stereotypical, overly focused behavior — just what we might
expect if matrix function were decreased relative to striosome function and the striosome-matrix
balance idea for controlling the degree of repetitiveness of behavior were correct (Crittenden et al.,
in prep). And the knockouts have a mild learning deficit. The combination of symptoms in the mice
is striking.
With our coworkers, including Carolyn Lacey, we now have the idea that the cholinergic
system is critically affected in the mice, which could fit with the heightened expression of CalDAGGEFI in the matrix compartment and the links between cholinergic control and repetitive behavior
suggested by our work and that of others. Putting this work together with other features of
striosome-matrix compartmentation is an exciting prospect in view of the potential relevance for
these genes in the human brain.
Striosomes and Limbic Circuits
Thanks to work in many labs, including our own, we quite early on had at least indirect evidence
that striosomes were related to the limbic system. With Frank Eblen, we made an explicit attempt
to find out what regions of the frontal cortex project to striosomes in non-human primate
experiments. Of all the sites in the frontal cortex that we injected with tracer, there were only two
from which we could trace differentially strong labeling in striosomes: the far-anterior anterior
cingulate cortex (now called the pregenual anterior cingulate cortex, pACC for short), and the farcaudal orbitofrontal cortex (Eblen and Graybiel, 1995). It was just becoming known in thenemerging PET human brain scanning studies that these regions had abnormal activity in states of
addiction and in obsessive-compulsive disorder. For us, this was remarkable: drugs that could
induce addictive states, particularly on repeated use, preferentially activated genes in the anterior
striatum and induced repetitive stereotypical behaviors. Frank and I had found inputs to these
anterior striosomes from regions that seemed to correspond to those that, in the human, were
abnormal in addictive and repetitive behavioral disorders (Fig. 16).
The anterior cingulate-orbitofrontal link suggested by the experiments with Frank Eblen,
along with the other links that we and others found (for example, with the midline thalamus
(Ragsdale and Graybiel, 1991)), led us to the notion that striosomes might interact with regions
related to emotional control at higher and lower levels on a scale from autonomic-endocrine and
Ann Martin Graybiel
15
visceromotor to deliberative behavior (Graybiel, 2008).
This set up the possibility that these dispersed
striosomal regions might bring a form of ‘limbic’ control
to the sensorimotor-associative processing going on in
the much larger, surrounding matrix. From the many
experiments that we and others did, there were some
remarkable instances of fairly sharp divisions between
the two compartments: the dendrites of striatal neurons
often tended to shy away from crossing the borders,
something that we examined in some detail with Ruth
Walker and Gordon Arbuthnott (Walker et al., 1993;
Walker and Graybiel, 1993), and with Marie-Francoise
Figure 16. Striosomes receive inputs from the
Chesselet; but there were also instances of border- pregenual anterior cingulate cortex and the
crossings, as we called them. Especially interesting was posterior orbitofrontal cortex. Schematic diagrams
illustrating sites of anterograde tracer injection
the idea that the cholinergic interneurons, which we had made in the anterior cingulate cortex and
found often to lie next to striosomes, might be orbitofrontal cortex of macaque monkeys. Of all
sites, only those illustrated in red produced
integrators across the boundaries, an idea pursued the
differential labeling of striosomes. From Eblen
elegantly by Aosaki and Kawaguchi (Aosaki and and Graybiel, 1995.
Kawaguchi, 1996; Miura et al., 2008).
All of this finally can be studied in detail now that genetically assisted identification of
neurons, viral transfection methods and optogenetic and other methods for manipulating activity
methods are becoming available. We have gone from barely being able to trace axons to their
terminals — the condition when I began in the field — to being able to identify neurons and their
processes in complete detail. This must be akin to what ardent surfers feel as they catch wave after
wave — we in neuroscience have the incredible privilege of using wave after wave of new methods!
Striosomes as Sources of Direct Input to Dopamine-Containing Neurons of the Midbrain
One other, key suggestion from quite early on, based on indirect evidence, was that striosomes
projected to or near to the dopamine-containing neurons in the midbrain. This issue has still not
been fully settled, but current single-fiber and targeted tracing done with genetically assisted
methods (Fujiyama et al., 2011; Watabe-Uchida et al., 2012) strongly suggests that neurons in
striosomes may be the only striatal neurons that project directly to the dopamine-containing
neurons. This situation would mean that striosomes are in a position to influence the very
dopaminergic system that can signal to other brain sites, including the striatum, about the salience
and reinforcement value of stimuli. This possibility suggests a powerful functional position for
striosomes not only in influencing reinforcement-guided behavior but also in influencing the
cognitive states that accompany visceromotive states. The striosomes, themselves, by receiving
limbic/emotion-related inputs, may be part of larger networks controlling positive and negative
emotional states and their bodily concommitants.
Some time ago, to begin to convey this idea, I proposed that the outputs of the basal ganglia
to the neocortex (via the thalamocortical systems with which they connect) might help to build
cognitive patterns much as classic studies have viewed them as control centers for the central
pattern generators of the brainstem and spinal cord (Graybiel, 1997). The nervous system probably
Ann Martin Graybiel
16
has techniques for using very simple mechanisms for creating behaviors that seem very complex.
Some of the signals that we have found — like the extra peaks, the oscillatory bursts and the
beginning-and-end signals — seem like good candidates for such computational or algorithmic
signals.
Striosome-Related Circuits and Emotional Decision-Making
Ken-ichi Amemori and I decided, based on all of this accumulating evidence, that it would be
important to record from striosome-projecting cortex to gain a hint of what striosomes might do.
Ken had developed the idea that striosomes might be important for, and perhaps integrate, both
positive and negative reinforcement signals transmitted to the striatum. Until we could record from
the striosomes themselves (the technical challenge that we are working on now), studying
striosome-projecting cortex could be highly informative. Ken and I adapted for use in monkeys a
well known test used to study human emotion, an approach-avoidance conflict task. The monkeys
would receive both positive (juice) and negative (airpuff) reinforcement, not one or the other. This
meant that the monkeys would have to decide how much reward made how much airpuff
worthwhile to experience. Out of this idea, and many long hours of recording in the pACC and
related cingulate cortex, we came up with an intriguing result (Amemori and Graybiel, 2012): there
were large numbers of anterior cingulate neurons that are active during the decision-making, with
about equal numbers of these firing more in relation to positive expected outcome or more for
negative expected outcome. But in one site, right in the pACC, and apparently corresponding to the
striosome-projecting zone, there were more of the negative type. Microstimulating that region, but
not the nearby regions, pushed the monkeys' decisions toward increased pessimism (more
avoidance; Fig. 17). And this effect could be cleanly reversed by giving the monkeys a dose of an
anxiolytic drug (diazepam).
These experiments brought us right back to
thinking about potential striosome-related circuits and
emotional control. Surprisingly, there seems to be no
effect of the same microstimulation when the monkeys
have to decide between two rewards of different size
(approach-approach conflict). This set of results could tie
in with the discoveries by Okehide Hikosaka and his
colleagues (Matsumoto and Hikosaka, 2007) that there is
a negative reinforcement pathway leading from the
habenula toward the dopamine-containing neurons of
the midbrain. Striosomes were found by Rajakumar to
Figure 17. Microstimulation of the pregenual
project, indirectly, to the same part of the habenula anterior cingulate cortex in its striosomeprojecting region influences cost-benefit decision(Rajakumar et al., 1993).
making in monkeys. The monkeys made decisions
Ken-ichi Amemori, Leif Gibb, Alexander to approach (receive) or avoid combinations of
Friedman and I are interested in the idea that striosomes food and airpuff. The diagram shows avoidance
responses as squares and approach responses as
might, together with the nearby cholinergic plus signs. The average pre-stimulation decision
interneurons, form a dispersed set of decision units that, boundary is shown by the interrupted red line, and
the average of the decisions made during
based on current context could weight relative good and stimulation as shown by the solid line. From
bad and influence basal ganglia outflow (Amemori et al., Amemori and Graybiel, 2012.
Ann Martin Graybiel
17
2011). Striosomes could provide the responsibility signals in a modular hierarchical learning
structure, as introduced by Doya and coworkers. This idea could fit well with the mixture-ofexperts template suggested by the earlier anatomy. Perhaps striosomes affect decision-making
when relative cost and benefit decisions are made. This idea makes the broad distributions of the
striosomes particularly interesting: they might form a distributed biasing mechanism within the
striatum — a gridwork for value (Graybiel, 2009).
Striatal Compartments in Relation to Neurologic and Neuropsychiatric Disorders
So far, the striosome-matrix architecture appears to be beyond the reach of human brain imaging
methods; getting adequate resolution at such a great depth has been a roadblock. Frustrating as this
has been, there are now hints from anatomical work on post-mortem brains that striosome and
matrix compartments are differentially affected in some disorders in humans. With Richard Faull
and his team in New Zealand, I had the privilege to join in examining the brains of patients who had
suffered from Huntington's disease. This work suggested that degeneration was more likely to
occur in striosomes in those individuals who had had early mood dysfunction than in those with
early motor signs (Tippett et al., 2007). Other work on human brains has reinforced the idea that
striosomes, or an imbalance in relative striosome/matrix function, might contribute to repetitive
behavior. Brotchie and colleagues found that in the brains of Parkinson's patients who had suffered
L-DOPA induced dyskinesias, striosomes had differentially increased pre-proenkephalin
expression, relative to the matrix (Henry et al., 2003). In our own lab, we worked with William
Langston to study MPTP-treated monkeys and found differential sparing of dopamine transporter
binding in striosomes in parts of the striatum in which the transporter signal was nearly
undetectable in the matrix. And in work on the brains of patients who suffered from DYT-3
dystonia, Goto and colleagues have reported differential loss of striosomes, which they relate to the
severe mood and motor dysfunction in these patients (Goto et al., 2005). Adding to this are studies
in animal models, including our own (Crittenden and Graybiel, 2011).
These and other studies point to the striosome-matrix architecture as one in which
compartmentally selective neurodegeneration could be important in disease. Striosomal neurons
are born and migrate out into the developing striatum very early, and they are for a prolonged
developmental period more mature than are the later-born neurons moving into the matrix. Early
genetic or environmental insults, including those occurring during the prenatal period, could
differentially affect these compartments and their developing specialized circuits. I hope that
imaging methods will soon allow us in the field to see into the depths of the forebrain with
adequate resolving power to help in identifying differential patterns of compartmental
involvement.
The Future
I am writing this essay just as methods are finally becoming powerful enough to let scientists
approach the deep brain with methods as powerful as those for some time available for work on the
neocortex. But it already seems likely that aspects of our
lives that are very basic to us as people depend on these deep brain regions. I have emphasized
here our ability to move smoothly from deliberative, conscious decision-making to semi-automatic
behavior, our capacity to include emotional states within our behavioral guidance systems as
Ann Martin Graybiel
18
individuals and as communities of people, and our vulnerability to states and disorders that pull
apart the balance of systems that seem to control these functions and our mental equilibrium. Given
all of this, it is no wonder that the catalogue of basal ganglia disorders has expanded from the
original extrapyramidal disorders — hypokinetic and hyperkinetic disorders and dystonias — to
include neuropsychiatric disorders ranging from obsessive-compulsive disorder and related OCspectrum disorders to mania and depression and to autistic and attention deficit disorders. This
realization adds great inspiration to work in this field, a source of inspiration that is matched by the
beauty of the brain itself.
Acknowledgments
I have been graced with talented students, post-doctoral fellows, and staff members, to each one of
whom I am grateful. I have mentioned the names of many of those who worked with me on the
striatum. I especially thank Diane Major and Henry Hall, who have been long-time lab members and
friends, along with Pat and Ray Harlan and Hu Dan. We run our lab as nearly a family, and I register
my thanks to each person as a tribute to what can come from talented, energized, and dedicated
people who work toward a great goal — in our case, understanding the brain and trying to use that
information to help humankind.
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