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The Big and the Small: Challenges of Imaging the Brain's Circuits
Jeff W. Lichtman, et al.
Science 334, 618 (2011);
DOI: 10.1126/science.1209168
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The Big and the Small: Challenges of
Imaging the Brain’s Circuits
Jeff W. Lichtman1* and Winfried Denk2*
The relation between the structure of the nervous system and its function is more poorly
understood than the relation between structure and function in any other organ system. We explore
why bridging the structure-function divide is uniquely difficult in the brain. These difficulties
also explain the thrust behind the enormous amount of innovation centered on microscopy in
neuroscience. We highlight some recent progress and the challenges that remain.
central theme of biology is the relation
between the structure and function of
things. By structure, we mean the physical form of something, a property that humans can apprehend by touch (if the object is
big enough) or by sight. Right now, the leading
edge of this effort is the field known by the general name “structural biology” but is focused
narrowly on the shapes of molecules in order to
provide insights into how proteins such as channels, enzymes, and transcription factors do their
jobs. The x-ray crystallography approach commonly used in structural biology does not generate images per se (producing instead diffraction
patterns), but with the help of computers humans
can change the data into a form that is interpretable intuitively by the visual system. Indeed, all
of “imaging” is a means of generating data about
an object that depend on location and that are
presented so they can be seen and hence interpreted by vision, our most powerful sense.
Going back in history, of course, there was
a time when structural biology dealt with larger
things: Discovering the functions of whole organs was center stage (1). Imaging was a critical
part of the endeavor too. With the exception of
the heart, which in many animals can be functionally understood without the need for magnification, most organs require microscopical
analysis of structure and functional dynamics
for the cellular organization (measured in micrometers) to be visible to the eye, whose limiting
resolution several orders of magnitude coarser.
Spanning 350 years, the microscope-enabled exploration of the cellular structure of organs has
been extremely fertile.
Indeed, for all organ systems, save one notable exception, it is now pretty much settled
how the structure of an organ is related to its
function—the exception being the nervous sys-
A
1
Molecular and Cellular Biology and Center for Brain Science
Harvard University, Cambridge, MA 02138, USA. 2Max-Planck
Institute for Medical Research, Department of Biomedical
Optics, 69120 Heidelberg, Germany.
*To whom correspondence should be addressed. E-mail:
[email protected] ( J.W.L.); [email protected] (W.D.)
618
tem, where much progress has been made at
the molecular and functional level. But notwithstanding the extraordinary insights of neurobiology’s foremost structural biologist, Cajal,
our understanding of the relation between the
structure and function of the brain remains primitive, especially when compared to other organ
systems. There is no other organ system where so
many common diseases and disorders have no
known histological trace. There is no other organ
system for which we still debate how many cell
types there are. There is no other organ system
for which one would seriously propose to image
the entire organ’s volume with an electron microscope. And there is also no other organ system
for which the complexity of the structure is so
great that earnest arguments can be made against
delving into structural analysis because such an
effort might well provide an unprecedentedly gigantic, yet totally incomprehensible, mass of data.
Here, we will explore why bridging the
structure-function divide is uniquely difficult in
the brain. These difficulties also explain the thrust
behind the enormous amount of innovation centered on microscopy in neuroscience, innovation that has been motivated by the special
challenges of understanding how the brain’s
functions are related to its especially complicated structure.
Problem Number 1: Immense Diversity of
Cell Types
One unique feature of the brain that frustrates
easy understanding of the relation between its
structure and function is the immense structural
and functional diversity of its cells. The nervous
system of Caenorhabditis elegans is miniscule,
only ~300 neurons (2), and yet nearly every single cell is unique in shape and function. In the
far larger vertebrate brains, there are certainly
structural classes of neurons that, although not
having identical morphologies, have enough
similarities within a class to be easily identifiable.
The retina is a good example. With its iterated
tiled structure, its cell classes are easy to recognize because they are repeated at regular intervals
and have particular morphologies and molecular properties. Despite this, it is an area of active
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research to determine the full extent of cell-type
diversity in this small part of the nervous system,
because the range of cell types continues to grow
as the analysis becomes more refined. Moreover,
neuronal cellular architecture is so variable from
one region to the next that no single area of the
brain serves as a guide for anything other than
itself. Few believe that learning the full extent
of retinal cellular diversity will be of much use
in trying to understand the diversity in the cerebral cortex.
Researchers have found ways to explore this
diversity by developing imaging-based modalities to categorize cells and synapses. On the
basis of the molecular peculiarities of cell types,
molecular biologists have engineered means of
generating tissue-specific expression of fluorescent markers in a wide and ever-growing range
of neuronal cell types. In some cases the rationale
is clear: The enzymes responsible for the synthesis of inhibitory transmitters are expressed
only in inhibitory cells (3). In other cases, the
molecular markers are useful but not well understood (4). The perhaps most powerful approach to cell classification uses antibodies. The
well-known field of immunohistochemistry has
had an extraordinary impact on cellular and subcellular analysis. However, there are special challenges in using antibodies to identify neuronal
processes. First, antibodies do not penetrate well
in thick tissues. Second, even if they did, the
sheer density of epitopes makes it nearly impossible to unambiguously associate the epitopes with a particular process of a particular cell.
Third, the range of markers necessary to delineate
the many different cell and synapse types is extraordinarily large. These problems all have the
same solution. By labeling arrays of serial sections with different antibodies (Fig. 1A), the threedimensional (3D) distribution of an array of epitopes
can be mapped in a volume and potentially assigned to different cells (5, 6). The z resolution is
given by the section thickness of 50 to 100 nm,
with the exciting possibility of a similar lateral
resolution achievable by using one of the new
super-resolution techniques (7) (Fig. 1, B and C).
Problem No. 2: Imaging Electrical and
Chemical Activity
A second unique feature of the nervous system
is that the structural connectivity may ultimately
determine function but does not constitute a map
of function. The nervous system depends on rapid reversals of membrane potential, known as
action potentials, to transmit signals between one
part of a neuron and another distant part and
depends on smaller, slower changes in member
potential at sites of synaptic contact (i.e., synaptic
potentials) to mediate the exchange of information between one cell and the next. Action potentials are typically only a few milliseconds in
duration, which means direct optical readout of
action-potential voltage is a challenge, because,
for whatever optical signal one might devise, only
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relatively few photons would likely be associated
with each impulse. Detecting action potentials
from many cells in a volume simultaneously
would be even more difficult. Synaptic potentials
can be orders of magnitude longer in duration
than action potentials but are smaller in size,
which poses other technical challenges. The development of small-molecule fluorescent calcium indicators, begun in the 1980s (8), showed
that the rise in intracellular calcium, a consequence of neuronal activity, could in many cases
serve as a substitute for the direct optical readout of action potential activity. Although in many
cases it is only a rough approximation of the
number of action potentials, “functional imaging,” as it is called, is a mainstay of linking neural function to individual neurons. Used often in
conjunction with two-photon (2P) microscopy
(9) and with its unique ability to image with high
resolution and high sensitivity inside scattering
tissue (10), calcium imaging is now performed
routinely in vivo (11, 12), in awake animals that
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Fig. 1. Fluorescence imaging modalities useful for studying circuits. (A) Array tomography immunofluorescence of mouse somatosensory cortex shows the location of several different synaptic proteins and
other neural markers in one piece of brain tissue. (Left) DAPI (4´,6-diamidino-2-phenylindole, gray) show
nuclei and yellow fluorescent protein (YFP) (green) shows processes of a subset of pyramidal cells and
presynaptic boutons labeled with synapsin (magenta). (Middle) Distribution of two glutamate (VGluT1 in
red, VGluT2 in yellow) and one g-aminobutyric acid (GABA) (VGAT in cyan) transporters in presynaptic
terminals. (Right) Several postsynaptic labels—GluR2 (blue), N-methyl-D-aspartate receptor 1 (NMDAR1,
white), and gephyrin (orange)—each next to presynaptic synapsin labeling (magenta). Scale bar indicates
10 mm. Adapted from (6). (B) Array tomography immunofluorescence imaged with STED, a super-resolution
optical technique showing large and small processes in a 3D volume image of mouse cerebral cortex. Dark
voids are cell somata, intimately surrounded by perinuclear baskets of non-neuritic microtubules. Also
visible is a dense profusion of large and small axonal and dendritic microtubule core bundles. Volume
depth = 1.3 mm. [Image provided by Forrest Collman, Emi Ling, Corrie DaCosta, and Stephen Smith,
Stanford University.] (C) Another super-resolution technique, STORM (stochastic optical reconstruction
microscopy), is used with array tomography to reveal a subcortical white-matter tract showing quasiparallel trajectories of numerous axons (volume depth = 2.8 mm). [Image provided by Hazen Babcock and
Xiaowei Zhuang, Harvard University, and Nick Weiler, Stanford.] (D) Layer 2/3 cortical neurons labeled
with brainbow transgenes (cells were transfected in utero by electroporation). [Image provided by K. Loulier
and J. Livet, Institut de la Vision, Paris.]
are head-fixed and virtually (13) or truly (14)
freely moving. Functional imaging has received
an additional boost from the development of genetically encoded calcium indicators (15), which
are finally becoming competitive (16, 17) with
chemically synthesized indicators in sensitivity
and speed. The genetically encoded indicators
have the added advantage of targetability to specific cell classes. Researchers still await equivalently powerful genetically encoded voltage
sensors, because these might provide the high
temporal resolution required to understand certain details of circuit function. Tantalizing new
proteorhodopsin-based probes are being developed that may be useful in this regard (18). The
fact that most brain structures extend over considerable volumes requires imaging into a tissue
volume rather than a thin sheet, another impediment to functional imaging.
Scattering of light blurs wide-field images
and strongly attenuates signals based on confocal
detection. The attenuation is much less for signals
based on 2P excitation. Nonetheless, imaging
deep into volumes remains an area requiring
new innovations. Penetration toward the deeper
layers of cerebral cortex has been shown to be
aided by combining 2P microscopy with adaptive optics (19, 20), amplified pulses (21), and
longer excitation wavelengths (22). But at best,
these approaches can extend the depth to about
1 mm. The imaging of subcortical structures requires the removal of overlying brain tissue (23)
or the insertion of penetrating optical elements
such as gradient-index lenses (24).
One recently rediscovered approach, which
has some of the advantages of 2P imaging in that
it excites fluorescent molecules in a restricted
optical section without excitation of fluorescence
above or below that region, is the so-called “lightsheet” microscopy, in which a plane of a volume
is illuminated by a narrow sheet of light and
detected via a wide-field camera (25, 26). One
advantage of this approach is that it can acquire
images of many cells simultaneously, giving
higher throughput for functional imaging than
otherwise possible (27) but with less depth
penetration than 2P imaging.
Activity or its suppression can be optically
triggered with high time resolution in cells expressing light-activated channels or transporters
with different ion selectivities (28, 29). In addition to controlling the illumination pattern, targeting to certain cell classes is possible by using
the same molecular tricks used for fluorescent
proteins. This allows, for example, the testing
of hypotheses such as whether particular cells
whose activity is correlated to a particular behavior are central or peripheral to its generation (30).
Problem No. 3: Neurons Extend Over
Vast Volumes
Since Cajal’s insight that nerve cells are functionally interconnected in a directional signaling
cascade, with axons playing the role of transmitter and dendrites and somata that of receivers,
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it has been appreciated that neural
connectivity holds the key to function. Although neuronal somata are
not unusually large, a neuron’s synaptic connections are distributed all
through their dendritic and axonal
branches. These branches often extend through tissue volumes that are
enormous when compared to the
actual volume of the cell itself. A
pyramidal neuron in the cerebral
cortex can have axonal branches
that cross to the other hemisphere
or go down to the brain stem or
even the spinal cord. The dendrites
of one pyramidal cell may be circumscribed by a volume of nearly
a cubic millimeter. All told, the length
of all the branches of one such cell
may exceed a centimeter in a mouse
and more than a meter in human
brain. Thus, fully describing the shape
of a single cortical neuron could require sampling a substantial percentage of an entire brain’s volume. But
if one wanted to document the sites
of all its synaptic connections, the
sampling would need to be done at
very high resolution to identify all
the fine branches containing preand postsynaptic sites. Indeed, if one
wished to “just” image the complete
cell geometry of one neuron along
with the complete geometry of the
set of all the neurons that are directly pre- and directly postsynaptic to
it, that volume would probably require imaging neuronal processes
that span nearly the entire brain volume. As already mentioned, seeing
into a volume of brain, even in fixed
material, is a long-standing challenge.
Indeed, the confocal microscope was
invented because of attempts to peer
into a tissue block of Golgi-stained
cerebral cortex, where out-of-focus
and scattered light undermined a
clear view (31). Recent progress with
clearing agents may be quite useful
as we try to understand ever larger
volumes of brain (32, 33).
But is it possible to image such a
large volume at very high resolution? In general in imaging, there
is a tradeoff between volume and
resolution: Large-volume imaging
is done with low spatial resolution
techniques, and high spatial resolution is accomplished over small volumes. Magnetic resonance imaging
(MRI) can provide an image of a
whole brain with ~1-mm3 voxels,
whereas an electron microscope can
render the structure of a synapse impinging on a dendritic spine by using
620
Fig. 2. The brain is organized over sizes that span 6 orders of magnitude. (A) The macroscopic brain, at the cm
scale, is organized into regions such as the lobes of the cortex, the cerebellum, the brainstem, and the spinal cord.
(B) At the millimeter scale, it is apparent that each brain region has neurons arranged in columns, layers, or other
kinds of clusters. Often the axons communicating between brain regions (e.g., in the white matter beneath the
cortex, which is tinted pink in this drawing) are separated from the sites where neuronal somata and their synaptic
interconnections reside (e.g., in the gray matter of the cerebral cortex that lies above the white matter). (C) At the
100-mm scale, it is apparent that within each region neurons extend dendrites locally and send axons into the
same or more distant sites. The most common excitatory neuron in the cerebral cortex is the pyramidal cell (shown
here), whose dendrites can span a cubic millimeter. (D) At the 10-mm scale, the structure of the individual branches
of a neuron become apparent. Many dendrites are studded with small processes (spines) where the axons of other
neurons establish excitatory synaptic connections. (E to G) The detailed structure of the brain visualized by using
EM. Successively higher resolution images of the same region of cerebrum acquired from a 29-nm section imaged
on tape with a SEM detecting secondary electrons. In (E), which spans 100 mm, many neuron cell bodies (N) and
their exiting processes can be seen along with occasional blood vessels, such as the capillary in the bottom right
(BV). In (F), more cellular detail is apparent. The dark rings are cross sections of myelinated axons, such as the one
labeled MY. The small dark objects are mitochondria (Mi), and the large polygonal objects are dendrites in cross
section (D). Occasional somata of neurons and glia are visible (S). Most of the remaining tissue falls into three
categories: glial processes (one highlighted in blue), presynaptic terminals (one tinted red), and dendrite spines
(green). (G) The synapse is shown in red at 10 times higher resolution. At 3 nm per pixel, the synaptic vesicles (SV)
are visible, along with the synaptic cleft (SC) and a membranous spine apparatus (SA) in the postsynaptic dendritic
spine. [Artwork by Julia Kuhl, and SEM images are from Richard Schalek, Ken Hayworth, and Bobby Kasthuri,
Harvard University.]
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Problem No. 4: The Detailed Structure Cannot
Be Resolved by Traditional Light Microscopy
The critical details of neuronal connectivity occur at the level of the synapse. Synapses are
densely packed, and in cerebral cortex synapses
often connect axons thinner than 100 nm to dendritic spines whose necks are thinner still. The
resolution of diffraction-limited light microscopy
is insufficient even when used in conjunction
with confocal detection, 2P excitation, or—for
fast imaging in clear specimens—selective-plane
illumination (see above), all so-called optical
sectioning techniques that eliminate signals from
out-of-focus structures.
Optical super-resolution techniques that truly break the diffraction barrier and rely on the
strong nonlinearities of stimulated emission–based
deterministic (38) or light activation–based stochastic (39, 40) molecular switching have matured in recent years (41, 42) and are beginning
to engage questions about synaptic function (43)
and other neurobiological problems (7, 44). Particularly important problems that are not yet fully
overcome include the ability to image multiple
colors and to observe dynamic processes at subwavelength resolution, even when they are located hundreds of micrometers below the brain’s
surface. There are also other serious limitations
related to the facts that the overall signal from
small structures is proportional to their size and
that often labeling densities, especially in vivo,
are not high enough to give a sufficiently low
noise image of the smallest structures. Moreover,
often it is not the physics of the imaging process
per se or the label density but rather intrinsic
properties of the tissue, especially refractive index inhomogeneities, that distort the wave front
and degrade resolution. Adaptive optics, a trick
borrowed from astronomy, in which it is used to
counteract the effects of Earth’s atmosphere, can
improve resolution, signal, and depth penetration,
in particular for in vivo imaging (19, 20). Lastly all these techniques are fluorescence-based,
A
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voxels of less than 100 nm3, a trillion-fold smaller
volume. Thus, it is an extraordinary challenge
to image large brain volumes at a resolution sufficient to find all the synapses (Fig. 2).
The solution requires the automation, even
industrialization, of imaging. Once the imaging
is accomplished by machines, without the need
for direct human oversight of each step, it becomes possible to increase the throughput by
running an instrument around the clock and potentially many imaging machines working in parallel. Automation also increases reliability, which
is essential because loss of even a small amount
of data can invalidate the entire data set. Over
the past several years there have been a number
of techniques developed to optimize the automation of imaging: optical ablation (34), optical
(35) and electron microscopic (36, 37) block face
imaging (Fig. 3), and increased automation of
stage motion and imaging steps in both standard
scanning light and electron microscopes.
B
y
1 m
z
C
Fig. 3. Volume EM data and their analysis. (A) Fly optic lobe data stack as taken with 8000 cycles of
SEM and focused ion beam ablation in 7-nm increments. Image is over a 28 mm by 28 mm by 56 mm
volume that starts in the medulla (bottom solid rendering) and shows the process crossings in the
outer chiasm (middle) and a dozen lamina cartridges (top). [Image courtesy of Shan Xu and Harald
Hess at Howard Hughes Medical Institute, Janelia Farm, and Zhiyiuan Lu and Ian Meinerzhagen at
Dalhousie University.] (B) A reslice through a stack taken on a diamond knife-based serial block face
electron microscope (SBEM). Tissue was from mouse retina. White circle highlights a synapse. [Image
courtesy of Kevin Briggman, Max-Planck Institute for Medical Research.] (C) All retinal bipolar cells in
a SBEM data set, measuring 80 mm by 120 mm by 130 mm, were reconstructed by using computeraided manual skeleton tracing. Cell bodies are in gray. [Image courtesy of Moritz Helmstaedter, MaxPlanck Institute for Medical Research.]
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which allows very selective labeling but can
make us forget the structural context.
Problem No. 5: Need for Dense or
Saturated Reconstruction
Our present notions of neural circuits are still
largely informed by Cajal’s ideas that showed
with small arrows the directional flow of information through circuits and pathways. For his
insights, the intrinsic inefficiency of the Golgi
stain, which labels less than 5% of the neurons,
was crucial. This sparse labeling allowed him to
propose correctly the law of dynamic polarization, in which axons send signals to dendrites and
somata and that information is then passed along
to the next neuron by a cell’s axon. But the sparse
labeling and his inability to see the signs (inhibitory or excitatory) of connections undermined an
understanding of how information was actually
processed by the circuits. Such fundamental
questions as the number of different inputs a cell
receives and the number of target cells a neuron
innervates remain unknown even a century later.
A small muscle that wiggles the mouse ear is the
only place in the mammalian nervous system for
which, in some sense, the entire connectivity
matrix has been worked out, by complete labeling
of all axons using transgenic fluorescent expression. This was possible because the relatively
large size and small number of axons allowed a
full rendering of all the connections with use of
light microscopy and computer-aided manual
reconstruction (45).
The randomness and sparseness of the Golgi
stain and more modern approaches to label subsets of neurons mean that in very few cases both
pre- and postsynaptic cells are actually labeled
and, even if they were, that fact could not be
discerned with certainty because of limited resolution. This problem is being solved by new transsynaptic virus techniques (46, 47) that may ensure
that all labeled cells are connected to one or a
small population of cells.
Sparseness is one way to avoid the melding
together of multiple nearby processes. Alternatives are giving different cells different colors,
which allows the identity of a neurite to be determined without having to trace it (31, 43)
(Fig. 1D), or increasing the resolution of the light
microscope to be able to trace it even in a dense
neuropil (see above). However, the highestresolution microscope is still the electron microscope (Fig. 2, E to G). In addition to its resolution,
electron microscopy (EM) provides a cellular
context that is difficult to obtain with fluorescence because all cell membranes and intracellular organelles can be made visible. Several ways
to improve serial-section EM have (re)emerged
during the last decade: improving z resolution,
increasing automation, as well as reducing section distortion and loss. Because modern scanning electron microscopes yield good contrast
and resolution, even at low electron energies,
when imaging plastic-embedded and heavymetal stained material, imaging the block face
622
between cuts (36, 37) or ion-beam abrasions (48)
(Fig. 3) yields well-aligned volume data. Moreover, thinner cuts can be taken, crucially improving the z resolution. The serial block-face
approach has been used in combination with
2P-based calcium imaging to investigate the wiring
of the direction-selectivity circuit in the retina
(49), showing for example a massive violation of
the so-called Peters’ rule, which essentially says
that connection probability is mainly a function
of axons and dendrites passing near each other.
Instead, connectivity is finely tuned to allow starburst cell dendrites to pass on their directional signals to the appropriate retinal ganglion cells (49).
Imaging and analysis speeds are further serious impediments to a full brain circuit reconstruction. The first, and still only, full nervous system
connectome is that of the several hundred cells
in a tiny worm, and still it took a decade. At the
moment a full cubic millimeter of brain volume
resolved to the level of seeing every synapse would
require many months or even years to image and
far longer to analyze. One approach to speed the
imaging step by parallelizing the imaging in a
transmission electron microscope (SEM) appears
promising (50). Faster scanning electron microscopy also seems to be on the horizon with better
detectors and new scanning strategies.
Advances in instrumentation and labeling
are giving us an unprecedented flood of data,
which, partly because they are not perfect, need
sophisticated computer science for analysis.
Computers can learn how to best analyze the data
by giving them a sufficient number of examples
even if we do not understand the computational
function that determines whether a voxel is inside
or outside a cell. In the end, artificial neural networks may help us to analyze the data with which
we then understand the brain (51).
As circuit analysis finally moves forward,
serious questions concerning its utility will be
raised. One obvious question concerns the variability in the structure of the brain at the synaptic
level. During the study of the mouse ear muscle
described above, it became clear that every instantiation of the wiring diagram was different
from every other one (45). Some will take such
variability to mean that nothing can be learned
from doing this kind of tedious, data-intensive,
and highly expensive work. Alternatively, it is
likely that one could learn a great deal about the
game of chess by watching one game, despite the
fact that it is highly unlikely that any two games
are identical. It is certainly possible that certain
circuit motifs will be recognizable and will be
interpretable in a number of contexts. The key
may be to make successful use of orthogonal data,
such as from functional imaging, to link structure
to a cellular or organismal behavior. The links
may help decipher the code by which neuronal
connections underlie all that we do.
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REVIEWS
REVIEWS
A. Karpova for commenting on the manuscript. Our own
work was supported by the NIH, Gatsby Charitable
Foundation, and a Collaborative Innovation Award (no.
43667) from Howard Hughes Medical Institute ( J.W.L.),
the Deutsche Forschungsgemeinschaft (W.D.), and the
Max-Planck Society (W.D.). W.D. has provided a technology license for block-face EM to Gatan, Incorporated.
10.1126/science.1209168
The Cell Biology of Synaptic Plasticity
constraints prevent us from addressing any single mechanism in depth; instead, our aim is to
provide a framework for understanding the cell
biology of synaptic plasticity.
Victoria M. Ho,1 Ji-Ann Lee,2 Kelsey C. Martin2,3,4*
Hippocampal Synaptic Plasticity
The successful study of the cell biology of synaptic plasticity requires a tractable experimental
model system. Ideally, such a model should consist of a defined population of identifiable neurons and be amenable to electrophysiological,
genetic, and molecular cell biological manipulations. Awell-studied model system for studying
plasticity in the adult vertebrate nervous system
is the rodent hippocampus (Fig. 1). Critical for
memory formation, the anatomy of the hippocampus renders it particularly suitable for electrophysiological investigation. It consists of three
sequential synaptic pathways (perforant, mossy
fiber, and Schaffer collateral pathways), each
with discrete cell body layers and axonal and
dendritic projections (Fig. 1). Synaptic plasticity
has been studied in all three hippocampal pathways. Distinct stimuli elicit changes in synaptic
Synaptic plasticity is the experience-dependent change in connectivity between neurons that
is believed to underlie learning and memory. Here, we discuss the cellular and molecular
processes that are altered when a neuron responds to external stimuli, and how these alterations
lead to an increase or decrease in synaptic connectivity. Modification of synaptic components
and changes in gene expression are necessary for many forms of plasticity. We focus on
excitatory neurons in the mammalian hippocampus, one of the best-studied model systems
of learning-related plasticity.
he circuitry of the human brain is composed of a trillion (1012) neurons and a
quadrillion (1015) synapses, whose connectivity underlies all human perception, emotion, thought, and behavior. Studies in a range
of species have revealed that the overall structure of the nervous system is genetically hardwired but that neural circuits undergo extensive
sculpting and rewiring in response to a variety
of stimuli. This process of experience-dependent
changes in synaptic connectivity is called synaptic plasticity.
Studies of synaptic plasticity have begun to
detail the molecular mechanisms that underlie
these synaptic changes. This research has examined a variety of cell biological processes, including synaptic vesicle release and recycling,
neurotransmitter receptor trafficking, cell adhesion, and stimulus-induced changes in gene expression within neurons. Taken together, these
studies have provided an initial molecular biological understanding of how nature and nurture
combine to determine our identities. As a result,
research on synaptic plasticity promises to provide insight into the biological basis of many
neuropsychiatric disorders in which experiencedependent brain rewiring goes awry.
Here we focus on long-lasting forms of plasticity that underlie learning and memory. We
consider, in turn, each component of the synapse:
the presynaptic compartment, the postsynaptic
compartment, and the synaptic cleft, and discuss
T
1
Interdepartmental Program in Neurosciences, University of
California–Los Angeles (UCLA), BSRB 390B, 615 Charles E.
Young Drive South, Los Angeles, CA 90095–1737, USA. 2Department of Biological Chemistry, UCLA, BSRB 310, 615
Charles E. Young Drive South, Los Angeles, CA 90095–1737,
USA. 3Department of Psychiatry and Biobehavioral Sciences,
UCLA, BSRB 390B, 615 Charles E. Young Drive South, Los
Angeles, CA 90095–1737, USA. 4Brain Research Institute,
UCLA, BSRB 390B, 615 Charles E. Young Drive South, Los
Angeles, CA 90095–1737, USA.
*To whom correspondence should be addressed. E-mail:
[email protected]
processes that undergo activity-dependent modifications to alter synaptic efficacy. Long-lasting
changes in synaptic connectivity require new
RNA and/or protein synthesis, and we discuss
how gene expression is regulated within neurons. We concentrate on studies of learning-related
plasticity at excitatory chemical synapses in the
rodent hippocampus because these provide extensive evidence for the cell biological mechanisms of plasticity in the vertebrate brain. Space
Downloaded from www.sciencemag.org on December 11, 2011
50. D. D. Bock et al., Nature 471, 177 (2011).
51. V. Jain, H. S. Seung, S. C. Turaga, Curr. Opin. Neurobiol.
20, 653 (2010).
Acknowledgments: We thank K. Briggman, H. Hess, J. Livet,
M. Helmstaedter, and S. Smith for providing images and
Record
Stimulate
CA1
Synaptic pathways
CA3
Perforant (axons from
entorhinal cortex)
Mossy fiber
(axons from dentate
granule cells)
Schaffer collateral
(axons from CA3
pyramidal cells)
Dentate gyrus
Fig. 1. Hippocampal synaptic plasticity. The rodent hippocampus can be dissected and cut into transverse
slices that preserve all three synaptic pathways. In the perforant pathway (purple), axons from the
entorhinal cortex project to form synapses (yellow circles) on dendrites of dentate granule cells; in the
mossy fiber pathway (green), dentate granule axons synapse on CA3 pyramidal neuron dendrites; and in
the Schaffer collateral pathway (brown), CA3 axons synapse on CA1 dendrites. The dentate, CA3, and CA1
cell bodies form discrete somatic layers (dark blue lines), projecting axons and dendrites into defined
regions. Electrodes can be used to stimulate axonal afferents and record from postsynaptic follower cells,
as illustrated for the Schaffer collateral (CA3-CA1) pathway.
www.sciencemag.org
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