Download Constraints on Somatotopic Organization in the Primary Motor Cortex

INVITED REVIEW
Constraints on Somatotopic Organization in the Primary
Motor Cortex
MARC H. SCHIEBER
Departments of Neurology, Neurobiology and Anatomy, Brain and Cognitive Science, and Physical Medicine and
Rehabilitation, the Center for Visual Science, and the Brain Injury Rehabilitation Program at St. Mary’s Hospital,
University of Rochester School of Medicine and Dentistry, Rochester, New York 14642
Received 9 January 2001; accepted in final form 5 July 2001
Schieber, Marc H. Constraints on somatotopic organization in the
primary motor cortex. J Neurophysiol 86: 2125–2143, 2001. Since the
1870s, the primary motor cortex (M1) has been known to have a
somatotopic organization, with different regions of cortex participating in control of face, arm, and leg movements. Through the middle
of the 20th century, it seemed possible that the principle of somatotopic organization extended to the detailed representation of different
body parts within each of the three major representations. The arm
region of M1, for example, was thought to contain a well-ordered,
point-to-point representation of the movements or muscles of the
thumb, index, middle, ring, and little fingers, the wrist, elbow, and
shoulder, as conveyed by the iconic homunculus and simiusculus. In
the last quarter of the 20th century, however, experimental evidence
has accumulated indicating that within-limb somatotopy in M1 is not
spatially discrete nor sequentially ordered. Rather, beneath gradual
somatotopic gradients of representation, the representations of different smaller body parts or muscles each are distributed widely within
the face, arm, or leg representation, such that the representations of
any two smaller parts overlap extensively. Appreciation of this underlying organization will be essential to further understanding of the
contribution to control of movement made by M1. Because no single
experiment disproves a well-ordered within-limb somatotopic organization in M1, here I review the accumulated evidence, using a framework of six major features that constrain the somatotopic organization
of M1: convergence of output, divergence of output, horizontal interconnections, distributed activation, effects of lesions, and ability to
reorganize. Review of the classic experiments that led to development
of the homunculus and simiusculus shows that these data too were
consistent with distributed within-limb somatotopy. I conclude with
speculations on what the constrained somatotopy of M1 might tell us
about its contribution to control of movement.
Somatotopic organization long has been the hallmark of the
primary motor cortex (M1). The concept of a cortical region
systematically organized to control movements of different
body parts was first hypothesized by Hughlings Jackson in the
1870s, based on his observations of certain epileptic patients in
whom convulsive movements systematically marched from
one part of the body to adjacent parts (Jackson 1958). The
existence of such a cortical region was demonstrated contemporaneously by Fritsch and Hitzig using electrical stimulation
of the canine cortex, one of the earliest demonstrations of a
specific function of a particular cortical region (Walshe 1948).
As techniques for electrical stimulation improved, increasingly
detailed maps of body part representation in M1 became available, culminating in the well-known summary diagrams of
Penfield’s homunculus (Penfield and Rasmussen 1950) and
Woolsey’s simiusculus (Woolsey et al. 1952). These icons of
neuroscience commonly are interpreted as showing a systematic, spatially organized, point-to-point mapping of control of
different body parts by different pieces of M1 cortex (Schott
1993). Indeed, in its ultimate form, Penfield’s homunculus
included a line representing the mediolateral ribbon of M1,
broken into sequential line segments representing different
body parts, down to different segments for the thumb, index,
middle, ring, and little fingers.
In the last quarter of the 20th century, however, experimental evidence has accumulated indicating that the control of
different body parts from M1 is not nearly so somatotopically
organized as the homunculus and simiusculus seem to suggest.
While it remains clear that the head, upper extremity, and
lower extremity have sequential and largely separate representations, the representations of smaller body parts are widely
distributed within these major regions. In retrospect, data obtained from the 1870s to the present can be seen to be consistent with this distributed organization as well. Consequently,
the territory controlling one body part overlaps extensively
with the territory controlling adjacent body parts. For example,
the M1 territory controlling the thumb overlaps extensively
with the territories controlling the fingers.
Here I review this evidence in a framework of six factors
that constrain the somatotopic organization of M1. 1) Convergent output from a large M1 territory controls any particular
body part, joint or muscle. 2) Divergent output of many single
M1 neurons reaches multiple spinal motoneuron pools. 3)
Horizontal connections interlink the cortex throughout a major
body part region. 4) Widely distributed activity appears in a
major body part region whenever any smaller body part is
moved. 5) Partial inactivation of a major region affects multiple smaller body parts simultaneously. 6) Plasticity limits the
degree to which control of a specific body part can be assigned
to a particular piece of cortex. Although I will deal mainly with
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INTRODUCTION
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the upper extremity region (from which the most experimental
evidence is available), these six factors appear to apply as well
to the representations of the face and lower extremity. No one
factor alone unequivocally disproves a detailed within-limb
somatotopy, nor can any single experiment. Yet considered
altogether, they compel us to conclude that control of each part
of the upper limb, lower limb, or face is widely distributed
within the overall representation. To progress in understanding
M1’s contribution to motor control, we must consider the
implications of these constraints on the somatotopic organization of M1.
CONVERGENCE
Outputs from large territories of M1 converge on the spinal
motoneuron pool of any given muscle. The cortical territory for
each muscle is so large as to preclude spatially separate territories for each muscle. Instead, the M1 territories from which
outputs converge on two upper extremity muscles overlap
extensively. This principle of convergence was articulated
most precisely by the work of Charles Phillips and his collaborators (described in the following sub-sections), but all studies
of movements evoked by stimulation of M1 have been consistent with such convergence and overlap, from the classical
studies with cortical surface stimulation that led to the homunculus and simiusculus, to more recent studies using intracortical microstimulation (ICMS).
Classical studies employing stimulation of the cortical
surface
Because the classical studies that employed stimulation of
the cortical surface commonly are assumed to have demonstrated a detailed within-limb somatotopic organization, I begin by reviewing exactly what was demonstrated in these
studies. By modern standards, the electrical stimuli employed
in these studies were intense and prolonged, exciting relatively
large regions of cortex, and evoked overt movements rather
than the brief flicks and twitches evoked by ICMS. Penfield
and Boldrey (1937) published a map of 77 precentral locations
from which cortical surface stimulation elicited movements of
the different digits of the hand in studies of 126 human subjects
(Fig. 1A). The overall region from which stimulation produced
finger movements extended 55 mm along the central sulcus.
Inspection of their figure shows that thumb movements were
elicited at both the lateral and medial limits of this region, as
were movements of the little finger, and of the other digits as
well. Furthermore, comparing the region from which finger
movements were evoked with the region from which arm
movements were evoked showed large, extensively overlapping territories representing different proximodistal parts of the
upper extremity (Fig. 1B).
Because data from multiple subjects were compiled in these
maps, inter-individual variation might have accounted for the
large and overlapping territories of different digits and more
FIG. 1. Convergence and overlap in Penfield’s data. Enlargements are shown from Figs. 12A and 25B of Penfield and Boldrey
(1937). The region enlarged is indicated by the rectangle drawn on the inset taken from their standardized map of the hemisphere,
but note that whereas the region shown in B extends laterally to the Sylvian fissure and therefore includes the face representation,
the region shown in A does not. A: locations from which finger movements were elicited in data compiled from 126 patients. If only
certain digits moved, they are indicated with Roman numerals: I ⫽ thumb through V ⫽ little finger. Black dots indicate locations
where stimulation elicited movement of all the digits. Note that, contrary to the discrete order implied by the homunculus, thumb
movements were elicited medially as well as laterally, and little finger movements were elicited laterally as well as medially. B:
outlines encompass the total territory from which movements of the fingers (E E E), entire hand (¦ ¦ ¦), or more proximal arm
(⫹ – ⫹ – ⫹) were evoked. Note the overlap of distal and proximal representations. (Reproduced with permission of the Literary
Executors of Wilder Penfield)
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proximal parts of the upper extremity. In single subjects, an
orderly, segregated somatotopic arrangement might have been
apparent. Inspection of records from single patients reveals,
however, that such was not the case. Figure 2 shows, for
example, detailed results of intraoperative stimulation in one
patient studied by Penfield and colleagues. Although an overall
somatotopic trend was apparent in this single case, with movements of the digits being evoked more often laterally along the
Rolandic fissure and movements of more proximal parts of the
upper extremity being evoked more often medially, movements of different parts were not elicited from discrete locations arrayed in simple somatotopic order. The thumb, for
example, was involved in the movements produced by stimulation at three points along the central sulcus, and at each of
these points stimulation evoked movements of other digits as
well (points marked by upside-down L, N, M). Finger movements were elicited from two more medial points as well (Z
and O), with the most medial of these (O) surrounded by other
points from which more proximal arm movements were
evoked (7, R, X). Thus a well-ordered somatotopic representation of the upper extremity was not evident in the details of
single cases such as this.
Penfield and Rasmussen (1950, p. 56), commenting on their
homunculus, noted: “A figurine of this sort cannot give an
accurate indication of the specific joints in which movement
takes place, for in most cases movement appears at more than
one joint simultaneously. . . . The motor homunculus may be
used as an aid to memory in regard to movement sequence and
the relative extent of cortex in which such movement finds
representation. It is a cartoon of representation in which sci-
FIG. 2. Details of intraoperative stimulation in an individual human patient. The sketch reproduced here from case records
shows the borders of the craniotomy (cross-hatching) and locations of paper markers placed at stimulated locations (upside-down
letters and numbers), both drawn by Penfield and colleagues on their standard map of the hemispheric surface based on their
intraoperative photograph. To this reproduced sketch, I have added selected details from the transcribed intraoperative notes
recording the results of stimulation at each location, linked to each marker by a straight line. No response to stimulation was
obtained at point B, 10, or 11; and no transcribed note was available for point H or 2. Note that, although movements of the digits
were generally obtained laterally and more proximal movements medially, movements of different digits or more proximal parts
of the extremity were not obtained from separate, somatotopically ordered points. This particular case was chosen here because of
the relatively large number of points stimulated along the precentral gyrus (in many other cases, because of the possible risk of
setting off seizures by stimulation of the motor cortex, many points along the postcentral gyrus were stimulated, and only a few
along the precentral gyrus; personal communication, Dr. William Feindel), and because no cortical lesion was evident at surgery.
The only lesion found was a meningeal cicatrix close to the midline (in the sketch reproduced here, the wandering dashed line close
to the midline delimited a territory of “whitening of the arachnoid”). Although a detailed description of the patient’s typical seizures
was not available, he had “Jacksonian epilepsy” with an epigastric aura. This patient (MO) was described by Penfield and
Rasmussen (1950, their Fig. 32) in reference to eye turning. Transcribed operative notes, an intraoperative photograph of the brain
with paper markers placed on the cortical surface, and the sketch reproduced here, all were provided courtesy of the Penfield
Archive (Montreal Neurological Institute, Dr. William Feindel, Curator).
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entific accuracy is impossible.” Although the overlapping representations of adjacent body parts observed by Penfield and
colleagues might have resulted from current spread across an
underlying discrete and orderly somatotopic representation, the
overlap also could have been a genuine feature of the underlying representation in M1.
The detailed results of similar studies on a rhesus monkey
and on a human from the work of Woolsey and colleagues are
illustrated in Fig. 3. In both species, evoked movements typically involved more than one digit and/or more proximal joint.
In both species, movements involving the thumb were elicited
by stimuli delivered at different locations scattered over much
of the upper extremity representation. Similarly, stimulation at
many different locations elicited movements involving the
little finger. Although the thumb appears more heavily represented in the lateral aspect of the upper extremity representation and the little finger appears more heavily represented
medially, the territory in which evoked movements involved
the thumb overlaps considerably with the territory in which
evoked movements involved the little finger. As with Penfield’s studies, given the possible spread of stimulating current,
Woolsey’s data would be consistent either with discrete, somatotopically segregated representations of the thumb and
little finger, or with overlapping representations in which outputs to the muscles serving each digit converge from large
cortical territories. The same argument would apply to other
pairings of digits, or pairings of digit and wrist, wrist and
elbow, and elbow and shoulder. In the text bracketing the
motor simiusculi (their Fig. 131), Woolsey and colleagues
wrote, “It must be emphasized . . . that this diagram is an
inadequate representation of the localization pattern, since in a
line drawing one cannot indicate the successive overlap which
is so characteristic a feature of cortical representation. . . .”
(Woolsey et al. 1952, p. 252).
While the examples illustrated above come from the work of
Penfield’s group and Woolsey’s group, similar evidence consistent with convergence and overlap was present in the detailed results of other investigators who employed cortical
surface stimulation in systematic exploration of M1. The number of such studies is too large for each to be mentioned here,
but some additional examples may illustrate two general features of this literature. First, the impression of discrete somatotopic order versus convergence and overlap varied with the
number of points stimulated. Stimulating a limited number of
widely spaced points along the central sulcus often demonstrated a progression from shoulder movements medially to
finger and thumb movements laterally (Bucy 1949; Fulton and
Keller 1932). Even in these studies, however, some points
failed to follow a strict somatotopic order. In studies sampling
a larger number of points, convergence and overlap became
more apparent. In studies of anthropoid apes, for example,
Leyton and Sherrington (1917) stimulated a relatively large
number of points in each animal studied, and listed 135 different combinations of primary, secondary, tertiary, and quaternary evoked upper extremity movements; these studies show
considerable overlap of the representation of different joints
FIG. 3. Convergence and overlap in Woolsey’s data. Maps of evoked movements obtained by Woolsey and colleagues (A) in
a monkey (Macaca irus) (Woolsey et al. 1952), and (B) in a human (Woolsey et al. 1979). Inset figures of an entire brain indicate
the areas enlarged. In A, dashed lines indicate areas in the anterior bank of the central sulcus (right) and posterior bank of the arcuate
sulcus (left) exposed for stimulation. In both A and B, figurines show which parts of the body moved on stimulation at each location,
with black shading indicating the most vigorous, cross-hatching intermediate and stippling the least movement. In both species, the
territories from which movements of the thumb and different fingers were evoked were large and extensively overlapping.
(Modified from Schieber 1990)
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and movements. Here, then, is a second general feature: focusing on only the initial or most prominent elicited movement
was more revealing of somatotopic order, whereas attending to
all the movements elicited by stimulation at each point suggested more extensive convergence and overlap (Beevor and
Horsley 1887; Ferrier 1873; Hines 1940; Murphy and Gellhorn
1945). For example, by focusing only on the primary movement evoked by stimulation at each site, and comparing nonadjacent joints (e.g., shoulder vs. fingers), Leyton and Sherrington demonstrated a gradual somatotopic progression
consistent with the homunculus and simiusculus (e.g., their
Figs. 16 and 17), even though their data are consistent with
extensive overlap when all movements of all joints were considered.
Studies in which muscle contraction was measured during
cortical surface stimulation, instead of observing evoked
movement, also were consistent with convergence and overlap.
Recording the tension developed by a number of monkey
hindlimb muscles, for example, revealed that cortical surface
stimulation only occasionally evoked contraction of one of the
recorded muscles alone; much more often, multiple muscles
contracted simultaneously, although the cortical locations from
which maximal contraction was evoked differed from muscle
to muscle (Chang et al. 1947). Similar results in humans have
been obtained in recent years by recording compound muscle
actions potentials in response to transcranial magnetic stimulation (Krings et al. 1998; Wassermann et al. 1992).
Were convergence and overlap artifactual?
Until the 1970s, much if not all of this evidence of convergence from large and overlapping territories moving different
body parts could have been attributed to the spread of stimulus
effects, for two reasons. First, relatively large stimulating currents (on the order of 0.5–1.5 mA) had to be used at the cortical
surface to evoke movements; in comparison, currents an order
of magnitude smaller evoked movements when applied to a
peripheral nerve. The large currents applied to a point at the
cortical surface inevitably spread through a considerable volume of tissue. At threshold for evoking simple flick movements (e.g., 10-ms pulses of 0.5–1.5 mA), for example, surface
stimulation evoked repetitive discharge in Betz cells up to 4
mm horizontally distant from the surface point stimulated
(Phillips 1956; see also Asanuma et al. 1976; Jankowska et al.
1975a). Direct excitation of corticospinal neurons by surface
stimulation thus occurred within a rather large area around the
stimulated point, but even a 4-mm horizontal spread of direct
Betz cell excitation could not account for the observed overlap
of up to 55 mm.
Second, corticospinal neurons at an even greater horizontal
distance in theory could be excited indirectly. Single electrical
stimuli delivered at the cortical surface evoked multiple descending volleys in the corticospinal tract. The earliest volley
(D-wave) was produced by direct excitation of corticospinal
neurons; later descending volleys (I-waves) resulted from excitation of intracortical neurons which indirectly (trans-synaptically) excited the corticospinal neurons (Patton and Amassian
1954). D-waves were evoked by current spread from the point
of surface stimulation through the superficial cortical layers,
exciting the corticospinal neuron somata in layer V (Patton and
Amassian 1954), or their axons still deeper (Landau et al.
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1965). At threshold for direct activation of corticospinal neurons, then, more superficial cortical interneurons were excited
as well. These interneurons could excite corticospinal neurons
not only directly beneath the stimulating electrode, but also
lateral to the electrode (see Horizontal interconnections, below). Horizontal spread through transynaptic excitation of corticospinal neurons might artifactually enlarge the cortical territories from which a given movement was evoked, producing
even more overlap. To limit such horizontal spread of excitation, vertical incisions in the cortex could be made to isolate
small (3 ⫻ 5 mm) islands of cortex; however, this experimental
manipulation failed to eliminate the extensively overlapping
territories (Murphy and Gellhorn 1945).
Nevertheless, it remained possible that if only a few, closely
packed corticospinal neurons could be excited directly, the
map of evoked movements would resolve into discrete territories for different movements or muscles. This possibility diminished, however, when Phillips and co-workers found that
brief (0.2-ms) low-amplitude, surface-anodal stimuli directly
excited corticospinal neurons without indirect excitation (Hern
et al. 1962). Recording intracellularly from baboon cervical
motoneurons, and accounting for current spread in the cortex,
they used such stimuli to demonstrate that the colony of corticospinal neurons projecting monosynaptically to a single
cervical motoneuron must, in many instances, be spread over a
cortical territory of at least several square millimeters (the
largest minimal territory they measured covered 20 mm2)
(Landgren et al. 1962). Moreover, the minimal territories containing corticospinal neurons projecting to radial, ulnar, or
median nerve motoneurons (i.e., innervating different muscles)
often overlapped. Thus even single motoneurons were shown
to receive converging input from relatively large cortical territories, which overlap with the territories providing input to
motoneurons of other muscles.
Studies employing ICMS
In the late 1960s and early 1970s, Asanuma and colleagues
developed the technique of ICMS. Rather than stimulating with
a large electrode touching the pial surface of the cortex, a
microelectrode was advanced into the M1 cortex and positioned close to layer V. Here, 0.2-ms pulses of only a few
microamperes, delivered in trains of 10 –12 pulses at approximately 300 Hz, could evoke visible movement or recordable
electromyographic (EMG) activity. Single 10-␮A, 0.2-ms
cathodal current pulses delivered in layer V were estimated to
directly excite neuronal somata within a radius of only 88 ␮m,
which in cat anterior sigmoid gyrus would encompass only
about 28 pyramidal neurons (Stoney et al. 1968). Initial studies
with ICMS indicated that a particular movement of a part of the
forelimb, or contraction of a particular muscle, was evoked by
threshold ICMS applied within a small columnar zone of
approximately 0.5–1 mm radius (Asanuma and Rosen 1972).
Multiple small efferent zones scattered in the overall forelimb
representation could be found for the same movement or muscle. Discrete efferent zones representing different movements
or muscles appeared intermingled like the different colors of
tiles in a mosaic.
The initial report of Asanuma and Rosen (1972) showed this
mosaic arrangement by superimposing data from 10 Cebus
monkeys (their Fig. 8). Subsequent studies from numerous
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laboratories in many species, including detailed studies of
single subjects, have continued to show that maps of threshold
responses evoked by ICMS include the same features. Two
examples are shown in Fig. 4. In anesthetized owl monkeys
(Fig. 4A), although a general within-limb somatotopic gradient
could be appreciated (distal representation stronger posterolaterally and proximal representation stronger anteromedially),
movement of a given part (such as the digits) was evoked by
ICMS at multiple foci scattered over a considerable portion of
the upper extremity representation, and these foci were intermingled with points at which stimulation elicited movements
of other parts of the limb (Gould et al. 1986). In awake
stump-tailed monkeys (Macaca arctoides, Fig. 4B), although
the thumb had more representation laterally, movement of any
given digit was evoked by ICMS at foci scattered over a large
portion of the upper extremity representation, and the territory
from which ICMS evoked movement of a given digit overlapped with the territory from which ICMS evoked movement
of any other digit (Kwan et al. 1978). A similar pattern of
scattered threshold foci for individual muscles, intermingled
with foci for other muscles, has been described by recording
evoked EMG activity during ICMS in squirrel monkeys (Donoghue et al. 1992; Strick and Preston 1978, 1982), macaques
(Humphrey 1986), and baboons (Waters et al. 1990). Thus
even with threshold ICMS, a particular movement of a given
body part, or a contraction of a specific muscle, is evoked by
stimulation at several sites scattered in the forelimb representation, and these sites are intermingled with sites where stimulation evokes other movements of the same body part, or
movements of adjacent body parts, or contractions of other
nearby muscles. At the same time, gradual somatotopic gradients—indicating gradual shifts in the part(s) most heavily
represented— can often be appreciated in ICMS maps of the
forelimb representation. Similar features appear in ICMS maps
of the face representation as well (Huang et al. 1988).
What is revealed with ICMS?
At this point, one might come away with the interpretation
that M1’s somatotopic organization consists of a mosaic of
small discrete zones, with each movement or muscle represented in multiple scattered zones. The large and overlapping
cortical territories demonstrated by the older methods of cortical surface stimulation then could have resulted from stimulation of many of these tiny zones at the same time. The extent
to which the efferent zones mapped by threshold ICMS are
actually discrete, however, is called into question by two major
considerations.
First, even with ICMS, stimulation does not occur entirely at
a single point. Many of the corticospinal neurons that discharge
in response to ICMS are excited directly at the soma or axon
hillock, and these neurons do indeed lie within a small zone
FIG. 4. Intracortical microstimulation maps. A: a selected portion is shown of the threshold intracortical microstimulation
(ICMS) map of the left hemisphere M1 in an anesthetized owl monkey, a species in which M1 lies on the surface of a relatively
lissencephalic cortex. Black dots mark stimulated points, and solid lines surround groups of points where stimulation evoked
movements of the same body part. Added colors emphasize regions where threshold ICMS evoked movements of different parts
of the upper extremity. Note that movements of a given part of the upper extremity, exemplified by the digits, were evoked from
several scattered foci, intermingled with foci from which movements of other parts were elicited. At the same time, an overall
somatotopic gradient [with heavier representation of the more distal parts (digits, wrist, and forearm) posterolaterally; and heavier
representation of the more proximal parts (shoulder and elbow) anteromedially] can be appreciated. The rectangle on the
hemispheric outline inset at bottom right indicates the region enlarged. ANK, ankle; CH, chin; Dig, digits; EL, elbow; FA, forearm;
M, mouth; NO, nose; SH, shoulder; TR, trunk; VIB, vibrissae; W, wrist (modified from Gould et al. 1986). B: map of locations
from which ICMS evoked movements of different fingers in an awake stump-tailed monkey. Different digits are indicated by 1 ⫽
thumb through 5 ⫽ little finger. Letters indicate the type of movement: f, flexion; e, extension; a, adduction; b, abduction. Flexion
of digits 2 through 5, for example, is denoted “25f.” Note that representation of movements of different digits are largely
intermingled, although the thumb tended to have a heavier representation more laterally (down). The anterior bank of the left central
sulcus has been unfolded, with the depth of the sulcus represented by the solid vertical line at right (length, 10 mm), and the lip
of the anterior bank represented by the dotted vertical line. Dashed vertical lines represent the borders between Brodmann’s areas,
and the outer dashed curve encloses the entire upper extremity representation (redrawn from Kwan et al. 1978).
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close to the electrode tip. As noted above, in cat M1, lowamplitude ICMS pulses have been estimated to directly excite
on the order of 28 pyramidal neuron somata within a radius of
88 ␮m (Stoney et al. 1968). In the baboon, a 0.2-ms ⫻ 5-␮A
pulse delivered in layer V was estimated to directly excite
90 –900 small and 1–5 large pyramidal neurons within a radius
of 40 –125 ␮m, whereas at 90 ␮A, a generous estimate of the
effective spread of stimulating current was only 0.6 mm
(Andersen et al. 1975). Direct excitation of neuronal somata or
axon hillocks by ICMS thus is reasonably focal.
Shortly after ICMS came into use, however, investigators
realized that the same pulses could excite additional corticospinal neurons at greater distances through two mechanisms.
One mechanism is direct excitation of the intracortical collaterals of pyramidal tract axons, which may extend horizontally
up to 1 mm away from the soma (Asanuma et al. 1976).1 A
second mechanism is indirect, trans-synaptic excitation. Indeed, the greatest part of the descending volley produced by
ICMS results from such trans-synaptic excitation of corticospinal neurons, even when the stimulating electrode is within
layer V and currents are as low as 5 ␮A or less (Jankowska et
al. 1975b). Moreover, repetitive ICMS of the same intracortical
point at frequencies of 200 – 400 Hz, the type of stimulation
needed to evoke detectable movement or EMG activity in the
studies described above, produces powerful temporal summation of this trans-synaptic excitation (Asanuma et al. 1976;
Jankowska et al. 1975b). Although most trans-synaptically
excited corticospinal neurons probably lie relatively close to
the ICMS microelectrode, some somata may be more distant.
Horizontally extending axon collaterals can produce excitatory
postsynaptic potentials (EPSPs) in M1 pyramidal tract neurons
within a 1- to 2-mm radius (Asanuma and Rosen 1973; Matsumura et al. 1996), and intracortical microstimulation can
excite some pyramidal tract neurons within this radius (Baker
et al. 1998). While most of the effects of ICMS result from
excitation of pyramidal tract neurons quite close to the microelectrode, a penumbra of other pyramidal tract neurons may be
excited as well. Quantitative estimates of what fraction of
observed muscle contraction or movement results from direct
excitation of local somata versus excitation of penumbral neurons are not available.
A modified ICMS technique recently has been developed
that avoids the temporal summation of repetitive stimulation at
high frequency (⬃300 Hz) used in conventional ICMS to
produce detectable output effects in quiescent animals. In
stimulus-triggered averaging (StTA), single ICMS pulses are
delivered at much lower frequencies (10 –20 Hz) while awake
monkeys perform active movements, and triggered averaging
then is used to extract the effects of these pulses from ongoing
voluntary EMG activity (Cheney and Fetz 1985). Because the
temporal summation of EPSPs is eliminated, StTA probably
produces much less of its effect via indirect, trans-synaptic
excitation. Yet maps made with StTA continue to show large
cortical territories for individual muscles that overlap extensively with the cortical territories of other muscles (Park et al.
2001).
This brings us to the second consideration: exactly what is
1
Indeed, recent studies of electrical excitation in slices of rat visual cortex
indicate extracellular stimulation excites axonal branches more readily than
axon initial segments or somata (Nowak and Bullier 1998a,b).
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being mapped with threshold electrical stimulation? Threshold
ICMS mapping in M1 entails placing the electrode tip at a
certain point in or near layer V, and gradually adjusting stimulus strength until on half of stimulation trials the discharge of
some motor units is just detected, either by recording EMG
activity, or by having enough motor units discharge to produce
an externally observable movement. With either assay, the
experimental observation means that the evoked output from
M1 to a particular muscle (or potentially a combination of
muscles when observing movement) was greater than the output to other muscles, not that output occurred to that muscle
alone. Output may well have occurred to motoneurons of other
muscles; such output to other muscles simply was insufficient
to cause them to discharge (or to discharge enough to produce
observable movement). The apparently discrete zones of ICMS
maps obtained with threshold stimuli thus represent the quantitatively greatest outputs, not qualitatively exclusive outputs.
What happens, then, if the stimulus strength is increased
beyond threshold? Are outputs to additional muscles revealed?
Phillips and colleagues recorded simultaneously from single
motor units in the thenar eminence (thumb muscles), in the first
dorsal interosseous muscle (FDI, an index finger muscle), and
in extensor digitorum communis (EDC, which extends the 4
fingers)2 while using ICMS to map the forelimb region of
baboon M1 (Andersen et al. 1975). Threshold stimulation at
most points evoked discharge in only one of the three motor
units. Using currents up to 80 ␮A, however, they found that the
three motor units recorded from these three muscles each could
be brought to discharge with ICMS at many points spread over
a wide cortical territory, and that the total territories from
which each motor unit could be discharged overlapped extensively (Fig. 5). Their calculations showed that spread of the
higher currents did not account for the overlap. Even at 90 ␮A,
a current larger than they routinely employed, a generous
estimate of the spread of current effective for direct stimulation
of somata and axons was only 0.6 mm, while the motor unit
territories overlapped several millimeters. Hence the colony
of Betz cells whose output excited each motor unit necessarily
was spread over a considerable cortical territory, largely intermingled with the colonies of Betz cells exciting the motor
units of the other muscles.3 Similar findings were obtained
with intracellular recordings from hindlimb motoneurons
(Jankowska et al. 1975a).
Subsequently, several investigators have confirmed that
ICMS maps show multiple scattered loci from which threshold
stimulation evokes movement about a particular joint, or EMG
activity in a particular muscle. In between are scattered threshold loci for other movements or muscles, forming a “complex
mosaic.” As stimulus intensity is increased systematically
above threshold, however, movements are produced at additional joints (Sessle and Wiesendanger 1982), or contractions
2
The large spatial extent of the “colony” of layer V neurons projecting to
the EDC motoneuron pool of macaques recently has been demonstrated
anatomically by retrograde transneuronal transport of rabies virus injected into
the EDC muscle belly (Rathelot and Strick 2000).
3
These authors did not consider excitation of additional, penumbral neurons
via horizontal axon collaterals or indirect trans-synaptic excitation. Even if one
considers a 2-mm penumbra of lesser excitation, however, the overlap they
demonstrated for cortical territories of different muscles is too extensive to
attribute entirely to spread of excitation and therefore indicates intermingling
of corticospinal neurons exciting different muscles.
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FIG. 5. Convergence and overlap demonstrated with ICMS. Results of ICMS at currents up to 80 ␮A in 12 electrode
penetrations (denoted A through N) down the anterior wall of a baboon’s central sulcus while recording 3 single motor units: one
in extensor digitorum communis (EDC, which extends all 4 fingers, radial nerve innervation); another in the thenar muscles
(Thenar, which act only on the thumb, median nerve innervation); and a 3rd in the 1st dorsal interosseous (FDI, which acts on the
index finger, ulnar nerve innervation). In each penetration, black dots indicate locations where ICMS was ineffective, whereas
numbers indicate threshold current (␮A) for evoking discharge of the motor unit. Lateral is to the viewer’s left; medial to the right.
With currents up to 20 ␮A (red), multiple small zones from which each motor unit could be discharged were identified. Although
the zones for the different motor units were largely interdigitated, on close inspection these small zones also overlapped to some
degree. At higher currents up to 40 (orange) and then 80 ␮A (yellow), the small zones for each motor unit expanded and coalesced
into large cortical territories, increasing their mutual overlap. Current spread could not account for this degree of convergence and
overlap in the cortical territories of these 3 motor units, which each were served by different peripheral nerves and each acted on
different digits (modified from Andersen et al. 1975).
are evoked in more and more muscles (Donoghue et al. 1992),
so that the loci for any particular muscle tend to expand and
coalesce, revealing the large total territory representing that
muscle, which overlaps extensively with the territories representing other muscles (Humphrey 1986; Sato and Tanji 1989).
This expansion and coalescence into large and overlapping
territories cannot be attributed entirely to current spread and
indirect, trans-synaptic excitation. Thus ICMS, like surface
stimulation, indicates convergence of M1 outputs from large
and overlapping M1 territories onto different muscles or movements.
Because even ICMS involves some spread of current to
multiple neurons, and because such current can excite neurons
both directly and indirectly, the question of whether stimulation at a single “point” in M1 actually produces output to more
than one muscle cannot be resolved with extracellular electrical
stimulation. The ideal experiment for resolving this question
(recording intracellularly from the spinal motoneurons of several muscles while stimulating intracellularly in the somata of
single corticospinal neurons in M1) remains technically inaccessible even now. In the 1980s, however, separate lines of
evidence developed rendering much of the previous arguments
moot.
DIVERGENCE
For many years neuroscientists generally believed that a
given corticospinal neuron made monosynaptic connections to
the motoneurons of only one muscle. These specific connections to particular muscles enabled the “upper motor neurons”
in M1 to selectively activate the muscles needed to perform
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fine, relatively independent movements (Phillips and Landau
1990). Shortly after Phillips and colleagues had articulated the
concept of convergence from wide M1 territories onto spinal
motoneurons, evidence appeared demonstrating that the output
projections from single M1 neurons often diverge to innervate
the motoneuron pools of more than one muscle.
Anatomic evidence of such divergence was obtained by
filling single corticospinal axons with horseradish peroxidase
(HRP), revealing that collateral branches of a single corticospinal axon often ramified over several spinal segments providing terminal arbors in the motoneuron pools of up to four
muscles (Fig. 6A) (Shinoda et al. 1981). Physiologic evidence
of divergence came from the use of spike-triggered averaging
of rectified EMG activity to identify functional, short-latency
connections from M1 neurons to spinal motoneuron pools (Fig.
6B) (Fetz and Cheney 1978, 1980). Many single M1 neurons
produced postspike effects, indicative of relatively direct corticomotoneuronal connections, in up to six different forearm
muscles. Spike-triggered averaging also has shown divergent
outputs from M1 neurons controlling the intrinsic muscles of
the hand; those used in the finest of relatively independent
movements (Buys et al. 1986; Lemon et al. 1986). Furthermore, a recent study indicates that the functional connections
of single M1 neurons may diverge, not only to different muscles moving the fingers and wrist, but also to muscles moving
the elbow and shoulder (McKiernan et al. 1998). These divergent projections from single M1 neurons obviously constrain
the degree to which M1’s output can be organized in a strict
within-limb somatotopy. The set of muscles receiving the
output of a single M1 neuron may act on multiple fingers; on
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CONSTRAINTS ON SOMATOTOPY IN M1
the fingers and the wrist; or even on the fingers, wrist, elbow,
and shoulder.
HORIZONTAL INTERCONNECTIONS
The concept of a strict somatotopic organization implied that
different sites within M1 acted on their output targets relatively
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independently. By providing site-specific outputs to selected
elements, the somatotopic map in M1 was thought to act like a
piano keyboard on which higher levels of the cortex could play
out motor programs. This notion has been supported by anatomic studies demonstrating that the majority of intracortical
connections within M1 are relatively local, spreading horizontally over a radius of only 1–2 mm. Lesions made by passing
a microelectrode through monkey M1 cortex radially (normal
to the pial surface) resulted in dense fiber degeneration spreading horizontally from the lesion over a radius of 200 –300 ␮m,
and less densely over a radius of 2–3 mm (Gatter and Powell
1978). Intracellular injections of HRP into cat pyramidal tract
cells showed axon collaterals spreading horizontally in layers
V and VI, densely over a radius of 0.5– 0.8 mm, and less
densely over 1.5–2.0 mm radius, with a few extending as far as
2–3 mm from the soma (Landry et al. 1980). Neurobiotin-filled
cat pyramidal neurons in layers II and III extend horizontal
axons collaterals within these layers for up to 1 mm (Keller and
Asanuma 1993).
These anatomic findings are consistent with studies demonstrating that the strongest physiologic interactions are found
between M1 neurons separated by 1–2 mm. Low-amplitude (4
␮A) ICMS in cat M1 evokes monosynaptic postsynaptic potentials (PSPs) in neurons within only a 0.5-mm horizontal
radius, and polysynaptic PSPs chiefly within 1-mm radius
(Asanuma and Rosen 1973). Spike-triggered averaging of intracellular potentials in monkey M1 likewise has shown that
EPSPs and inhibitory postsynaptic potentials (IPSPs) are strongest and most common within 1–2 mm (direct rather than
horizontal distance) of the triggering neuron (Matsumura et al.
1996). The observations that two sequential 20-␮A ICMS
pulses delivered through the same electrode produce intracortical facilitation of evoked EMG, whereas sequential pulses
delivered through electrodes separated horizontally by 1.5–2.0
mm do not also support the notion that the strongest physiologic interactions between M1 neurons occur within 1.0 mm,
although the same ICMS pulses do affect the discharge of some
pyramidal tract neurons 1.5–2.0 mm away (Baker et al. 1998).
Similarly, synchronous discharges in the action potential trains
of two neurons (indicating shared or serial inputs) are found
more commonly when the two neurons are close enough to be
recorded from the same microelectrode, and the likelihood of
finding synchronous discharge decreases with horizontal separation until synchrony is rarely detected between neurons
FIG. 6. Divergence in the projection of single corticospinal neurons. A: a
horseradish peroxidase (HRP)–filled corticospinal axon has been reconstructed
in the transverse plane of the ventral horn of the monkey spinal cord. The cord
midline and central canal are to the left; the lateral column to the right. The
filled corticospinal axon enters the spinal gray matter from the lateral column
and branches repeatedly, ultimately giving off terminal ramifications in the
outlined motoneuron pools of 4 different muscles (reproduced from Shinoda et
al. 1981). B: averages of rectified electromyographic (EMG) activity are shown
from 6 muscles [extensor digitorum secundi et tertii (ED23), extensor carpi
ulnaris (ECU), extensor digitorum quarti et quinti (ED45), extensor digitorum
communis (EDC), extensor carpi radialis longus (ECRL), and extensor carpi
radialis brevis (ECRB)] that act on the wrist and/or fingers in macaque
monkeys. Each average of rectified EMG was triggered from several thousand
spikes discharged by the simultaneously recorded M1 neuron whose averaged
action potential is shown in the top trace. The brief (⬃10 ms) peaks that appear
in each of the 1st 4 EMG traces shortly after the neuron spike indicate that
motoneurons innervating these 4 muscles received synaptic excitation at a
short and fixed latency following the discharge of action potentials by the M1
neuron (modified from Fetz and Cheney 1980).
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separated by more than 2.0 mm (Grammont and Riehle 1999;
Kwan et al. 1987; Riehle et al. 1997; Smith and Fetz 1989).
Horizontal interconnections extending 1–2 mm may mediate
interactions between M1 neurons contributing to the control of
different muscles acting about the same joint (Capaday et al.
1998), or neighboring joints of the same extremity (Kwan et al.
1987).
Although the strongest intracortical interconnections thus
occur within a 1-mm radius of a given pyramidal neuron,
recent anatomical studies have shown that many M1 neurons
extend axon collaterals even further horizontally, interconnecting much larger regions of M1 (Fig. 7). HRP injections in the
ICMS-defined digit region of monkey M1 revealed that neurons near the injection site extended terminal arbors throughout
the upper extremity region, including the territory where
threshold ICMS had evoked movements of the shoulder, elbow, or wrist (Huntley and Jones 1991). Conversely, neuronal
somata throughout the upper extremity representation were
filled retrogradely by the HRP injection in the digit region,
indicating that neurons throughout the upper extremity territory
send axon collaterals into a single focus within the digit representation. When the injection was placed laterally, close to
the face representation, labeled terminals and somata still were
found in the ICMS-defined shoulder representation, 7– 8 mm
medial to the injection site. Similarly, when Fast Blue (FB) was
injected in the digit representation, and Diamidino Yellow
(DY) was injected 7– 8 mm away in the shoulder region of
monkey M1, retrogradely labeled FB and DY somata were
found intermingled throughout the M1 cortex between the two
injections, including some double-labeled neurons (Tokuno
and Tanji 1993).
Horizontally projecting axon collaterals interconnecting the
entire upper extremity representation have been demonstrated
as well in the cat and the rat, where the collaterals have been
shown to arise predominantly from pyramidal neurons in layers
III and V, and to have predominantly excitatory, glutamatergic
effects (Aroniadou and Keller 1993; Keller 1993; Weiss and
Keller 1994). In monkey M1, inhibitory, GABAergic neurons
have predominantly vertically oriented projections (DeFelipe
and Jones 1985), although the axons of GABAergic basket
cells may project horizontally for 1–3 mm. Furthermore, the
effective range of intracortical inhibition may be much greater
(Kujirai et al. 1993), in part because local inhibitory interneurons may receive excitatory inputs from long-range horizontal
projections within M1 (Jacobs and Donoghue 1991). Longrange horizontal interconnections within M1 thus provide a
substrate for information to be interchanged through a network
distributed widely through the M1 upper extremity representation, again limiting the degree to which a particular M1 site
can be associated with control of a particular body part.
Besides long-range intrinsic connections within M1, afferent
inputs to M1 also show considerable horizontal distribution.
Given that any particular locus within M1 tends to receive
somatosensory input from the same body part moved by ICMS
at the locus (Murphy et al. 1978; Rosen and Asanuma 1972),
it is not surprising that somatosensory inputs to M1 also have
a scattered and intermingled distribution (Lemon 1981; Wong
et al. 1978). In large part, somatosensory inputs to M1 arrive
FIG. 7. Horizontal interconnections in the M1 upper extremity representation. After using ICMS to map the M1 upper extremity
representation in a macaque monkey’s left hemisphere, HRP was injected in the low-threshold digit representation at the site
indicated by the large, filled black circle with surrounding coarse-stippled penumbra. This injection resulted in widespread terminal
labeling (fine stippling in A) and retrograde filling of neuronal somata (small black dots in B). ICMS was delivered at sites indicated
by the large black dots in both A and B. Regions where stimulation at many contiguous sites elicited movement of the same body
part are delimited with dashed lines, exceptional sites being indicated individually. Stars indicate points where stimulation up to
40 ␮A failed to evoke observable movement. The solid line at the right indicates the central sulcus, and data from its anterior bank
have been represented as if projected to the hemisphere’s surface. Scale bars at bottom represent 1 mm. Anterior is to the left;
posterior, right; medial, up; lateral, down (modified from Huntley and Jones 1991).
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CONSTRAINTS ON SOMATOTOPY IN M1
via short U-fibers from the primary somatosensory cortex,
fibers that arborize over a considerable rostrocaudal distance in
M1. In macaques, these corticocortical axons may give off two
to three terminal arborizations separated by up to 800 ␮m
(DeFelipe et al. 1986). Similar arborization patterns have been
found for corticocortical afferents to M1 from area 5 in the cat
(Kakei et al. 1996). Thalamocortical afferents to M1 from the
cat ventroanterior and ventrolateral (VA/VL) nuclear complex
distribute their terminal fields even more extensively, some
covering areas up to 5.0 ⫻ 4.8 mm (almost 25 mm2) in
rostrocaudal and mediolateral dimensions (Shinoda et al.
1993). Both corticocortical and thalamocortical afferents thus
distribute their information widely in the M1 forelimb representation.
The functional role played by long horizontal connections
within M1 remains uncertain. Nevertheless, physiologic studies in awake behaving animals have demonstrated a number of
types of correlations between the discharge of M1 neurons that
may in part be mediated by these long horizontal interconnections. One form of correlation between relatively distant M1
neurons has been demonstrated by averaging the intracellular
(IC) potential of one neuron triggered from the extracellular
(EC) action potentials of a second neuron (Matsumura et al.
1996). These averages frequently reveal a broad depolarization
of the IC neuron straddling the triggering spikes of the EC
neuron. Such average synchronous excitation potentials
(ASEPs) indicate that the IC and EC neurons both receive
some sort of synchronous excitation. ASEPs, although most
common and most intense in pairs of neurons separated by ⬍2
mm, also have been found in pairs of neurons separated by up
to 4.5 mm in monkey M1.
A second type of long-range correlation has been demonstrated by examining the trial-by-trial variation in the discharge
of monkey M1 neurons averaged over 600 ms during a reaching task (Maynard et al. 1999). The trial-by-trial variation in
average discharge rate of two neurons was more likely to be
correlated if the two neurons had similar preferred directions,
suggesting that functionally similar neurons receive shared
inputs that fluctuated from trial to trial. The strength of such
correlations did not depend on the horizontal distance between
the two neurons, however. Although interneuronal separations
of only up to 2 mm were examined, that the correlation
strength was independent of separation distance suggests that
this type of correlation extends beyond 2 mm.
The most extensive evidence of long-range interactions
within M1, however, comes from studies of local field potential
(LFP) oscillations, which occur synchronously over regions of
the M1 upper extremity representation extending 14 mm mediolaterally along the central sulcus of monkeys (Donoghue et
al. 1998; Murthy and Fetz 1992, 1996a). These LFP oscillations are coherent with simultaneous oscillations in EMG activity (Baker et al. 1997; Hari and Salenius 1999). Neurons at
sites separated by up to 10 mm have been found to have
oscillatory modulation of their discharge in phase with these
LFP oscillations, and pairs of such neurons often show peaks in
cross-correlograms of their spike discharges recorded during
LFP oscillations (Baker et al. 1999; Murthy and Fetz 1996b).
The fact that such correlations during LFP oscillations can be
found between neurons in the left and right M1 indicates that
intrinsic horizontal connections are unlikely to be the sole
anatomic basis for such widespread synchronization. NeverJ Neurophysiol • VOL
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theless, horizontal connections intrinsic to the M1 upper extremity representation may contribute to synchronous LFP
oscillations, associating the widespread neurons needed to perform a coordinated movement of the entire extremity.
DISTRIBUTED ACTIVATION
For many years, authorities debated whether it was muscles
per se, or the movements they produced, that were represented
somatotopically in M1 (Phillips 1975). The convergence of M1
outputs to single motoneuron pools from wide and overlapping
cortical territories, and the divergence of output from single
M1 neurons to multiple motoneuron pools, both necessarily
constrain any somatotopic representation of individual muscles
in M1. The overlapping cortical territories of different muscles
raise the possibility, however, that different combinations of
activity in multiple muscles are represented at different cortical
sites. Voluntary movements, even movements of a single joint
or a single finger, typically involve simultaneous contractions
of multiple muscles (Beevor 1903; Schieber 1995). The simultaneous contractions of a such a set of muscles producing
movement of one body part might be represented at one location in M1, while the simultaneous contractions of a partially
overlapping set of muscles producing movement of a different
body part might be represented at a different location. Although electrical stimulation mapping had also suggested overlap of movement representations (above), electrical stimulation
is unlikely to mimic accurately the natural cortical activation
that occurs during voluntary movements.
Since the 1960s, a number of techniques (including single
neuron recording, functional neuroimaging, and magnetoencephalography) have become available for probing cortical
activity during voluntary movements performed by awake subjects. A well-ordered, discrete somatotopic organization of M1
would imply that movements of different body parts involve
activation of spatially distinct regions of M1, with these regions arrayed in somatotopic order. Somatotopically ordered
activation during voluntary movements should be demonstrable with these modern techniques.
Experimental studies examining M1 activity during movements of different parts of the upper extremity, however, have
revealed relatively little evidence of activation in spatially
distinct regions of M1. In monkeys performing individuated
movements of each finger and of the wrist, single neurons were
found to discharge in relation to movements of several different fingers, which obviously constrains the degree to which
movements of different fingers could be represented in spatially distinct regions of M1 (Schieber and Hibbard 1993).
Moreover, the M1 territories containing neurons active during
movements of different fingers were virtually coextensive,
with little evidence of a somatotopic shift in the center of
activity from lateral to medial for movements of the thumb
through little finger and wrist (Fig. 8). Similarly, functional
magnetic resonance imaging (fMRI) in humans has shown
extensive overlap of the cortical territories activated during
performance of thumb, index finger, ring finger or wrist movements (Sanes et al. 1995). Magnetoencephalography in humans
likewise has shown that the dipole sources of the neuromagnetic fields generated during movements of different digits are
not arrayed in somatotopic order, either in a single subject or
averaged across multiple subjects (Cheyne et al. 1991; Salenius
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M. H. SCHIEBER
FIG. 8. Distributed activation in M1 during finger movements. A: colored
spheres each represent a single neuron recorded in the left hemisphere M1 as
a monkey performed individuated movements (flexion or extension) of each
right-hand digit and of the right wrist. Each neuron was consistently related to
at least 1 movement, although most neurons were related to multiple different
finger and/or wrist movements. The sphere representing each neuron is centered at the location of the recorded neuron in the anterior bank of the central
sulcus, with the hemispheric surface above, white matter below, lateral to the
viewer’s right and medial to the left. Each sphere is sized according to its
greatest change in discharge frequency during any of the movements; the white
spheres at left constitute a scale from 0 to 200 spikes/s, with centers 1 mm
apart. Each sphere representing a neuron is colored according to the movement
for which that neuron’s greatest discharge occurred: thumb, red; index finger,
orange; middle, yellow; ring, green; little, blue; wrist, violet. Neurons bestrelated to movements of each digit or the wrist were intermingled throughout
the same cortical territory. B: centroids of discharge frequency changes calculated for each flexion movement and each extension movement are shown in
the same coordinate system as in A, with the scale of white spheres as a visual
anchor. Rather than shifting progressively across the field of active cortex for
thumb through little finger and wrist movements, these centroids all are
clustered together in the center of the field, with only a slight shift for
movements of different digits or the wrist, reflecting the extensive overlap of
the representations of different movements (modified from Schieber and
Hibbard 1993).
et al. 1997). These studies all indicate that movements of
different fingers are not mediated by activity in different,
spatially segregated regions, somatotopically arrayed in M1.
Rather, they suggest that movement of any finger is mediated
by the activity of neurons widely distributed in the M1 upper
extremity representation.
Strict somatotopic organization of M1 also would predict
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that the territory active during movements of multiple fingers
should be larger than the territory activated during movement
of a single digit. When this hypothesis has been tested experimentally, however, the extent and amplitude of activation in
the primary sensorimotor cortex has been found to be significantly larger during movement of a single finger than during
simultaneous movement of multiple fingers (Kitamura et al.
1993; Remy et al. 1994). Such results indicate that the process
of moving multiple fingers is not simply the sum of activating
multiple separate M1 territories, each controlling a different
finger; rather, moving a single finger without the others requires more M1 activity than moving multiple fingers simultaneously. Presumably, such extra activation occurs because,
besides controlling the motion of the one finger, M1 actively
participates in stabilizing other parts of the upper extremity
during the individuated movement of a particular finger (Humphrey and Reed 1983; Schieber 1990).
Could the distributed activation observed in awake behaving
subjects reflect activation of somatotopically organized cortex,
with one region producing the movement and other regions
stabilizing other body parts? If one considers only studies of
activation in awake behaving subjects, this interpretation certainly is possible. But ICMS should have demonstrated such an
underlying somatotopic organization (see Figs. 4 and 5), and
the divergence of output from single M1 neurons to muscles
that move all four fingers and the wrist (Fig. 6), or to muscles
acting on both the digits and the shoulder (McKiernan et al.
1998), would certainly limit the degree of somatotopic segregation of these representations. Distributed representation provides a more parsimonious interpretation of all these observations considered together.
Several studies have demonstrated comparatively small, somatotopically ordered shifts in the location of activation during
movements of different parts of the upper extremity. It must be
recognized, however, that these shifts are detected by using
analytic approaches that minimize the contribution of activation common to different movements. For example, somatotopically ordered shifts may be detected in the centroids of
activation calculated for movements of different fingers. These
shifts are small, however, compared with the total spatial
extent of the territory activated. In monkeys, the centroids of
activation during different finger and wrist movements were
found to be spread over 2 mm along the central sulcus, whereas
the field containing active neurons extended 8 –9 mm (Fig. 8)
(Schieber and Hibbard 1993). In humans, centroids of fMRI
activation for movements of different fingers may be spread
over 2.46 mm (no greater than the thickness of the cortex
itself!) (Beistener et al. 2001; Hlustik et al. 2001; Indovina and
Sanes 2001), whereas the total extent of the hand representation along the central sulcus is roughly 50 mm (Hlustik et al.
2001; Penfield and Boldrey 1937). In both species, comparing
the spatial separation of the centroids with the spatial extent of
the hand representation indicates that the territories activated
during movements of different fingers must overlap extensively.
Analytic techniques that make even less use of the activation
common to different movements have demonstrated greater
apparent separation. Although the territory of fMRI activation
during thumb movement overlaps extensively with that during
little finger movement, difference images that subtract away
the shared activation have shown that the activation peak
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during thumb movements is lateral to that during little finger
movements (Kleinschmidt et al. 1997). Similarly, when the
activation peaks during thumb, index finger, wrist, elbow, and
shoulder movements were compared using positron emission
tomography (PET), a somatotopic progression from lateral to
medial was demonstrated (Grafton et al. 1993). These observations become consistent with observations of distributed
activation, as well as with observations of convergence, divergence, and horizontal interconnections, when the gradual shift
in peak activation or centroid of activation is recognized to be
present on a base of extensively overlapping representation.
PARTIAL INACTIVATION
A strictly somatotopic organization of M1 also would predict that in some instances lesions should affect certain parts of
the upper extremity without affecting others. In human patients, where lesions of M1 may be produced by a variety of
disease processes, the resulting upper extremity weakness affects distal strength in the fingers and wrist more profoundly
than proximal strength in the shoulder and elbow (Bucy 1949;
Colebatch and Gandevia 1989). Uncommonly, more selective
lesions weaken the fingers and wrist much more profoundly
than the elbow and shoulder (Foerster 1936). Even in these
cases, however, some weakness is evident at these proximal
joints, unless the weakness of the hand itself is minimal. Even
more uncommonly, human patients have been reported with
weakness greater in some fingers than in others. Most often, the
thumb is the weakest digit, with some weakness of the index as
well (Lee et al. 1998; Terao et al. 1993). Greatest weakness of
the thumb (and index) could result from a greater representation of the thumb and index throughout the M1 hand area,
rather than selective involvement of a region controlling only
the thumb. Other cases have been reported, however, in which
the little and ring fingers were weakest (Foerster 1936; Kim
2001; Phan et al. 2000; Schieber 1999). Notably absent are
cases in which the index, middle, or ring fingers were the
weakest. Human cases thus suggest that, rather than discrete
regions of M1 controlling different parts of the upper extremity, control of each part is mediated by an extensive territory
that overlaps with the territories controlling other parts. Nevertheless, on top of this widely distributed control of each
finger, two somatotopic gradients may be present, consistent
with the general order suggested by the homunculus. First, the
proximal upper extremity is represented more heavily medially
than laterally, while the reverse is true for the distal upper
extremity. Second, within the distal representation, the thumb
and index are represented more heavily laterally than medially,
while the little and ring fingers are represented more heavily
medially than laterally.
The exact location and extent of lesions in human cases, of
course, cannot be controlled experimentally. Relatively few
investigations in experimental animals have attempted to correlate the location of a lesions within the M1 upper extremity
representation with the resulting motor deficits in the upper
extremity. In monkeys performing individuated finger movements, however, partial inactivation of the M1 hand area produced by intracortical injection of the GABAA agonist, muscimol, impaired some finger movements more than others, but
which finger movements were impaired was unrelated to the
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cus (Schieber and Poliakov 1998). Similarly, when muscimol
was injected at loci where ICMS evoked thumb and index
finger movements, movements of the whole hand were impaired (Brochier et al. 1999). Microinfarction of ICMS defined
hand representation in squirrel monkeys resulted not only in
decreased use of the hand, but also in tonic flexion at the elbow
and adduction of the extremity close to the torso, similar to the
involuntary tonic posturing of the upper extremity seen in
human patients after much more extensive lesions of M1 (Friel
and Nudo 1998). The deficits produced by controlled lesions in
animal studies, like those resulting from lesions produced by
disease in humans, suggest that control of each finger, and of
each more proximal joint, is widely distributed in the M1 upper
extremity representation.
PLASTICITY
Observations indicative of what we now call plasticity are
almost as old as stimulation mapping of M1. In their classic
study of somatotopic organization of M1 in the great apes, for
example, Leyton and Sherrington (1917) took pains to describe
the “functional instability of cortical motor points.” They
found that after the movement evoked by stimulation of a given
point was first identified, intervening stimulation of the same
point or other nearby points could result in facilitation, reversal, or deviation of the movement evoked when stimulation of
the given point subsequently was repeated. These investigators
inferred that “. . . the functional instability of cortical motor
points are indicative of the enormous wealth of mutual associations existing between the separable motor cortical points,
and those associations must be a characteristic part of the
machinery by which the synthetic powers of that cortex are
made possible.” When M1 was considered to contain a pointto-point somatotopic map of body parts, movements, or muscles, with each corticospinal neuron monosynaptically connected to one and only one spinal motoneuron pool, the
possibility of plastic reorganization in M1 seemed remote. The
convergence, divergence, horizontal interconnections, and distributed activation described above, however, provide a substrate that would appear capable of considerable plastic reorganization.
As reviewed in detail elsewhere (Nudo et al. 2001; Sanes
and Donoghue 2000), in the past decade M1 has been shown to
undergo plastic reorganization in response to a variety of
changes, including peripheral lesions, central lesions, and motor skill acquisition. Here we focus only on the latter, which
may be most relevant to the normal organization of somatotopic representation in M1. In a variety of motor skill acquisition paradigms, the M1 representation of the trained body
parts has been shown to enlarge, typically at the expense of the
representations of less trained body parts. In rats trained to
reach and grab small food pellets, for example, the ICMS
defined M1 representation of digit and wrist movements was
expanded at the expense of elbow and shoulder movement
representation (Kleim et al. 1998). In monkeys trained to
retrieve small objects, ICMS mapping likewise revealed expansion of the digit representation, whereas in monkeys trained
to perform forearm pronation/supination movements the representation of forearm movements expanded (Nudo et al.
1996). These changes are progressive as training continues,
and reverse after training stops (Fig. 9).
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M. H. SCHIEBER
FIG. 9. M1 Reorganization in relation to motor training. A: after initial ICMS mapping of the distal forelimb representation
(Baseline), a squirrel monkey was trained for 11 days on a Kluver board and then re-mapped (Training I), with little change in the
total area devoted to digit (red), wrist/forearm (green), digit and wrist/forearm (yellow, DIG ⫹ W/FA), digit and more proximal
(blue, DIG ⫹ PROX), or wrist/forearm and more proximal (purple, W/FA ⫹ PROX) movements. After additional training on the
Kluver board for 39 days, re-mapping showed increases in the percent of the total distal forelimb area devoted to movements used
in retrieving food morsels from the small wells on the board: digit and wrist/forearm, finger flexion and wrist extension, and finger
flexion/extension movements (Training II). Then, after 4 mo without practice, re-mapping showed a reversion of these changes back
to baseline (Extinction), and finally after another 30 days training, re-mapping showed that similar changes had occurred again
(Reaquisition). Only points from which stimulation evoked movements of the digits, wrist, or pronation/supination movements of
the forearm are shown in these maps; points eliciting only movements of the elbow and/or shoulder are not included, although they
typically surrounded the distal forelimb representations anteriorly (up), laterally (left) and medially (right) as indicated by “prox”
in the baseline map. B: bargraphs show the percentage of the total distal forelimb representation in each map from which specific
movements were elicited in each phase of the study (modified from Nudo et al. 1996).
Additional evidence obtained in human subjects indicates
that M1 reorganization occurs both within a single session and
over the longer term needed to acquire a complex skill. When
normal human subjects, initially unaware of any sequence,
practice a repeating sequence of finger movements instructed
by visual cues, the amplitude and extent of finger muscle
representation assessed by trans-cranial magnetic stimulation
increases in M1 contralateral to the performing hand as the
speed of performance increases over a single day of training
(Pascual-Leone et al. 1994). Several days of such training
produce progressive expansion of finger muscle representation,
whether the training involves physical practice or only mental
rehearsal (Pascual-Leone et al. 1995). Practicing a finger
movement sequence over several weeks results in greater fMRI
activation of the M1 hand representation during performance
of the practiced sequence than during performance of a comparable, but unpracticed sequence (Karni et al. 1995). An
example of very long-term changes related to motor skill is
found in experienced Braille readers, whose M1 representation
of the first dorsal interosseous muscle (used to sweep the tip of
the index finger over Braille letters) is expanded in M1 contralateral to their reading finger (Pascual-Leone et al. 1993).
If reorganization of M1 in normal subjects can be driven by
J Neurophysiol • VOL
learning and practicing a particular skill, then reorganization is
likely to be proceeding continuously as each individual performs the motor tasks used frequently in their daily life. The
patterns of representation in M1 thus are likely to change as an
individual performs more of one motor activity and less of
another from day to day. Such a continual process of reorganization places yet another constraint on somatotopic organization in M1.
WHY SHOULD THE PRIMARY MOTOR CORTEX
HAVE SO DISTRIBUTED AN ORGANIZATION?
The ease with which we can comprehend a well-ordered,
discrete, somatotopic representation makes the concept of somatotopy an attractive organizing principle with which to
understand the function of the primary motor cortex. Somatotopy seems so straightforward that it ought be so. The primary
somatosensory cortex (S1) has a well-ordered somatotopic
representation, and the primary visual cortex (V1) has a wellordered retinotopic representation. The evidence reviewed
above indicating that within-limb somatotopy in M1 is limited,
and that a more complex, widely distributed organization exists
instead, therefore is likely to reflect important features of
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CONSTRAINTS ON SOMATOTOPY IN M1
2139
FIG. 10. Cortical pianos. A: the standard
piano keyboard constitutes a well-ordered
map of musical notes. Here the 42 white
keys have been colored to distinguish each of
them. Although the strict order and single
representation make it easy to locate each
note, these same features make it impossible
for a pianist to play certain combinations of
notes, such as the 5 notes marked by black
dots beneath. B: a nonstandard keyboard can
be created by re-representing each note at
multiple locations and in a wide variety of
orders. While this arrangement appears more
complex, with each note represented over a
wide territory that overlaps with the territories representing many other notes, this distributed organization provides ready access
to many more combinations of notes than the
standard keyboard. The 5 adjacent notes
marked by black dots in B, for example, are
the same notes indicated in A.
functional organization in M1.4 I close with speculations as to
what these features might be.
One such feature may be the dimensionality of the information processed in M1. Well-ordered representations exist where
a two-dimensional receptor sheet (the skin surface or retina,
respectively) can be mapped isomorphically onto the twodimensional cortex. Movements and the muscles that generate
them are three-dimensional, however, and cannot be mapped
simply into a two-dimensional cortex. The number of dimensions represented in M1 is arguably much more than three, if
each muscle, each degree of freedom at each joint, and each
kinematic or dynamic parameter of movement constitutes a
possible dimension. Even in S1, the most discrete and wellordered somatotopic representation of the different fingers is
found in area 3b, where cutaneous inputs predominate
(Iwamura et al. 1983a; Pons et al. 1987). In areas 1 and 2,
where cutaneous inputs are combined with inputs from deep
mechanoreceptors in joints and muscles, increasing numbers of
receptive fields span multiple digits, and somatotopic organization becomes more complex, particularly in awake animals
(Iwamura et al. 1980, 1983b, 1993; Pons et al. 1985). In
contrast, V1 represents additional dimensions by nesting them
within the two-dimensional retinotopic representation. Ocular
dominance columns, orientation columns, and color blobs can
be considered additional dimensions of visual stimuli, the
representations of which are nested within the two-dimensional
representation of each retinotopic location. Little evidence of
such a fine-grained nesting has been found in M1, however,
which presumably reflects some additional difference in cortical processing for control of movement versus perception of
sensory stimuli.
4
Indeed, the resolution of somatotopic organization in area 3b exceeds that
which would be expected based on the divergence of thalamocortical afferents
carrying somatosensory input from a given finger, and the overlap of thalamocortical afferents carrying input from different fingers (Garraghty et al. 1989;
Rausell et al. 1998). The precise somatotopy in area 3b therefore indicates that
active mechanisms normally increase the somatotopic resolution in S1, in
contrast to the mechanisms organizing M1.
J Neurophysiol • VOL
A second feature of functional organization may have to do
with what needs to be processed simultaneously by M1. The
well-ordered representations in S1 (area 3b in particular) and
V1 are thought to be computationally advantageous because
two adjacent receptors are much more likely to receive similar
input simultaneously than two distant receptors. If a mechanoreceptor on the thumb is responding to an indenting stimulus,
for example, another mechanoreceptor on the thumb is much
more likely to be responding simultaneously than a mechanoreceptor on the little finger. Some economy of neural
processing presumably is achieved by representing thumb
mechanoreceptors close to one another, with little finger
mechanoreceptors represented at a distance. In the much
less likely event that the thumb and little finger are indented
simultaneously, however, the requisite neural processing is
more costly than if the thumb and little finger mechanoreceptor representations were intermingled with one another.
Control of movement, particularly the control provided by
M1, is fundamentally different. Innumerable combinations of
muscle contractions and movements with relatively similar
likelihood must be represented. In this way M1 provides the
capacity to generate a huge repertoire of movements, as well as
the potential to generate previously unperformed movements.
To achieve these abilities, the organizational substrate of M1
must be able to access virtually any different combination of
muscle contractions and body part movements with equal
facility. A well-ordered, discrete, somatotopic representation
would limit its ability to do so. Such a well-ordered somatotopic representation in M1 often has been likened to a piano
keyboard, on which other cortical areas play out movements, as
illustrated in Fig. 10A, where colors have been added to the
white keys to identify individual notes. Although many different tunes can be played on such a keyboard, a 21st century
composer might be disappointed that certain combinations of
notes simply cannot be played. For example, a single pianist
cannot play the five notes indicated by black dots in Fig. 10A.
If, however, a modern two-dimensional keyboard is created in
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M. H. SCHIEBER
which each note (white keys only for simplicity) is re-represented at multiple locations (Fig. 10B), then at some location
the desired combination of five notes can be accessed as easily
as any other combination. Distributed organization thus can
provide much more flexible access to a wide variety of combinations.
In theory, all possible combinations could be equally represented. In practice, equivalent representation of all possible
combinations might come at the cost of an excessively large
cortical area. More area is required to re-represent any element
multiple times (compare the size of Fig. 10, A vs. B), and the
right connections must be established and maintained throughout a relatively large area, with relatively long conduction
delays. A compromise therefore might exist in which more
frequently used combinations are represented at more locations
than less frequently used combinations. Hence when activated
by electrical stimulation, the extent of cortical territory representing these frequently used combinations, and the corresponding body parts, would appear magnified relative to other
less frequently used combinations and body parts. As the
individual learns new motor skills (or quits practicing old ones)
such a distributed system (with the underlying structural substrates of convergence, divergence, and horizontal interconnection) could be reorganized to represent new combinations (at
the expense of now less used combinations) more readily than
a well-ordered somatotopic system.
Distributed organization also provides greater resistance to
the disruptive effects of lesions. A tiny lesion the size of a
single piano key, for example, could eliminate any ability to
produce a given note (such as the pale yellow E) in the
well-ordered keyboard of Fig. 10A. The same tiny lesion would
go virtually unnoticed in the distributed keyboard of Fig. 10B.
A considerably larger lesion covering many keys, would be
needed to noticeably compromise use of any given element on
the distributed keyboard. When such a lesion occurs, however,
use of many notes all along the musical scale would be compromised, consistent with the observations reviewed above that
inactivation in the M1 hand representation sufficient to produce
detectable deficits affects movements of multiple fingers, not
just one.
In a distributed system, networked by convergence, divergence, and horizontal interconnections, somatotopic organization is theoretically unnecessary. Even if one subset of locations representing a particular element (such as the pale yellow
E, or thumb flexion) is active during one movement, and
another subset is active during another movement, no a priori
requirement forces the two different subsets to be spatially
segregated. Movement control from the primary motor cortex
is not distributed to the point of homogeneity, however. As
noted above, the face, arm and leg representations are distinct
from one another. Within the upper extremity representation,
gradual somatotopic gradients also can be identified on top of
an underlying distributed representation. At some point, the
costs of distributed representation outweigh the benefits, which
may have to do with a third feature of functional organization
in M1, the biomechanical interactions of the body parts being
controlled.
The degree of somatotopic segregation in M1 generally
parallels the biomechanical independence of different body
parts. Thumb movements are biomechanically independent of
lip movements, and the representations of the thumb and lips
J Neurophysiol • VOL
therefore can be quite segregated in M1. Movements of the
thumb and the wrist are not so independent. Extrinsic muscles
acting on the thumb (flexor pollicis longus, extensor pollicis
longus, and abductor pollicis longus) act across the wrist as
well, and because the proximal segment of the thumb is connected to the wrist directly, motion of the thumb will exert
interaction torques at the wrist. Precise control of thumb movement therefore will always require some control of the wrist,
even when the wrist is being stabilized so as not to move when
the thumb does. Because movement of the thumb always
requires some degree of simultaneous control of the wrist, then,
representation of the thumb and the wrist is intermingled to a
considerable degree in M1. Even more intermingled in M1 are
representations of thumb and fingers. Movements of the different digits are not entirely independent (Hager-Ross and
Schieber 2000; Schieber 1991). In functional uses of the hand,
even when performing sophisticated tasks such as typing or
playing the piano, the thumb and fingers are in motion simultaneously (Engel et al. 1997; Fish and Soechting 1992; Santello
and Soechting 1998).
The need to control a wide variety of movements in biomechanically coupled, simultaneously moving body parts may
have constrained evolution of a well-ordered, spatially segregated, discrete somatotopic map in M1. Indeed, when Hughlings Jackson initially proposed localization of control of
movements in the brain, he recognized that, “. . . since the
movements of the thumb and fingers could scarcely be developed for any useful purpose without fixation of the wrist . . . ,
we should a priori be sure that the center discharged, although it
might represent movements in which the thumb had the leading
part, must represent also certain other movements of the forearm,
upper arm, etc., which serve subordinately” (Jackson 1958, p. 69).
The author thanks J. Gardinier for preparing illustrations, M. Hayles for
editorial comments, and the anonymous reviewers for helpful critiques.
This work was supported by National Institutes of Health Grants R01-NS27686 and P41-RR-09283.
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