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
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
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.
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
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 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 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 scientific 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 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.
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-µA, 0.2-ms
cathodal current pulses delivered in layer V were estimated to directly
excite neuronal somata within a radius of only 88 µm, 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 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 close to the electrode tip.
As noted above, in cat M1, low-amplitude ICMS pulses have been
estimated to directly excite on the order of 28 pyramidal neuron somata
within a radius of 88 µm (Stoney et al. 1968). In the
baboon, a 0.2-ms × 5-µA 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 µm, whereas at 90 µA, 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 µA 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 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 µA, 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 µA, 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 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 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 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 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 µm, 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 µA) 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-µA 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 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
). Long-range 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 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 µm (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. Nevertheless, 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 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 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 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 mediolateral location of the inactivation along
the central sulcus (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 point-to-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).
|
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 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 well-ordered 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 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 two-dimensional 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 well-ordered 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.
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 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 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).
![]() |
ACKNOWLEDGMENTS |
---|
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-NS-27686 and P41-RR-09283.
![]() |
FOOTNOTES |
---|
Address for reprint requests: University of Rochester Medical Center, Dept. of Neurology, 601 Elmwood Ave., Box 673, Rochester, NY 14642 (E-mail: mhs{at}cvs.rochester.edu).
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
).
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.
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.
Received 9 January 2001; accepted in final form 5 July 2001.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|