Motor cortex in voluntary movements a distributed system for distributed functions pdf
MOTOR CORTEX
C RC P R E S S
Boca Raton London New York Washington, D.C.
Alexa Riehle and Eilon Vaadia
IN VOLUNTARY
MOVEMENTS
A DISTRIBUTED SYSTEM
FOR DISTRIBUTED FUNCTIONS
EDITED BY
Library of Congress Cataloging-in-Publication Data Motor cortex in voluntary movements : a distributed system for distributed functions / edited by Alexa Riehle and Eilon Vaadia. p. cm. Includes bibliographical references and index.
ISBN 0-8493-1287-6 (alk. paper) 1. Motor cortex. 2. Human locomotion. I. Riehle, Alexa. II. Vaadia, Eilon. III. Series. QP383.15.M68 2005 612.8
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Methods & New Frontiers
in NeuroscienceOur goal in creating the Methods & New Frontiers in Neuroscience series is to present the insights of experts on emerging experimental techniques and theoretical concepts that are or will be at the vanguard of the study of neuroscience. Books in the series cover topics ranging from methods to investigate apoptosis to modern techniques for neural ensemble recordings in behaving animals. The series also covers new and exciting multidisciplinary areas of brain research, such as compu- tational neuroscience and neuroengineering, and describes breakthroughs in classical fields such as behavioral neuroscience. We want these to be the books every neuro- scientist will use in order to graduate students and postdoctoral fellows when they are looking for guidance to start a new line of research.
Each book is edited by an expert and consists of chapters written by the leaders in a particular field. Books are richly illustrated and contain comprehensive bibli- ographies. Chapters provide substantial background material relevant to the partic- ular subject; hence, they are not only “methods” books. They contain detailed tricks of the trade and information as to where these methods can be safely applied. In addition, they include information about where to buy equipment and about Web sites that are helpful in solving both practical and theoretical problems.
We hope that as the volumes become available, the effort put in by us, by the publisher, by the book editors, and by the individual authors will contribute to the further development of brain research. The extent to which we achieve this goal will be determiend by the utility of these books.
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Preface
Voluntary movement is undoubtedly the overt basis of human behavior. Without movement we cannot walk, nourish ourselves, communicate, or interact with the environment. This is one of the reasons why the motor cortex was one of the first cortical areas to be explored experimentally. Historically, the generation of motor commands was thought to proceed in a rigidly serial and hierarchical fashion. The traditional metaphor of the piano presents the premotor cortex “playing” the upper motoneuron keys of the primary motor cortex (M1), which in turn activate with strict point-to-point connectivity the lower motoneurons of the spinal cord. Years of research have taught us that we may need to reexamine almost all aspects of this model. Both the premotor and the primary motor cortex project directly to the spinal cord in highly complex overlapping patterns, contradicting the simple hierarchical view of motor control. The task of generating and controlling movements appears to be subdivided into a number of subtasks that are accomplished through parallel distributed processing in multiple motor areas. Multiple motor areas may increase the behavioral flexibility by responding in a context-related way to any constraint within the environment. Furthermore, although more and more knowledge is accu- mulating, there is still an ongoing debate about what is represented in the motor cortex: dynamic parameters (such as specific muscle activation), kinematic param- eters of the movement (for example, its direction and speed), or even more abstract parameters such as the context of the movement. Given the great scope of the subject considered here, this book focuses on some new perspectives developed from con- temporary monkey and human studies. Moreover, many topics receive very limited treatment.
wo chapters, uses functional neuroanatomy
and imaging studies to describe motor cortical function. The objective of
is to describe the major components of the structural framework employed by the cerebral cortex to generate and control skeletomotor function. Dum and Strick focus on motor areas in the frontal lobe that are the source of corticospinal projec- tions to the ventral horn of the spinal cord in primates. These cortical areas include the primary motor cortex (M1) and the six premotor areas that project directly to it. The results presented lead to an emerging view that motor commands can arise from multiple motor areas and that each of these motor areas makes a specialized contri- bution to the planning, execution, or control of voluntary movement. The purpose
Chapter 2 is to provide an overview of the contribution of functional magnetic
resonance imaging (fMRI) to some of the prevailing topics in the study of motor control and the function of the primary motor cortex. Kleinschmidt and Toni claim that in several points the findings of functional neuroimaging seem to be in apparent disagreement with those obtained with other methods, which cannot always be attributed to insufficient sensitivity of this noninvasive technique. In part, it may
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reflect the indirect and spatio-temporally imprecise nature of the fMRI signal, but these studies remain informative by virtue of the fact that usually the whole brain is covered. Not only does fMRI reveal plausible brain regions for the control of localized effects, but the distribution of response foci and the correlation of effects observed at many different sites can assist in the guidance of detailed studies at the mesoscopic or microscopic spatio-temporal level. A prudently modest view might conclude that fMRI is at present primarily a tool of exploratory rather than explan- atory value.
evolution of individuated
finger movements. Schieber, Reilly, and Lang demonstrate that rather than acting as a somatotopic array of upper motor neurons, each controlling a single muscle that moves a single finger, neurons in the primary motor cortex (M1) act as a spatially distributed network of very diverse elements, many of which have outputs that diverge to facilitate multiple muscles acting on different fingers. This biological control of a complex peripheral apparatus initially may appear unnecessarily com- plicated compared to the independent control of digits in a robotic hand, but can be understood as the result of concurrent evolution of the peripheral neuromuscular apparatus and its descending control from the motor corte
simultaneous movements of the two arms, as a simple example of complex move- ments, and may serve to test whether and how the brain generates unique represen- tations of complex movements from their constituent elements. Vaadia and Cardoso present evidence that bimanual representations indeed exist, both at the
de Oliveira
level of single neurons and at the level of neuronal populations (in local field potentials). They further show that population firing rates and dynamic interactions between the hemispheres contain information about the bimanual movement to be executed. In
whether the motor cortex codes the spatial aspects (kinematics) of motor output, such as direction, velocity, and position, or primarily controls, muscles, and forces (dynamics). Although the weight of evidence is in favor of M1 controlling spatial output, the effect of limb biomechanics and forces on motor cortex activity is beyond dispute. The author proposes that the motor cortex indeed codes for the most behaviorally relevant spatial variables and that both spatial variables and limb bio- mechanics are reflected in motor cortex activity
issue of how theoretical concepts guide experimental design and data analysis. Scott describes two conceptual frameworks for interpreting neural activity during reach- ing: sensorimotor transformations and internal models. He claims that sensorimotor transformation have been used extensively over the past 20 years to guide neuro- physiological experiments on reaching, whereas internal models have only recently had an impact on experimental design. Furthermore, the chapter demonstrates how the notion of internal models can be used to explore the neural basis of movement by describing a new experimental tool that can sense and perturb multiple-joint planar mov
motor cortex. MacKay notes that from their earliest recognition, oscillatory EEG signals in the sensorimotor cortex have been associated with stasis: a lack of move- ment, static postures, and possibly physiological tremor. It is now established that
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10-, 20-, and 40-Hz motor cortical oscillations are associated with constant, sustained muscle contractions, again a static condition. Sigma band oscillations of about 14 Hz may be indicative of maintained active suppression of a motor response. The dynamic phase at the onset of an intended movement is preceded by a marked decrease in oscillatory power, but not all frequencies are suppressed. Fast gamma oscillations coincide with movement onset. Moreover, there is increasing evidence that oscilla- tory potentials of even low frequencies (4–12 Hz) may be linked to dynamic episodes of movement. Most surprisingly, the 8-Hz cortical oscillation — the neurogenic component of physiological tremor — is emerging as a major factor in shaping the pulsatile dynamic microstructure of movement, and possibly in coordinating diverse actions performed together
Chapter 8 , Riehle discusses the main aspects of
preparatory processes in the motor cortex. Preparation for action is thought to be based on central processes, which are responsible for maximizing the efficiency of motor performance. A strong argument in favor of such an efficiency hypothesis of preparatory processes is the fact that providing prior information about movement parameters or removing time uncertainty about when to move significantly shortens reaction time. The types of changes in the neuronal activity of the motor cortex, and their selectivity during preparation, are portrayed and compared with other cortical areas that are involved in motor behavior. Furthermore, linking motor cortical activity directly to behavioral performance showed that the trial-by-trial correlation between single neuron firing rates and reaction time revealed strong task-related cortical dynamics. Finally, the cooperative interplay among neurons, expressed by precise synchronization of their action potentials, is illustrated and compared with changes in the firing rate of the same neurons. New concepts including the notion of coor- dinated ensemble activity and their functional implication during movement prepa- ration are discussed. In the last chapter of
Chapter 9 , Jeannerod poses
the question of the role of the motor cortex in motor cognition. The classical view of the primary motor cortex holds that it is an area devoted to transferring motor execution messages that have been elaborated upstream in the cerebral cortex. More recently, however, experimental data have pointed to the fact that the relation of motor cortex activity to the production of movements is not as simple as was thought on the basis of early stimulation experiments. This revision of motor cortical function originated from two main lines of research, dealing first with the plasticity of the somatotopic organization of the primary motor cortex, and second with its involve- ment in cognitive functions such as motor imagery.
explores various
conditions of mapping between sensory input and motor output. Brasted and Wise claim that studies on the role of the motor cortex in voluntary movement usually focus on standard sensorimotor mapping, in which movements are directed toward sensory cues. Sensorimotor behavior can, however, show much greater flexibility. Some variants rely on an algorithmic transform between the location of the cue and that of the target. The well-known “antisaccade” task and its analogues in reaching serve as special cases of such transformational mapping, one form of nonstandard mapping. Other forms of nonstandard mapping differ strongly: they are arbitrary. In arbitrary sensorimotor mapping, the cue’s location has no systematic spatial rela- tionship with the response. The authors explore several types of arbitrary mapping,
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Chapter 11 , Shadmehr, Donchin,
deal with internal models that transform the desired
Hwang, Hemminger, and Rao
movement into a motor command. When one moves the hand from one point to another, the brain guides the arm by relying on neural structures that estimate the physical dynamics of the task. Internal models are learned with practice and are a fundamental part of voluntary motor control. What do internal models compute, and which neural structures perform that computation? The authors approach these questions by considering a task where the physical dynamics of reaching movements are altered by force fields that act on the hand. Many studies suggest that internal models are sensorimotor transformations that map a desired sensory state of the arm into an estimate of forces; i.e., a model of the inverse dynamics of the task. If this computation is represented as a population code via a flexible combination of basis functions, then one can infer activity fields of the bases from the patterns of gener- alization. Shadmehr and colleagues provide a mathematical technique that facilitates this inference by analyzing trial-by-trial changes in performance. Results suggest that internal models are computed with bases that are directionally tuned to limb motion in intrinsic coordinates of joints and muscles, and this tuning is modulated multiplicatively as a function of static position of the limb. That is, limb position acts as a gain field on directional tuning. Some of these properties are consistent with activity fields of neurons in the motor cortex and the cerebellum. The authors suggest that activity fields of these cells are reflected in human behavior in the way that we learn and generalize patterns of dynamics in reaching movements. In the
Chapter 12 , Padoa-Schioppa, Bizzi, and Mussa-Ivaldi
address the question of the cortical control of motor learning. In robotic systems, engineers coordinate the action of multiple motors by writing computer codes that specify how the motors must be activated for achieving the desired robot motion and for compensating unexpected disturbance. Humans and animals follow another path. Something akin to programming is achieved in nature by the biological mech- anisms of synaptic plasticity — that is, by the variation in efficacy of neural trans- mission brought about by past history of pre- and post-synaptic signals. However, robots and animals differ in another important way. Robots have a fixed mechanical structure and dimensions. In contrast, the mechanics of muscles, bones, and liga- ments change in time. Because of these changes, the central nervous system must continuously adapt motor commands to the mechanics of the body. Adaptation is a form of motor learning. Here, a view of motor learning is presented that starts from the analysis of the computational problems associated with the execution of the simplest gestures. The authors discuss the theoretical idea of internal models and present some evidence and theoretical considerations suggesting that internal models of limb dynamics may be obtained by the combination of simple modules or “motor primitives.” Their findings suggest that the motor cortical areas include neurons that process well-acquired movements as well as neurons that change their behavior during and after being exposed to a new task.
evoted to the reconstruction of movements using brain activity. For decades, science fiction authors anticipated the view that comput- ers can be made to communicate directly with the brain. Now, a rapidly expanding science community is making this a reality. In
Chapter 13 , Carmena and Nicolelis Copyright © 2005 CRC Press LLC
present and discuss the recent research in the field of brain–machine interfaces (BMI) conducted mainly on nonhuman primates. In fact, this research field has supported the contention that we are at the brink of a technological revolution, where artificial devices may be “integrated” in the multiple sensory, motor, and cognitive represen- tations that exist in the primate brain. These studies have demonstrated that animals can learn to utilize their brain activity to control the displacements of computer cursors, the movements of simple and elaborate robot arms, and, more recently, the reaching and grasping movements of a robot arm. In addition to the current research performed in rodents and primates, there are also preliminary studies using human subjects. The ultimate goal of this emerging field of BMI is to allow human subjects to interact effortlessly with a variety of actuators and sensory devices through the expression of their voluntary brain activity, either for augmenting or restoring sen- sory, motor, and cognitive function. In the last chapter
Chapter 14 , Pfurtscheller,
deal with BMIs, which transform signals originating from
Neuper, and Birbaumer
the human brain into commands that can control devices or applications. BCIs provide a new nonmuscular communication channel, which can be used to assist patients who have highly compromised motor functions, as is the case with patients suffering from neurological diseases such as amyotrophic lateral sclerosis (ALS) or brainstem stroke. The immediate goal of current research in this field is to provide these users with an opportunity to communicate with their environment. Present- day BCI systems use different electrophysiological signals such as slow cortical potentials, evoked potentials, and oscillatory activity recorded from scalp or subdural electrodes, and cortical neuronal activity recorded from implanted electrodes. Due to advances in methods of signal processing, it is possible that specific features automatically extracted from the electroencephalogram (EEG) and electrocortico- gram (ECoG) can be used to operate computer-controlled devices. The interaction between the BCI system and the user, in terms of adaptation and learning, is a challenging aspect of any BCI development and application.
It is the increased understanding of neuronal mechanisms of motor functions, as reflected in this book, that led to the success of BCI. Yet, the success in tapping and interpreting neuronal activity and interfacing it with a machine that eventually executes the subject’s intention is amazing, considering the limited understanding we have of the system as a whole.
Perhaps ironically, the proof of our understanding of motor cortical activity will stem from how effectively we, as external observers of the brain, can tap into it and make use of it.
Alexa Riehle Eilon Vaadia Copyright © 2005 CRC Press LLC
Dedication to Hanns-Günther Riehle
Copyright © 2005 CRC Press LLC
Editors Alexa Riehle received a B.Sc. degree in biology (main topic: deciphering microcir-
cuitries in the frog retina) from the Free University, Berlin, Germany, in 1976, and a Ph.D. degree in neurophysiology (main topic: neuronal mechanisms of temporal aspects of color vision in the honey bee) from the Biology Department of the Free University in 1980.
From 1980 to 1984, she was a postdoctoral fellow at the National Center for Scientific Research (CNRS) in Marseille, France (main topic: neuronal mechanisms of elementary motion detectors in the fly visual system). In 1984, she moved to the Cognitive Neuroscience Department at the CNRS and has been mainly interested since then in the study of cortical information processing and neural coding in cortical ensembles during movement preparation and execution in nonhuman primates.
Eilon Vaadia graduated from the Hebrew University of Jerusalem (HUJI) in 1980
and joined the Department of Physiology at Hadassah Medical School after post- doctoral studies in the Department of Biomedical Engineering at Johns Hopkins University Medical School in Baltimore, Maryland.
Vaadia studies cortical mechanisms of sensorimotor functions by combining experimental work (recordings of multiple unit activity in the cortex of behaving animals) with a computational approach. He is currently the director of the Depart- ment of Physiology and the head of the Ph.D. program at the Interdisciplinary Center for Neural Computation (ICNC) at HUJI, and a director of a European advanced course in computational neuroscience.
Copyright © 2005 CRC Press LLC
Contributors James Ashe
Institute of Cognitive Sciences National Center for Scientific Research
Laboratory for Computational Motor Control
Department of Biomedical Engineering Johns Hopkins School of Medicine Baltimore, Maryland
Eun-Jung Hwang
Laboratory for Computational Motor Control
Department of Biomedical Engineering Johns Hopkins School of Medicine Baltimore, Maryland
Marc Jeannerod
(ISC-CNRS) Bron, France
Medicine Pittsburgh, Pennsylvania
Andreas Kleinschmidt
Cognitive Neurology Unit Department of Neurology Johann Wolfgang Goethe University Frankfurt am Main, Germany
Catherine E. Lang
University of Rochester Department of Neurology Rochester, New York
William A. MacKay
Department of Physiology University of Toronto Toronto, Ontario, Canada
Sarah E. Hemminger
Department of Neurobiology University of Pittsburgh School of
Veterans Affairs Medical Center Brain Sciences Center University of Minnesota Minneapolis, Minnesota
Peter J. Brasted
Emilio Bizzi
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology Cambridge, Massachusetts
Niels Birbaumer
Institute of Medical Psychology and Behavioral Neurobiology
Eberhard-Karls-University of Tübingen Tübingen, Germany
Laboratory of Systems Neuroscience National Institute of Mental Health National Institutes of Health Bethesda, Maryland
Richard P. Dum
Simone Cardoso de Oliveira
German Primate Center Cognitive Neuroscience Laboratory Göttingen, Germany
Jose M. Carmena
Center for Neuroengineering Department of Neurobiology Duke University Medical Center Durham, North Carolina
Opher Donchin
Laboratory for Computational Motor Control
Department of Biomedical Engineering Johns Hopkins School of Medicine Baltimore, Maryland
Copyright © 2005 CRC Press LLC
Ferdinando A. Mussa-Ivaldi
Peter L. Strick
Stephen H. Scott
Centre for Neuroscience Studies Department of Anatomy and Cell
Biology Canadian Institutes of Health Research
Group in Sensory-Motor Systems Queen’s University Kingston, Ontario
Reza Shadmehr
Laboratory for Computational Motor Control
Department of Biomedical Engineering Johns Hopkins School of Medicine Baltimore, Maryland
Veterans Affairs Medical Center for the Neural Basis of Cognition
Marc H. Schieber
Department of Neurobiology University of Pittsburgh Pittsburgh, Pennsylvania
Ivan Toni
F.C. Donders Center for Cognitive Neuroimaging
Nijmegen, The Netherlands
Eilon Vaadia
Department of Physiology Hadassah Medical School The Hebrew University Jerusalem, Israel
Steven P. Wise
Laboratory of Systems Neuroscience National Institute of Mental Health National Institutes of Health Bethesda, Maryland
University of Rochester Department of Neurology Rochester, New York
Marseille, France
Departments of Physiology, Physical Medicine and Rehabilitation, and Biomedical Engineering
Department of Neurobiology Harvard Medical School Boston, Massachusetts
Northwestern University Chicago, Illinois
Christa Neuper
Ludwig Boltzmann Institute of Medical Informatics and Neuroinformatics
Graz University of Technology Graz, Austria
Miguel A.L. Nicolelis
Department of Neurobiology Duke University Medical Center Durham, North Carolina
Camillo Padoa-Schioppa
Gert Pfurtscheller
Natinoal Center for Scientific Research (INCM-CNRS)
Laboratory of Brain–Computer Interfaces
Graz University of Technology Graz, Austria
Ashwini K. Rao
Columbia University Medical Center Program in Physical Therapy Neurological Institute New York, New York
Karen T. Reilly
University of Rochester Department of Neurology Rochester, New York
Alexa Riehle
Mediterranean Institute for Cognitive Neuroscience
Copyright © 2005 CRC Press LLC
Table of Contents
Richard P. Dum and Peter L. Strick
William A. MacKay Copyright © 2005 CRC Press LLC
Gert Pfurtscheller, Christa Neuper, and Niels Birbaumer Copyright © 2005 CRC Press LLC
Functional Neuroanatomy and Imaging
Motor Areas in the
1 Frontal Lobe: The Anatomical Substrate for the Central Control
of Movement
Richard P. Dum and Peter L. StrickCONTENTS
0-8493-1287-6/05/$0.00+$1.50 © 2005 by CRC Press LLC
1.1 INTRODUCTION
The objective of this chapter is to describe the major components of the structural framework employed by the cerebral cortex to generate and control skeletomotor function. We will focus on motor areas in the frontal lobe that are the source of corticospinal projections to the ventral horn of the spinal cord in primates. These cortical areas include the primary motor cortex (M1) and the six premotor areas that project directly to M1. We will begin by examining anatomical and physiological evidence that demonstrates how each of these cortical areas directly accesses spinal cord mechanisms involved in the generation and control of movement. This evidence suggests that all these cortical areas have some direct involvement in movement execution. Then we will examine how the pattern of cortical and subcortical inputs could shape the functional role of each cortical area in motor control. We will show that each of these cortical areas receives a unique pattern of cortical and subcortical input. Taken together, these results have led to an emerging view that motor commands can arise from multiple motor areas and that each of these motor areas makes a specialized contribution to the planning, execution, or control of voluntary movement. In this chapter, we will describe some of the relevant anatomical and physiological evidence that has led to this viewpoint.
Given the breadth of the subject considered here, our review will focus on new perspectives developed from contemporary primate studies. Even with this focus, many topics will receive limited treatment. For instance, the physiological and behavioral studies that provide evidence of differential involvement of each motor area in the generation and control of movement are beyond the scope of this chapter. For further insight into the historical development of this field and a broader coverage
1–11
of related issues, numerous reviews on this and related topics are available. In
12 addition, the corticospinal system has been the subject of a recent book.
1.2 FUNCTIONAL ANATOMY RIMARY OTOR ORTEX
1.2.1 P M C
The primary motor cortex (M1) owes its name to the fact that thresholds for evoking movement with electrical stimulation are lower here than in any other cortical
13–15
region. (For historical review, see Reference 12.) Anatomically, M1 corresponds
SMA
(F3) PE
M1 SI
preSMA (F6) PEc PEci CMAd9m PGm CMAv CMAr
CGp
V6A 23a,b 24a,b
CgS PEc
V6 PE
V6A MIP CS
V6 PEip SEF
PEc
VIP PE prePMd (F7)
LIP PMd
9l (F2) M1
IPS
SI
(F1) AIP FEF PG (F4)IPS 46d PFG
PS 46v PMv (F5) ArS PF PFGop 12l
OFC PFop LS PrCO
SII Ig 1 cm
FIGURE 1.1 Identification of cortical areas in the macaque monkey. The cingulate sulcus(CgS), lateral sulcus (LS), and intraparietal sulcus (IPS) are unfolded and each fundus is indicated by a dashed line. The borders between cytoarchitectonic areas are delineated with dotted lines.
M1 and the premotor areas are shaded. Abbreviations: AIP, LIP, MIP, VIP: anterior, lateral, medial, and ventral intraparietal areas; ArS: arcuate sulcus; CGp: posterior cingulate gyrus; CMAd, CMAv, CMAr: dorsal, ventral, and rostral cingulate motor areas; CS: central sulcus; F1 to F7: cytoarchitectonic areas in the frontal lobe according to Matelli 77,248 et al. ; FEF: frontal eye fields; Ig: granular insular cortex; M1: primary motor cortex;
OFC: orbital frontal cortex; PMd: dorsal premotor area; PMv: ventral premotor area; PrCO: precentral opercular cortex; prePMd: pre-premotor area, dorsal; preSMA: presupplementary motor area; PS: principal sulcus; SEF: supplementary eye field; SI: primary somatosensory cortex; SII: secondary somatosensory cortex; SMA: supplementary motor area; PE, PEc, PEci, 249 PF, PFG, PFop, PG, PGm, Pgop: parietal areas after Pandya and Selzer ; V6A, V6: posterior 177 181 parietal areas after Galletti et al. ; 9m, 9l, 46d, 46v, 12l: prefrontal areas after Walker and 186 Barbas and Pandya.
to cytoarchitectonic area 4, which is identified by the presence of giant pyramidal
16–18
cells in cortical layer V. Based on these definitions, M1 is located in the anterior bank of the central sulcus and on the adjacent caudal portion of the precentral gyrus (Figure 1.1). (For more complete reviews, see References 4,5,9,12.)
1.2.1.1 Organization Based on Intracortical Stimulation
Our view of the organization of M1 as based on electrical stimulation has evolved with advances in stimulation techniques. Classically, surface stimulation suggested that M1 contained a “motor map” that was a single, contiguous representation of
14,15
the body. (For reviews, see References 4 and 12.) In this map, the leg, trunk, arm, and face formed a medial to lateral procession across M1 with the distal musculature of each limb located in the central sulcus. Electrical stimulation with microelectrodes inserted into the cortex lowered the amount of current necessary to
19
evoke movement by a factor of 100. Although this advance allowed a much more detailed exploration of the cortex, intracortical stimulation confirmed the overall
19–32 somatotopy of leg, arm, and face representation described by surface stimulation.
Thus, electrical stimulation of M1 generated a somatotopic motor map with relatively sharp boundaries between major body parts.
The organization of movements generated by intracortical stimulation within each major body part, however, was more complex than that produced by surface A consistent observation was that the same move-
22–32
ment could be evoked at multiple, spatially separate sites. Although this obser- vation precluded an orderly somatotopy, the general features of this map were reproducible. Within the arm representation of macaque monkeys, distal limb move- ments (fingers and wrist) tended to form a central core that was surrounded by a horseshoe of proximal limb movements (elbow and shoulder) (Color Figure
22,33
1.2A). Some intermingling of distal and proximal limb movements occurred at the borders. This organizational structure has been confirmed with single-pulse,
34
stimulus-triggered averaging (Color Figure 1.2B). The presence of multiple repre- sentations of an individual movement/muscle in M1 has been proposed as an arrange- ment that allows a muscle to engage in multiple synergies with other muscles acting at the same or different joints. (See Reference 35.)
20,26,28,32
Other studies utilizing intracortical stimulation reported even more com- plex patterns of muscle activation. For example, stimulation at some sites in M1 evoked reciprocal activation of wrist antagonists, whereas at other sites it caused
26
their co-contraction. Some stimulus locations evoked movements of several joints at barely differing thresholds. Thus, multiple-joint movements could also be evoked by relatively localized stimulation. These more complex relationships may allow “automatic” coordination of postural stabilization of the proximal limb during object manipulation by the distal limb musculature.
More recently, long trains (0.5 to 1.0 sec) of supra-threshold intracortical stim- ulation have been reported to evoke coordinated forelimb movements in the awake
36
primate (Color Figure 1.2C). Each stimulation site produced a stereotyped posture in which the arm moved to the same final position regardless of its posture at the initiation of stimulation. In the most complex example, the monkey formed a frozen pose with the hand in a grasping position in front of the open mouth. The map of final hand location in the workspace in front of the monkey included both M1 and the premotor cortex (Color Figure 1.2C). In many respects, these results were a more
- Please see color insert following page 170.
Medial A
Ce ntra l Sulc us, An terior Bank
Elbow
ElbowWrist
WristShoulder l s
Shoulder ra u d
Elbow st
Digits n Digits + Wrist Digits o u R Digits + Wrist F
Wrist Wrist Wrist Shoulder
Shoulder Area 3a Area 4
Area 6 2 mm
FIGURE 1.2 (see color figure) Intracortical stimulation maps of M1 in macaque monkeys.Note that in each map, hand movements form a central core (red). (A) Summary map of the movements evoked by intracortical stimulation (2–30 µA) in an awake macaque monkey. (Adapted with permission from Reference 22.) (B) Summary map of muscle representation in M1 derived from stimulus-triggered averages of rectified EMG activity (15 µA at 15 Hz) in an awake monkey. Sites that influenced only proximal muscles are indicated by light shading
, those that influenced only distal muscles by dark shading, and those sites that influenced both proximal and distal muscles by intermediate shading. Sites of significant stimulus-triggered averages of rectified EMG activity for the shorthead of biceps (BIS, blue) and extensor digitorum communis (EDC, red) are indicated with size-coded dots (3, 4, 5,
6 S.D. levels above pre-trigger level baseline activity). (Adapted with permission from Ref- erence 34.) (C) Summary of hand and arm postures produced by long train (0.5 sec), high intensity (25–150 µA) intracortical stimulation in M1, the PMd, and the PMv of an awake monkey. Arm sites evoked postures involving the arm but without changes in the configuration of the hand. Hand + arm indicates sites where stimulation evoked postures involving both the hand and arm. Hand to mouth indicates sites that evoked grasp-like movements of the hand which was brought to the mouth. Bimodal/defensive indicates sites where neurons received visual input and stimulation moved the arm into a defensive posture. See text for further explanation. (Adapted with permission from Reference 36.)
37
detailed equivalent of observations made initially by Ferrier who reported that in M1 “long-continued stimulation brings the hand to the mouth, and at the same time the angle of the mouth is retracted and elevated.” The interpretation of these complex movements is limited by the fact that intracortical stimulation primarily activates
38,39
neurons trans-synaptically, and thereby enlarges its sphere of activation. (See also References 40,41.) At the extreme, long stimulus trains and high stimulus intensities open the route for interactions at multiple levels, including local, cortical, subcortical, and spinal. Thus, intracortical stimulation is unable to determine the
Central Sulcus
10
5 B C T Hindlim Trunk ArS
5
F n u d u s
10
CS 2 mm EDC15 Arm BIS Hand + arm Face Bimodal/defensive Distal Distal + Proximal Proximal Hand to Mouth
FIGURE 1.2 (continued)output structure of M1 unambiguously or to ascertain the functional organization of a cortical motor area.
1.2.1.2 Output of Single Corticomotoneuronal Cells
A more focused approach to examining the output structure of M1 has been to determine the axonal branching patterns of single corticospinal neurons. Both phys- iological and anatomical studies provide evidence that single corticospinal neurons may have a rather widespread influence in the spinal cord. A substantial proportion
42 of corticospinal neurons (43%) innervates several segments of the spinal cord.
Reconstruction of individual corticospinal axons filled with an intracellular tracer
43
reveals terminal arbors located in as many as four separate motor nuclei. Thus, a single corticospinal axon can directly influence several muscles.
These anatomical observations are consistent with the results of studies employ- ing the spike-triggered averaging technique to examine the divergence of single
44–49
corticomotoneuronal (CM) cells. (For review see Reference 6.) In this technique, electromyographic (EMG) activity of a sampled muscle was averaged following each action potential of a single CM cell. Averaged muscle activity exhibiting facilitation or suppression at a short latency after the spike was considered to indicate a connection between the CM cell and the muscle’s motoneurons. Most CM cells (71%) produced post-spike effects in two or more muscles (mean = 3.1, maximum
49
10 of 24. Many of the post-spike effects were confined to distal muscles (45%) and some were found in proximal muscles (10%). Remarkably, the remaining 45% of CM neurons produced post-spike effects in both distal and proximal muscles. This result strongly suggests that single CM neurons can influence muscles at both proximal and distal joints.
The size of the branching patterns of individual CM cells appears to be related to the muscles they innervate. CM cells that influence both proximal and distal muscles have wider branching patterns than those that project to either proximal or
49
distal muscles. In addition, half of the CM cells that facilitate intrinsic hand muscles
48
targeted just one of the muscles sampled. These observations suggest that CM cells have more restricted branching to distal muscles than they do to proximal muscles.
50–52
Lemon and colleagues have emphasized, on the basis of electrophysiological data from macaque and squirrel monkeys, that direct CM projections are important
35
for the control of grasp. Although Schieber has argued that restricted branching is not a requirement for producing individuated finger movements, the restricted branching of some CM cells suggests that they may be specialized to control individual finger muscles.
The limited branching patterns of some CM neurons as well as the observation
42,46
that small clusters of CM neurons tend to innervate the same motoneuron pool may explain why intracortical stimulation can evoke contractions of a single muscle
19
at threshold. This raises the possibility that a framework for muscle representation exists at the level of small clusters of neurons. On the other hand, the highly divergent projections of many CM neurons are consistent with some of the more complex, multiple-joint movements observed with other variations of the intracortical stimu-
26,36
lation technique. Thus, adjustment of the parameters of intracortical stimulation may promote access to different structural features of the output organization of M1 as well as other portions of the motor system.
1.2.1.3 Peripheral Input to M1
Another type of map within M1 concerns the responses of its neurons to peripheral somatosensory stimulation. In both New and Old World primates, neurons in the caudal part of the forelimb representation of M1 were activated by peripheral input
25,53–55
predominantly from cutaneous afferents. In contrast, neurons in the rostral part of the M1 forelimb representation were driven by peripheral afferents originating largely from muscles or joints. A similar segregation of peripheral input has been
24
54
observed in the hindlimb representation of M1 in the macaque. Strick and Preston have proposed that the segregation of peripheral inputs within M1 may represent a functional specialization designed to solve tasks demanding high levels of sen- sory–motor integration. For example, the portion of the hand representation in M1 that receives largely cutaneous input may be specialized to control finger coordina- tion during object manipulation. Thus, the internal organization of M1 is quite complicated and may include multiple, overlapping maps of sensory input and motor output.
REMOTOR REAS
1.2.2 P A
The identification and characterization of the premotor cortex has been the subject
2,9,15,56–61
of some controversy and considerable revision over the last century. The term “premotor cortex” was originally applied to the portion of agranular cortex
56,62