On somatotopic representation centers for finger movements in human primary motor cortex and supplementary motor area

Why do different motor cortical areas activate the same muscles?

The cortex contains multiple motor areas, including the primary motor cortex (M1) and supplementary motor area (SMA). Many muscles are represented in both the M1 and SMA, but the reason for this dual representation remains unclear. Previous work has shown that the M1 and SMA representations of a specific human muscle can be differentiated according to their functional connectivity with different brain areas located outside of the motor cortex. It is our perspective that this differential functional connectivity may be the neural substrate that allows an individual muscle to be accessed by distinct neural processes, such as those implementing volitional vs. postural task control. Here, we review existing human and animal literature suggesting how muscles are represented in the M1 and SMA and how these brain regions have distinct functions. We also discuss potential studies to further elucidate the distinct roles of the SMA and M1 in normal and dysfunctional motor control.

Cortico-striatal activity associated with fidget spinner use: an fMRI study

Fidget spinners are said to be a very successful toy, and it's said that it has a good impact on attention for children with ADHD and hand motor control. However, there is limited scientific evidence to support these claims, and there is a lack of data on neurobiological responses to rotating fidget spinners. To better understand the mechanism whereby fidget spinners affect motor behavior, we tried to identify the neural correlates of rotating fidget spinners using functional magnetic resonance imaging and non-magnetic fidget spinners with five types of ease of rotation. As a result, we confirmed that the pre/postcentral gyrus, middle temporal gyrus, supplementary motor area (SMA), cerebellum, and striatum are activated when rotating spinners. Furthermore, the SMA was activated more with easier-to-rotate spinners. Additionally, a psychophysiological interaction analysis revealed increased functional connectivity between the SMA and the caudate while rotating fidget spinners compared to just holding them. These results suggest that the fine motor control associate with spinning a fidget spinner is supported by the cortico-striatal circuits involved in planning and reward.

Chord skill: learning optimized hand postures and bimanual coordination

This reaction time study tested the hypothesis that in the case of finger movements skilled motor control involves the execution of learned hand postures. After delineating hypothetical control mechanisms and their predictions an experiment is described involving 32 participants who practiced 6 chord responses. These responses involved the simultaneous depression of one, two or three keys with either four right-hand fingers or two fingers of both hands. After practicing each of these responses for 240 trials, the participants performed the practiced and also novel chords with the familiar and with the unfamiliar hand configuration of the other practice group. The results suggest that participants learned hand postures rather than spatial or explicit chord representations. Participants practicing with both hands also developed a bimanual coordination skill. Chord execution was most likely slowed by interference between adjacent fingers. This interference seemed eliminated with practice for some chords but not for others. Hence, the results support the notion that skilled control of finger movements is based on learned hand postures that even after practice may be slowed by interference between adjacent fingers.

Morphology, Connectivity, and Encoding Features of Tactile and Motor Representations of the Fingers in the Human Precentral and Postcentral Gyrus

Despite the tight coupling between sensory and motor processing for fine manipulation in humans, it is not yet totally clear which specific properties of the fingers are mapped in the precentral and postcentral gyrus. We used fMRI to compare the morphology, connectivity, and encoding of the motor and tactile finger representations (FRs) in the precentral and postcentral gyrus of 25 5-fingered participants (8 females). Multivoxel pattern and structural and functional connectivity analyses demonstrated the existence of distinct motor and tactile FRs within both the precentral and postcentral gyrus, integrating finger-specific motor and tactile information. Using representational similarity analysis, we found that the motor and tactile FRs in the sensorimotor cortex were described by the perceived structure of the hand better than by the actual hand anatomy or other functional models (finger kinematics, muscles synergies). We then studied a polydactyly individual (i.e., with a congenital 6-fingered hand) showing superior manipulation abilities and divergent anatomic-functional hand properties. The perceived hand model was still the best model for tactile representations in the precentral and postcentral gyrus, while finger kinematics better described motor representations in the precentral gyrus. We suggest that, under normal conditions (i.e., in subjects with a standard hand anatomy), the sensorimotor representations of the 5 fingers in humans converge toward a model of perceived hand anatomy, deviating from the real hand structure, as the best synthesis between functional and structural features of the hand.SIGNIFICANCE STATEMENT Distinct motor and tactile finger representations exist in both the precentral and postcentral gyrus, supported by a finger-specific pattern of anatomic and functional connectivity across modalities. At the representational level, finger representations reflect the perceived structure of the hand, which might result from an adapting process harmonizing (i.e., uniformizing) the encoding of hand function and structure in the precentral and postcentral gyrus. The same analyses performed in an extremely rare polydactyly subject showed that the emergence of such representational geometry is also found in neuromechanical variants with different hand anatomy and function. However, the harmonization process across the precentral and postcentral gyrus might not be possible because of divergent functional-structural properties of the hand and associated superior manipulation abilities.

More comprehensive proprioceptive stimulation of the hand amplifies its cortical processing

Corticokinematic coherence (CKC) quantifies the phase coupling between limb kinematics and cortical neurophysiological signals reflecting proprioceptive feedback to the primary sensorimotor (SM1) cortex. We studied whether the CKC strength or cortical source location differs between proprioceptive stimulation (i.e., actuator-evoked movements) of right-hand digits (index, middle, ring, and little). Twenty-one volunteers participated in magnetoencephalography measurements during which three conditions were tested: 1) simultaneous stimulation of all four fingers at the same frequency, 2) stimulation of each finger separately at the same frequency, and 3) simultaneous stimulation of the fingers at finger-specific frequencies. CKC was computed between MEG responses and accelerations of the fingers recorded with three-axis accelerometers. CKC was stronger (P < 0.003) for the simultaneous (0.52 ± 0.02) than separate (0.45 ± 0.02) stimulation at the same frequency. Furthermore, CKC was weaker (P < 0.03) for the simultaneous stimulation at the finger-specific frequencies (0.38 ± 0.02) than for the separate stimulation. CKC source locations of the fingers were concentrated in the hand region of the SM1 cortex and did not follow consistent finger-specific somatotopic order. Our results indicate that proprioceptive afference from the fingers is processed in partly overlapping cortical neuronal circuits, which was demonstrated by the modulation of the finger-specific CKC strengths due to proprioceptive afference arising from simultaneous stimulation of the other fingers of the same hand as well as overlapping cortical source locations. Finally, comprehensive simultaneous proprioceptive stimulation of the hand would optimize functional cortical mapping to pinpoint the hand region, e.g., prior brain surgery.

NEW & NOTEWORTHY Corticokinematic coherence (CKC) can be used to study cortical proprioceptive processing and localize proprioceptive hand representation. Our results indicate that proprioceptive stimulation delivered simultaneously at the same frequency to fingers (D2–D4) maximizes CKC strength allowing robust and fast localization of the human hand region in the sensorimotor cortex using MEG.

Tactile perception of right middle fingertip suppresses excitability of motor cortex supplying right first dorsal interosseous muscle

The present study examined whether tactile perception of the fingertip modulates excitability of the motor cortex supplying the intrinsic hand muscle and whether this modulation is specific to the fingertip stimulated and the muscle and hand tested. Tactile stimulation was given to one of the five fingertips in the left or right hand, and transcranial magnetic stimulation eliciting motor evoked potential in the first dorsal interosseous muscle (FDI) or abductor digiti minimi was given 200 ms after the onset of tactile stimulation. The corticospinal excitability of the FDI at rest was suppressed by the tactile stimulation of the right middle fingertip, but such suppression was absent for the other fingers stimulated and for the other muscle or hand tested. The persistence and amplitude of the F-wave was not significantly influenced by tactile stimulation of the fingertip in the right hand. These findings indicate that tactile perception of the right middle fingertip suppresses excitability of the motor cortex supplying the right FDI at rest. The suppression of corticospinal excitability was absent during tonic contraction of the right FDI, indicating that the motor execution process interrupts the tactile perception-induced suppression of motor cortical excitability supplying the right FDI. These findings are in line with a view that the tactile perception of the right middle finger induces surround inhibition of the motor cortex supplying the prime mover of the finger neighboring the stimulated finger.

Forearm and Hand Muscles Exhibit High Coactivation and Overlapping of Cortical Motor Representations

Most of the motor mapping procedures using navigated transcranial magnetic stimulation (nTMS) follow the conventional somatotopic organization of the primary motor cortex (M1) by assessing the representation of a particular target muscle, disregarding the possible coactivation of synergistic muscles. In turn, multiple reports describe a functional organization of the M1 with an overlapping among motor representations acting together to execute movements. In this context, the overlap degree among cortical representations of synergistic hand and forearm muscles remains an open question. This study aimed to evaluate the muscle coactivation and representation overlapping common to the grasping movement and its dependence on the stimulation parameters. The nTMS motor maps were obtained from one carpal muscle and two intrinsic hand muscles during rest. We quantified the overlapping motor maps in size (area and volume overlap degree) and topography (similarity and centroid Euclidean distance) parameters. We demonstrated that these muscle representations are highly overlapped and similar in shape. The overlap degrees involving the forearm muscle were significantly higher than only among the intrinsic hand muscles. Moreover, the stimulation intensity had a stronger effect on the size compared to the topography parameters. Our study contributes to a more detailed cortical motor representation towards a synergistic, functional arrangement of M1. Understanding the muscle group coactivation may provide more accurate motor maps when delineating the eloquent brain tissue during pre-surgical planning.

Representational Similarity Scores of Digits in the Sensorimotor Cortex Are Associated with Behavioral Performance

Previous studies aimed to unravel a digit-specific somatotopy in the primary sensorimotor (SM1) cortex. However, it remains unknown whether digit somatotopy is associated with motor preparation and/or motor execution during different types of tasks. We adopted multivariate representational similarity analysis to explore digit activation patterns in response to a finger tapping task (FTT). Sixteen healthy young adults underwent magnetic resonance imaging, and additionally performed an out-of-scanner choice reaction time task (CRTT) to assess digit selection performance. During both the FTT and CRTT, force data of all digits were acquired using force transducers. This allowed us to assess execution-related interference (i.e., digit enslavement; obtained from FTT & CRTT), as well as planning-related interference (i.e., digit selection deficit; obtained from CRTT) and determine their correlation with digit representational similarity scores of SM1. Findings revealed that digit enslavement during FTT was associated with contralateral SM1 representational similarity scores. During the CRTT, digit enslavement of both hands was also associated with representational similarity scores of the contralateral SM1. In addition, right hand digit selection performance was associated with representational similarity scores of left S1. In conclusion, we demonstrate a cortical origin of digit enslavement, and uniquely reveal that digit selection is associated with digit representations in primary somatosensory cortex (S1). Significance statement In current systems neuroscience, it is of critical importance to understand the relationship between brain function and behavioral outcome. With the present work, we contribute significantly to this understanding by uniquely assessing how digit representations in the sensorimotor cortex are associated with planning- and execution-related digit interference during a continuous finger tapping and a choice reaction time task. We observe that digit enslavement (i.e., execution-related interference) finds its origin in contralateral digit representations of SM1, and that deficits in digit selection (i.e., planning-related interference) in the right hand during a choice reaction time task are associated with more overlapping digit representations in left S1. This knowledge sheds new light on the functional contribution of the sensorimotor cortex to everyday motor skills.

Compartmentalized dynamics within a common multi-area mesoscale manifold represent a repertoire of human hand movements

The human hand is unique in the animal kingdom for unparalleled dexterity, ranging from complex prehension to fine finger individuation. How does the brain represent such a diverse repertoire of movements? We evaluated mesoscale neural dynamics across the human "grasp network," using electrocorticography and dimensionality reduction methods, for a repertoire of hand movements. Strikingly, we found that the grasp network represented both finger and grasping movements alike. Specifically, the manifold characterizing the multi-areal neural covariance structure was preserved during all movements across this distributed network. In contrast, latent neural dynamics within this manifold were surprisingly specific to movement type. Aligning latent activity to kinematics further uncovered distinct submanifolds despite similarities in synergistic coupling of joints between movements. We thus find that despite preserved neural covariance at the distributed network level, mesoscale dynamics are compartmentalized into movement-specific submanifolds; this mesoscale organization may allow flexible switching between a repertoire of hand movements.

Action Observation Responses Are Influenced by Movement Kinematics and Target Identity

In order to inform the debate whether cortical areas related to action observation provide a pragmatic or a semantic representation of goal-directed actions, we performed 2 functional magnetic resonance imaging (fMRI) experiments in humans. The first experiment, involving observation of aimless arm movements, resulted in activation of most of the components known to support action execution and action observation. Given the absence of a target/goal in this experiment and the activation of parieto-premotor cortical areas, which were associated in the past with direction, amplitude, and velocity of movement of biological effectors, our findings suggest that during action observation we could be monitoring movement kinematics. With the second, double dissociation fMRI experiment, we revealed the components of the observation-related cortical network affected by 1) actions that have the same target/goal but different reaching and grasping kinematics and 2) actions that have very similar kinematics but different targets/goals. We found that certain areas related to action observation, including the mirror neuron ones, are informed about movement kinematics and/or target identity, hence providing a pragmatic rather than a semantic representation of goal-directed actions. Overall, our findings support a process-driven simulation-like mechanism of action understanding, in agreement with the theory of motor cognition, and question motor theories of action concept processing.

Precise finger somatotopy revealed by focal motor cortex injury

BACKGROUND

Somatotopy is considered the hallmark of the primary motor cortex. While this is fundamentally true for the major body parts (head, upper and lower extremities), evidence supporting the existence of within-limb somatotopy is scarce.

METHOD

We report a young man presenting recurrent ischemic strokes with selective finger weakness in whom serial motor cortex mapping procedures were performed.

RESULT

Following the first stroke, which largely spared the motor cortex, motor mapping displayed overlap of the motor representations of the hand muscles. The second focal stroke, affecting the lateral part of the hand knob, resulted in selective loss of the first dorsal interosseous muscle motor evoked potentials while sparing those of the adductor digiti minimi muscle. This observation is in apparent contradiction with the first mapping results that suggested complete overlap of motor representations.

DISCUSSION

Our mapping results provide evidence for the existence of very precise within-limb somatotopy and confirm the proposed homuncular order, whereby lateral fingers are represented laterally and medial fingers medially. The discrepancy between the initial and subsequent mapping results is discussed in light of functional organization of the primary motor cortex.

Assessment of Somatosensory Reorganization by Functional Magnetic Resonance Imaging After Hand Replantation

INTRODUCTION

Amputation of the hand is a rare and extremely intense trauma. Replanting and allografting after this type of injury require a major reorganization of the brain. Brain plasticity, though better known in the context of disorders of the central nervous system, is just as indispensable when the extremities are damaged.

MATERIALS AND METHODS

A 17-year-old patient underwent replantation of the nondominant hand after transmetaphyseal amputation after traumatic injury. After 18 days in hospital and subsequent treatment in a physical rehabilitation center, the patient attended clinical and radiology follow-up sessions over the next 2 years.

RESULTS

The management of this patient led to an excellent functional outcome in conjunction with successful social and professional reintegration. Electromyography at 18 months confirmed nerve regrowth. Functional magnetic resonance imaging was done at 2 years to evaluate cerebral plasticity. Motor function, largely dependent on the primary motor area, is aided by the addition of secondary and accessory motor areas for both simple and complex movements. A change in sensory information is stimulation in its own right hemisphere and increases solicitation of the contralateral precentral and postcentral gyrus.

CONCLUSIONS

There seems to be a real reversible dynamic plasticity under the balance of inhibitory and excitatory influences exerted on the cortical neurons. Any disruption of this balance requires the brain to adapt to the new circumstances to reestablish the hand as a functioning part of the body.

Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain-Computer Interface perspective

For severely paralyzed people, Brain-Computer Interfaces (BCIs) can potentially replace lost motor output and provide a brain-based control signal for augmentative and alternative communication devices or neuroprosthetics. Many BCIs focus on neuronal signals acquired from the hand area of the sensorimotor cortex, employing changes in the patterns of neuronal firing or spectral power associated with one or more types of hand movement. Hand and finger movement can be described by two groups of movement features, namely kinematics (spatial and motion aspects) and kinetics (muscles and forces). Despite extensive primate and human research, it is not fully understood how these features are represented in the SMC and how they lead to the appropriate movement. Yet, the available information may provide insight into which features are most suitable for BCI control. To that purpose, the current paper provides an in-depth review on the movement features encoded in the SMC. Even though there is no consensus on how exactly the SMC generates movement, we conclude that some parameters are well represented in the SMC and can be accurately used for BCI control with discrete as well as continuous feedback. However, the vast evidence also suggests that movement should be interpreted as a combination of multiple parameters rather than isolated ones, pleading for further exploration of sensorimotor control models for accurate BCI control.

Spatial Resolution and Imaging Encoding fMRI Settings for Optimal Cortical and Subcortical Motor Somatotopy in the Human Brain

There is much controversy about the optimal trade-off between blood-oxygen-level-dependent (BOLD) sensitivity and spatial precision in experiments on brain’s topology properties using functional magnetic resonance imaging (fMRI). The sparse empirical evidence and regional specificity of these interactions pose a practical burden for the choice of imaging protocol parameters. Here, we test in a motor somatotopy experiment the impact of fMRI spatial resolution on differentiation between body part representations in cortex and subcortical structures. Motor somatotopy patterns were obtained in a block-design paradigm and visually cued movements of face, upper and lower limbs at 1.5, 2, and 3 mm spatial resolution. The degree of segregation of the body parts’ spatial representations was estimated using a pattern component model. In cortical areas, we observed the same level of segregation between somatotopy maps across all three resolutions. In subcortical areas the degree of effective similarity between spatial representations was significantly impacted by the image resolution. The 1.5 mm 3D EPI and 3 mm 2D EPI protocols led to higher segregation between motor representations compared to the 2 mm 3D EPI protocol. This finding could not be attributed to differential BOLD sensitivity or delineation of functional areas alone and suggests a crucial role of the image encoding scheme – i.e., 2D vs. 3D EPI. Our study contributes to the field by providing empirical evidence about the impact of acquisition protocols for the delineation of somatotopic areas in cortical and sub-cortical brain regions.

Psycho-Neurobiology and Its Potential Influence on Psychotherapy: Being, Doing, and the Risk of Scientism

Neuroscientific information may transform the modern practice of psychotherapy. Still we must pay heed to the most salient of the common factors generating therapeutic change: the relationship between patient and therapist. Likewise, brain and body are both part of mind and we ignore this at our clinical peril. Research on affective, cognitive, mnemic, somatic, psychophysiologic, developmental, and integrative mental processes, amongst others, must hold to a high standard of translation from basic scientific findings or we risk practicing a psychotherapy enslaved to an authoritarian scientism as a substitute for the creation of unfettered intimacy and engagement. A balanced approach is required if in trauma treatment, for example, we are to be both potential beneficiaries of understanding what is in our human heads while not losing track of our very human hearts. Each clinician need develop a basic knowledge of neuroscience in order to critically assess the meanings of new findings and their proper place in the practice of all the psychotherapies.

Ipsilateral finger representations in the sensorimotor cortex are driven by active movement processes, not passive sensory input

Hand and finger movements are mostly controlled through crossed corticospinal projections from the contralateral hemisphere. During unimanual movements, activity in the contralateral hemisphere is increased while the ipsilateral hemisphere is suppressed below resting baseline. Despite this suppression, unimanual movements can be decoded from ipsilateral activity alone. This indicates that ipsilateral activity patterns represent parameters of ongoing movement, but the origin and functional relevance of these representations is unclear. In this study, we asked whether ipsilateral representations are caused by active movement or whether they are driven by sensory input. Participants alternated between performing single finger presses and having fingers passively stimulated while we recorded brain activity using high-field (7T) functional imaging. We contrasted active and passive finger representations in sensorimotor areas of ipsilateral and contralateral hemispheres. Finger representations in the contralateral hemisphere were equally strong under passive and active conditions, highlighting the importance of sensory information in feedback control. In contrast, ipsilateral finger representations in the sensorimotor cortex were stronger during active presses. Furthermore, the spatial distribution of finger representations differed between hemispheres: the contralateral hemisphere showed the strongest finger representations in Brodmann areas 3a and 3b, whereas the ipsilateral hemisphere exhibited stronger representations in premotor and parietal areas. Altogether, our results suggest that finger representations in the two hemispheres have different origins: contralateral representations are driven by both active movement and sensory stimulation, whereas ipsilateral representations are mainly engaged during active movement. NEW & NOTEWORTHY Movements of the human body are mostly controlled by contralateral cortical regions. The function of ipsilateral activity during movements remains elusive. Using high-field neuroimaging, we investigated how human contralateral and ipsilateral hemispheres represent active and passive finger presses. We found that representations in contralateral sensorimotor cortex are equally strong during both conditions. Ipsilateral representations were mostly present during active movement, suggesting that sensorimotor areas do not receive direct sensory input from the ipsilateral hand.

How does human motor cortex regulate vocal pitch in singers?

Vocal pitch is used as an important communicative device by humans, as found in the melodic dimension of both speech and song. Vocal pitch is determined by the degree of tension in the vocal folds of the larynx, which itself is influenced by complex and nonlinear interactions among the laryngeal muscles. The relationship between these muscles and vocal pitch has been described by a mathematical model in the form of a set of 'control rules'. We searched for the biological implementation of these control rules in the larynx motor cortex of the human brain. We scanned choral singers with functional magnetic resonance imaging as they produced discrete pitches at four different levels across their vocal range. While the locations of the larynx motor activations varied across singers, the activation peaks for the four pitch levels were highly consistent within each individual singer. This result was corroborated using multi-voxel pattern analysis, which demonstrated an absence of patterned activations differentiating any pairing of pitch levels. The complex and nonlinear relationships between the multiple laryngeal muscles that control vocal pitch may obscure the neural encoding of vocal pitch in the brain.

Musical training induces functional and structural auditory-motor network plasticity in young adults

Playing music requires a strong coupling of perception and action mediated by multimodal integration of brain regions, which can be described as network connections measured by anatomical and functional correlations between regions. However, the structural and functional connectivities within and between the auditory and sensorimotor networks after long-term musical training remain largely uninvestigated. Here, we compared the structural connectivity (SC) and resting-state functional connectivity (rs-FC) within and between the two networks in 29 novice healthy young adults before and after musical training (piano) with those of another 27 novice participants who were evaluated longitudinally but with no intervention. In addition, a correlation analysis was performed between the changes in FC or SC with practice time in the training group. As expected, participants in the training group showed increased FC within the sensorimotor network and increased FC and SC of the auditory-motor network after musical training. Interestingly, we further found that the changes in FC within the sensorimotor network and SC of the auditory-motor network were positively correlated with practice time. Our results indicate that musical training could induce enhanced local interaction and global integration between musical performance-related regions, which provides insights into the mechanism of brain plasticity in young adults.

Modulation of Functional Connectivity in Auditory-Motor Networks in Musicians Compared with Nonmusicians

Correlation of spontaneous fluctuations at rest between anatomically distinct brain areas are proposed to reflect the profile of individual a priori cognitive biases, coded as synaptic efficacies in cortical networks. Here, we investigate functional connectivity at rest (rs-FC) in musicians and nonmusicians to test for differences in auditory, motor, and audiomotor connectivity. As expected, musicians had stronger rs-FC between the right auditory cortex (AC) and the right ventral premotor cortex than nonmusicians, and this stronger rs-FC was greater in musicians with more years of practice. We also found reduced rs-FC between the motor areas that control both hands in musicians compared with nonmusicians, which was more evident in the musicians whose instrument required bimanual coordination and as a function of hours of practice. Finally, we replicated previous morphometric data to show an increased volume in the right AC in musicians, which was greater in those with earlier musical training, and that this anatomic feature was in turn related to greater rs-FC between auditory and motor systems. These results show that functional coupling within the motor system and between motor and auditory areas is modulated as a function of musical training, suggesting a link between anatomic and functional brain features.

Structure, function, and control of the human musculoskeletal network

The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle's role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments.

The Role of Human Primary Motor Cortex in the Production of Skilled Finger Sequences

Human primary motor cortex (M1) is essential for producing dexterous hand movements. Although distinct subpopulations of neurons are activated during single-finger movements, it remains unknown whether M1 also represents sequences of multiple finger movements. Using novel multivariate functional magnetic resonance imaging (fMRI) analysis techniques and combining evidence from both 3T and 7T fMRI data, we found that after 5 d of intense practice, premotor and parietal areas encoded the different movement sequences. There was little or no evidence for a sequence representation in M1. Instead, activity patterns in M1 could be fully explained by a linear combination of patterns for the constituent individual finger movements, with the strongest weight on the first finger of the sequence. Using passive replay of sequences, we show that this first-finger effect is due to neuronal processes involved in the active execution, rather than to a hemodynamic nonlinearity. These results suggest that M1 receives increased input from areas with sequence representations at the initiation of a sequence, but that M1 activity itself relates to the execution of component finger presses only. These results improve our understanding of the representation of finger sequences in the human neocortex after short-term training and provide important methodological advances for the study of long-term skill development.SIGNIFICANCE STATEMENT There is clear evidence that human primary motor cortex (M1) is essential for producing individuated finger movements, such as pressing a button. Over and above its involvement in movement execution, it is less clear whether M1 also plays a role in learning and controlling sequences of multiple finger movements, such as when playing the piano. Using cutting-edge multivariate fMRI analysis and carefully controlled experiments, we demonstrate here that, while premotor areas clearly show a sequence representation, activity patterns in M1 can be fully explained from the patterns for individual finger movements. The results provide important new insights into the interplay of M1 and premotor cortex for learning of sequential movements.

Novel imaging techniques to study the functional organization of the human brain

Despite more than a century of investigation into the cortical organization of motor function, the existence of motor somatotopy is still debated. We review functional magnetic resonance imaging (fMRI) studies examining motor somatotopy in the cerebral cortex. In spite of a substantial overlap of representations corresponding to different body parts, especially in non-primary motor cortices, geographic approaches are capable of revealing somatotopic ordering. From the iconic homunculus in the contralateral primary cortex to the subtleties of ipsilateral somatotopy and its relations with lateralization, we outline potential reasons for the lack of segregation between motor representations. Among these are the difficulties in distinguishing activity that arises from multiple muscular effectors, the need for flexible motor control and coordination of complex movements through functional integration and artefacts in fMRI. Methodological advances with regard to the optimization of experimental design and fMRI acquisition protocols as well as improvements in spatial registration of images and indices aiming at the quantification of the degree of segregation between different functional representations are inspected. Additionally, we give some hints as to how the functional organization of motor function might be related to various anatomical landmarks in brain morphometry.

Differentiation of motor evoked potentials elicited from multiple forearm muscles: An investigation with high-density surface electromyography

Transcranial magnetic stimulation (TMS) is a non-invasive method to measure corticospinal excitability of the primary motor cortex. However, motor evoked potentials (MEPs) elicited by TMS in a target muscle are variable; inconsistent MEPs may be due to overlapping cortical muscle representations and/or volume conduction from neighbouring muscles. The source of variable muscle responses may not be apparent using conventional bipolar electromyography (EMG), particularly over areas with several distinct neighbouring muscles (e.g. the forearm). High-density surface EMG (HDsEMG) may provide a useful means to investigate the underlying variability in amplitude and spatial distribution of MEPs. Here, we investigated the spatial distribution of MEPs in the forearm extensors using HDsEMG. HDsEMG consisted of a 16×5 grid of surface electrodes placed on the right (dominant) dorsal forearm over the extensor carpi radialis (ECR), ulnaris (ECU) and extensor digitorum communis finger extensors (EDC). MEP amplitude and distribution were recorded from 100 to 170% of resting (RMT) and active motor threshold (AMT). The distribution of MEPs was correlated to the activity recorded during selective, isometric contractions of the ECR, ECU, middle (EDC-D3) and ring (EDC-D4) finger extensors to determine the spatial distribution of MEPs in the forearm extensors. Although ECR was the hotspot, resting MEP spatial distribution was primarily correlated to that of EDC-D4 and ECU. With background ECR activation, the spatial distribution of MEPs correlated strongly with ECR. Further, while holding a background ECR contraction, EDC-D4 and ECU MEPs increased with greater stimulation intensity. Our results suggest that HDsEMG provides a useful way to differentiate which wrist extensor muscles are activated by TMS.

The effect of current flow direction on motor hot spot allocation by transcranial magnetic stimulation

The objective of this study was to investigate the significance of pulse configurations and current direction for corticospinal activation using transcranial magnetic stimulation (TMS). In 11 healthy subjects (8 female), a motor map for the motor evoked potentials (MEPs) recorded from the first dorsal interosseus (FDI), abductor digiti minimi (ADM), extensor carpi radialis, and biceps brachii (BB) muscles of the dominant side was established. Starting from a manually determined hot spot of the FDI representation, we measured MEPs at equal oriented points on an hexagonal grid, with 7 MEPs recorded at each point, using the following pulse configurations: posteriorly directed monophasic (Mo-P), anteriorly directed monophasic (Mo-A), biphasic with the more relevant second cycle oriented posteriorly (Bi-P) as well as a reversed biphasic condition (Bi-A). For each pulse configuration, a hot spot was determined and a center of gravity (CoG) was calculated. We found that the factor current direction had an effect on location of the CoG-adjusted hot spot in the cranio-caudal axis but not in the latero-medial direction with anteriorly directed pulses locating the CoG more anteriorly and vice versa. In addition, the CoG for the FDI was more laterally than the cortical representations for the abductor digiti minimi (ADM) and extensor carpi radialis (ECR) which were registered as well. The results indicate that direction of the current pulse should be taken into account for determination of the motor representation of a muscle by TMS.

A synergy-based hand control is encoded in human motor cortical areas

How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses.

DOI:http://dx.doi.org/10.7554/eLife.13420.001

The human hand can perform an enormous range of movements with great dexterity. Some common everyday actions, such as grasping a coffee cup, involve the coordinated movement of all four fingers and thumb. Others, such as typing, rely on the ability of individual fingers to move relatively independently of one another.

This flexibility is possible in part because of the complex anatomy of the hand, with its 27 bones and their connecting joints and muscles. But with this complexity comes a huge number of possibilities. Any movement-related task – such as picking up a cup – can be achieved via many different combinations of muscle contractions and joint positions. So how does the brain decide which muscles and joints to use?

One theory is that the brain simplifies this problem by encoding particularly useful patterns of joint movements as distinct units or “synergies”. A given task can then be performed by selecting from a small number of synergies, avoiding the need to choose between huge numbers of options every time movement is required.

Leo et al. now provide the first direct evidence for the encoding of synergies by the human brain. Volunteers lying inside a brain scanner reached towards virtual objects – from tennis rackets to toothpicks – while activity was recorded from the area of the brain that controls hand movements. As predicted, the scans showed specific and reproducible patterns of activity. Analysing these patterns revealed that each corresponded to a particular combination of joint positions. These activity patterns, or synergies, could even be ‘decoded’ to work out which type of movement a volunteer had just performed.

Future experiments should examine how the brain combines synergies with sensory feedback to allow movements to be adjusted as they occur. Such findings could help to develop brain-computer interfaces and systems for controlling the movement of artificial limbs.

DOI:http://dx.doi.org/10.7554/eLife.13420.002

Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.

Classification of finger vibrotactile input using scalp EEG

While there are many output brain-computer interface (output BCIs) studies, few have examined the input pathway, namely decoding the sensory input. To examine the possibility of building a BCI with sensory input using scalp electroencephalography (EEG), this study builds a classifier based on Local Fisher Discriminant Analysis (LFDA) and Gaussian Mixture Model (GMM) to classify neural activity generated by vibrotactile sensory stimuli delivered to the fingers. Small vibrators were placed on the fingertips of the participant. They vibrated one by one in a random sequence while the participant sat still with eyes closed. EEG data were recorded and later used to classify which finger was vibrated. There were two tasks: one focusing on differentiating between ipsilateral fingers, the other one focusing on differentiating contralateral fingers. Decoding accuracies were high in both tasks: 97.6% and 99.3% respectively. Event-related EEG features in both amplitude and power domain are discussed.

Neuronal correlates of voluntary facial movements

Whereas the somatotopy of finger movements has been extensively studied with neuroimaging, the neural foundations of facial movements remain elusive. Therefore, we systematically studied the neuronal correlates of voluntary facial movements using the Facial Action Coding System (FACS, Ekman et al., 2002). The facial movements performed in the MRI scanner were defined as Action Units (AUs) and were controlled by a certified FACS coder. The main goal of the study was to investigate the detailed somatotopy of the facial primary motor area (facial M1). Eighteen participants were asked to produce the following four facial movements in the fMRI scanner: AU1+2 (brow raiser), AU4 (brow lowerer), AU12 (lip corner puller) and AU24 (lip presser), each in alternation with a resting phase. Our facial movement task induced generally high activation in brain motor areas (e.g., M1, premotor cortex, supplementary motor area, putamen), as well as in the thalamus, insula, and visual cortex. BOLD activations revealed overlapping representations for the four facial movements. However, within the activated facial M1 areas, we could find distinct peak activities in the left and right hemisphere supporting a rough somatotopic upper to lower face organization within the right facial M1 area, and a somatotopic organization within the right M1 upper face part. In both hemispheres, the order was an inverse somatotopy within the lower face representations. In contrast to the right hemisphere, in the left hemisphere the representation of AU4 was more lateral and anterior compared to the rest of the facial movements. Our findings support the notion of a partial somatotopic order within the M1 face area confirming the “like attracts like” principle (Donoghue et al., 1992). AUs which are often used together or are similar are located close to each other in the motor cortex.

Disrupting the supplementary motor area makes physical effort appear less effortful

The perception of physical effort is relatively unaffected by the suppression of sensory afferences, indicating that this function relies mostly on the processing of the central motor command. Neural signals in the supplementary motor area (SMA) correlate with the intensity of effort, suggesting that the motor signal involved in effort perception could originate from this area, but experimental evidence supporting this view is still lacking. Here, we tested this hypothesis by disrupting neural activity in SMA, in primary motor cortex (M1), or in a control site by means of continuous theta-burst transcranial magnetic stimulation, while measuring effort perception during grip forces of different intensities. After each grip force exertion, participants had the opportunity to either accept or refuse to replicate the same effort for varying amounts of reward. In addition to the subjective rating of perceived exertion, effort perception was estimated on the basis of the acceptance rate, the effort replication accuracy, the influence of the effort exerted in trial t on trial t+1, and pupil dilation. We found that disruption of SMA activity, but not of M1, led to a consistent decrease in effort perception, whatever the measure used to assess it. Accordingly, we modeled effort perception in a structural equation model and found that only SMA disruption led to a significant alteration of effort perception. These findings indicate that effort perception relies on the processing of a signal originating from motor-related neural circuits upstream of M1 and that SMA is a key node of this network.

Motor Cortex Activity During Functional Motor Skills: An fNIRS Study

Assessments of brain activity during motor task performance have been limited to fine motor movements due to technological constraints presented by traditional neuroimaging techniques, such as functional magnetic resonance imaging. Functional near-infrared spectroscopy (fNIRS) offers a promising method by which to overcome these constraints and investigate motor performance of functional motor tasks. The current study used fNIRS to quantify hemodynamic responses within the primary motor cortex in twelve healthy adults as they performed unimanual right, unimanual left, and bimanual reaching, and stepping in place. Results revealed that during both unimanual reaching tasks, the contralateral hemisphere showed significant activation in channels located approximately 3 cm medial to the C3 (for right-hand reach) and C4 (for left-hand reach) landmarks. Bimanual reaching and stepping showed activation in similar channels, which were located bilaterally across the primary motor cortex. The medial channels, surrounding Cz, showed significantly higher activations during stepping when compared to bimanual reaching. Our results extend the viability of fNIRS to study motor function and build a foundation for future investigation of motor development in infants during nascent functional behaviors and monitor how they may change with age or practice.

Hand use predicts the structure of representations in sensorimotor cortex

Fine finger movements are controlled by the population activity of neurons in the hand area of primary motor cortex. Experiments using microstimulation and single-neuron electrophysiology suggest that this area represents coordinated multi-joint, rather than single-finger movements. However, the principle by which these representations are organized remains unclear. We analyzed activity patterns during individuated finger movements using functional magnetic resonance imaging (fMRI). Although the spatial layout of finger-specific activity patterns was variable across participants, the relative similarity between any pair of activity patterns was well preserved. This invariant organization was better explained by the correlation structure of everyday hand movements than by correlated muscle activity. This also generalized to an experiment using complex multi-finger movements. Finally, the organizational structure correlated with patterns of involuntary co-contracted finger movements for high-force presses. Together, our results suggest that hand use shapes the relative arrangement of finger-specific activity patterns in sensory-motor cortex.

[The motor organization of cerebral cortex and the role of the mirror neuron system. Clinical impact for rehabilitation]

The basic characteristics of Penfield homunculus (somatotopy and unique representation) have been questioned. The existence of a defined anatomo-functional organization within different segments of the same region is controversial. The presence of multiple motor representations in the primary motor area and in the parietal lobe interconnected by parieto-frontal circuits, which are widely overlapped, form a complex organization. Both features support the recovery of functions after brain injury. Regarding the movement organization, it is possible to yield a relevant impact through the understanding of actions and intentions of others, which is mediated by the activation of mirror-neuron systems. The implementation of cognitive functions (observation, image of the action and imitation) from the acute treatment phase allows the activation of motor representations without having to perform the action and it plays an important role in learning motor patterns.

Evidence for a role for the dorsal anterior cingulate cortex in disengaging from an incorrect action

Adjusting for an error requires both disengaging from the wrong course of action and initiating a corrective response. The dorsal anterior cingulate cortex (dACC) has been implicated in both these processes in the decision-making and action monitoring literatures. Here, we aimed to distinguish between these putative functions with a variant of the Eriksen flanker task that manipulated response requirements (i.e. one or two finger responses). We found that two event-related potentials originating from the dACC (error-related negativity (ERN) and anterior N2) only reflected the representation of the incorrect response: these waveforms were larger when the incorrect response involved two fingers rather than one finger. The increase in ERN magnitude was also accompanied by a reduction in spontaneous error corrections. These results argue that activity in the dACC reflects a process involved in disengaging from an ongoing incorrect action, clearing the way for the correct response.

Decoding the individual finger movements from single-trial functional magnetic resonance imaging recordings of human brain activity

Multivariate pattern classification analysis (MVPA) has been applied to functional magnetic resonance imaging (fMRI) data to decode brain states from spatially distributed activation patterns. Decoding upper limb movements from non-invasively recorded human brain activation is crucial for implementing a brain-machine interface that directly harnesses an individual's thoughts to control external devices or computers. The aim of this study was to decode the individual finger movements from fMRI single-trial data. Thirteen healthy human subjects participated in a visually cued delayed finger movement task, and only one slight button press was performed in each trial. Using MVPA, the decoding accuracy (DA) was computed separately for the different motor-related regions of interest. For the construction of feature vectors, the feature vectors from two successive volumes in the image series for a trial were concatenated. With these spatial-temporal feature vectors, we obtained a 63.1% average DA (84.7% for the best subject) for the contralateral primary somatosensory cortex and a 46.0% average DA (71.0% for the best subject) for the contralateral primary motor cortex; both of these values were significantly above the chance level (20%). In addition, we implemented searchlight MVPA to search for informative regions in an unbiased manner across the whole brain. Furthermore, by applying searchlight MVPA to each volume of a trial, we visually demonstrated the information for decoding, both spatially and temporally. The results suggest that the non-invasive fMRI technique may provide informative features for decoding individual finger movements and the potential of developing an fMRI-based brain-machine interface for finger movement.

Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography

We investigated how well repetitive finger tapping movements can be decoded from scalp electroencephalography (EEG) signals. A linear decoder with memory was used to infer continuous index finger angular velocities from the low-pass filtered fluctuations of the amplitude of a plurality of EEG signals distributed across the scalp. To evaluate the accuracy of the decoder, the Pearson's correlation coefficient (r) between the observed and predicted trajectories was calculated in a 10-fold cross-validation scheme. We also assessed attempts to decode finger kinematics from EEG data that was cleaned with independent component analysis (ICA), EEG data from peripheral sensors, and EEG data from rest periods. A genetic algorithm (GA) was used to select combinations of EEG channels that maximized decoding accuracies. Our results (lower quartile r = 0.18, median r = 0.36, upper quartile r = 0.50) show that delta-band EEG signals contain useful information that can be used to infer finger kinematics. Further, the highest decoding accuracies were characterized by highly correlated delta band EEG activity mostly localized to the contralateral central areas of the scalp. Spectral analysis of EEG also showed bilateral alpha band (8–13 Hz) event related desynchronizations (ERDs) and contralateral beta band (20–30 Hz) event related synchronizations (ERSs) localized over central scalp areas. Overall, this study demonstrates the feasibility of decoding finger kinematics from scalp EEG signals.

Neurologic heuristics and artistic whimsy: the cerebral cartography of Wilder Penfield

Neurosurgeon Wilder Penfield played a singularly important role in expanding our knowledge of functional neuroanatomy and neurophysiology in the twentieth century. Trained under Charles Sherrington, William Osler, and Otfrid Foerster, Penfield was an early leader in efforts to map the cerebral cortex via direct electrical stimulation of the brain. In 1937, Penfield introduced an entirely new concept for illustrating the relative sizes and locations of discrete functional regions within the sensorimotor cortex--the homunculus-to exemplify the "order and comparative extent" of specific functional regions. Over the subsequent two decades, Penfield and colleagues introduced several more "little men" to portray the functional maps of other important brain structures (i.e., supplementary motor area, insular cortex, thalamus). These later homunculi were more crudely drawn, and Penfield referred to them as essentially heuristic devices. The actual intent in producing these homunculi remains uncertain, and despite the extraordinary impact of these artistic renderings on the field, the question is raised as to whether the allure of the artwork seemed to wrest control from-and then to guide-the dissemination of science, rather than the other way around.

A multivariate method to determine the dimensionality of neural representation from population activity

How do populations of neurons represent a variable of interest? The notion of feature spaces is a useful concept to approach this question: According to this model, the activation patterns across a neuronal population are composed of different pattern components. The strength of each of these components varies with one latent feature, which together are the dimensions along which the population represents the variable. Here we propose a new method to determine the number of feature dimensions that best describes the activation patterns. The method is based on Gaussian linear classifiers that use only the first d most important pattern dimensions. Using cross-validation, we can identify the classifier that best matches the dimensionality of the neuronal representation. We test this method on two datasets of motor cortical activation patterns measured with functional magnetic resonance imaging (fMRI), during (i) simultaneous presses of all fingers of a hand at different force levels and (ii) presses of different individual fingers at a single force level. As expected, the new method shows that the representation of force is low-dimensional; the neural activation for different force levels is scaled versions of each other. In comparison, individual finger presses are represented in a full, four-dimensional feature space. The approach can be used to determine an important characteristic of neuronal population codes without knowing the form of the underlying features. It therefore provides a novel tool in the building of quantitative models of neuronal population activity as measured with fMRI or other approaches.

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Neural population activity represents external variables using multiple feature dimensions.

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We present a general method to determine the dimensionality of this feature space.

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Primary motor cortex represents force through a low-dimensional feature space.

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Individual finger movements are represented using a four-dimensional feature space.

Movement activation and inhibition in Parkinson's disease: a functional imaging study

BACKGROUND

Parkinson's disease (PD), traditionally considered a movement disorder, has been shown to affect executive function such as the ability to adapt behavior in response to new environmental situations.

OBJECTIVE

to identify the impact of PD on neural substrates subserving two specific components of normal movement which we refer to as activation (initiating an un-cued response) and inhibition (suppressing a cued response).

METHODS

We used fMRI to measure pre-movement processes associated with activating an un-cued response and inhibiting a cued response plan in 13 PD (ON anti-parkinsonian medications) and 13 control subjects. Subjects were shown a visual arrow cue followed by a matched or mismatched response target that instructed them to respond with a right, left, or bilateral button press. In mismatched trials, an un-cued (new) response was initiated, or the previously cued response was suppressed.

RESULTS

We were able to isolate pre-movement responses in dorsolateral prefrontal cortex, specifically in the right hemisphere. During the activation of an un-cued movement, PD subjects showed decreased activity in the putamen and increased cortical activity in bilateral DLPFC, SMA, subcentral gyrus and inferior frontal operculum. During inhibition of a previously cued movement, the PD group showed increased activation in SMA, S1/M1, premotor and superior parietal areas.

CONCLUSION

Right DLPFC plays a role in pre-movement processes, and DLPFC activity is abnormal in PD. Decreased specificity of responses was observed in multiple ROI's. The basal ganglia are involved in circuits that coordinate activation and inhibition involved in action selection as well as execution.

Two distinct ipsilateral cortical representations for individuated finger movements

Movements of the upper limb are controlled mostly through the contralateral hemisphere. Although overall activity changes in the ipsilateral motor cortex have been reported, their functional significance remains unclear. Using human functional imaging, we analyzed neural finger representations by studying differences in fine-grained activation patterns for single isometric finger presses. We demonstrate that cortical motor areas encode ipsilateral movements in 2 fundamentally different ways. During unimanual ipsilateral finger presses, primary sensory and motor cortices show, underneath global suppression, finger-specific activity patterns that are nearly identical to those elicited by contralateral mirror-symmetric action. This component vanishes when both motor cortices are functionally engaged during bimanual actions. We suggest that the ipsilateral representation present during unimanual presses arises because otherwise functionally idle circuits are driven by input from the opposite hemisphere. A second type of representation becomes evident in caudal premotor and anterior parietal cortices during bimanual actions. In these regions, ipsilateral actions are represented as nonlinear modulation of activity patterns related to contralateral actions, an encoding scheme that may provide the neural substrate for coordinating bimanual movements. We conclude that ipsilateral cortical representations change their informational content and functional role, depending on the behavioral context.

The motor system and its disorders

The motor system has been intensively studied using the emerging neuroimaging technologies over the last twenty years. These include early applications of positron emission tomography of brain perfusion, metabolic rate and receptor function, as well as functional magnetic resonance imaging, tractography from diffusion weighted imaging, and transcranial magnetic stimulation. Motor system research has the advantage of the existence of extensive electrophysiological and anatomical information from comparative studies which enables cross-validation of new methods. We review the impact of neuroimaging on the understanding of diverse motor functions, including motor learning, decision making, inhibition and the mirror neuron system. In addition, we show how imaging of the motor system has supported a powerful platform for bidirectional translational neuroscience. In one direction, it has provided the opportunity to study safely the processes of neuroplasticity, neural networks and neuropharmacology in stroke and movement disorders and offers a sensitive tool to assess novel therapeutics. In the reverse direction, imaging of clinical populations has promoted innovations in cognitive theory, experimental design and analysis. We highlight recent developments in the analysis of structural and functional connectivity in the motor system; the advantages of integration of multiple methodologies; and new approaches to experimental design using formal models of cognitive-motor processes.

Plasticity in cortical motor upper-limb representation following stroke and rehabilitation: two longitudinal multi-joint FMRI case-studies

Motor dysfunction and recovery following stroke and rehabilitation are associated with primary motor cortex plasticity. To better track these effects we studied two patients with sub-acute sub-cortical stroke causing hemiparesis, who underwent an effective behavioral treatment termed Constraint Induced Movement Therapy (CIMT). The therapy involves 2 weeks of intensive motor training of the hemiparetic limb coupled with immobilization of the unaffected limb. The study included a longitudinal series of clinical evaluations and fMRI scans, before and after the treatment. The fMRI task included wrist, elbow, or ankle movements. Activity in the M1 upper-limb region of control subjects was stable, strictly contralateral, and similar in amplitude for elbow and wrist movements. These findings reflect the well-known contralateral motor control and support the idea of overlapping representations of adjacent joints in M1. In both patients, pre-CIMT activation patterns in M1 were tested twice and did not change significantly, were contralateral, and included elbow-wrist differences. Following CIMT, the clinical condition of both patients improved and three fMRI-explored prototypes were found: First, cluster position remained constant; Second, ipsilateral activity appeared in the unaffected hemispheres during hemiparetic movements; Third, patient-specific elbow-wrist inter and intra hemispheric differences were modified. All effects were long-lasting. We suggest that overlapping representations of adjacent joints contributed to the cortical plasticity observed following CIMT. Our findings should be confirmed by studying larger groups of homogeneous patients. Nevertheless, this study introduces multi-joint imaging studies and shows that it is both possible and valuable to carry it out in stroke patients.

Integration of sensory and motor representations of single fingers in the human cerebellum

The cerebellum is thought to play a key role in the integration of sensory and motor events. Little is known, however, about how sensory and motor maps in the cerebellum superimpose. In the present study we investigated the relationship between these two maps for the representation of single fingers. Participants made isometric key presses with individual fingers or received vibratory tactile stimulation to the fingertips while undergoing high-resolution functional magnetic resonance imaging (fMRI). Using multivariate analysis, we have demonstrated that the ipsilateral lobule V and VIII show patterns of activity that encode, within the same region, both which finger pressed and which finger was stimulated. The individual finger-specific activation patches are smaller than 3 mm and only show a weak somatotopic organization. To study the superposition of sensory and motor maps, we correlated the finger-specific patterns across the two conditions. In the neocortex, sensory stimulation of one digit led to activation of the same patches as force production by the same digit; in the cerebellum, these activation patches were organized in an uncorrelated manner. This suggests that, in the cerebellum, a movement of a particular finger is paired with a range of possible sensory outcomes. In summary, our results indicate a small and fractured representation of single digits in the cerebellum and suggest a fundamental difference in how the cerebellum and the neocortex integrate sensory and motor events.

Vascular contributions to pattern analysis: comparing gradient and spin echo fMRI at 3T

Multivariate pattern analysis is often assumed to rely on signals that directly reflect differences in the distribution of particular neural populations. The source of the signal used in these analyses remains unclear however, and an alternative model suggests that signal from larger draining veins may play a significant role. The current study was designed to investigate the vascular contribution to pattern analyses at 3T by comparing the results obtained from gradient and spin echo data. Classification analyses were carried out comparing line orientations in V1, tone frequencies in A1, and responses from different fingers in M1. In all cases, classification accuracy in the spin echo data was not significantly different from chance. In contrast, classification accuracies in the gradient echo data were significantly above chance, and significantly higher than the accuracies observed for the spin echo data. These results suggest that at the field strength and spatial resolution used for the majority of fMRI studies, a considerable proportion of the signal used by pattern analysis originates in the vasculature.