Human Somatosensory Cortex Is Modulated during Motor Planning.

Limb movement direction can be inferred from local field potentials in motor cortex during movement execution. Yet, it remains unclear to what extent intended hand movements can be predicted from brain activity recorded during movement planning. Here, we set out to probe the directional-tuning of oscillatory features during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. We recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. Fronto-parietal LFP low-frequency power predicted hand-movement direction during planning while execution was largely mediated by higher frequency power and low-frequency phase in motor areas. By contrast, Phase-Amplitude Coupling showed uniform modulations across directions. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%). The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans.

Review of the definition of cerebral palsy (CP) has focused on disorders of movement and posture development that cause activity limitations. Recent research provides new insights into CP, showing that activity limitation in individuals with hemiplegic CP is not exclusively caused by disorders related to movement execution, but is also related to the planning of movements. In this review article, converging evidence is presented suggesting that, in addition to movement execution impairments, impairments in movement planning may also limit the performance of activities of daily living. Evidence from studies of fingertip-force planning and anticipatory planning in object manipulation tasks is discussed.

This study aimed to explore the impact of exercise training modes on sensory and motor-related cortex excitability using functional near-infrared spectroscopy technology (fNIRS) and reveal specific cortical effects. Twenty participants with no known health conditions took part in a study involving passive, active, and resistance tasks facilitated by an upper-limb robot, using a block design. The participants wore functional near-infrared spectroscopy (fNIRS) devices throughout the experiment to monitor changes in cortical blood oxygen levels during the tasks. The fNIRS optode coverage primarily targeted key areas of the brain cortex, including the primary motor cortex (M1), primary somatosensory cortex (S1), supplementary motor area (SMA), and premotor cortex (PMC) on both hemispheres. The study evaluated cortical activation areas, intensity, and lateralization values. Passive movement primarily activates M1 and part of S1, while active movement mainly activates contralateral M1 and S1. Resistance training activates brain regions in both hemispheres, including contralateral M1, S1, SMA, and PMC, as well as ipsilateral M1, S1, SMA, and PMC. Resistance movement also activates the ipsilateral sensorimotor cortex (S1, SMA, PMC) more than active or passive movement. Active movement has higher contralateral activation in M1 compared to passive movement. Resistance and active movements increase brain activity more than passive movement. Different movements activate various cortical areas equally on both sides, but lateralization differs. The correlation between lateralization of brain regions is significant in the right cortex but not in the left cortex during three movement patterns. All types of exercise boost motor cortex excitability, but resistance exercise activates both sides of the motor cortex more extensively. The PMC is crucial for intense workouts. The right cortex shows better coordination during motor tasks than the left. fNIRS findings can help determine the length of treatment sessions.

Introduction: Parkinson’s disease (PD) reduces an individual’s capacity for automaticity which limits their ability to perform two tasks simultaneously, negatively impacting daily function. Understanding the neural correlates of dual tasks (DTs) may pave the way for targeted therapies. To better understand automaticity in PD, we aimed to explore whether individuals with differing DT performances possessed differences in brain morphologic characteristics. Methods: Data were obtained from 34 individuals with PD and 47 healthy older adults including (1) demographics (age, sex), (2) disease severity (Movement Disorder Society – Unified Parkinson’s Disease Rating Scale [MDS-UPDRS], Hoehn and Yahr, levodopa equivalent daily dose [LEDD]), (3) cognition (Montreal Cognitive Assessment), (4) LEDD, (5) single-task and DT performance during a DT-timed-up-and-go test utilizing a serial subtraction task, and (6) cortical thicknesses and subcortical volumes obtained from volumetric MRI. Participants were categorized as low or high DT performers if their combined DT effect was greater than the previously determined mean value for healthy older adults (μ = –74.2). Nonparametric testing using Quade’s ANCOVA was conducted to compare cortical thicknesses and brain volumes between the highDT and lowDT groups while controlling for covariates: age, sex, MDS-UPDRS part III, LEDD, and intracranial volume. Secondarily, similar comparisons were made between the healthy older adult group and the highDT and lowDT groups. Lastly, a hierarchical linear regression model was conducted regressing combined DT effect on covariates (block one) and cortical thicknesses (block 2) in stepwise fashion. Results: The highDT group had thicker cortices than the lowDT group in the right primary somatosensory cortex (p = 0.001), bilateral primary motor cortices (p ≤ 0.001, left; p = 0.002, right), bilateral supplementary motor areas (p = 0.001, left; p < 0.001, right), and mean of the bilateral hemispheres (p = 0.001, left; p < 0.001, right). Of note, left primary cortex thickness (p = 0.002), left prefrontal cortex thickness (p < 0.001), and right supplementary motor area thickness (p = 0.003) differed when adding a healthy comparison group. Additionally, the regression analysis found that the left paracentral lobule thickness explained 20.8% of the variability in combined DT effect (p = 0.011) beyond the influence of covariates. Conclusions: These results suggest regions underlying DT performance, specifically, a convergence of neural control relying on sensorimotor integration, motor planning, and motor activation to achieve higher levels of DT performance for individuals with PD. Introduction: Parkinson’s disease (PD) reduces an individual’s capacity for automaticity which limits their ability to perform two tasks simultaneously, negatively impacting daily function. Understanding the neural correlates of dual tasks (DTs) may pave the way for targeted therapies. To better understand automaticity in PD, we aimed to explore whether individuals with differing DT performances possessed differences in brain morphologic characteristics. Methods: Data were obtained from 34 individuals with PD and 47 healthy older adults including (1) demographics (age, sex), (2) disease severity (Movement Disorder Society – Unified Parkinson’s Disease Rating Scale [MDS-UPDRS], Hoehn and Yahr, levodopa equivalent daily dose [LEDD]), (3) cognition (Montreal Cognitive Assessment), (4) LEDD, (5) single-task and DT performance during a DT-timed-up-and-go test utilizing a serial subtraction task, and (6) cortical thicknesses and subcortical volumes obtained from volumetric MRI. Participants were categorized as low or high DT performers if their combined DT effect was greater than the previously determined mean value for healthy older adults (μ = –74.2). Nonparametric testing using Quade’s ANCOVA was conducted to compare cortical thicknesses and brain volumes between the highDT and lowDT groups while controlling for covariates: age, sex, MDS-UPDRS part III, LEDD, and intracranial volume. Secondarily, similar comparisons were made between the healthy older adult group and the highDT and lowDT groups. Lastly, a hierarchical linear regression model was conducted regressing combined DT effect on covariates (block one) and cortical thicknesses (block 2) in stepwise fashion. Results: The highDT group had thicker cortices than the lowDT group in the right primary somatosensory cortex (p = 0.001), bilateral primary motor cortices (p ≤ 0.001, left; p = 0.002, right), bilateral supplementary motor areas (p = 0.001, left; p < 0.001, right), and mean of the bilateral hemispheres (p = 0.001, left; p < 0.001, right). Of note, left primary cortex thickness (p = 0.002), left prefrontal cortex thickness (p < 0.001), and right supplementary motor area thickness (p = 0.003) differed when adding a healthy comparison group. Additionally, the regression analysis found that the left paracentral lobule thickness explained 20.8% of the variability in combined DT effect (p = 0.011) beyond the influence of covariates. Conclusions: These results suggest regions underlying DT performance, specifically, a convergence of neural control relying on sensorimotor integration, motor planning, and motor activation to achieve higher levels of DT performance for individuals with PD.

Hand movement distribution in the motor cortex: the influence of a concurrent task and motor imagery

The connections of cingulate cortex with visual, motor, and parahippocampal cortices in the rabbit brain are evaluated by using a modified Brodmann cytoarchitectural scheme, electrophysiological mapping techniques, and the pathway tracers horseradish peroxidase (HRP) and tritiated amino acids. Rabbit cingulate cortex can be divided into areas 25, 24, and 29. Area 29 is of particular interest because area 29d has a lateral extension with a granular layer IV, area 29b has a caudal extension in which the connections differ from anterior area 29b, and there is a prominent area 29e. Cytoarchitectural delineation of the lateral border of area 29d with area 17 closely approximates the medial edge of the visual field representation in area 17 as determined electrophysiologically. The main interconnections between visual and cingulate cortices occur between cingulate areas 24b and 29d and visual areas 18 and medial parts of area 17. Projections between areas 29d and 18 are organized in a loose topographic fashion with rostral parts of each and caudal parts of each being reciprocally connected. Neurons mainly in superficial layer II-III of areas 17 and 18 project to layer I of area 29d, while the reciprocal projection originates from neurons in layer V of area 29d and project mainly to layer I of areas 17 and 18. The medial portion of motor area 8 projects to areas 18 and 29d and has a smaller projection to area 17. Postsubicular area 48 is reciprocally connected with area 29d, and it also projects to areas 29b and c. The subiculum projects to areas 29a and 29c but only to the anterior two-thirds of area 29b not the posterior one-third. Rostral area 29d receives the most extensive intrinsic cingulate projections including those from all major cytoarchitectural divisions. Interconnections between areas 29d and 29b appear to be topographically organized in the rostrocaudal plane. Area 29c projects more heavily to area 29b than vice versa. Finally area 29d projects mainly to area 24b in anterior cingulate cortex. In conclusion, rostral area 29d has extensive connections with visual areas 17 and 18, motor area 8, and all subdivisions of cingulate cortex. In light of these connections, it may play a pivotal role in associative functions of the rabbit cerebral cortex including visuomotor integration.

Motor cortical points are linked by intrinsic horizontal connections having a recurrent network topology. However, it is not known whether neural activity can propagate over the area covered by these intrinsic connections and whether there are spatial anisotropies of synaptic strength, as opposed to synaptic density. Moreover, the mechanisms by which activity spreads have yet to be determined. To address these issues, an 8 × 8 microelectrode array was inserted in the forelimb area of the cat motor cortex (MCx). The centre of the array had a laser etched hole ∼500 μm in diameter. A microiontophoretic pipette, with a tip diameter of 2-3 μm, containing bicuculline methiodide (BIC) was inserted in the hole and driven to a depth of 1200-1400 μm from the cortical surface. BIC was ejected for ∼2min from the tip of the micropipette with positive direct current ranging between 20 and 40 nA in different experiments. This produced spontaneous nearly periodic bursts (0.2-1.0 Hz) of multi-unit activity in a radius of about 400 μm from the tip of the micropipette. The bursts of neural activity spread at a velocity of 0.11-0.24 ms⁻¹ (mean=0.14 mm ms⁻¹, SD=0.05)with decreasing amplitude.The area activated was on average 7.22 mm² (SD=0.91 mm²), or ∼92% of the area covered by the recording array. The mode of propagation was determined to occur by progressive recruitment of cortical territory, driven by a central locus of activity of some 400 μm in radius. Thus, activity did not propagate as a wave. Transection of the connections between the thalamus and MCx did not significantly alter the propagation velocity or the size of the recruited area, demonstrating that the bursts spread along the routes of intrinsic cortical connectivity. These experiments demonstrate that neural activity initiated within a small motor cortical locus (≤ 400 μm in radius) can recruit a relatively large neighbourhood in which a variety of muscles acting at several forelimb joints are represented. These results support the hypothesis that the MCx controls the forelimb musculature in an integrated and anticipatory manner based on a recurrent network topology

Understanding the relationship between neural activity in motor cortex and muscle activity during movements is important both for basic science and for the design of neural prostheses. While there has been significant work in decoding muscle EMG from neural data, decoders often require many parameters which make the analysis susceptible to overfitting, which reduces generalizability and makes the results difficult to interpret. To address this issue, we recorded simultaneous neural activity from the motor cortices (M1/PMd) of rhesus monkeys performing an arm-reaching task while recording EMG from arm muscles. In this work, we focused on relating the mean neural activity (averaged across reach trials to one target) to the corresponding mean EMG. We reduced the dimensionality of the neural data and found that the curvature of the low-dimensional (low-D) neural activity could be used as a signature of muscle activity. Using this signature, and without directly fitting EMG data to the neural activity, we derived neural axes based on reaches to only one reach target (< 5% of the data) that could explain EMG for reaches across multiple targets (average R(2) = 0.65). Our results suggest that cortical population activity is tightly related to muscle EMG measurements, predicting a lag between cortical activity and movement generation of 47.5 ms. Furthermore, our ability to predict EMG features across different movements suggests that there are fundamental axes or directions in the low-D neural space along which the neural population activity moves to activate particular muscles.

Neurons in the somatosensory cortex are exquisitely sensitive to mechanical stimulation of the skin surface. The location, velocity, direction, and adaptation of tactile stimuli on the skin’s surface are discriminable features of somatosensory processing, however the representation and processing of dynamic tactile arrays in the human somatosensory cortex are poorly understood. The principal aim of this study was to map the relation between dynamic saltatory pneumatic stimuli at discrete traverse velocities on the glabrous hand and the resultant pattern of evoked BOLD response in the human brain. Moreover, we hypothesized that the hand representation in contralateral Brodmann Area (BA) 3b would show a significant dependence on stimulus velocity. Saltatory pneumatic pulses (60 ms duration, 9.5 ms rise/fall) were repetitively sequenced through a 7-channel TAC-Cell array at traverse velocities of 5, 25, and 65 cm/s on the glabrous hand initiated at the tips of D2 (index finger) and D3 (middle finger) and sequenced towards the D1 (thumb). The resulting hemodynamic response was sampled during 3 functional MRI scans (BOLD) in 20 neurotypical right-handed adults at 3T. Results from each subject were inserted to the one-way ANOVA within-subjects and one sample t-test to evaluate the group main effect of all three velocities stimuli and each of three different velocities, respectively. The stimulus evoked BOLD response revealed a dynamic representation of saltatory pneumotactile stimulus velocity in a network consisting of the contralateral primary hand somatosensory cortex (BA3b), associated primary motor cortex (BA4), posterior insula, and ipsilateral deep cerebellum. The spatial extent of this network was greatest at the 5 and 25 cm/s pneumotactile stimulus velocities.

In the planning of a movement, the brain has the opportunity to delay the incorporation of accuracy requirements of the motor plan in order to reduce the reaction time by up to 100 ms (average: 32 ms). Such shortening of reaction time is observed here when the first phase of the movement consists of a transport phase. This forces us to reconsider the hypothesis that motor plans are fully defined before movement onset.

Motivation: This study is to design and implement a dense inductively coupled wireless resonator array to enhance functional MRI signal sensitivity in the human somatosensory and motor cortex. Goal(s): This study is to design and evaluate a dense inductively coupled wireless resonator array (ICWRA) specifically for the human somatosensory and motor cortex. Approach: The methodology integrates this proposed coil with a secondary 48-channel receive head array to enhance the signal-to-noise ratio (SNR) and functional sensitivity of magnetic resonance imaging. Results: The study demonstrates the feasibility and advantages of this ICWRA designed in improving functional sensitivity and SNR in ultra-high field fMRI applications. Impact: This study is to design and implement a dense inductively coupled wireless resonator array (ICWRA) to enhance functional MRI signal sensitivity in the human somatosensory and motor cortex.

The primary motor (M1) and somatosensory (S1) cortices play critical roles in motor control but the signaling between these structures is poorly understood. To fill this gap, we recorded – in three participants in an ongoing human clinical trial (NCT01894802) for people with paralyzed hands – the responses evoked in the hand and arm representations of M1 during intracortical microstimulation (ICMS) in the hand representation of S1. We found that ICMS of S1 activated some M1 neurons at short, fixed latencies consistent with monosynaptic activation. Additionally, most of the ICMS-evoked responses in M1 were more variable in time, suggesting indirect effects of stimulation. The spatial pattern of M1 activation varied systematically: S1 electrodes that elicited percepts in a finger preferentially activated M1 neurons excited during that finger’s movement. Moreover, the indirect effects of S1 ICMS on M1 were context dependent, such that the magnitude and even sign relative to baseline varied across tasks. We tested the implications of these effects for brain-control of a virtual hand, in which ICMS conveyed tactile feedback. While ICMS-evoked activation of M1 disrupted decoder performance, this disruption was minimized using biomimetic stimulation, which emphasizes contact transients at the onset and offset of grasp, and reduces sustained stimulation.

Acute aerobic exercise has been shown to improve fine motor skills and alter activation of the motor cortex (M1). The intensity of exercise may influence M1 activation, and further impact whole-body motor skill performance. The aims of the current study were to compare a whole-body motor skill via a piano task following moderate-intensity training (MIT) and high-intensity interval training (HIIT), and to determine if M1 activation is linked to any such changes in performance. Nine subjects (seven females and two males), aged 18 ± 1 years completed a control, MIT, and HIIT trial followed by administration of a piano performance task. M1 activation was evaluated by measuring oxyhemoglobin (O2Hb) and hemoglobin difference (Hbdiff) changes during post-exercise piano performance using functional near-infrared spectroscopy (fNIRS). The results indicate that piano performance scores were higher after the MIT trial, but not HIIT trial, compared to the control trial. A negative relationship was detected between heart rate during HIIT and post-HIIT piano scores. M1 activation (as measured by Hbdiff) was significantly increased after the HIIT trial. M1 activation was also positively associated with piano performance when exercise trials (HIIT + MIT) and all trials (HIIT + MIT + Control) were combined. We found that acute moderate-intensity exercise led to an improvement in complex motor skill performance while higher-intensity exercise increased M1 activation. These results demonstrate that moderate-intensity exercise can prime the nervous system for the acquisition of whole-body motor skills, suggesting that similar exercise protocols may be effective in improving the outcomes of other motor tasks performed during regular routines of daily life (e.g., sporting tasks, activities of daily living or rehabilitation). In addition, it appears that improvements in motor task performance may be driven by M1 activation. Our findings provide new mechanistic insight into the complex relationship between exercise intensity, M1 activation, and whole-body motor skill performance.

Multi-finger receptive field properties in primary somatosensory cortex: A revised account of the spatiotemporal integration functions of area 3b

Movements are defining characteristics of all behaviors. Animals walk around, move their eyes to explore the world or touch structures to learn more about them. So far we only have some basic understanding of how the brain generates movements, especially when we want to understand how different areas of the brain interact with each other. In this study we investigated the influence of sensory object information on grasp planning in four different brain areas involved in vision, touch, movement planning, and movement generation in the parietal, somatosensory, premotor and motor cortex. We trained one monkey to grasp objects that he either saw or touched beforehand while continuously recording neural spiking activity with chronically implanted floating multi-electrode arrays. The animal was instructed to sit in the dark and either look at a shortly illuminated object or reach out and explore the object with his hand in the dark before lifting it up. In a first analysis we confirmed that the animal not only memorizes the object in both tasks, but also applies an object-specific grip type, independent of the sensory modality. In the neuronal population, we found a significant difference in the number of tuned units for sensory modalities during grasp planning that persisted into grasp execution. These differences were sufficient to enable a classifier to decode the object and sensory modality in a single trial exclusively from neural population activity. These results give valuable insights in how different brain areas contribute to the preparation of grasp movement and how different sensory streams can lead to distinct neural activity while still resulting in the same action execution.