Changes in Resting State Functional Connectivity Associated with Dynamic Adaptation of Wrist Movements.

Dynamic adaptation is an error-driven process of adjusting planned motor actions to changes in task dynamics (Shadmehr, 2017). Adapted motor plans are consolidated into memories that contribute to better performance on re-exposure. Consolidation begins within 15 min following training (Criscimagna-Hemminger and Shadmehr, 2008), and can be measured via changes in resting state functional connectivity (rsFC). For dynamic adaptation, rsFC has not been quantified on this timescale, nor has its relationship to adaptative behavior been established. We used a functional magnetic resonance imaging (fMRI)-compatible robot, the MR-SoftWrist (Erwin et al., 2017), to quantify rsFC specific to dynamic adaptation of wrist movements and subsequent memory formation in a mixed-sex cohort of human participants. We acquired fMRI during a motor execution and a dynamic adaptation task to localize brain networks of interest, and quantified rsFC within these networks in three 10-min windows occurring immediately before and after each task. The next day, we assessed behavioral retention. We used a mixed model of rsFC measured in each time window to identify changes in rsFC with task performance, and linear regression to identify the relationship between rsFC and behavior. Following the dynamic adaptation task, rsFC increased within the cortico-cerebellar network and decreased interhemispherically within the cortical sensorimotor network. Increases within the cortico-cerebellar network were specific to dynamic adaptation, as they were associated with behavioral measures of adaptation and retention, indicating that this network has a functional role in consolidation. Instead, decreases in rsFC within the cortical sensorimotor network were associated with motor control processes independent from adaptation and retention.SIGNIFICANCE STATEMENT Motor memory consolidation processes have been studied via functional magnetic resonance imaging (fMRI) by analyzing changes in resting state functional connectivity (rsFC) occurring more than 30 min after adaptation. However, it is unknown whether consolidation processes are detectable immediately (<15 min) following dynamic adaptation. We used an fMRI-compatible wrist robot to localize brain regions involved in dynamic adaptation in the cortico-thalamic-cerebellar (CTC) and cortical sensorimotor networks and quantified changes in rsFC within each network immediately after adaptation. Different patterns of change in rsFC were observed compared with studies conducted at longer latencies. Increases in rsFC in the cortico-cerebellar network were specific to adaptation and retention, while interhemispheric decreases in the cortical sensorimotor network were associated with alternate motor control processes but not with memory formation.

Resting-state brain and spinal cord networks in humans are functionally integrated.

In the absence of any task, both the brain and spinal cord exhibit spontaneous intrinsic activity organised in a set of functionally relevant neural networks. However, whether such resting-state networks (RSNs) are interconnected across the brain and spinal cord is unclear. Here, we used a unique scanning protocol to acquire functional images of both brain and cervical spinal cord (CSC) simultaneously and examined their spatiotemporal correspondence in humans. We show that the brain and spinal cord activities are strongly correlated during rest periods, and specific spinal cord regions are functionally linked to consistently reported brain sensorimotor RSNs. The functional organisation of these networks follows well-established anatomical principles, including the contralateral correspondence between the spinal hemicords and brain hemispheres as well as sensory versus motor segregation of neural pathways along the brain-spinal cord axis. Thus, our findings reveal a unified functional organisation of sensorimotor networks in the entire central nervous system (CNS) at rest.

Neural Substrates of Muscle Co-contraction during Dynamic Motor Adaptation.

As we learn to perform a motor task with novel dynamics, the central nervous system must adapt motor commands and modify sensorimotor transformations. The objective of the current research is to identify the neural mechanisms underlying the adaptive process. It has been shown previously that an increase in muscle co-contraction is frequently associated with the initial phase of adaptation and that co-contraction is gradually reduced as performance improves. Our investigation focused on the neural substrates of muscle co-contraction during the course of motor adaptation using a resting-state fMRI approach in healthy human subjects of both genders. We analyzed the functional connectivity in resting-state networks during three phases of adaptation, corresponding to different muscle co-contraction levels and found that change in the strength of functional connectivity in one brain network was correlated with a metric of co-contraction, and in another with a metric of motor learning. We identified the cerebellum as the key component for regulating muscle co-contraction, especially its connection to the inferior parietal lobule, which was particularly prominent in early stage adaptation. A neural link between cerebellum, superior frontal gyrus and motor cortical regions was associated with reduction of co-contraction during later stages of adaptation. We also found reliable changes in the functional connectivity of a network involving primary motor cortex, superior parietal lobule and cerebellum that were specifically related to the motor learning.SIGNIFICANCE STATEMENT It is well known that co-contracting muscles is an effective strategy for providing postural stability by modulating mechanical impedance and thereby allowing the central nervous system to compensate for unfamiliar or unexpected physical conditions until motor commands can be appropriately adapted. The present study elucidates the neural substrates underlying the ability to modulate the mechanical impedance of a limb as we learn during motor adaptation. Using resting-state fMRI analysis we demonstrate that a distributed cerebellar-parietal-frontal network functions to regulate muscle co-contraction with the cerebellum as its key component.

Brain-spinal cord interaction in long-term motor sequence learning in human: An fMRI study.

The spinal cord is important for sensory guidance and execution of skilled movements. Yet its role in human motor learning is not well understood. Despite evidence revealing an active involvement of spinal circuits in the early phase of motor learning, whether long-term learning engages similar changes in spinal cord activation and functional connectivity remains unknown. Here, we investigated spinal-cerebral functional plasticity associated with learning of a specific sequence of visually-guided joystick movements (sequence task) over six days of training. On the first and last training days, we acquired high-resolution functional images of the brain and cervical cord simultaneously, while participants practiced the sequence or a random task while electromyography was recorded from wrist muscles. After six days of training, the subjects' motor performance improved in the sequence compared to the control condition. These behavioral changes were associated with decreased co-contractions and increased reciprocal activations between antagonist wrist muscles. Importantly, early learning was characterized by activation in the C8 level, whereas a more rostral activation in the C6-C7 was found during the later learning phase. Motor sequence learning was also supported by increased spinal cord functional connectivity with distinct brain networks, including the motor cortex, superior parietal lobule, and the cerebellum at the early stage, and the angular gyrus and cerebellum at a later stage of learning. Our results suggest that the early vs. late shift in spinal activation from caudal to rostral cervical segments synchronized with distinct brain networks, including parietal and cerebellar regions, is related to progressive changes reflecting the increasing fine control of wrist muscles during motor sequence learning.

Investigating the human spinal sensorimotor pathways through functional magnetic resonance imaging.

Most of our knowledge about the human spinal ascending (sensory) and descending (motor) pathways comes from non-invasive electrophysiological investigations. However, recent methodological advances in acquisition and analyses of functional magnetic resonance imaging (fMRI) data from the spinal cord, either alone or in combination with the brain, have allowed us to gain further insights into the organization of this structure. In the current review, we conducted a systematic search to produced somatotopic maps of the spinal fMRI activity observed through different somatosensory, motor and resting-state paradigms. By cross-referencing these human neuroimaging findings with knowledge acquired through neurophysiological recordings, our review demonstrates that spinal fMRI is a powerful tool for exploring, in vivo, the human spinal cord pathways. We report strong cross-validation between task-related and resting-state fMRI in accordance with well-known hemicord, postero-anterior and rostro-caudal organization of these pathways. We also highlight the specific advantages of using spinal fMRI in clinical settings to characterize better spinal-related impairments, predict disease progression, and guide the implementation of therapeutic interventions.

Neural Basis of Sensorimotor Plasticity in Speech Motor Adaptation.

When we speak, we get correlated sensory feedback from speech sounds and from the muscles and soft tissues of the vocal tract. Here we dissociate the contributions of auditory and somatosensory feedback to identify brain networks that underlie the somatic contribution to speech motor learning. The technique uses a robotic device that selectively alters somatosensory inputs in combination with resting-state fMRI scans that reveal learning-related changes in functional connectivity. A partial correlation analysis is used to identify connectivity changes that are not explained by the time course of activity in any other learning-related areas. This analysis revealed changes related to behavioral improvements in movement and separately, to changes in auditory perception: Speech motor adaptation itself was associated with connectivity changes that were primarily in non-motor areas of brain, specifically, to a strengthening of connectivity between auditory and somatosensory cortex and between presupplementary motor area and the inferior parietal lobule. In contrast, connectively changes associated with alterations to auditory perception were restricted to speech motor areas, specifically, primary motor cortex and inferior frontal gyrus. Overall, our findings show that during adaptation, somatosensory inputs result in a broad range of changes in connectivity in areas associated with speech motor control and learning.

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

Error-related Persistence of Motor Activity in Resting-state Networks.

The relationship between neural activation during movement training and the plastic changes that survive beyond movement execution is not well understood. Here we ask whether the changes in resting-state functional connectivity observed following motor learning overlap with the brain networks that track movement error during training. Human participants learned to trace an arched trajectory using a computer mouse in an MRI scanner. Motor performance was quantified on each trial as the maximum distance from the prescribed arc. During learning, two brain networks were observed, one showing increased activations for larger movement error, comprising the cerebellum, parietal, visual, somatosensory, and cortical motor areas, and the other being more activated for movements with lower error, comprising the ventral putamen and the OFC. After learning, changes in brain connectivity at rest were found predominantly in areas that had shown increased activation for larger error during task, specifically the cerebellum and its connections with motor, visual, and somatosensory cortex. The findings indicate that, although both errors and accurate movements are important during the active stage of motor learning, the changes in brain activity observed at rest primarily reflect networks that process errors. This suggests that error-related networks are represented in the initial stages of motor memory formation.

Simultaneous Brain-Cervical Cord fMRI Reveals Intrinsic Spinal Cord Plasticity during Motor Sequence Learning.

The spinal cord participates in the execution of skilled movements by translating high-level cerebral motor representations into musculotopic commands. Yet, the extent to which motor skill acquisition relies on intrinsic spinal cord processes remains unknown. To date, attempts to address this question were limited by difficulties in separating spinal local effects from supraspinal influences through traditional electrophysiological and neuroimaging methods. Here, for the first time, we provide evidence for local learning-induced plasticity in intact human spinal cord through simultaneous functional magnetic resonance imaging of the brain and spinal cord during motor sequence learning. Specifically, we show learning-related modulation of activity in the C6-C8 spinal region, which is independent from that of related supraspinal sensorimotor structures. Moreover, a brain-spinal cord functional connectivity analysis demonstrates that the initial linear relationship between the spinal cord and sensorimotor cortex gradually fades away over the course of motor sequence learning, while the connectivity between spinal activity and cerebellum gains strength. These data suggest that the spinal cord not only constitutes an active functional component of the human motor learning network but also contributes distinctively from the brain to the learning process. The present findings open new avenues for rehabilitation of patients with spinal cord injuries, as they demonstrate that this part of the central nervous system is much more plastic than assumed before. Yet, the neurophysiological mechanisms underlying this intrinsic functional plasticity in the spinal cord warrant further investigations.

Somatic and Reinforcement-Based Plasticity in the Initial Stages of Human Motor Learning.

As one learns to dance or play tennis, the desired somatosensory state is typically unknown. Trial and error is important as motor behavior is shaped by successful and unsuccessful movements. As an experimental model, we designed a task in which human participants make reaching movements to a hidden target and receive positive reinforcement when successful. We identified somatic and reinforcement-based sources of plasticity on the basis of changes in functional connectivity using resting-state fMRI before and after learning. The neuroimaging data revealed reinforcement-related changes in both motor and somatosensory brain areas in which a strengthening of connectivity was related to the amount of positive reinforcement during learning. Areas of prefrontal cortex were similarly altered in relation to reinforcement, with connectivity between sensorimotor areas of putamen and the reward-related ventromedial prefrontal cortex strengthened in relation to the amount of successful feedback received. In other analyses, we assessed connectivity related to changes in movement direction between trials, a type of variability that presumably reflects exploratory strategies during learning. We found that connectivity in a network linking motor and somatosensory cortices increased with trial-to-trial changes in direction. Connectivity varied as well with the change in movement direction following incorrect movements. Here the changes were observed in a somatic memory and decision making network involving ventrolateral prefrontal cortex and second somatosensory cortex. Our results point to the idea that the initial stages of motor learning are not wholly motor but rather involve plasticity in somatic and prefrontal networks related both to reward and exploration. SIGNIFICANCE STATEMENT: In the initial stages of motor learning, the placement of the limbs is learned primarily through trial and error. In an experimental analog, participants make reaching movements to a hidden target and receive positive feedback when successful. We identified sources of plasticity based on changes in functional connectivity using resting-state fMRI. The main finding is that there is a strengthening of connectivity between reward-related prefrontal areas and sensorimotor areas in the basal ganglia and frontal cortex. There is also a strengthening of connectivity related to movement exploration in sensorimotor circuits involved in somatic memory and decision making. The results indicate that initial stages of motor learning depend on plasticity in somatic and prefrontal networks related to reward and exploration.

Structure of plasticity in human sensory and motor networks due to perceptual learning.

As we begin to acquire a new motor skill, we face the dual challenge of determining and refining the somatosensory goals of our movements and establishing the best motor commands to achieve our ends. The two typically proceed in parallel, and accordingly it is unclear how much of skill acquisition is a reflection of changes in sensory systems and how much reflects changes in the brain's motor areas. Here we have intentionally separated perceptual and motor learning in time so that we can assess functional changes to human sensory and motor networks as a result of perceptual learning. Our subjects underwent fMRI scans of the resting brain before and after a somatosensory discrimination task. We identified changes in functional connectivity that were due to the effects of perceptual learning on movement. For this purpose, we used a neural model of the transmission of sensory signals from perceptual decision making through to motor action. We used this model in combination with a partial correlation technique to parcel out those changes in connectivity observed in motor systems that could be attributed to activity in sensory brain regions. We found that, after removing effects that are linearly correlated with somatosensory activity, perceptual learning results in changes to frontal motor areas that are related to the effects of this training on motor behavior and learning. This suggests that perceptual learning produces changes to frontal motor areas of the brain and may thus contribute directly to motor learning.

FASB: an integrated processing pipeline for Functional Analysis of simultaneous Spinal cord-Brain fMRI.

Simultaneous functional magnetic resonance imaging (fMRI) of the spinal cord and brain represents a powerful method for examining both ascending sensory and descending motor pathways in humans in vivo . However, its image acquisition protocols, and processing pipeline are less well established. This limitation is mainly due to technical difficulties related to spinal cord fMRI, and problems with the logistics stemming from a large field of view covering both brain and cervical cord. Here, we propose an acquisition protocol optimized for both anatomical and functional images, as well as an optimized integrated image processing pipeline, which consists of a novel approach for automatic modeling and mitigating the negative impact of spinal voxels with low temporal signal to noise ratio (tSNR). We validate our integrated pipeline, named FASB, using simultaneous fMRI data acquired during the performance of a motor task, as well as during resting-state conditions. We demonstrate that FASB outperforms the current spinal fMRI processing methods in three domains, including motion correction, registration to the spinal cord template, and improved detection power of the group-level analysis by removing the effects of participant-specific low tSNR voxels, typically observed at the disk level. Using FASB, we identify significant task-based activations in the expected sensorimotor network associated with a unilateral handgrip force production task across the entire central nervous system, including the contralateral sensorimotor cortex, thalamus, striatum, cerebellum, brainstem, as well as ipsilateral ventral horn at C5-C8 cervical levels. Additionally, our results show significant task-based functional connectivity between the key sensory and motor brain areas and the dorsal and ventral horns of the cervical cord. Overall, our proposed acquisition protocol and processing pipeline provide a robust method for characterizing the activation and functional connectivity of distinct cortical, subcortical, brainstem and spinal cord regions in humans.

DeepRetroMoCo: deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI.

Background and purpose: There are distinct challenges in the preprocessing of spinal cord fMRI data, particularly concerning the mitigation of voluntary or involuntary movement artifacts during image acquisition. Despite the notable progress in data processing techniques for movement detection and correction, applying motion correction algorithms developed for the brain cortex to the brainstem and spinal cord remains a challenging endeavor. Methods: In this study, we employed a deep learning-based convolutional neural network (CNN) named DeepRetroMoCo, trained using an unsupervised learning algorithm. Our goal was to detect and rectify motion artifacts in axial T2*-weighted spinal cord data. The training dataset consisted of spinal cord fMRI data from 27 participants, comprising 135 runs for training and 81 runs for testing. Results: To evaluate the efficacy of DeepRetroMoCo, we compared its performance against the sct_fmri_moco method implemented in the spinal cord toolbox. We assessed the motion-corrected images using two metrics: the average temporal signal-to-noise ratio (tSNR) and Delta Variation Signal (DVARS) for both raw and motion-corrected data. Notably, the average tSNR in the cervical cord was significantly higher when DeepRetroMoCo was utilized for motion correction, compared to the sct_fmri_moco method. Additionally, the average DVARS values were lower in images corrected by DeepRetroMoCo, indicating a superior reduction in motion artifacts. Moreover, DeepRetroMoCo exhibited a significantly shorter processing time compared to sct_fmri_moco. Conclusion: Our findings strongly support the notion that DeepRetroMoCo represents a substantial improvement in motion correction procedures for fMRI data acquired from the cervical spinal cord. This novel deep learning-based approach showcases enhanced performance, offering a promising solution to address the challenges posed by motion artifacts in spinal cord fMRI data.

Perceptual learning in sensorimotor adaptation.

Motor learning often involves situations in which the somatosensory targets of movement are, at least initially, poorly defined, as for example, in learning to speak or learning the feel of a proper tennis serve. Under these conditions, motor skill acquisition presumably requires perceptual as well as motor learning. That is, it engages both the progressive shaping of sensory targets and associated changes in motor performance. In the present study, we test the idea that perceptual learning alters somatosensory function and in so doing produces changes to human motor performance and sensorimotor adaptation. Subjects in these experiments undergo perceptual training in which a robotic device passively moves the subject's arm on one of a set of fan-shaped trajectories. Subjects are required to indicate whether the robot moved the limb to the right or the left and feedback is provided. Over the course of training both the perceptual boundary and acuity are altered. The perceptual learning is observed to improve both the rate and extent of learning in a subsequent sensorimotor adaptation task and the benefits persist for at least 24 h. The improvement in the present studies varies systematically with changes in perceptual acuity and is obtained regardless of whether the perceptual boundary shift serves to systematically increase or decrease error on subsequent movements. The beneficial effects of perceptual training are found to be substantially dependent on reinforced decision-making in the sensory domain. Passive-movement training on its own is less able to alter subsequent learning in the motor system. Overall, this study suggests perceptual learning plays an integral role in motor learning.

Functionally specific changes in resting-state sensorimotor networks after motor learning.

Motor learning changes the activity of cortical motor and subcortical areas of the brain, but does learning affect sensory systems as well? We examined in humans the effects of motor learning using fMRI measures of functional connectivity under resting conditions and found persistent changes in networks involving both motor and somatosensory areas of the brain. We developed a technique that allows us to distinguish changes in functional connectivity that can be attributed to motor learning from those that are related to perceptual changes that occur in conjunction with learning. Using this technique, we identified a new network in motor learning involving second somatosensory cortex, ventral premotor cortex, and supplementary motor cortex whose activation is specifically related to perceptual changes that occur in conjunction with motor learning. We also found changes in a network comprising cerebellar cortex, primary motor cortex, and dorsal premotor cortex that were linked to the motor aspects of learning. In each network, we observed highly reliable linear relationships between neuroplastic changes and behavioral measures of either motor learning or perceptual function. Motor learning thus results in functionally specific changes to distinct resting-state networks in the brain.

Shared and specific independent components analysis for between-group comparison.

Independent component analysis (ICA) has been extensively used in individual and within-group data sets in real-world applications, but how can it be employed in a between-groups or conditions design? Here, we propose a new method to embed group membership information into the FastICA algorithm so as to extract components that are either shared between groups or specific to one or a subset of groups. The proposed algorithm is designed to automatically extract the pattern of differences between different experimental groups or conditions. A new constraint is added to the FastICA algorithm to simultaneously deal with the data of multiple groups in a single ICA run. This cost function restricts the specific components of one group to be orthogonal to the subspace spanned by the data of the other groups. As a result of performing a single ICA on the aggregate data of several experimental groups, the entire variability of data sets is used to extract the shared components. The results of simulations show that the proposed algorithm performs better than the regular method in both the reconstruction of the source signals and classification of shared and specific components. Also, the sensitivity to detect variations in the amplitude of shared components across groups is enhanced. A rigorous proof of convergence is provided for the proposed iterative algorithm. Thus, this algorithm is guaranteed to extract and classify shared and specific independent components across different experimental groups and conditions in a systematic way.

Repeated hippocampal seizures lead to brain-wide reorganization of circuits and seizure propagation pathways.

Repeated seizure activity can lead to long-term changes in seizure dynamics and behavior. However, resulting changes in brain-wide dynamics remain poorly understood. This is due partly to technical challenges in precise seizure control and in vivo whole-brain mapping of circuit dynamics. Here, we developed an optogenetic kindling model through repeated stimulation of ventral hippocampal CaMKII neurons in adult rats. We then combined fMRI with electrophysiology to track brain-wide circuit dynamics resulting from non-afterdischarge (AD)-generating stimulations and individual convulsive seizures. Kindling induced widespread increases in non-AD-generating stimulation response and ipsilateral functional connectivity and elevated anxiety. Individual seizures in kindled animals showed more significant increases in brain-wide activity and bilateral functional connectivity. Onset time quantification provided evidence for kindled seizure propagation from the ipsilateral to the contralateral hemisphere. Furthermore, a core of slow-migrating hippocampal activity was identified in both non-kindled and kindled seizures, revealing a novel mechanism of seizure sustainment and propagation.

Brain-wide neural dynamics of poststroke recovery induced by optogenetic stimulation.

Poststroke optogenetic stimulations can promote functional recovery. However, the circuit mechanisms underlying recovery remain unclear. Elucidating key neural circuits involved in recovery will be invaluable for translating neuromodulation strategies after stroke. Here, we used optogenetic functional magnetic resonance imaging to map brain-wide neural circuit dynamics after stroke in mice treated with and without optogenetic excitatory neuronal stimulations in the ipsilesional primary motor cortex (iM1). We identified key sensorimotor circuits affected by stroke. iM1 stimulation treatment restored activation of the ipsilesional corticothalamic and corticocortical circuits, and the extent of activation was correlated with functional recovery. Furthermore, stimulated mice exhibited higher expression of axonal growth-associated protein 43 in the ipsilesional thalamus and showed increased Synaptophysin+/channelrhodopsin+ presynaptic axonal terminals in the corticothalamic circuit. Selective stimulation of the corticothalamic circuit was sufficient to improve functional recovery. Together, these findings suggest early involvement of corticothalamic circuit as an important mediator of poststroke recovery.

A Single Session of Robot-Controlled Proprioceptive Training Modulates Functional Connectivity of Sensory Motor Networks and Improves Reaching Accuracy in Chronic Stroke.

BACKGROUND: Passive robot-generated arm movements in conjunction with proprioceptive decision making and feedback modulate functional connectivity (FC) in sensory motor networks and improve sensorimotor adaptation in normal individuals. This proof-of-principle study investigates whether these effects can be observed in stroke patients. METHODS: A total of 10 chronic stroke patients with a range of stable motor and sensory deficits (Fugl-Meyer Arm score [FMA] 0-65, Nottingham Sensory Assessment [NSA] 10-40) underwent resting-state functional magnetic resonance imaging before and after a single session of robot-controlled proprioceptive training with feedback. Changes in FC were identified in each patient using independent component analysis as well as a seed region-based approach. FC changes were related to impairment and changes in task performance were assessed. RESULTS: A single training session improved average arm reaching accuracy in 6 and proprioception in 8 patients. Two networks showing training-associated FC change were identified. Network C1 was present in all patients and network C2 only in patients with FM scores >7. Relatively larger C1 volume in the ipsilesional hemisphere was associated with less impairment ( r = 0.83 for NSA, r = 0.73 for FMA). This association was driven by specific regions in the contralesional hemisphere and their functional connections (supramarginal gyrus with FM scores r = 0.82, S1 with NSA scores r = 0.70, and cerebellum with NSA score r = -0.82). CONCLUSION: A single session of robot-controlled proprioceptive training with feedback improved movement accuracy and induced FC changes in sensory motor networks of chronic stroke patients. FC changes are related to functional impairment and comprise bilateral sensory and motor network nodes.