Unilateral, 3D Arm Movement Kinematics Are Encoded in Ipsilateral Human Cortex.

There is increasing evidence that the hemisphere ipsilateral to a moving limb plays a role in planning and executing movements. However, the exact relationship between cortical activity and ipsilateral limb movements is uncertain. We sought to determine whether 3D arm movement kinematics (speed, velocity, and position) could be decoded from cortical signals recorded from the hemisphere ipsilateral to the moving limb. By having invasively monitored patients perform unilateral reaches with each arm, we also compared the encoding of contralateral and ipsilateral limb kinematics from a single cortical hemisphere. In four motor-intact human patients (three male, one female) implanted with electrocorticography electrodes for localization of their epileptic foci, we decoded 3D movement kinematics of both arms with accuracies above chance. Surprisingly, the spatial and spectral encoding of contralateral and ipsilateral limb kinematics was similar, enabling cross-prediction of kinematics between arms. These results clarify our understanding that the ipsilateral hemisphere robustly contributes to motor execution and supports that the information of complex movements is more bihemispherically represented in humans than has been previously understood.SIGNIFICANCE STATEMENT Although limb movements are traditionally understood to be driven by the cortical hemisphere contralateral to a moving limb, movement-related neural activity has also been found in the ipsilateral hemisphere. This study provides the first demonstration that 3D arm movement kinematics can be decoded from human electrocorticographic signals ipsilateral to the moving limb. Surprisingly, the spatial and spectral encoding of contralateral and ipsilateral limb kinematics was similar. The finding that specific kinematics are encoded in the ipsilateral hemisphere demonstrates that the ipsilateral hemisphere contributes to the execution of unilateral limb movements, improving our understanding of motor control. Additionally, the bihemisheric representation of voluntary movements has implications for the development of neuroprosthetic systems for reaching and for neurorehabilitation strategies following cortical injuries.

Contralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors.

BACKGROUND AND PURPOSE: There are few effective therapies to achieve functional recovery from motor-related disabilities affecting the upper limb after stroke. This feasibility study tested whether a powered exoskeleton driven by a brain-computer interface (BCI), using neural activity from the unaffected cortical hemisphere, could affect motor recovery in chronic hemiparetic stroke survivors. This novel system was designed and configured for a home-based setting to test the feasibility of BCI-driven neurorehabilitation in outpatient environments. METHODS: Ten chronic hemiparetic stroke survivors with moderate-to-severe upper-limb motor impairment (mean Action Research Arm Test=13.4) used a powered exoskeleton that opened and closed the affected hand using spectral power from electroencephalographic signals from the unaffected hemisphere associated with imagined hand movements of the paretic limb. Patients used the system at home for 12 weeks. Motor function was evaluated before, during, and after the treatment. RESULTS: Across patients, our BCI-driven approach resulted in a statistically significant average increase of 6.2 points in the Action Research Arm Test. This behavioral improvement significantly correlated with improvements in BCI control. Secondary outcomes of grasp strength, Motricity Index, and the Canadian Occupational Performance Measure also significantly improved. CONCLUSIONS: The findings demonstrate the therapeutic potential of a BCI-driven neurorehabilitation approach using the unaffected hemisphere in this uncontrolled sample of chronic stroke survivors. They also demonstrate that BCI-driven neurorehabilitation can be effectively delivered in the home environment, thus increasing the probability of future clinical translation. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT02552368.

Fast-scale network dynamics in human cortex have specific spectral covariance patterns.

Whether measured by MRI or direct cortical physiology, infraslow rhythms have defined state invariant cortical networks. The time scales of this functional architecture, however, are unlikely to be able to accommodate the more rapid cortical dynamics necessary for an active cognitive task. Using invasively monitored epileptic patients as a research model, we tested the hypothesis that faster frequencies would spectrally bind regions of cortex as a transient mechanism to enable fast network interactions during the performance of a simple hear-and-repeat speech task. We term these short-lived spectrally covariant networks functional spectral networks (FSNs). We evaluated whether spectrally covariant regions of cortex, which were unique in their spectral signatures, provided a higher degree of task-related information than any single site showing more classic physiologic responses (i.e., single-site amplitude modulation). Taken together, our results showing that FSNs are a more sensitive measure of task-related brain activation and are better able to discern phonemic content strongly support the concept of spectrally encoded interactions in cortex. Moreover, these findings that specific linguistic information is represented in FSNs that have broad anatomic topographies support a more distributed model of cortical processing.

Frequency-specific mechanism links human brain networks for spatial attention.

Selective attention allows us to filter out irrelevant information in the environment and focus neural resources on information relevant to our current goals. Functional brain-imaging studies have identified networks of broadly distributed brain regions that are recruited during different attention processes; however, the dynamics by which these networks enable selection are not well understood. Here, we first used functional MRI to localize dorsal and ventral attention networks in human epileptic subjects undergoing seizure monitoring. We subsequently recorded cortical physiology using subdural electrocorticography during a spatial-attention task to study network dynamics. Attention networks become selectively phase-modulated at low frequencies (δ, θ) during the same task epochs in which they are recruited in functional MRI. This mechanism may alter the excitability of task-relevant regions or their effective connectivity. Furthermore, different attention processes (holding vs. shifting attention) are associated with synchrony at different frequencies, which may minimize unnecessary cross-talk between separate neuronal processes.

Stimulation-Evoked Effective Connectivity (SEEC): An in-vivo approach for defining mesoscale corticocortical connectivity.

BACKGROUND: Cortical electrical stimulation is a versatile technique for examining the structure and function of cortical regions and for implementing novel therapies. While electrical stimulation has been used to examine the local spread of neural activity, it may also enable longitudinal examination of mesoscale interregional connectivity. NEW METHOD: Here, we sought to use intracortical microstimulation (ICMS) in conjunction with recordings of multi-unit action potentials to assess the mesoscale effective connectivity within sensorimotor cortex. Neural recordings were made from multielectrode arrays placed into sensory, motor, and premotor regions during surgical experiments in three squirrel monkeys. During each recording, single-pulse ICMS was repeatably delivered to a single region. Mesoscale effective connectivity was calculated from ICMS-evoked changes in multi-unit firing. RESULTS: Multi-unit action potentials were able to be detected on the order of 1 ms after each ICMS pulse. Across sensorimotor regions, short-latency (< 2.5 ms) ICMS-evoked neural activity strongly correlated with known anatomical connections. Additionally, ICMS-evoked responses remained stable across the experimental period, despite small changes in electrode locations and anesthetic state. COMPARISON WITH EXISTING METHODS: Previous imaging studies investigating cross-regional responses to stimulation are limited to utilizing indirect hemodynamic responses and thus lack the temporal specificity of ICMS-evoked responses. CONCLUSIONS: These results show that monitoring ICMS-evoked neural activity, in a technique we refer to as Stimulation-Evoked Effective Connectivity (SEEC), is a viable way to longitudinally assess effective connectivity, enabling studies comparing the time course of connectivity changes with the time course of changes in behavioral function.

Nonuniform high-gamma (60-500 Hz) power changes dissociate cognitive task and anatomy in human cortex.

High-gamma-band (>60 Hz) power changes in cortical electrophysiology are a reliable indicator of focal, event-related cortical activity. Despite discoveries of oscillatory subthreshold and synchronous suprathreshold activity at the cellular level, there is an increasingly popular view that high-gamma-band amplitude changes recorded from cellular ensembles are the result of asynchronous firing activity that yields wideband and uniform power increases. Others have demonstrated independence of power changes in the low- and high-gamma bands, but to date, no studies have shown evidence of any such independence above 60 Hz. Based on nonuniformities in time-frequency analyses of electrocorticographic (ECoG) signals, we hypothesized that induced high-gamma-band (60-500 Hz) power changes are more heterogeneous than currently understood. Using single-word repetition tasks in six human subjects, we showed that functional responsiveness of different ECoG high-gamma sub-bands can discriminate cognitive task (e.g., hearing, reading, speaking) and cortical locations. Power changes in these sub-bands of the high-gamma range are consistently present within single trials and have statistically different time courses within the trial structure. Moreover, when consolidated across all subjects within three task-relevant anatomic regions (sensorimotor, Broca's area, and superior temporal gyrus), these behavior- and location-dependent power changes evidenced nonuniform trends across the population. Together, the independence and nonuniformity of power changes across a broad range of frequencies suggest that a new approach to evaluating high-gamma-band cortical activity is necessary. These findings show that in addition to time and location, frequency is another fundamental dimension of high-gamma dynamics.

A cortical injury model in a non-human primate to assess execution of reach and grasp actions: implications for recovery after traumatic brain injury.

BACKGROUND: Technological advances in developing experimentally controlled models of traumatic brain injury (TBI) are prevalent in rodent models and these models have proven invaluable in characterizing temporal changes in brain and behavior after trauma. To date no long-term studies in non-human primates (NHPs) have been published using an experimentally controlled impact device to follow behavioral performance over time. NEW METHOD: We have employed a controlled cortical impact (CCI) device to create a focal contusion to the hand area in primary motor cortex (M1) of three New World monkeys to characterize changes in reach and grasp function assessed for 3 months after the injury. RESULTS: The CCI destroyed most of M1 hand representation reducing grey matter by 9.6 mm3, 12.9 mm3, and 15.5 mm3 and underlying corona radiata by 7.4 mm3, 6.9 mm3, and 5.6 mm3 respectively. Impaired motor function was confined to the hand contralateral to the injury. Gross hand-use was only mildly affected during the first few days of observation after injury while activity requiring skilled use of the hand was impaired over three months. COMPARISON WITH EXISTING METHOD(S): This study is unique in establishing a CCI model of TBI in an NHP resulting in persistent impairments in motor function evident in volitional use of the hand. CONCLUSIONS: Establishing an NHP model of TBI is essential to extend current rodent models to the complex neural architecture of the primate brain. Moving forward this model can be used to investigate novel therapeutic interventions to improve or restore impaired motor function after trauma.

Effects of tDCS on spontaneous spike activity in a healthy ambulatory rat model.

BACKGROUND: The neurophysiological effects of transcranial direct current stimulation (tDCS) are typically described with respect to changes in cortical excitability, defined by using transcranial magnetic stimulation pulses to determine changes in motor evoked potentials. However, how individual cortical neurons change firing patterns under the influence of tDCS is largely unknown. While the relatively weak currents produced in the brain by tDCS may not be adequate to directly depolarize neuronal membranes, ongoing neuronal activity, combined with subthreshold changes in membrane polarization might be sufficient to alter the threshold for neural firing. OBJECTIVES: The purpose of this study was to determine the effects of tDCS on neurophysiological activity in motor cortex of freely moving, healthy rats. METHODS: In nine healthy, ambulatory rats, each studied under six different stimulation conditions varying in current intensity (maximum current density = 39.8 A/m2 at 0.4 mA) and polarity (anodal or cathodal), neural activity was analyzed in response to 20 min of tDCS applied through bone screws insulated from the overlying scalp. RESULTS: After analysis of 480 multi-unit channels that satisfied a rigid set of neurophysiological criteria, we found no systematic effect of tDCS stimulation condition on firing rate or firing pattern. Restricting the analysis to the most responsive units, subtle, but statistically significant changes occurred only in the highest intensity anodal condition. CONCLUSIONS: These results confirm that at current densities typically used in human or animal tDCS studies, observed effects of tDCS are likely to occur via mechanisms other than direct neuronal depolarization.

The Cortical Physiology of Ipsilateral Limb Movements.

Whereas voluntary movements have long been understood to derive primarily from the cortical hemisphere contralateral to a moving limb, substantial cortical activations also occur in the same-sided, or ipsilateral, cortical hemisphere. These ipsilateral motor activations have recently been shown to be useful to decode specific movement features. Furthermore, in contrast to the classical understanding that unilateral limb movements are solely driven by the contralateral hemisphere, it appears that the ipsilateral hemisphere plays an active and specific role in the planning and execution of voluntary movements. Here we review the movement-related activations observed in the ipsilateral cortical hemisphere, interpret this evidence in light of the potential roles of the ipsilateral hemisphere in the planning and execution of movements, and describe the implications for clinical populations.

Chronic stability of single-channel neurophysiological correlates of gross and fine reaching movements in the rat.

While substantial task-related neural activity has been observed during motor tasks in rodent primary motor cortex and premotor cortex, the long-term stability of these responses in healthy rats is uncertain, limiting the interpretability of longitudinal changes in the specific patterns of neural activity associated with learning or motor recovery following injury. This study examined the stability of task-related neural activity associated with execution of two distinct reaching tasks in healthy rodents. A novel automated rodent behavioral apparatus was constructed and rats were trained to perform a reaching task combining a 'gross' lever press and a 'fine' pellet retrieval. In each animal, two chronic microelectrode arrays were implanted in motor cortex spanning the caudal forelimb area (rodent primary motor cortex) and the rostral forelimb area (rodent premotor cortex). We recorded multiunit spiking and local field potential activity from 10 days to 7-10 weeks post-implantation to characterize the patterns of neural activity observed during each task component and analyzed the consistency of channel-specific task-related neural activity. Task-related changes in neural activity were observed on the majority of channels. While the task-related changes in multi-unit spiking and local field potential spectral power were consistent over several weeks, spectral power changes were more stable, despite the trade-off of decreased spatial and temporal resolution. These results show that neural activity in rodent primary and premotor cortex is associated with specific phases of reaching movements with stable patterns of task-related activity across time, establishing the relevance of the rodent for future studies designed to examine changes in task-related neural activity during recovery from focal cortical lesions.

The impact of high grade glial neoplasms on human cortical electrophysiology.

OBJECTIVE: The brain's functional architecture of interconnected network-related oscillatory patterns in discrete cortical regions has been well established with functional magnetic resonance imaging (fMRI) studies or direct cortical electrophysiology from electrodes placed on the surface of the brain, or electrocorticography (ECoG). These resting state networks exhibit a robust functional architecture that persists through all stages of sleep and under anesthesia. While the stability of these networks provides a fundamental understanding of the organization of the brain, understanding how these regions can be perturbed is also critical in defining the brain's ability to adapt while learning and recovering from injury. METHODS: Patients undergoing an awake craniotomy for resection of a tumor were studied as a unique model of an evolving injury to help define how the cortical physiology and the associated networks were altered by the presence of an invasive brain tumor. RESULTS: This study demonstrates that there is a distinct pattern of alteration of cortical physiology in the setting of a malignant glioma. These changes lead to a physiologic sequestration and progressive synaptic homogeneity suggesting that a de-learning phenomenon occurs within the tumoral tissue compared to its surroundings. SIGNIFICANCE: These findings provide insight into how the brain accommodates a region of "defunctionalized" cortex. Additionally, these findings may have important implications for emerging techniques in brain mapping using endogenous cortical physiology.

Electrophysiological Sequelae of Hemispherotomy in Ipsilateral Human Cortex.

Objectives: Hemispheric disconnection has been used as a treatment of medically refractory epilepsy and evolved from anatomic hemispherectomy to functional hemispherectomies to hemispherotomies. The hemispherotomy procedure involves disconnection of an entire hemisphere with limited tissue resection and is reserved for medically-refractory epilepsy due to diffuse hemispheric disease. Although it is thought to be effective by preventing seizures from spreading to the contralateral hemisphere, the electrophysiological effects of a hemispherotomy on the ipsilateral hemisphere remain poorly defined. The objective of this study was to evaluate the effects of hemispherotomy on the electrophysiologic dynamics in peri-stroke and dysplastic cortex. Methods: Intraoperative electrocorticography (ECoG) was recorded from ipsilateral cortex in 5 human subjects with refractory epilepsy before and after hemispherotomy. Power spectral density, mutual information, and phase-amplitude coupling were measured from the ECoG signals. Results: Epilepsy was a result of remote perinatal stroke in three of the subjects. In two of the subjects, seizures were a consequence of dysplastic tissue: one with hemimegalencephaly and the second with Rasmussen's encephalitis. Hemispherotomy reduced broad-band power spectral density in peri-stroke cortex. Meanwhile, hemispherotomy increased power in the low and high frequency bands for dysplastic cortex. Functional connectivity was increased in lower frequency bands in peri-stroke tissue but not affected in dysplastic tissue after hemispherotomy. Finally, hemispherotomy reduced band-specific phase-amplitude coupling in peristroke cortex but not dysplastic cortex. Significance: Disconnecting deep subcortical connections to peri-stroke cortex via a hemispherotomy attenuates power of oscillations and impairs the transfer of information from large-scale distributed brain networks to the local cortex. Hence, hemispherotomy reduces heterogeneity between neighboring cortex while impairing phase-amplitude coupling. In contrast, dysfunctional networks in dysplastic cortex lack the normal connectivity with distant networks. Therefore hemispherotomy does not produce the same effects.

Decoding three-dimensional reaching movements using electrocorticographic signals in humans.

OBJECTIVE: Electrocorticography (ECoG) signals have emerged as a potential control signal for brain-computer interface (BCI) applications due to balancing signal quality and implant invasiveness. While there have been numerous demonstrations in which ECoG signals were used to decode motor movements and to develop BCI systems, the extent of information that can be decoded has been uncertain. Therefore, we sought to determine if ECoG signals could be used to decode kinematics (speed, velocity, and position) of arm movements in 3D space. APPROACH: To investigate this, we designed a 3D center-out reaching task that was performed by five epileptic patients undergoing temporary placement of ECoG arrays. We used the ECoG signals within a hierarchical partial-least squares (PLS) regression model to perform offline prediction of hand speed, velocity, and position. MAIN RESULTS: The hierarchical PLS regression model enabled us to predict hand speed, velocity, and position during 3D reaching movements from held-out test sets with accuracies above chance in each patient with mean correlation coefficients between 0.31 and 0.80 for speed, 0.27 and 0.54 for velocity, and 0.22 and 0.57 for position. While beta band power changes were the most significant features within the model used to classify movement and rest, the local motor potential and high gamma band power changes, were the most important features in the prediction of kinematic parameters. SIGNIFICANCE: We believe that this study represents the first demonstration that truly three-dimensional movements can be predicted from ECoG recordings in human patients. Furthermore, this prediction underscores the potential to develop BCI systems with multiple degrees of freedom in human patients using ECoG.

Optimizing the Detection of Wakeful and Sleep-Like States for Future Electrocorticographic Brain Computer Interface Applications.

Previous studies suggest stable and robust control of a brain-computer interface (BCI) can be achieved using electrocorticography (ECoG). Translation of this technology from the laboratory to the real world requires additional methods that allow users operate their ECoG-based BCI autonomously. In such an environment, users must be able to perform all tasks currently performed by the experimenter, including manually switching the BCI system on/off. Although a simple task, it can be challenging for target users (e.g., individuals with tetraplegia) due to severe motor disability. In this study, we present an automated and practical strategy to switch a BCI system on or off based on the cognitive state of the user. Using a logistic regression, we built probabilistic models that utilized sub-dural ECoG signals from humans to estimate in pseudo real-time whether a person is awake or in a sleep-like state, and subsequently, whether to turn a BCI system on or off. Furthermore, we constrained these models to identify the optimal anatomical and spectral parameters for delineating states. Other methods exist to differentiate wake and sleep states using ECoG, but none account for practical requirements of BCI application, such as minimizing the size of an ECoG implant and predicting states in real time. Our results demonstrate that, across 4 individuals, wakeful and sleep-like states can be classified with over 80% accuracy (up to 92%) in pseudo real-time using high gamma (70-110 Hz) band limited power from only 5 electrodes (platinum discs with a diameter of 2.3 mm) located above the precentral and posterior superior temporal gyrus.

Current Challenges Facing the Translation of Brain Computer Interfaces from Preclinical Trials to Use in Human Patients.

Current research in brain computer interface (BCI) technology is advancing beyond preclinical studies, with trials beginning in human patients. To date, these trials have been carried out with several different types of recording interfaces. The success of these devices has varied widely, but different factors such as the level of invasiveness, timescale of recorded information, and ability to maintain stable functionality of the device over a long period of time all must be considered in addition to accuracy in decoding intent when assessing the most practical type of device moving forward. Here, we discuss various approaches to BCIs, distinguishing between devices focusing on control of operations extrinsic to the subject (e.g., prosthetic limbs, computer cursors) and those focusing on control of operations intrinsic to the brain (e.g., using stimulation or external feedback), including closed-loop or adaptive devices. In this discussion, we consider the current challenges facing the translation of various types of BCI technology to eventual human application.

The role of resting state networks in focal neocortical seizures.

OBJECTIVE: The role of resting state functional networks in epilepsy is incompletely understood. While some pathologic diagnoses have been shown to have maintained but altered resting state connectivity, others have implicated resting state connectivity in disease progression. However little is known about how these resting state networks influence the behavior of a focal neocortical seizure. METHODS: Using data taken from invasively monitored patients with intractable focal neocortical epilepsy, we evaluated network connectivity (as determined by oscillatory covariance of the slow cortical potential (<0.5 Hz)) as it relates to neocortical seizure foci both in the interictal and ictal states. RESULTS: Similar to what has been shown in the past for sleep and anesthesia, electophysiologic resting state networks that are defined by this slow cortical potential covariance maintain their topographic correlation structure throughout an ictal event. Moreover, in the context of focal epilepsy in which the seizure has a specific site of onset, seizure propagation is not chaotic or random. Rather, the seizure (reflected by an elevation of high frequency power) preferentially propagates along the network that contains the seizure onset zone. SIGNIFICANCE: Taken together, these findings further undergird the fundamental role of resting state networks, provide novel insights into the network-influenced behavior of seizures, and potentially identify additional targets for surgical disconnection including informing the location for the completion of multiple subpial transections (MSPTs).

Characterization of the effects of the human dura on macro- and micro-electrocorticographic recordings.

OBJECTIVE: Electrocorticography (ECoG) electrodes implanted on the surface of the brain have recently emerged as a potential signal platform for brain-computer interface (BCI) systems. While clinical ECoG electrodes are currently implanted beneath the dura, epidural electrodes could reduce the invasiveness and the potential impact of a surgical site infection. Subdural electrodes, on the other hand, while slightly more invasive, may have better signals for BCI application. Because of this balance between risk and benefit between the two electrode positions, the effect of the dura on signal quality must be determined in order to define the optimal implementation for an ECoG BCI system. APPROACH: This study utilized simultaneously acquired baseline recordings from epidural and subdural ECoG electrodes while patients rested. Both macro-scale (2 mm diameter electrodes with 1 cm inter-electrode distance, one patient) and micro-scale (75 µm diameter electrodes with 1 mm inter-electrode distance, four patients) ECoG electrodes were tested. Signal characteristics were evaluated to determine differences in the spectral amplitude and noise floor. Furthermore, the experimental results were compared to theoretical effects produced by placing epidural and subdural ECoG contacts of different sizes within a finite element model. MAIN RESULTS: The analysis demonstrated that for micro-scale electrodes, subdural contacts have significantly higher spectral amplitudes and reach the noise floor at a higher frequency than epidural contacts. For macro-scale electrodes, while there are statistical differences, these differences are small in amplitude and likely do not represent differences relevant to the ability of the signals to be used in a BCI system. CONCLUSIONS: Our findings demonstrate an important trade-off that should be considered in developing a chronic BCI system. While implanting electrodes under the dura is more invasive, it is associated with increased signal quality when recording from micro-scale electrodes with very small sizes and spacing. If recording from larger electrodes, such as traditionally used clinically, the signal quality of epidural recordings is similar to that of subdural recordings.

A novel data-driven approach to preoperative mapping of functional cortex using resting-state functional magnetic resonance imaging.

BACKGROUND: Recent findings associated with resting-state cortical networks have provided insight into the brain's organizational structure. In addition to their neuroscientific implications, the networks identified by resting-state functional magnetic resonance imaging (rs-fMRI) may prove useful for clinical brain mapping. OBJECTIVE: To demonstrate that a data-driven approach to analyze resting-state networks (RSNs) is useful in identifying regions classically understood to be eloquent cortex as well as other functional networks. METHODS: This study included 6 patients undergoing surgical treatment for intractable epilepsy and 7 patients undergoing tumor resection. rs-fMRI data were obtained before surgery and 7 canonical RSNs were identified by an artificial neural network algorithm. Of these 7, the motor and language networks were then compared with electrocortical stimulation (ECS) as the gold standard in the epilepsy patients. The sensitivity and specificity for identifying these eloquent sites were calculated at varying thresholds, which yielded receiver-operating characteristic (ROC) curves and their associated area under the curve (AUC). RSNs were plotted in the tumor patients to observe RSN distortions in altered anatomy. RESULTS: The algorithm robustly identified all networks in all patients, including those with distorted anatomy. When all ECS-positive sites were considered for motor and language, rs-fMRI had AUCs of 0.80 and 0.64, respectively. When the ECS-positive sites were analyzed pairwise, rs-fMRI had AUCs of 0.89 and 0.76 for motor and language, respectively. CONCLUSION: A data-driven approach to rs-fMRI may be a new and efficient method for preoperative localization of numerous functional brain regions.

Using ipsilateral motor signals in the unaffected cerebral hemisphere as a signal platform for brain-computer interfaces in hemiplegic stroke survivors.

Brain-computer interface (BCI) systems have emerged as a method to restore function and enhance communication in motor impaired patients. To date, this has been applied primarily to patients who have a compromised motor outflow due to spinal cord dysfunction, but an intact and functioning cerebral cortex. The cortical physiology associated with movement of the contralateral limb has typically been the signal substrate that has been used as a control signal. While this is an ideal control platform in patients with an intact motor cortex, these signals are lost after a hemispheric stroke. Thus, a different control signal is needed that could provide control capability for a patient with a hemiparetic limb. Previous studies have shown that there is a distinct cortical physiology associated with ipsilateral, or same-sided, limb movements. Thus far, it was unknown whether stroke survivors could intentionally and effectively modulate this ipsilateral motor activity from their unaffected hemisphere. Therefore, this study seeks to evaluate whether stroke survivors could effectively utilize ipsilateral motor activity from their unaffected hemisphere to achieve this BCI control. To investigate this possibility, electroencephalographic (EEG) signals were recorded from four chronic hemispheric stroke patients as they performed (or attempted to perform) real and imagined hand tasks using either their affected or unaffected hand. Following performance of the screening task, the ability of patients to utilize a BCI system was investigated during on-line control of a one-dimensional control task. Significant ipsilateral motor signals (associated with movement intentions of the affected hand) in the unaffected hemisphere, which were found to be distinct from rest and contralateral signals, were identified and subsequently used for a simple online BCI control task. We demonstrate here for the first time that EEG signals from the unaffected hemisphere, associated with overt and imagined movements of the affected hand, can enable stroke survivors to control a one-dimensional computer cursor rapidly and accurately. This ipsilateral motor activity enabled users to achieve final target accuracies between 68% and 91% within 15 min. These findings suggest that ipsilateral motor activity from the unaffected hemisphere in stroke survivors could provide a physiological substrate for BCI operation that can be further developed as a long-term assistive device or potentially provide a novel tool for rehabilitation.