Loss of motor function is a common deficit following stroke insult and often manifests as persistent upper extremity (UE) disability which can affect a survivor’s ability to participate in activities of daily living. Recent research suggests the use of brain–computer interface (BCI) devices might improve UE function in stroke survivors at various times since stroke. This randomized crossover-controlled trial examines whether intervention with this BCI device design attenuates the effects of hemiparesis, encourages reorganization of motor related brain signals (EEG measured sensorimotor rhythm desynchronization), and improves movement, as measured by the Action Research Arm Test (ARAT). A sample of 21 stroke survivors, presenting with varied times since stroke and levels of UE impairment, received a maximum of 18–30 h of intervention with a novel electroencephalogram-based BCI-driven functional electrical stimulator (EEG-BCI-FES) device. Driven by spectral power recordings from contralateral EEG electrodes during cued attempted grasping of the hand, the user’s input to the EEG-BCI-FES device modulates horizontal movement of a virtual cursor and also facilitates concurrent stimulation of the impaired UE. Outcome measures of function and capacity were assessed at baseline, mid-therapy, and at completion of therapy while EEG was recorded only during intervention sessions. A significant increase in r-squared values [reflecting Mu rhythm (8–12 Hz) desynchronization as the result of attempted movements of the impaired hand] presented post-therapy compared to baseline. These findings suggest that intervention corresponds with greater desynchronization of Mu rhythm in the ipsilesional hemisphere during attempted movements of the impaired hand and this change is related to changes in behavior as a result of the intervention. BCI intervention may be an effective way of addressing the recovery of a stroke impaired UE and studying neuromechanical coupling with motor outputs.Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02098265.

Stroke is a leading cause of persistent upper extremity (UE) motor disability in adults. Brain–computer interface (BCI) intervention has demonstrated potential as a motor rehabilitation strategy for stroke survivors. This sub-analysis of ongoing clinical trial (NCT02098265) examines rehabilitative efficacy of this BCI design and seeks to identify stroke participant characteristics associated with behavioral improvement. Stroke participants (n = 21) with UE impairment were assessed using Action Research Arm Test (ARAT) and measures of function. Nine participants completed three assessments during the experimental BCI intervention period and at 1-month follow-up. Twelve other participants first completed three assessments over a parallel time-matched control period and then crossed over into the BCI intervention condition 1-month later. Participants who realized positive change (≥1 point) in total ARAT performance of the stroke affected UE between the first and third assessments of the intervention period were dichotomized as “responders” (<1 = “non-responders”) and similarly analyzed. Of the 14 participants with room for ARAT improvement, 64% (9/14) showed some positive change at completion and approximately 43% (6/14) of the participants had changes of minimal detectable change (MDC = 3 pts) or minimally clinical important difference (MCID = 5.7 points). Participants with room for improvement in the primary outcome measure made significant mean gains in ARATtotal score at completion (ΔARATtotal = 2, p = 0.028) and 1-month follow-up (ΔARATtotal = 3.4, p = 0.0010), controlling for severity, gender, chronicity, and concordance. Secondary outcome measures, SISmobility, SISadl, SISstrength, and 9HPTaffected, also showed significant improvement over time during intervention. Participants in intervention through follow-up showed a significantly increased improvement rate in SISstrength compared to controls (p = 0.0117), controlling for severity, chronicity, gender, as well as the individual effects of time and intervention type. Participants who best responded to BCI intervention, as evaluated by ARAT score improvement, showed significantly increased outcome values through completion and follow-up for SISmobility (p = 0.0002, p = 0.002) and SISstrength (p = 0.04995, p = 0.0483). These findings may suggest possible secondary outcome measure patterns indicative of increased improvement resulting from this BCI intervention regimen as well as demonstrating primary efficacy of this BCI design for treatment of UE impairment in stroke survivors.Clinical Trial Registration: ClinicalTrials.gov, NCT02098265.

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Segmenting the human brain into networks has been a useful approach in analyzing functional connectivity. Brain network bundling can determine which regions are engaged and if they are working together. The thalamus (THL) and basal ganglia (BSL) regions in the subcortical network are linked to multiple cortical areas due to their roles in neural circuitry outlined in the cortico-basal ganglia-thalamo cortical map. Here we explore their coupling with the default mode network (DMN), frontoparietal network (FPN), salience network (SAN), attention network (ATN), sensorimotor network (SSM), visual network (VIS), and auditory network (AUD) using the energy landscape technique. Energy landscape analysis helps identify the statistical differences in functional behaviors between the healthy control and patient groups, which are obtained from the fMRI activity time courses of the 9 internetworks. In this work, we focused on studying 107 schizophrenic patients and 86 healthy controls and obtained the constructed activity patterns and disconnectivity graphs of each subject. The differences between two groups are compared. The results from bundling THL and BSL with the DMN, FPN, SAN, ATN, SSM, VIS, and AUD shows that these regions are more strongly coupled in controls than in patients. After performing energy calculations and heat map generations, we observed several lower energy band states that are common among all control and patient subjects. The potential implications of these common band states are discussed.

Computational neuroscience models can be used to understand neural dynamics in the brain and these dynamics change as the physiological and other conditions like aging. One such approach we have used in this work is Energy Landscape analysis based on resting-state fMRI data. The dataset consists of 70 subjects with normal cognitive function, of which 23 are young adults and 47 are old adults. In this analysis, disconnectivity graphs and activity patterns are generated and using connectivity statistics among seven prominent brain networks. To study brain dynamic behaviors, we perform sliding window studies on the dataset and observe local minima of each window evolving in time. By varying the window shift from multiple seconds to 1 second, we can obtain statistics and evaluate the speed and activity pattern holding time of individual and group subjects. We found that older subjects can hold the brain states for a longer time but then jump to other dominated brain state local minima with a large hamming distance, whereas young subjects change dominated local minima more frequently but with a small hamming distance of 1 or 2. In fact, when averaged over the full time course, old subjects have more stable brain states local minima compared to young subjects. For both young and old subjects, the default mode network (DMN) and visual network (VIS) are coupled but for young subjects the two networks are on and off together and strongly correlated. For old subjects, there is an extra dominated brain state local minimum that the DMN and attention network (ATN) are correlated and anti-correlated with (VIS) and sensory-motor networks (SMN). This state may suggest old subjects are more capable of focusing on brain internal models and not getting influenced by external visual and sensory factors than young subjects.