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Introduction: Brain-computer interface (BCI)-EEG is a promising intervention for improving motor function after stroke. However, brain changes following intervention on a BCI-EEG system are not yet fully understood. We examined changes in resting state functional connectivity (RSFC) MRI in the motor network defined by 6 key regions in the left and right primary motor cortex (M1), left and right supplementary motor area (SMA), and left and right premotor cortex (PMC). Additionally, we investigated brain-behavior correlation between rsFC and a battery of outcome measures including the Barthel Index (BI), the Stroke Impact Scale(SIS), and the Action Research Arm Test (ARAT). Methods: Fifteen stroke patients with persistent mild to severe upper extremity impairment following ischemic stroke received intervention using BCI-EEG and were tested before (T1) and at 2-3 weeks (T2) mid intervention. 11 of these patients were also tested a third time at 4-6 weeks at the end of intervention (T3). Eyes closed, 10 minute resting fMRI and anatomical scans were acquired on a GE 3T MRI scanner. Right hemisphere stroke patients’ scans were flipped so that as a group the lesion was in the left (L) hemisphere and the impaired limb right (R). Seed region based connectivity analyses were performed to examine changes in RSFC over time and in inter-hemispheric and intra-hemispheric connectivity, and correlations between brain changes and behavioral changes were investigated. Results: BCI-EEG intervention led to significant increase in intra-hemispheric connectivity (p = .03) from T1 to T3. Inter-hemispheric connectivity increased from T1 to T3, trending towards significance (p = .06). Significant positive correlations were observed between changes in RSFC (L.M1 and L.PMC, L.M1 and R.PMC, L.SMA and R.PMC, and R.PMC and R.SMA) and change in upper extremity BI score (p ranging from .01 to .001); changes in RSFC between L.PMC and R.PMC correlated with hand strength on the SIS (p = .03). A trend was observed between increase in RSFC (L.M1 and R.PMC) and increase in total ARAT score but this was not significant. Conclusions: Results suggest that BCI-EEG intervention facilitate changes in RSFC in the motor network in stroke patients and these changes are associated with improved outcomes.

Introduction: Brain-computer interface (BCI) is an emerging technology for stroke rehabilitation, but little is known about how the administration of these therapies affects brain and behavior. We examine relationships between BCI therapy dose, intensity, and frequency with changes in laterality index (LI), Action Research Arm Test (ARAT), and Stroke Impact Scale (SIS). Methods: We collected ARAT and SIS scores as well as anatomical and functional MRI of 16 stroke patients with upper extremity motor impairment before, during, and after therapy using a BCI system with tongue and functional electrical stimulation. We acquired fMRI during finger tapping of each hand and computed LI with 3 mask sets at 2 thresholds. Changes from baseline LI and behavioral scores were assessed for correlation with cumulative therapy sessions, cumulative BCI runs, and overall runs/session using Spearman analysis and generalized estimating equations (GEE). Changes from baseline were also compared between subjects with different therapy frequencies. Results: Increased sessions correlated with changes in impaired finger tapping LI in 2 of 6 mask-threshold combinations (p Conclusions: Patterns of brain activation shift toward contralesional hemisphere with additional therapy sessions and additional BCI runs. Increased BCI therapy intensity also correlates with increased self-reported strength. When using BCI in stroke rehabilitation, therapy frequency may be less important than dose and intensity, and neuroimaging and self-report measures may be more sensitive to differences in therapy administration.

Introduction: Brain-computer interface (BCI) is an emerging technology for stroke rehabilitation, but little is known about brain changes associated with its use. We examine changes in laterality index (LI) and functional connectivity (FC) during hand movements associated with BCI interventional therapy. Methods: We collected anatomical and functional MRI of 8 stroke patients with upper extremity motor impairment before, during, and after up to 6 weeks of therapy using a BCI system with tongue and functional electrical stimulations. We acquired functional images during imagined (MI) and executed (ME) tapping and squeezing of each hand; not all subjects performed all tasks. Two subjects’ scans were flipped so that as a group the lesion was left (L) and the impaired limb right (R). We computed LI using 3 mask sets: whole brain, motor network, and motor cortex. Group-level analyses examined FC changes to motor network seeds using AFNI and Matlab NBS toolbox. Results: BCI intervention led to decreased average LI during tapping. Overall, R taps shifted L to bilateral; all L taps shifted bilateral to R. Trends were consistent across all masks at thresholds p Conclusions: BCI interventional therapy of the impaired hand leads to more bilateral brain activity, while more lateralized activation was seen of the unimpaired hand to its corresponding contralateral motor regions. With different patterns of change observed during tasks using the impaired or unimpaired hand, lesioned brain areas may respond differently to BCI than unlesioned counterparts.

Background: In this ongoing study we are testing a closed-loop neurological feedback device that can facilitate functional recovery in stroke patients with upper extremity motor deficits. Methods: This device combines Brain Computer Interface (BCI) and functional electrical muscle stimulation (FES), together with tongue stimulation (TS) in order to utilize the subject’s intention-to-move with the stimulated output. FMRI is used to examine the brain plasticity changes secondary to the rehabilitation. Subjects, wearing a 16-channel EEG cap, are first trained to voluntarily modulate beta and mu rhythms as they use motor imagery or execution of left and right hand squeezing task and then trained to use this imagery or execution to control the movement of a cursor, either to the right or the left depending on the presentation of a target rectangle shown on the screen. Once subjects achieve consistent accuracy in doing this task, FES in conjunction with TS is used. The subject is then asked to perform the task with the stimulation of FES and TS linked to their task performance. Results: Two chronic stroke patients (mean age=57, 1 male, more than 1 year postonset) were able to complete the entire 3-week BCI training course and to perform the tasks at a > 70% success rate. Cortical activation recorded by EEG in response to attempted paretic arm movement became concentrated over the contralateral motor areas. Similar changes were confirmed by fMRI measures (Figure 1). Although neither subject showed any improvement on the Action Research Arm Test (ARAT), subjects self-reported increased strength, less spasticity and a greater range of movement in their paretic arm. Conclusion: Our preliminary results indicate that training with the device may lead to brain plasticity changes toward normalization of cortical activation patterns and promote behavioral improvements for stroke patients even in their chronic stage.

Objective: Stroke can have important effects on widespread brain regions resulting in network disruption. This study investigates the changes in spontaneous activity in the brain after stroke using resting-state functional connectivity(FC) MRI. Methods: Acute ischemic stroke patients (N=22, 11 cortical, mean age=60, 10F) were recruited within 7 days of stroke onset(timepoint 1, V1). Eleven of these patients were also scanned at timepoint 2(V2), approximately 3 months later. Age-matched healthy controls (N=17, mean age = 56, 7F) and 7 patients with risk factors for stroke (mean age=67, 1F) were also recruited in the study. Ten minute eyes-closed resting-state fMRI scans were collected along with a high resolution anatomical scan. We examined FC in the language network consisting of ten regions extracted based on functional activations on a phonemic fluency task performed during a separate scan. We also examined the correlation of brain FC with behavioral performance on the task outside the scanner. Results: We examined correlation between every seed region and every other region in the network(45 seed region-pairs). Compared to age-matched controls, acute strokes showed significantly reduced inter-hemispheric connectivity, specifically between inferior frontal and temporal regions(p Conclusions: These results suggest that acute strokes show reduced brain connectivity between contralateral regions in the language network; however, this connectivity pattern is restored in the sub-acute stage.