Browsing by Author "Grossman, Robert"

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    Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation
    (Elsevier, 2020) Bhagat, Nikunj A.; Yozbatiran, Nuray; Sullivan, Jennifer L.; Paranjape, Ruta; Losey, Colin; Hernandez, Zachary; Keser, Zafer; Grossman, Robert; Francisco, Gerard E.; O'Malley, Marcia K.; Contreras-Vidal, Jose L.; Mechatronics and Haptic Interfaces Laboratory
    Brain-machine interfaces (BMI) based on scalp EEG have the potential to promote cortical plasticity following stroke, which has been shown to improve motor recovery outcomes. However, the efficacy of BMI enabled robotic training for upper-limb recovery is seldom quantified using clinical, EEG-based, and kinematics-based metrics. Further, a movement related neural correlate that can predict the extent of motor recovery still remains elusive, which impedes the clinical translation of BMI-based stroke rehabilitation. To address above knowledge gaps, 10 chronic stroke individuals with stable baseline clinical scores were recruited to participate in 12 therapy sessions involving a BMI enabled powered exoskeleton for elbow training. On average, 132 ± 22 repetitions were performed per participant, per session. BMI accuracy across all sessions and subjects was 79 ± 18% with a false positives rate of 23 ± 20%. Post-training clinical assessments found that FMA for upper extremity and ARAT scores significantly improved over baseline by 3.92 ± 3.73 and 5.35 ± 4.62 points, respectively. Also, 80% participants (7 with moderate-mild impairment, 1 with severe impairment) achieved minimal clinically important difference (MCID: FMA-UE >5.2 or ARAT >5.7) during the course of the study. Kinematic measures indicate that, on average, participants’ movements became faster and smoother. Moreover, modulations in movement related cortical potentials, an EEG-based neural correlate measured contralateral to the impaired arm, were significantly correlated with ARAT scores (ρ = 0.72, p < 0.05) and marginally correlated with FMA-UE (ρ = 0.63, p = 0.051). This suggests higher activation of ipsi-lesional hemisphere post-intervention or inhibition of competing contra-lesional hemisphere, which may be evidence of neuroplasticity and cortical reorganization following BMI mediated rehabilitation therapy.
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    Using Self-Organizing Maps to discover functional relationships of brain areas from fMRI images
    (2014-04-23) O'Driscoll, Patrick; Merenyi, Erzsebet; Kelly, Kevin F.; Robinson, Jacob T.; Grossman, Robert; Karmonik, Christof
    This thesis combines a Conscious Self-Organizing Map (SOM) with an interactive clustering method to analyze functional Magnetic Resonance Imaging (fMRI) data to produce improved brain maps compared to maps produced at The Methodist Hospital and in the literature focusing on similar problems. My new maps exhibit an increased level of symmetry, contiguity, coincidence with functional region, and more complete mapping of functional regions. The examined fMRI data contains brain activations of a subject repeatedly executing willed motion in response to a visual stimulus. Clustering the data from this experiment first determines the optimal preprocessing steps for cluster extraction, and second proves that the Conscious SOM provides a valid brain map that identifies interacting brain regions during the sequence of willed motion. I determined that the geometric rectification, motion correction, temporal smoothing, and normalization preprocessing steps facilitate the best clustering.