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Williams Roberson, S.

Publications and source records attributed to Williams Roberson, S..

2 recordsLinked to original sources

Frontoparietal activity related to neuropsychological assessment of working memory

Executive functions, including working memory, are typically assessed clinically with neuropsychological instruments. In contrast, computerized tasks are used to test these cognitive functions in laboratory human and animal studies. Little is known of how neural activity captured by laboratory tasks relates to ability measured by clinical instruments and, by extension, clinical diagnoses of pathological conditions. We therefore sought to determine what aspects of neural activity elicited in laboratory tasks are predictive of performance in neuropsychological instruments. We recorded neural activity from intracranial electrodes implanted in human epilepsy patients as they performed laboratory working memory tasks. These patients had completed neuropsychological instruments preoperatively, including the Weschler Adult Intelligent Scale and the Wisconsin Card Sorting test. Our results revealed that increased high-gamma (70-150 Hz) power in the prefrontal and parietal cortex after presentation of visual stimuli to be remembered was indicative of lower performance in the neuropsychological tasks. On the other hand, we observed a positive correlation between high-frequency power amplitude in the delay period of the laboratory tasks and neuropsychological performance. Our results demonstrate how neural activity around task events relates to executive function and may be associated with clinical diagnosis of specific cognitive deficits.

neuroscience↗

Brain-state modeling using electroencephalography: Application to adaptive closed-loop neuromodulation for epilepsy

The progress of developing an effective closed-loop neuromodulation system for many neurological pathologies is hindered by the difficulties in accurately capturing a useful representation of a brains instantaneous functional state. Existing approaches rely on expert labeling of electroencephalography data to develop biomarkers of neurophysiological pathology. These techniques do not capture the highly complex functional states of the brain that are presumed to exist between labeled states or allow for the likely possibility of variation among identically labeled states. Thus, we propose BrainState, a self-supervised technique to model an arbitrarily complex instantaneous functional state of a brain using neural multivariate timeseries data. Application of BrainState to intracranial electroencephalography data from patients with epilepsy was able to capture diverse pre-seizure states and quantify nuanced effects of neuromodulation. We anticipate that BrainState will enable the development of sophisticated closed-loop neuromodulation systems for a diverse array of neurological pathologies.

neuroscience↗