Publications by authors named "R D Rothermel"

Article Synopsis
  • The research focuses on developing a deep convolutional neural network (DCNN) method to classify brain tracts, aiming to better predict short-term language improvement after surgery using brain connectivity data.
  • A three-step method was used, involving enhancing the classification of brain tracks, analyzing language networks, and employing machine learning to assess language improvement before and after surgery.
  • Results showed a significant increase in accuracy for predicting postoperative language improvements, suggesting that this approach could help doctors identify patients who may benefit most from early surgical intervention.
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Purpose: A prominent view of language acquisition involves learning to ignore irrelevant auditory signals through functional reorganization, enabling more efficient processing of relevant information. Yet, few studies have characterized the neural spatiotemporal dynamics supporting rapid detection and subsequent disregard of irrelevant auditory information, in the developing brain. To address this unknown, the present study modeled the developmental acquisition of cost-efficient neural dynamics for auditory processing, using intracranial electrocorticographic responses measured in individuals receiving standard-of-care treatment for drug-resistant, focal epilepsy.

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This prospective study determined the use of intracranially recorded spectral responses during naming tasks in predicting neuropsychological performance following epilepsy surgery. We recruited 65 patients with drug-resistant focal epilepsy who underwent preoperative neuropsychological assessment and intracranial EEG recording. The Clinical Evaluation of Language Fundamentals evaluated the baseline and postoperative language function.

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During a verbal conversation, our brain moves through a series of complex linguistic processing stages: sound decoding, semantic comprehension, retrieval of semantically coherent words, and overt production of speech outputs. Each process is thought to be supported by a network consisting of local and long-range connections bridging between major cortical areas. Both temporal and extratemporal lobe regions have functional compartments responsible for distinct language domains, including the perception and production of phonological and semantic components.

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This retrospective cohort study investigated 53 patients with drug-resistant focal epilepsy and identified factors predictive of long-term satisfaction of patients and families following extraoperative intracranial EEG (iEEG) recording. The mixed model analysis assessed the utility of intracranial EEG (iEEG) predictor variables, including the seizure-onset zone (SOZ), modulation index (MI), and naming-related high-gamma activity. Modulation index, quantifying the coupling between high-frequency activity at >80 Hz and local slow wave at 3-4 Hz, effectively functions as a surrogate marker of the burden of interictal spike-and-slow-wave discharges.

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