Biology subjects

Bernabei, J. M.

Publications and source records attributed to Bernabei, J. M..

4 recordsLinked to original sources

bioRxiv2022

A Taxonomy of Seizure Spread Patterns, Speed of Spread, and Associations With Structural Connectivity

Although seizure detection algorithms are widely used to localize seizure onset on intracranial EEG in epilepsy patients, relatively few studies focus on seizure activity beyond the seizure onset zone to direct treatment of surgical patients with epilepsy. To address this gap, we develop and compare fully automated deep learning algorithms to detect seizure activity on single channels, effectively quantifying spread when deployed across multiple channels. Across 275 seizures in 71 patients, we discover that the extent of seizure spread across the brain and the timing of seizure spread between temporal lobe regions is associated with both surgical outcomes and the brains structural connectivity between temporal lobes. Finally, we uncover a hierarchical structure of seizure spread patterns highlighting the relationship between clusters of seizures. Collectively, these findings underscore the broad utility in quantifying seizure activity past seizure onset to identify novel mechanisms of seizure evolution and its relationship to potential seizure freedom.

neuroscience↗

bioRxiv2022

"Primed to Perform:" Dynamic white matter graph communicability may drive metastable network representations of enhanced preparatory cognitive control

Spontaneous neural activity has become increasingly linked to behavioral and cognitive output. A specific cognitive control mode, proactive control, uses prior information to plan and prepare the brain to be particularly sensitive to incoming goal-directed stimuli. Little is known about specific proactive mechanisms implemented via preparatory patterns of spontaneous neural activity, that may enable dynamically enhanced cognitive performance. In this study, humans implanted with intracranial electrodes performed a simple cognitive task. For each subject, pre-trial spectral power and communicability-based features from both grey and white matter nodes were extracted to identify preparatory control states that were "primed to perform". The anatomical structure and topology of these states across subjects demonstrated a critical role for white matter communicability in decoding and intrinsically controlling preparatory network activity. Our results provide novel insights for putative cognitive network control and may be studied to develop prosthetic approaches for individuals with cognitive deficits.

neuroscience↗

bioRxiv2021

White Matter Signals Reflect Information Transmission Between Brain Regions During Seizures

White matter supports critical brain functions such as learning and memory, modulates the distribution of action potentials, and transmits neural information between brain regions. Notably, neuronal cell bodies exist in deeper white matter tissue, neurotransmitter vesicles are released directly in white matter, and white matter blood-oxygenation level dependent (BOLD) signals are detectable across a range of different tasks--all appearing to reflect a dynamic, active tissue from which recorded signals can reveal meaningful information about the brain. Yet, the signals within white matter have largely been ignored. Here, we elucidate the properties of white matter signals using intracranial EEG in a bipolar montage. We show that such signals capture the communication between brain regions and differentiate pathophysiologies of epilepsy. In direct contradiction to past assumptions that white matter functional signals provide little value, we show that white matter recordings can elucidate brain function and pathophysiology. Broadly, white matter functional recordings acquired through implantable devices may provide a wealth of currently untapped knowledge about the neurobiology of disease.

neuroscience↗

bioRxiv2019

How sensitive is functional connectivity to electrode resampling on intracranial EEG? Implications for personalized network models in drug-resistant epilepsy

Focal epilepsy is a clinical condition arising from disordered brain networks. Network models hold promise to map these networks, localize seizure generators, and inform targeted interventions to control seizures. However, incomplete sampling of epileptic brain due to sparse placement of intracranial electrodes may profoundly affect model results. In this study, we evaluate the robustness of several published network measures applied to intracranial electrode recordings and propose an algorithm, using network resampling, to determine confidence in model results. We retrospectively subsampled intracranial EEG data from 28 patients who were implanted with grid, strip, and depth electrodes during evaluation for epilepsy surgery. We recalculated global and local network metrics after both randomly and systematically resampling subsets of intracranial EEG electrode contacts. We found that sensitivity to incomplete sampling varied significantly across network metrics, and that this sensitivity was independent of the distance of removed contacts from the seizure onset zone. We present an algorithm, using random resampling, to compute patient-specific confidence intervals for network localizations on both global and nodal network statistics. Our findings highlight the difference in robustness between commonly used network metrics and provide tools to assess confidence in intracranial network localization. We present these techniques as an important step toward assessing the accuracy of intracranial electrode implants and translating personalized network models of seizures into rigorous, quantitative approaches to invasive therapy.

bioengineering↗