Changes in intracranial neurophysiology associated with acute COVID-19 infection

Sellers, Kristin K.; Stapper, Noah; Maya, Daniela A. Astudillo; Henderson, Catherine; Khambhati, Ankit N.; Fan, Joline M.; Rao, Vikram R.; Chang, Edward F.; Krystal, Andrew D.

doi:10.1016/j.clinph.2023.01.012

Cited by 3 publications

(1 citation statement)

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“…In the aforementioned studies, beyond the decoding model utilized for prediction, the neuro-markers derived from neural data were crucial for decoding efficacy. The majority of previous efforts to detect psychiatric symptoms and behaviors have focused on classical spectral power features [ 38 , 62 , 66 , 70 , 71 ]. It is not clear that spectral power is the best feature for decoding complex cognitive-emotional phenomena such as fear/defensive behaviors.…”

Section: Introductionmentioning

confidence: 99%

Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior

Liu,

Younk,

Drahos

et al. 2024

Preprint

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ObjectiveMany psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.ApproachWe analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.Main resultsBand power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring<110 ms for training and<1 ms for inference.SignificanceOur results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

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