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

Autor:	Liu J; Institute of Electrical and Micro Engineering, EPFL, Lausanne, Switzerland.; Neuro-X Institute, EPFL, Geneva, Switzerland., Younk R; Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA., Drahos LM; Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA., Nagrale SS; Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA., Yadav S; Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA., Widge AS; Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.; These authors jointly supervised this work., Shoaran M; Institute of Electrical and Micro Engineering, EPFL, Lausanne, Switzerland.; Neuro-X Institute, EPFL, Geneva, Switzerland.; These authors jointly supervised this work.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Jun 26. Date of Electronic Publication: 2024 Jun 26.
DOI:	10.1101/2024.06.06.597165
Abstrakt:	Objective: Many 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. Approach: We 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 Results: Band 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. Significance: Our 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.
Databáze:	MEDLINE
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