Quantifying defensive behavior and threat response through integrated headstage accelerometry

Younk, Rebecca; Widge, Alik S.

doi:10.1016/j.jneumeth.2022.109725

Cited by 1 publication

(4 citation statements)

References 14 publications

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“…As illustrated in Figure 2(c) , our decoders demonstrated better performance in capturing these broader timescales (trends or global means) than short-term variability in freezing score. This observation aligns with our previous behavioral research, which indicated that the mean freezing score across subjects correlated more closely with the mean accelerometry jerk and bar press rate, than when analyzing individual subjects [ 83 ]. This may be attributed to the averaging process across subjects, which effectively smoothed away local variance while preserving global trends, thereby rendering freezing score more comparable with other measured behaviors.…”

Section: Discussionsupporting

confidence: 90%

“…This research builds upon our previous work, which underscored that these metrics each capture unique facets of defensive behavior [ 83 ]. The variation in encoding between behaviors suggests that they may have distinct neural substrates, i.e., that a closed-loop system designed to modulate defensive processes might need to control different aspects of cortical/amygdala physiology depending on the exact process being targeted.…”

Section: Discussionmentioning

confidence: 93%

“…Quantification of defensive behavior adhered to the methodology established in a prior study [ 83 ]. The change in total acceleration, herein referred to as “accelerometry jerk”, was computed as:

where

is the accelerometry jerk as a function of time t , and

, and

are the voltages of the accelerometer in

, and

axes, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Freezing and bar press suppression are canonical defensive behaviors that have been studied for decades [ 20 – 22 , 80 – 82 ]. Accelerometry jerk is a newer metric we have proposed and shown to correlate with, but not fully overlap the two other measures [ 83 ]. We developed a decoding framework based on Light Gradient-Boosting Machine (LightGBM), which outperformed other state-of-the-art ML-based decoders in both accuracy and latency.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 93%

where

is the accelerometry jerk as a function of time t , and

, and

are the voltages of the accelerometer in

, and

axes, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Liu,

Younk,

Drahos

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Quantifying defensive behavior and threat response through integrated headstage accelerometry

Cited by 1 publication

References 14 publications

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

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

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