Paired Electrical Pulse Trains for Controlling Connectivity in Emotion-Related Brain Circuitry

Lo, Meng-chen; Younk, Rebecca; Widge, Alik S.

doi:10.1109/tnsre.2020.3030714

Cited by 18 publications

(19 citation statements)

References 35 publications

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“…An adaptor connected the recording head stage (RHD 2132, Intan Technologies LLC, Los Angeles, CA, USA) to two Mill-max male-male connectors (8 channels each, Part number: ED90267-ND, Digi-Key Electronics, Thief River Falls, MN, USA). For additional details on data collection, please refer to (Lo et al, 2020). We analyze here 250 s of LFP data from one channel.…”

Section: In Vivo Datamentioning

confidence: 99%

A state space modeling approach to real-time phase estimation

Wodeyar

Schatza

Widge

et al. 2021

eLife

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Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the OpenEphys acquisition system, making it widely available for use in experiments.

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Section: In Vivo Datamentioning

confidence: 99%

A state space modeling approach to real-time phase estimation

Wodeyar

Schatza

Widge

et al. 2021

eLife

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“…There is a growing set of TMS and similar tools for manipulating brain connectivity. 79, 80 For any individual patient, the approach we describe permits the identification of which DCM feature(s) are driving that patient’s vulnerability. Although this is still a speculative claim, it may be possible to then target and normalize those specific features.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Towards Biophysical Markers of Depression Vulnerability

Pinotsis

Fitzgerald

See

et al. 2021

Preprint

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A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. We constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

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“…Once they reached a minimum rate of 10 presses per minute on VI-60, they moved on to electrode implantation and a behavioral paradigm post-surgery. Microelectrode bundles as described in Lo et al, (2020) were implanted in the prefrontal cortex and amygdala, and the results of electrophysiologic recordings will be described in a separate paper.…”

Section: Methodsmentioning

confidence: 99%

Quantifying defensive behavior and threat response through integrated headstage accelerometry

Younk

Widge

2021

Preprint

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Background Defensive and threat-related behaviors are common targets of investigation, because they model aspects of human mental illness. These behaviors are typically quantified by video recording and post hoc analysis. Those quantifications can be laborious and/or computationally intensive. Depending on the analysis method, the resulting measurements can be noisy or inaccurate. Other defensive behaviors, such as suppression of operant reward seeking, require extensive animal pre-training. New Method We demonstrate a method for quantifying defensive behavior (immobility or freezing) by 3-axis accelerometry integrated with an electrophysiology headstage. We tested multiple pre-processing and smoothing methods, and correlated them against two common methods for quantification: freezing as derived from standard video analysis, and suppression of operantly shaped bar pressing. We assessed these three methods' ability to track defensive behavior during a standard threat conditioning and extinction paradigm. Results The best approach to tracking defensive behavior from accelerometry was Gaussian filter smoothing of the first derivative (change score or jerk). Behavior scores from this method reproduced canonical conditioning and extinction curves at the group level. At the individual level, timepoint-to-timepoint correlations between accelerometry, video, and bar press metrics were statistically significant but modest (largest r=0.53, between accelerometry and bar press). Comparison with existing methods The integration with standard electrophysiology systems and relatively lightweight signal processing may make accelerometry particularly well suited to detect behavior in resource-constrained or real-time applications. At the same time, there were modest cross-correlations between all three methods for quantifying defensive behavior. Conclusions Accelerometry analysis allows researchers already using electrophysiology to assess defensive behaviors without the need for additional behavioral measures or video. The similarities in behavioral tracking and modest correlations between each metric suggest that each measures a distinct aspect of defensive behavior. Accelerometry is a viable alternative to current defensive measurements, and its non-overlap with other metrics may allow a more sophisticated dissection of threat responses in future experiments.

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Paired Electrical Pulse Trains for Controlling Connectivity in Emotion-Related Brain Circuitry

Cited by 18 publications

References 35 publications

A state space modeling approach to real-time phase estimation

A state space modeling approach to real-time phase estimation

Towards Biophysical Markers of Depression Vulnerability

Quantifying defensive behavior and threat response through integrated headstage accelerometry

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