Unsupervised discovery of behaviorally relevant brain states in rats playing hide-and-seek

Bagi, Bence; Brecht, Michael; Sanguinetti-Scheck, Juan Ignacio

doi:10.1016/j.cub.2022.04.068

Cited by 9 publications

(7 citation statements)

References 52 publications

(75 reference statements)

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“…21,26,27 A recent interesting study applied HMMs to cell recordings in the rat PFC and found states that correlated with hide-and-seek behaviors, as well as states corresponding to previously undetected behaviors. 58 In general, although HMM models applied to brain data have shown to be a promising strategy to uncover unlabeled states, further work is needed to assess their performance in continuous paradigms in a manner that rigorously establishes the correspondence between states and external stimuli and/or behavior.…”

Section: Discussionmentioning

confidence: 99%

Brain dynamics and spatiotemporal trajectories during threat processing

Misra,

Pessoa

2024

Preprint

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In the past decades, functional MRI research has investigated mental states and their brain bases in largely static fashion based on evoked responses during blocked and event-related designs. Despite some progress in naturalistic designs, our understanding of threat processing remains largely limited to those obtained with standard paradigms. In the present paper, we applied Switching Linear Dynamical Systems to uncover the dynamics of threat processing during a continuous threat-of-shock paradigm. Importantly, unlike studies in systems neuroscience that frequently assume that systems are decoupled from external inputs, we characterized both endogenous and exogenous contributions to dynamics. First, we demonstrated that the SLDS model learned the regularities of the experimental paradigm, such that states and state transitions estimated from fMRI time series data from 85 ROIs reflected both the proximity of the circles and their direction (approach vs. retreat). After establishing that the model captured key properties of threat-related processing, we characterized the dynamics of the states and their transitions. The results revealed that threat processing can profitably be viewed in terms of dynamic multivariate patterns whose trajectories are a combination of intrinsic and extrinsic factors that jointly determine how the brain temporally evolves during dynamic threat. We propose that viewing threat processing through the lens of dynamical systems offers important avenues to uncover properties of the dynamics of threat that are not unveiled with standard experimental designs and analyses.

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Section: Discussionmentioning

confidence: 99%

Brain dynamics and spatiotemporal trajectories during threat processing

Misra,

Pessoa

2024

Preprint

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“…Factors learned from GPFA have brain-wide representation ( Fig 8 ), confirming a previous study that showed that brain-wide latent states are correlated to spontaneous observable behaviors in mice [ 18 ]. Another study done in rats show the ability to distinguish between different brain states during naturalistic hide-and-seek play [ 46 ]. We show that in studying brain activity generated from an unconstrained, spontaneous setting across hours and days, multiple spatiotemporal features at different time scales correlate to behavior in humans.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal dynamics of human high gamma discriminate naturalistic behavioral states

et al. 2022

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In analyzing the neural correlates of naturalistic and unstructured behaviors, features of neural activity that are ignored in a trial-based experimental paradigm can be more fully studied and investigated. Here, we analyze neural activity from two patients using electrocorticography (ECoG) and stereo-electroencephalography (sEEG) recordings, and reveal that multiple neural signal characteristics exist that discriminate between unstructured and naturalistic behavioral states such as “engaging in dialogue” and “using electronics”. Using the high gamma amplitude as an estimate of neuronal firing rate, we demonstrate that behavioral states in a naturalistic setting are discriminable based on long-term mean shifts, variance shifts, and differences in the specific neural activity’s covariance structure. Both the rapid and slow changes in high gamma band activity separate unstructured behavioral states. We also use Gaussian process factor analysis (GPFA) to show the existence of salient spatiotemporal features with variable smoothness in time. Further, we demonstrate that both temporally smooth and stochastic spatiotemporal activity can be used to differentiate unstructured behavioral states. This is the first attempt to elucidate how different neural signal features contain information about behavioral states collected outside the conventional experimental paradigm.

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“…The high-rate state is unstable under perturbations and when noise is added, the system will spontaneously transition to the low-rate state. The duration of the time in the high-rate state can be interpreted as a form of working memory ( Katori et al 2011 ), but here we consider the duration as a phase of activity ( Bagi et al 2022 ) that can lead to perseveration when the barrier is too high and a large perturbation is required for a state transition. Medications act on parameters of the model to raise or lower the boundary and affect catatonia symptoms.…”

Section: Methodsmentioning

confidence: 99%

Mechanistic modeling as an explanatory tool for clinical treatment of chronic catatonia

Roberts

Conour²

2022

Front. Pharmacol.

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Mathematical modeling of neural systems is an effective means to integrate complex information about the brain into a numerical tool that can help explain observations. However, the use of neural models to inform clinical decisions has been limited. In this study, we use a simple model of brain circuitry, the Wilson-Cowan model, to predict changes in a clinical measure for catatonia, the Bush-Francis Catatonia Rating Scale, for use in clinical treatment of schizophrenia. This computational tool can then be used to better understand mechanisms of action for pharmaceutical treatments, and to fine-tune dosage in individual cases. We present the conditions of clinical care for a residential patient cohort, and describe methods for synthesizing data to demonstrated the functioning of the model. We then show that the model can be used to explain effect sizes of treatments and estimate outcomes for combinations of medications. We conclude with a demonstration of how this model could be personalized for individual patients to inform ongoing treatment protocols.

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Unsupervised discovery of behaviorally relevant brain states in rats playing hide-and-seek

Cited by 9 publications

References 52 publications

Brain dynamics and spatiotemporal trajectories during threat processing

Brain dynamics and spatiotemporal trajectories during threat processing

Spatiotemporal dynamics of human high gamma discriminate naturalistic behavioral states

Mechanistic modeling as an explanatory tool for clinical treatment of chronic catatonia

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