Decision-making dynamics are predicted by arousal and uninstructed movements

Hulsey, Daniel; Zumwalt, Kevin; Mazzucato, Luca; McCormick, David A.; Jaramillo, Santiago

doi:10.1016/j.celrep.2024.113709

Cited by 5 publications

(3 citation statements)

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“…We trained mice to perform an auditory decision-making task, imaged neuronal spike-related activity in AC and PPC and used GLM-HMM modeling to identify distinct states of optimal and suboptimal behavioral performance each with unique decision-making strategies. Similar to the performance states identified during visual decision-making (Ashwood et al, 2022; Bolkan et al, 2022; Hulsey et al, 2024), we identified one optimal state, characterized by high task performance, and two suboptimal states in which mice were biased in their choice behavior toward either left or right choices (Figure 1E-G). Interestingly, though psychometrics in the suboptimal states were poor, we did find conservation of psychometric slope, indicating that animals were still engaged in the task despite adopting a suboptimal strategy.…”

Section: Discussionsupporting

confidence: 59%

mentioning

confidence: 99%

“…Animals trained to expertly perform decision-making tasks predominantly occupy an optimal performance state, as evidenced by high task accuracy and enhanced attention to sensory information (Harris & Thiele, 2011). When animals occupy suboptimal performance states, although they are still engaged in decision-making behavior, they experience increased lapses in choices, and increased variability on correlates of arousal such as pupil diameter and uninstructed movements (Hulsey et al, 2024).Task-relevant information, such as sensory stimuli and behavioral choices, is encoded across sensory and association-level cortex (Runyan et al, 2017;Steinmetz et al, 2019;Tseng et al, 2022). The primary auditory cortex (AC) processes incoming sound information but is also modulated by task performance (…”

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confidence: 99%

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Different state-dependence of population codes across cortex

Bandi,

Runyan

2024

Preprint

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During perceptual decision-making, behavioral performance varies with changes in internal states such as arousal, motivation, and strategy. Yet it is unknown how these internal states affect information coding across cortical regions involved in differing aspects of sensory perception and decision-making. We recorded neural activity from the primary auditory cortex (AC) and posterior parietal cortex (PPC) in mice performing a navigation-based sound localization task. We then modeled transitions in the behavioral strategies mice used during task performance. Mice transitioned between three latent performance states with differing decision-making strategies: an optimal state and two sub-optimal states characterized by choice bias and frequent errors. Performance states strongly influenced population activity patterns in association but not sensory cortex. Surprisingly, activity of individual PPC neurons was better explained by external inputs and behavioral variables during suboptimal behavioral performance than in the optimal performance state. Furthermore, shared variability across neurons (coupling) in PPC was strongest in the optimal state. In AC, shared variability was similarly weak across all performance states. Together, these findings indicate that neural activity in association cortex is more strongly linked to internal state than in sensory cortex.

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Section: Discussionsupporting

confidence: 59%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Different state-dependence of population codes across cortex

Bandi,

Runyan

2024

Preprint

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From innate to instructed: A new look at perceptual decision-making

Oesch,

Ryan,

Churchland

2024

Current Opinion in Neurobiology

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Orexin population activity precisely reflects net body movement across behavioral and metabolic states

Tesmer,

Viskaitis,

Donegan

et al. 2024

Preprint

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Hypothalamic orexin/hypocretin neurons (HONs) are key orchestrators of metabolism and adaptive behaviors. HON activity varies rapidly across behavioral and metabolic states: active during periods of high arousal, and silent during quiescence. However, it is unknown whether HON activity tracks specific behaviors, or a general aspect of all behaviors, and whether this depends on metabolic state. After recording HON population dynamics in awake behaving mice, we performed a spectral analysis of HON encoding of spontaneous body movements and behaviors recorded via video-tracking. Multivariate analyses of distinct HON activity bandwidths revealed that movements were encoded at a higher frequency bandwidth than blood glucose, and imposing distinct metabolic states did not interfere with HON movement encoding. HON population activity tracked the total amount of body movement across multiple classified behaviors with a high degree of precision. At key projection targets, orexin/hypocretin peptide outputs correlated with self-initiated movement in a projection-specific manner, indicating functional heterogeneity in HON outputs. Finally, we found that body movement was not encoded to the same extent in other key neural clusters related to arousal or energy. Collectively, these data reveal a general principle behind HON activation in behaving animals, that is orthogonal to their reactions to metabolic shifts. This not only enhances our understanding of the fundamental roles of HONs in brain-body coordination, but also suggests novel non-invasive methods for monitoring HON population activity using video-tracked biometrics, which are pivotal for future investigations into how movement and energy demands are encoded by the brain.

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Decision-making dynamics are predicted by arousal and uninstructed movements

Cited by 5 publications

References 42 publications

Different state-dependence of population codes across cortex

Different state-dependence of population codes across cortex

From innate to instructed: A new look at perceptual decision-making

Orexin population activity precisely reflects net body movement across behavioral and metabolic states

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