Prenatal stress dysregulates resting-state functional connectivity and sensory motifs

Rezaei, Zahra; Jafari, Zahra; Afrashteh, Navvab; Torabi, Reza; Singh, Surjeet; Kolb, Bryan; Davidsen, Jörn; Mohajerani, Majid H.

doi:10.1016/j.ynstr.2021.100345

Cited by 3 publications

(1 citation statement)

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“…Previous approaches to resolving this complexity in mesoscale cortical imaging have included unsupervised methods of motif detection 34 or experimentally generated templates of neural activity 19,20,26,37-39 . While elegant solutions, existing unsupervised approaches require computational resources that limit scalability and experimental templates are best suited to circumscribed neural circuits.…”

Section: Introductionmentioning

confidence: 99%

A Markovian neural barcode representing mesoscale cortical spatiotemporal dynamics

Culp,

Ashby,

George

et al. 2024

Preprint

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Mesoscale cortical dynamics consist of stereotyped patterns of recurring activity motifs, however the constraints and rules governing how these motifs assemble over time is not known. Here we propose a Continuous Time Markov Chain model that probabilistically describes the temporal sequence of activity motifs using Markov Elements derived using semi-binary non-negative matrix factorization. Although derived from a discovery sample, these can be applied to new recordings from new mice. Unwrapping the associated transition probability matrix creates a ‘Markovian neural barcode’ describing the probability of Markov element transitions as a compact and interpretable representation of neocortical dynamics. We show broad utility across a range of common mesoscale cortical imaging applications, ranging from time-locked events to pathological models. Moreover, it allows the discovery of new and emergent Markov Elements that unmask the flexibility of constraints governing cortical dynamics. The Markovian neural barcode provides a novel and powerful tool to characterize cortical function.

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