Brain activity recordings from multiple locations from the brain at rest often demonstrate dynamic spatiotemporal reconfigurations of oscillatory activity. These oscillatory patterns are observed both using indirect recordings of neural activity, such as resting-state functional magnetic resonance imaging (rs-fMRI), or direct recording of neural activity with electrophysiology. The origin and role of such oscillatory patterns and their relationship between those observed with fMRI and electrophysiology is the subject of much investigation. Here I present computational work that sheds light on these questions. In our first study, I analyzed data from the lab of Alessandro Gozzi to demonstrate that changes in the resting-state fMRI connectivity caused by chemogenetic chronic inhibition of the prefrontal cortex (PFC) are reflected by an increase of slow oscillatory power (<4 Hz) in the local field potential and an increase of coherence in those bands, suggesting that rs-fMRI connectivity primarily reflects low-frequency neural oscillations. In the second study, I analyzed data from the lab of Nelson Totah to investigate how prefrontal cortical oscillations are modulated by variations in the activity of ascending neuromodulatory systems. Noradrenergic Locus Coeruleus (LC) is a major neuromodulatory system and is believed to regulate the excitability of cortical targets by an en-masse synchronous increase of firing, and the changes in excitability. A recent study, however, has demonstrated a low observed correlation among LC pairs during anesthesia and has paved the way for potential ensemble-based modulation of the cortex. Here, utilizing the non-negative matrix factorization technique on populations of tens of simultaneously recorded neurons in anesthetized rats, we found multiple ensembles of LC cells with diverse spatiotemporal properties. Aligning the ongoing rhythms of the prefrontal cortex (PFC) around the activation of different LC ensembles, we found that the pattern of oscillations in the prefrontal cortex varied dynamically depending on which LC ensemble was active. Our results demonstrate that the spontaneous activity of LC ensembles can be associated with different oscillatory cortical states.

A computational study of the origin of brain oscillations at multiple scales / Noei, Shahryar. - (2022 Nov 07), pp. 1-117. [10.15168/11572_356181]

A computational study of the origin of brain oscillations at multiple scales

Noei, Shahryar
2022-11-07

Abstract

Brain activity recordings from multiple locations from the brain at rest often demonstrate dynamic spatiotemporal reconfigurations of oscillatory activity. These oscillatory patterns are observed both using indirect recordings of neural activity, such as resting-state functional magnetic resonance imaging (rs-fMRI), or direct recording of neural activity with electrophysiology. The origin and role of such oscillatory patterns and their relationship between those observed with fMRI and electrophysiology is the subject of much investigation. Here I present computational work that sheds light on these questions. In our first study, I analyzed data from the lab of Alessandro Gozzi to demonstrate that changes in the resting-state fMRI connectivity caused by chemogenetic chronic inhibition of the prefrontal cortex (PFC) are reflected by an increase of slow oscillatory power (<4 Hz) in the local field potential and an increase of coherence in those bands, suggesting that rs-fMRI connectivity primarily reflects low-frequency neural oscillations. In the second study, I analyzed data from the lab of Nelson Totah to investigate how prefrontal cortical oscillations are modulated by variations in the activity of ascending neuromodulatory systems. Noradrenergic Locus Coeruleus (LC) is a major neuromodulatory system and is believed to regulate the excitability of cortical targets by an en-masse synchronous increase of firing, and the changes in excitability. A recent study, however, has demonstrated a low observed correlation among LC pairs during anesthesia and has paved the way for potential ensemble-based modulation of the cortex. Here, utilizing the non-negative matrix factorization technique on populations of tens of simultaneously recorded neurons in anesthetized rats, we found multiple ensembles of LC cells with diverse spatiotemporal properties. Aligning the ongoing rhythms of the prefrontal cortex (PFC) around the activation of different LC ensembles, we found that the pattern of oscillations in the prefrontal cortex varied dynamically depending on which LC ensemble was active. Our results demonstrate that the spontaneous activity of LC ensembles can be associated with different oscillatory cortical states.
7-nov-2022
XXXIV
2021-2022
Università degli Studi di Trento
Cognitive Science
Panzeri, Stefano
no
Inglese
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/356181
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