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Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published on: May 27, 2020

Exploring the network dynamics underlying brain activity during rest.

Joana Cabral¹, Morten L Kringelbach², Gustavo Deco³

¹Theoretical and Computational Neuroscience Group, Center of Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain; Department of Psychiatry, University of Oxford, Oxford, United Kingdom.

Progress in Neurobiology

|January 7, 2014

Summary

This summary is machine-generated.

Brain activity during rest involves complex functional networks. Models show these networks emerge at critical system points, aligning with neuroimaging data but requiring updates for new findings.

Keywords:

Anatomical networks Computational modelling Functional networks Resting-state

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Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Resting-state brain activity research has grown since the 1990s.
Neuroimaging reveals spontaneous, dynamic functional networks during rest.
Existing computational models offer insights but lack a complete mechanistic explanation.

Purpose of the Study:

To review key findings in resting-state brain activity across neuroimaging modalities.
To discuss the analysis of brain networks and their link to mental health.
To survey large-scale computational models of resting-state dynamics in health and disease.

Main Methods:

Review of neuroimaging studies (fMRI, EEG, MEG).
Analysis of anatomical and functional brain network definitions.
Examination of large-scale computational models of brain dynamics.

Main Results:

Resting-state functional networks emerge when models operate at the edge of a bifurcation.
Network dynamics at critical points show patterns shaped by anatomical structure.
Models achieve optimal fit with empirical BOLD functional connectivity.

Conclusions:

Models operating at critical points explain key resting-state network features.
Future models must incorporate faster dynamics and non-stationary connectivity.
A comprehensive mechanistic explanation for resting-state activity remains an ongoing pursuit.