Application of the DySCo measures to a simulated dataset.

Application of the DySCo measures to a simulated dataset.

<p>i) simulated signals and the five underlying covariance patterns, corresponding to brain states. ii) The sliding window covariance matrix computed using the DySCo formula. iii) Reconfiguration speed with a lag of 100 frames shows peak corresponding to the switches between brain states (the...

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Bibliographic Details
Main Author: Giuseppe de Alteriis (20846230) (author)
Other Authors: Oliver Sherwood (20846233) (author), Alessandro Ciaramella (20136480) (author), Robert Leech (326879) (author), Joana Cabral (329646) (author), Federico E Turkheimer (14657534) (author), Paul Expert (831007) (author)
Published: 2025
Subjects:
Medicine
Neuroscience
Biotechnology
Space Science
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
na &# 239
applied mostly empirically
temporal interaction patterns
main dfc approaches
dynamic functional connectivity
consistent across time
dimensional brain recordings
computationally efficient way
common theoretical framework
full dfc analysis
temporal scales
full potential
dimensional datasets
computationally demanding
common language
memory efficient
brain configurations
brain activity
use dfc
prevented dfc
dfc matrix
dfc consists
dfc community
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without loss
voxel level
varying matrix
time applications
synthetic dataset
single framework
results derived
matrix space
magnitude faster
informational complexity
general framework
eigenvector space
easily afforded
computational efficiency
comprehensive set
clear view
associated repository
analysis paradigm
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