Steven L Bressler Director Cognitive Neurodynamics Lab Center for Complex Systems amp Brain Sciences Florida Atlantic University The goal to characterize the largescale brain networks responsible for cognition ID: 661456
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Slide1
Source-ResolvedConnectivity Analysis
Steven L. Bressler
Director, Cognitive Neurodynamics Lab
Center for Complex Systems & Brain Sciences
Florida Atlantic UniversitySlide2
The goal:
to
characterize the large-scale brain networks responsible for cognition
The approach: apply functional connectivity analysis to source-resolved neural data
Source-resolved neural data:
unit activity, LFP,
iEEG
, ECoG
[extensions of connectivity analysis to source-unresolved data (EEG, MEG, fMRI) require additional assumptions and procedures, and they are controversial ]Slide3
Network science:
compute network nodes, edges, and other network metrics, e.g., efficiency
Network configuration comparison:
use pattern classification to test whether network metrics categorize cognitive state Functional connectivity estimators: Undirected: correlation, mutual information,
spectral coherence
Directed:
mvar
coefficient, transfer entropy,
Granger causality, directed transfer function,
partial directed coherenceSlide4
A visuo-motor task performed by macaque monkeys
Nodes and edges (both undirected and directed) based on beta oscillations in prestimulus LFPs from visual cortex.
Spatial pattern of top-down peak spectral Granger causality from extrastriate cortex (V4, TEO) to V1.
Categorization of task rule by SVM pattern classification: feature is spatial pattern of top-down beta-frequency peak spectral Granger causalitySlide5
Identification of Oscillatory Activity from Power SpectraSlide6
Identification of Undirected Network Edge by Coherence SpectraSlide7
Identification of Directed Network Edge by Granger Causality Spectra