Book chapter
How Can I Analyze Large-Scale Intrinsic Functional Networks with iEEG?
Intracranial EEG, pp 539-556
01 Jan 2023
Abstract
An intrinsic functional brain network is a set of discrete, spatial elements that exhibit statistically dependent activity (“functional connectivity”) with each other in a largely state-invariant manner (e.g. across wakeful rest, task performance, and sleep). Large-scale intrinsic networks—involving coupling between distant brain regions—were initially discovered with human functional neuroimaging (fMRI) based on hemodynamic signals. Though fMRI studies suggest critical relevance of these networks to brain function, findings remain challenging to interpret given the low temporal resolution and indirect nature of fMRI. Human iEEG is poised as a unique method that can deliver fundamental insights into the neurophysiological connectivity processes in intrinsic networks. In this chapter, we review iEEG analysis methods that have been used to identify electrophysiological networks closely resembling those found using classical fMRI functional connectivity. We focus on amplitude and phase coupling within multiple frequency bands as measures of iEEG intrinsic connectivity. We review evidence that iEEG connectivity shows state-invariant patterns of inter-regional coupling across multiple contexts. Moreover, we review applications of intrinsic iEEG connectivity patterns in predicting the roles of discrete neuronal populations in cognitive function. Finally, we explore how iEEG sheds light on the cognitive relevance of temporal dynamics within and between intrinsic networks.
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Details
- Title
- How Can I Analyze Large-Scale Intrinsic Functional Networks with iEEG?
- Creators
- Aaron Kucyi - Drexel UniversitySepideh Sadaghiani - University of Illinois Urbana-Champaign
- Publication Details
- Intracranial EEG, pp 539-556
- Publisher
- Springer Nature
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Other Identifier
- 991021463663604721