Toward Meta-Connectomic Ergodicity in Neuroimaging
Poster No:
1908
Submission Type:
Abstract Submission
Authors:
Jonathan Towne1, Victor Lami2, Heath Pardoe3, Jose Cavazos1, Peter Fox1
Institutions:
1UT Health San Antonio, San Antonio, TX, 2UCLA Health, Los Angeles, CA, 3Florey Institute of Neuroscience and Mental Health, Melbourne, VIC
First Author:
Co-Author(s):
Introduction:
Ergodicity in a dynamical system asserts that group observations at a single time point are equivalent to a single-individual observation over time. In the brain, this would mandate that network properties derived from cross-sectional data will be observed longitudinally in individuals. The implication for neuroimaging, if ergodicity holds, is that meta-analytic sampling can access network architecture (data structures) useful for detecting networks per-subject.
Ergodicity has been implicitly shown in healthy subjects by graph theory (Crossley et al., 2013) and other analytics (Smith et al., 2009). In these studies, equivalent functional architecture was identified by connectomic meta-analysis of task-based studies and connectomic analysis of temporally concatenated rs-fMRI. Diseases follow collectively similar yet individually distinct patterns (shown in trans-diagnostic meta-analyses: Vanasse et al., 2021; Towne et al., 2023), motivating disease-specific ergodic hypotheses. We present evidence of ergodicity in a temporal lobe epilepsy (TLE) cohort.
Ergodicity has been implicitly shown in healthy subjects by graph theory (Crossley et al., 2013) and other analytics (Smith et al., 2009). In these studies, equivalent functional architecture was identified by connectomic meta-analysis of task-based studies and connectomic analysis of temporally concatenated rs-fMRI. Diseases follow collectively similar yet individually distinct patterns (shown in trans-diagnostic meta-analyses: Vanasse et al., 2021; Towne et al., 2023), motivating disease-specific ergodic hypotheses. We present evidence of ergodicity in a temporal lobe epilepsy (TLE) cohort.
Methods:
A meta-connectomic (cross-sectional) model of TLE pathology was derived from published coordinate data reported in case-control contrasts (n=74 TLE experiments), using Meta-analytic Graph Theory Modeling (M-GTM; Figure 1). For the same set of nodes, connectomic models were derived for from primary (per-subject) rs-fMRI scans (n = 37 patients, 19 healthy controls), using the FMRIB Software Library (FSL) to compute functional connectivity. Models were compared by modularity analysis and node topology metrics (e.g. centrality).
·Figure 1. Meta-Analytic Graph Theory Modeling (M-GTM) on the BrainMap Community Portal
Results:
TLE networks identified cross-sectionally (case-control contrasts) were observed longitudinally in TLE, not controls (Figure 2). Two TLE modules were found meta-analytically (limbic & language networks) and present individually. The medial dorsal nucleus was the strongest hub unique to the TLE models (hubness = 0.5); other strong TLE hubs (common to both TLE models) included the hippocampus, MDN thalamus, caudate body, superior temporal gyrus, & inferior parietal lobule.
·Figure 2. Ergodicity in TLE shown by meta-connectomic and connectomic graph model congruence
Conclusions:
Ergodicity was demonstrated in TLE. Critics purport ergodicity to imply individuals are identical. We suggest network structure is similar cross-sectionally (mean coherent structure) but exhibited ergodically over time. These results motivate the application of meta-analytic functional network models in primary data, to develop per-subject biomarkers.
Modeling and Analysis Methods:
Bayesian Modeling
fMRI Connectivity and Network Modeling 2
Methods Development 1
Task-Independent and Resting-State Analysis
Other Methods
Keywords:
Computational Neuroscience
Design and Analysis
Epilepsy
FUNCTIONAL MRI
Meta- Analysis
Modeling
Morphometrics
Multivariate
Statistical Methods
Workflows
1|2Indicates the priority used for review
Provide references using author date format
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