The challenge of non-ergodicity in network neuroscience

John D. Medaglia, Deepa M. Ramanathan, Umesh M. Venkatesan, Frank Gerard Hillary

Research output: Contribution to journalReview article

9 Citations (Scopus)

Abstract

Ergodicity can be assumed when the structure of data is consistent across individuals and time. Neural network approaches do not frequently test for ergodicity in data which holds important consequences for data integration and intepretation. To demonstrate this problem, we present several network models in healthy and clinical samples where there exists considerable heterogeneity across individuals. We offer suggestions for the analysis, interpretation, and reporting of neural network data. The goal is to arrive at an understanding of the sources of non-ergodicity and approaches for valid network modeling in neuroscience.

Original languageEnglish (US)
Pages (from-to)148-153
Number of pages6
JournalNetwork: Computation in Neural Systems
Volume22
Issue number1-4
DOIs
StatePublished - Mar 1 2011

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Neurosciences

All Science Journal Classification (ASJC) codes

  • Neuroscience (miscellaneous)

Cite this

Medaglia, John D. ; Ramanathan, Deepa M. ; Venkatesan, Umesh M. ; Hillary, Frank Gerard. / The challenge of non-ergodicity in network neuroscience. In: Network: Computation in Neural Systems. 2011 ; Vol. 22, No. 1-4. pp. 148-153.
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The challenge of non-ergodicity in network neuroscience. / Medaglia, John D.; Ramanathan, Deepa M.; Venkatesan, Umesh M.; Hillary, Frank Gerard.

In: Network: Computation in Neural Systems, Vol. 22, No. 1-4, 01.03.2011, p. 148-153.

Research output: Contribution to journalReview article

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