TY - JOUR
T1 - The challenge of non-ergodicity in network neuroscience
AU - Medaglia, John D.
AU - Ramanathan, Deepa M.
AU - Venkatesan, Umesh M.
AU - Hillary, Frank Gerard
PY - 2011/3
Y1 - 2011/3
N2 - 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.
AB - 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.
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U2 - 10.3109/09638237.2011.639604
DO - 10.3109/09638237.2011.639604
M3 - Review article
C2 - 22149675
AN - SCOPUS:82955164010
SN - 0954-898X
VL - 22
SP - 148
EP - 153
JO - Network: Computation in Neural Systems
JF - Network: Computation in Neural Systems
IS - 1-4
ER -