Journal article
The challenge of non-ergodicity in network neuroscience
Network (Bristol), v 22(1-4), pp 148-153
Mar 2011
PMID: 22149675
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
Metrics
Details
- Title
- The challenge of non-ergodicity in network neuroscience
- Creators
- John D. Medaglia - Pennsylvania State UniversityDeepa M. Ramanathan - Pennsylvania State UniversityUmesh M. Venkatesan - Pennsylvania State UniversityFrank G. Hillary - Penn State Milton S. Hershey Medical Center
- Publication Details
- Network (Bristol), v 22(1-4), pp 148-153
- Publisher
- Informa UK, Ltd
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000297757300009
- Scopus ID
- 2-s2.0-82955164010
- Other Identifier
- 991019297056004721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic
- Neurosciences