Journal article

Spatio-temporal dynamics of resting-state brain networks improve single-subject prediction of schizophrenia diagnosis

Akhil Kottaram, Leigh Johnston, Eleni Ganella, Christos Pantelis, Ramamohanarao Kotagiri, Andrew Zalesky

HUMAN BRAIN MAPPING | WILEY | Published : 2018


Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding-window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as bioma..

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Awarded by National Health and Medical Research Council of Australia

Awarded by NHMRC

Awarded by Melbourne Bioinformatics at the University of Melbourne, Australia

Funding Acknowledgements

Dataset 1: The authors acknowledge Everall, I., and Bousman C., as well as the financial support of the CRC for Mental Health. The Cooperative Research Centre (CRC) program is an Australian Government Initiative. The authors wish to acknowledge the CRC Scientific Advisory Committee, in addition to the contributions of study participants, clinicians at recruitment services, staff at the Murdoch Children's Research Institute, staff at the Australian Imaging, Bio-markers and Lifestyle Flagship Study of Aging, and research staff at the Melbourne Neuropsychiatry Centre, including Pantelis, C. (lead clinician) and coordinators Phassouliotis, C., Merritt, A., and research assistants, Burnside, A., Cross, H., Gale, S., and Tahtalian, S. Participants for this study were sourced, in part, through the Australian Schizophrenia Research Bank (ASRB), which is supported by the National Health and Medical Research Council of Australia (Enabling Grant N. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. We thank the Chief Investigators and ASRB Manager: Carr, V., Schall, U., Scott, R., Jablensky, A., Mowry, B., Michie, P., Catts, S., Henskens, F., Pantelis, C., Loughland, C. We acknowledge the help of Jason Bridge for ASRB database queries. C Pantelis was supported by a NHMRC Senior Principal Research Fellowship (IDs: 628386 & 1105825). A Zalesky is supported by the NHMRC Senior Research Fellowship B (APP1136649).The authors are thankful to the high performance computing facility provided by Melbourne Bioinformatics at the University of Melbourne, Australia (project ID UOM0015). The authors thank two anonymous reviewers for very helpful comments on an earlier version of this manuscript.