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

Journal article


Akhil Kottaram, Leigh A. Johnston, Eleni P Ganella, Christos Pantelis, Ramamohanarao Kotagiri and Andrew Zalesky. (2018). Spatio-temporal dynamics of resting-state brain networks improve single-subject prediction of schizophrenia diagnosis. Human Brain Mapping. 39(9), pp. 3663-3681. https://doi.org/10.1002/hbm.24202
AuthorsAkhil Kottaram, Leigh A. Johnston, Eleni P Ganella, Christos Pantelis, Ramamohanarao Kotagiri and Andrew Zalesky
Abstract

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 biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting‐state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting‐state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting‐state dynamics are not trivially attributable to sampling variability and head motion.

Keywordsdynamic functional connectivity; resting‐state fMRI; schizophrenia; single‐subject predicition; spatio‐temporal dynamics; support vector machine
Year2018
JournalHuman Brain Mapping
Journal citation39 (9), pp. 3663-3681
PublisherJohn Wiley & Sons, Inc.
ISSN1065-9471
Digital Object Identifier (DOI)https://doi.org/10.1002/hbm.24202
Scopus EID2-s2.0-85046727181
Publisher's version
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Controlled
Publication process dates
Deposited13 May 2021
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