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Spatio-temporal dynamics of resting-state brain networks improve single-subject prediction of schizophrenia diagnosis
Akhil Kottaram Leigh A. Johnston Eleni P Ganella Christos Pantelis Ramamohanarao Kotagiri Andrew Zalesky
Akhil Kottaram
Leigh A. Johnston
Eleni P Ganella
Christos Pantelis
Ramamohanarao Kotagiri
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.
Keywords
dynamic functional connectivity, resting‐state fMRI, schizophrenia, single‐subject predicition, spatio‐temporal dynamics, support vector machine
Date
2018
Type
Journal article
Journal
Human Brain Mapping
Book
Volume
39
Issue
9
Page Range
3663-3681
Article Number
ACU Department
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Relation URI
Source URL
Event URL
Open Access Status
License
File Access
Controlled