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

Human Brain Mapping
Akhil KottaramAndrew 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%, where...Continue Reading

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Citations

May 7, 2020·Human Brain Mapping·Barnaly Rashid, Vince Calhoun
Jan 22, 2019·Human Brain Mapping·Akhil KottaramAndrew Zalesky
Oct 15, 2019·Frontiers in Neuroscience·Suchita BhingeTülay Adali
Apr 13, 2020·European Archives of Psychiatry and Clinical Neuroscience·YanYan WeiJiJun Wang
Jul 15, 2020·Journal of Neural Engineering·Thomas BoltonDimitri Van De Ville
Jul 22, 2021·Journal of Neural Engineering·Maliheh AhmadiArdalan Aarabi
Dec 29, 2021·European Journal of Neurology : the Official Journal of the European Federation of Neurological Societies·Yuhan LuoGang Liu

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