Functional MRI connectivity accurately distinguishes cases with psychotic disorders from healthy controls, based on cortical features associated with neurodevelopment

Morgan, Sarah E., Young, Jonathan, Patel, Ameera X., Whitaker, Kirstie J., Scarpazza, Cristina, van Amelsvoort, Therese, Marcelis, Machteld, van Os, Jim, Donohoe, Gary, Mothersill, David, Corvin, Aiden, Arango, Celso, Mechelli, Andrea, van den Heuvel, Martijn, Kahn, Rene S., McGuire, Philip, Brammer, Michael and Bullmore, Edward T. (2021) Functional MRI connectivity accurately distinguishes cases with psychotic disorders from healthy controls, based on cortical features associated with neurodevelopment. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6 (12). pp. 1125-1134. ISSN 2451-9022

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Abstract

Background:
Machine learning (ML) can distinguish cases with psychotic disorder from healthy controls based on magnetic resonance imaging (MRI) data, but it is not yet clear which MRI metrics are the most informative for case-control ML, or how ML algorithms relate to the underlying biology.

Methods:
We analyzed multimodal MRI data from 2 independent case-control studies of psychotic disorders (cases, n = 65, 28; controls, n = 59, 80) and compared ML accuracy across 5 selected MRI metrics from 3 modalities. Cortical thickness, mean diffusivity, and fractional anisotropy were estimated at each of 308 cortical regions, as well as functional and structural connectivity between each pair of regions. Functional connectivity data were also used to classify nonpsychotic siblings of cases (n = 64) and to distinguish cases from controls in a third independent study (cases, n = 67; controls, n = 81).

Results:
In both principal studies, the most informative metric was functional MRI connectivity: The areas under the receiver operating characteristic curve were 88% and 76%, respectively. The cortical map of diagnostic connectivity features (ML weights) was replicable between studies (r = 0.27, p < .001); correlated with replicable case-control differences in functional MRI degree centrality and with a prior cortical map of adolescent development of functional connectivity; predicted intermediate probabilities of psychosis in siblings; and was replicated in the third case-control study.

Conclusions:
ML most accurately distinguished cases from controls by a replicable pattern of functional MRI connectivity features, highlighting abnormal hubness of cortical nodes in an anatomical pattern consistent with the concept of psychosis as a disorder of network development.

Item Type:	Article
Uncontrolled Keywords:	Digital radiology; Dysconnectivity; Machine learning; Magnetic resonance imaging; Network neuroscience; Psychosis
Subjects:	R Medicine > R Medicine (General) R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions:	School of Business > Staff Research and Publications
Depositing User:	Clara Chan
Date Deposited:	28 Apr 2022 12:08
Last Modified:	28 Apr 2022 12:08
URI:	https://norma.ncirl.ie/id/eprint/5595

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