Functional Magnetic Resonance Imaging Connectivity Accurately Distinguishes Cases With Psychotic Disorders From Healthy Controls, Based on Cortical Features Associated With Brain Network Development.
Patel, Ameera X
Whitaker, Kirstie J
van Amelsvoort, Thérèse
van Os, Jim
van den Heuvel, Martijn
Kahn, René S
Biol Psychiatry Cogn Neurosci Neuroimaging
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Morgan, S., Young, J., Patel, A. X., Whitaker, K. J., Scarpazza, C., van Amelsvoort, T., Marcelis, M., et al. (2021). Functional Magnetic Resonance Imaging Connectivity Accurately Distinguishes Cases With Psychotic Disorders From Healthy Controls, Based on Cortical Features Associated With Brain Network Development.. Biol Psychiatry Cogn Neurosci Neuroimaging https://doi.org/10.1016/j.bpsc.2020.05.013
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.
This study was supported by grants from the European Commission (PSYSCAN - Translating neuroimaging findings from research into clinical practice; ID: 603196) and the NIHR Cambridge Biomedical Research Centre (Mental Health). SEM holds a Henslow Fellowship at Lucy Cavendish College, University of Cambridge, funded by the Cambridge Philosophical Society. KJW was funded by an Alan Turing Institute Research Fellowship under EPSRC Research grant TU/A/000017. MPvdH was supported by a NWO VIDI and ALW open grant and a MQ fellowship. GD is supported by grants from the ERC (grant 677467) and SFI (12/IP/1359). ETB is supported by a NIHR Senior Investigator Award.
European Commission (603196)
Medical Research Council (MC_G0802534)
External DOI: https://doi.org/10.1016/j.bpsc.2020.05.013
This record's URL: https://www.repository.cam.ac.uk/handle/1810/306636
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Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/