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Dissimilarity in sulcal width patterns in the cortex can be used to identify patients with schizophrenia with extreme deficits in cognitive performance

By Joost Janssen, Covadonga M. Díaz-Caneja, Clara Alloza, Anouck Schippers, Lucía de Hoyos, Javier Santonja, Pedro M. Gordaliza, Elizabeth Buimer, Neeltje van Haren, Wiepke Cahn, Celso Arango, René S. Kahn, Hilleke Hulshoff Pol, Hugo Schnack

Posted 04 Feb 2020
bioRxiv DOI: 10.1101/2020.02.04.932210 (published DOI: 10.1093/schbul/sbaa131)

Background: Schizophrenia is a biologically complex disorder with multiple regional deficits in cortical brain morphology. In addition, interindividual heterogeneity of cortical morphological metrics is larger in patients with schizophrenia when compared to healthy controls. Exploiting interindividual differences in severity of cortical morphological deficits in patients instead of focusing on group averages may aid in detecting biologically informed homogeneous subgroups. Methods: The Person-Based Similarity Index (PBSI) of brain morphology indexes an individual's morphometric similarity across numerous cortical regions amongst a sample of healthy subjects. We extended the PBSI such that it indexes morphometric similarity of an independent individual (e.g., a patient) with respect to healthy control subjects. By employing a normative modeling approach on longitudinal data, we determined an individual's degree of morphometric dissimilarity to the norm. We calculated the PBSI for sulcal width (PBSI-SW) in patients with schizophrenia and healthy control subjects (164 patients, 164 healthy controls; 656 MRI scans) and associated it with cognitive performance and cortical sulcation index. Results: A subgroup of patients with markedly deviant PBSI-SW showed extreme deficits in cognitive performance and cortical sulcation. Progressive reduction of PBSI-SW in the schizophrenia group relative to healthy controls was driven by these deviating individuals. Conclusions: By explicitly leveraging interindividual differences in severity of PBSI-SW deficits, neuroimaging-driven subgrouping of patients is feasible. As such, our results pave the way for future applications of morphometric similarity indices for subtyping of clinical populations.

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