Cortico-cognition coupling in treatment resistant schizophrenia

Multivariate brain-behaviour associations in psychiatric disorders

Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.

Neuroanatomical comparison of treatment-resistant and treatment-responsive schizophrenia patients using the cloud-based brain magnetic resonance image segmentation and parcellation system: An MRIcloud study

Recent developments in neuroimaging have improved our understanding of the biological mechanisms underlying schizophrenia. However, neuroimaging findings in treatment-resistant schizophrenia (TRS) remain unclear. In the present study, we aimed to explore potential neuroanatomical regions that may be associated with treatment resistance in schizophrenia patients by comparing neuroanatomical regions of TRS and non-TRS patients using the MRICloud method. A total of 33 schizophrenia patients (meeting DSM 5 diagnostic criteria for schizophrenia) were included in the study. Patients were dichotomized into TRS (n = 18) and non-TRS (n = 15) groups, and all patients underwent MRI. Neuroanatomical regions of TRS and non-TRS patients were compared using the MRICloud method. Disease severity was measured using the Positive and Negative Syndrome Scale (PANSS). Interestingly, a statistically significant greater left Corpus Collosum (CC) thickness was found in TRS patients compared to non-TRS patients. It is clear that further studies comparing TRS patients with non-TRS patients are needed, and these studies should focus on the circuits in the corpus callosum that are thought to play a role in treatment resistance. Further longitudinal studies are also needed to complement the cross-sectional studies, using a multimodal imaging approach in the patients with clearly defined TRS criteria.