Latent clinical-anatomical dimensions of schizophrenia

Abstract

Widespread structural brain abnormalities have been consistently reported in schizophrenia, but their relation to the heterogeneous clinical manifestations remains unknown. In particular, it is unclear whether anatomical abnormalities in discrete regions give rise to discrete symptoms, or whether distributed abnormalities give rise to the broad clinical profile associated with schizophrenia. Here we apply a multivariate data-driven approach to investigate covariance patterns between multiple symptom domains and distributed brain abnormalities in schizophrenia. Structural MRI and clinical data were derived from one discovery sample (133 patients, 113 controls) and one independent validation sample (108 patients, 69 controls). Disease-related voxel-wise brain abnormalities were estimated using deformation based morphometry. Partial least squares analysis was used to comprehensively map clinical, neuropsychological and demographic data onto distributed deformation in a single multivariate model. The analysis identified three latent clinical-anatomical dimensions that collectively accounted for 55% of the covariance between clinical data and brain deformation. The first latent clinical-anatomical dimension was replicated in an independent sample, encompassing cognitive impairments, negative symptom severity and brain abnormalities within the default mode and visual networks. This cognitive-negative dimension was associated with low socioeconomic status and was represented across multiple races. Altogether, we identified a continuous cognitive-negative dimension of schizophrenia, centered on two intrinsic networks. By simultaneously taking into account both clinical manifestations and neuroanatomical abnormalities, the present results open new avenues for multi-omic stratification and biotyping of individuals with schizophrenia.

Multimodal phenotypic axes of Parkinson’s disease

“…This finding is reminiscent of previous reports, where even hard clustering solutions often sorted patients into broad disease severity categories [20,24,48,49,79]. Indeed, recent studies have taken an explicitly dimensional approach, attempting to find low-dimensional projections of clinical-behavioral and neuroimaging data [39,41,71,78]. In clinical practice, methods like SNF situate individual patients in a biologically-comprehensive feature space that can then guide more objective clinical decisions about diagnosis and prognosis.…”

Multimodal phenotypic axes of Parkinson’s disease

“…This finding is reminiscent of previous reports, where even hard clustering solutions often sorted patients into broad disease severity categories 5,[41][42][43][44] . Indeed, recent studies have taken an explicitly dimensional approach, attempting to find low-dimensional projections of clinical-behavioral and neuroimaging data 45,[52][53][54] . In clinical practice, methods like SNF situate individual patients in a biologically-comprehensive feature space that can then guide more objective clinical decisions about diagnosis and prognosis.…”

Schizophrenia polygenic risk during typical development reflects multiscale cortical organization

“…More complex interactions of microstructure and function on regional vulnerability may be captured by the groupings of cortical areas into cytoarchitectural types and functional networks. Indeed, recent studies of schizophrenia [17, 33] and high PRS-SCZ in healthy adults [34] suggest differential sensitivity of histological-defined cytoarchitectural types [35] and functional networks [36]. Finally, population-level effects of schizophrenia and other major psychiatric disorders can be used to illustrate the concordance of genetic risk for schizophrenia with disorder-related neuroanatomical phenotypes.…”