Mapping the Heterogeneous Phenotype of Schizophrenia and Bipolar Disorder Using Normative Models

Abstract

IMPORTANCE Schizophrenia and bipolar disorder are severe and complex brain disorders characterized by substantial clinical and biological heterogeneity. However, case-control studies often ignore such heterogeneity through their focus on the average patient, which may be the core reason for a lack of robust biomarkers indicative of an individual's treatment response and outcome.OBJECTIVES To investigate the degree to which case-control analyses disguise interindividual differences in brain structure among patients with schizophrenia and bipolar disorder and to map the brain alterations linked to these disorders at the level of individual patients. DESIGN, SETTING, AND PARTICIPANTSThis study used cross-sectional, T1-weighted magnetic resonance imaging data from participants recruited for the Thematically Organized Psychosis study from October 27, 2004, to October 17, 2012. Data were reanalyzed in 2017 and 2018. Patients were recruited from inpatient and outpatient clinics in the Oslo area of Norway, and healthy individuals from the same catchment area were drawn from the national population registry.MAIN OUTCOMES AND MEASURES Interindividual differences in brain structure among patients with schizophrenia and bipolar disorder. Voxel-based morphometry maps were computed, which were used for normative modeling to map the range of interindividual differences in brain structure. RESULTS Thisstudy included 218 patients with schizophrenia spectrum disorders (mean [SD] age, 30 [9.3] years; 126 [57.8%] male), of whom 163 had schizophrenia (mean [SD] age, 31 [8.7] years; 105 [64.4%] male) and 190 had bipolar disorder (mean [SD] age, 34 [11.3] years; 79 [41.6%] male), and 256 healthy individuals (mean [SD] age, 34 [9.5] years; 140 [54.7%] male). At the level of the individual, deviations from the normative model were frequent in both disorders but highly heterogeneous. Overlap of more than 2% among patients was observed in only a few loci, primarily in frontal, temporal, and cerebellar regions. The proportion of alterations was associated with diagnosis and cognitive and clinical characteristics within clinical groups. Patients with schizophrenia, on average, had significantly reduced gray matter in frontal regions, cerebellum, and temporal cortex. In patients with bipolar disorder, mean deviations were primarily present in cerebellar regions.CONCLUSIONS AND RELEVANCE This study found that group-level differences disguised biological heterogeneity and interindividual differences among patients with the same diagnosis. This finding suggests that the idea of the average patient is a noninformative construct in psychiatry that falls apart when mapping abnormalities at the level of the individual patient. This study presents a workable route toward precision medicine in psychiatry.

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

“…However, the robustness and generalizability of such studies have been brought into question (Dinga et al, ), which may be partly due to substantial brain heterogeneity within groups, which has been illustrated in terms of morphometry in schizophrenia (Alnæs et al, ). Alternatively, dimensional measures such as brain age prediction (Kaufmann et al, In press) and normative modeling (Marquand et al, ; Marquand, Rezek, Buitelaar, & Beckmann, ) have shown promising results in elucidating brain heterogeneity in mental disorders such as schizophrenia (Wolfers et al, ) and attention deficit/hyperactivity disorder (Wolfers et al, ). An important consideration is the multicausal nature of depression, which means that it is likely that including other measures such as psychosocial, cognitive, and genetic factors would increase the explained variance, beyond brain imaging by itself.…”

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

“…However, the robustness and generalizability of such studies have been brought into question (Dinga et al, 2019), which may be partly due to substantial brain heterogeneity within groups, which has been illustrated in terms of morphometry in schizophrenia . Alternatively, dimensional measures such as brain age prediction and normative modelling Marquand, Rezek, Buitelaar, & Beckmann, 2016) have shown promising results in elucidating brain heterogeneity in mental disorders such as schizophrenia (Wolfers et al, 2018) and attention deficit/hyperactivity disorder .…”

Using Brain-Based Phenotyping to Improve Discovery in Psychiatry

“…First, Wolfers et al 1 report typical group-level outcomes, which show the usual kinds of changes reported in SMI: gray matter reductions in frontal, temporal, and cerebellar regions in people with schizophrenia and gray matter reductions in the frontal cortex in individuals with bipolar disorder. However, in a section that is to our knowledge unique to their analysis, the authors also demonstrated localizations of extreme individual deviations by voxel and by group.…”

“…But even such a broad approach has been insufficient to find even 1 informative biomarker to specify conventional psychosis diagnoses. 2 Similarly to the suggestion of Wolfers et al, 1 the B-SNIP Consortium went on to apply clustering algorithms to the data to identify neurobiologically driven disease subtypes. The B-SNIP Consortium has previously reported these subtype clusters, called Biotypes , and shown that in these novel groups biological markers fall not on conventional diagnoses but rather on the Biotypes.…”

“…To frame the question: we start with a biomarker battery to complexly classify a dimension such as psychosis with biomarkers selected to reflect important brain characteristics in the disorder, as speculated by Wolfers et al 1 In sum, we create clusters of individuals with common biological indicators rather than a common set of symptom manifestations. These new entities could be like other biological disease examples in medicine in which multiple biomarkers collectively define a disease entity (eg, diabetes).…”