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The research topic “Dimensions & Spectra” focuses on understanding mental disorders not as discrete categories, but as continuous spectra that span from subclinical behavioural variations in the general population to manifest psychiatric illness symptoms. This approach provides a conceptual bridge between basic science and clinical psychiatry. It supports a shift from rigid diagnostic labels towards more fine-grained, quantitative characterizations of mental health and illness. In the long term, this work aims to inform early detection strategies, individualized risk assessment, and more nuanced outcome measures for future preventive and therapeutic interventions.

This dimensional perspective is central our work in investigating how e.g. personality traits, schizotypy, and subthreshold symptoms map onto the broader disease spectra. By combining psychometrics, neuroimaging, and genetics, our group aims to identify intermediate phenotypes that help explain why and how some individuals develop disorders while others do not.

Using advanced MRI morphometry and multivariate statistics, we investigate how dimensional measures of mental health and related traits are linked to structural and functional variation in key brain networks. Particular emphasis is placed on regions implicated in self-processing, salience, and higher-order cognition, such as the precuneus and fronto-thalamo-striatal circuits. By quantifying these relationships, our research aims to go beyond simple case–control contrasts and instead describe graded neurobiological changes along the mental health spectra.

One key concept within our research area is schizotypy, which captures psychosis-proneness through positive, negative, and disorganized dimensions that mirror symptom clusters seen in schizophrenia. Rather than focusing solely on clinical populations, we study these dimensions in healthy or high-risk individuals to trace subtle cognitive, emotional, and behavioural variations that may precede illness onset. This approach allows the identification of quantitative markers (such as specific cognitive profiles or imaging-derived brain metrics) that vary systematically with psychosis liability across the spectrum.

The dimensional framework is also applied to affective symptoms, especially depressive and anxiety-related traits. This helps to disentangle shared and distinct mechanisms linking mood, cognition, and emotional experiences. Longitudinal and cross-sectional designs are used to determine whether certain dimensional profiles predict transitions to clinical states or support resilience. In doing so, the group contributes to transdiagnostic models that cut across traditional boundaries between schizophrenia, bipolar disorder, and major depression.

Another important aspect of this research line is the integration of environmental and genetic factors into dimensional models. By incorporating polygenic risk scores and measures of environmental adversity, the group examines how risk and protective factors shape individual positions on the psychosis spectrum. This allows for the identification of subgroups who may carry high genetic liability but show limited symptom expression, suggesting potential resilience mechanisms.