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From mechanistic insight towards clinical implementation using normative modeling

Nature Computational Science volume 2, pages 278–280 (2022)Cite this article

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Normative modeling is considered one of the most promising avenues towards personalized medicine. The integration of multimodal, mechanistic and lifespan modeling will play an essential role, but significant challenges need to be overcome before this promise can be turned into reality.

Normative modeling has already been widely used to study individual-level differences associated with mental illnesses such as schizophrenia4, bipolar disorder5, autism6, attention-deficit/hyperactivity disorder7, mild cognitive impairment8 and Alzheimer’s disease9, with particular focus on changes in brain volume. An increasingly popular application area is the determination of the brain age, that is, the comparison of age estimated from brain structure with the chronological age, with the aim to characterize disease mechanisms related to brain development10,11. As detailed in the literature2, normative modeling approaches can differ in terms of the directionality of the mapping, either with age as the response variable (as in brain age models) or as one of the predictors. This directionality affects the interpretation of an individual’s deviation from the normative range, as well as the model coefficients, and should thus be considered carefully2. The popularity of normative modeling in psychiatric research is driven by the substantial biological heterogeneity of most mental illnesses, contributed to by the still largely symptom-based definition of diagnostic constructs. These are unlikely to align well with the biology of mental illness12,13, and limit the specificity of biological research using such criteria to define patient populations14. Consequently, little progress has been made in translating the extensive biological research performed in psychiatry into an improvement of the clinical management of mental illness, which comprise several of the globally most severe disease burdens. The hope is that normative models will aid in deciphering the biological complexity of these conditions and allow a more accurate, mechanistically informative determination of illness biology at the individual subject level.

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Fig. 1: Conceptual overview of normative modeling and proposed extensions.

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Acknowledgements

This study was supported by the Deutsche Forschungsgemeinschaft (DFG), SCHW 1768/1-1 and the German Federal Ministry of Education and Research (BMBF, grants 01KU1905A and 01ZX1904A).

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  1. Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Thilo Rieg & Emanuel Schwarz

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  1. Thilo Rieg
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  2. Emanuel Schwarz
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Correspondence to Emanuel Schwarz.

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Nature Computational Science thanks Richard Bethlehem and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Rieg, T., Schwarz, E. From mechanistic insight towards clinical implementation using normative modeling. Nat Comput Sci 2, 278–280 (2022). https://doi.org/10.1038/s43588-022-00248-7

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