Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes

Abstract

The ability of our current psychiatric nosology to accurately delineate clinical populations and inform effective treatment plans has reached a critical point with only moderately successful interventions and high relapse rates. These challenges continue to motivate the search for approaches to better stratify clinical populations into more homogeneous delineations, to better inform diagnosis and disease evaluation, and prescribe and develop more precise treatment plans. The promise of brain-based subtyping based on neuroimaging data is that finding subgroups of individuals with a common biological signature will facilitate the development of biologically grounded, targeted treatments. This review provides a snapshot of the current state of the field in empirical brain-based subtyping studies in child, adolescent, and adult psychiatric populations published between 2019 and March 2022. We found that there is vast methodological exploration and a surprising number of new methods being created for the specific purpose of brain-based subtyping. However, this methodological exploration and advancement is not being met with rigorous validation approaches that assess both reproducibility and clinical utility of the discovered brain-based subtypes. We also found evidence for a collaboration crisis, in which methodological exploration and advancements are not clearly grounded in clinical goals. We propose several steps that we believe are crucial to address these shortcomings in the field. We conclude, and agree with the authors of the reviewed studies, that the discovery of biologically grounded subtypes would be a significant advancement for treatment development in psychiatry.

Keywords: Biomarkers; Biotypes; Clinical utility; Neuroimaging; Precision medicine; Reproducibility.

Figure 1.. Data modalities, dimensionality reduction & feature selection techniques, and clustering algorithms used.

A. Data modalities used. Data-integration refers to the integration or fusion of either functional or structural data with psychometric or clinical data that was then used as input to the clustering algorithms. B. Dimensionality reduction and feature selection techniques used. C. Clustering algorithms used. Algorithms in dark grey are unsupervised approaches, while those in light grey are semi-supervised methods. CHIMERA, Clustering of Heterogeneous Disease Effects via Distribution Matching of Imaging Patterns; CT, Cortical Thickness; dNTiC, Dynamic-N-way tri-clustering; DTI, Diffusion Tensor Imaging; FA, Fractional Anisotropy; HYDRA, Heterogeneity through Discriminative Analysis; S-GIMME, Subgrouping – Group Iterative Multiple Model Estimation. SA, Surface Area; Vol, Volume.

Figure 2.. Cluster reproducibility and cluster utility strategies used across the 38 papers.

A. Cluster reproducibility strategies. B. Cluster utility strategies.

Figure 3.. Relationship between a studies most stringent validation strategies used to assess cluster reproducibility and cluster utility (jittered).

Several studies used more than one validation strategy for both reproducibility and utility. Here, we performed a spearman rank-order correlation on the most stringent reproducibility versus most stringent clinical utility strategy used within each study. An inverse relationship (ρ = −0.10) was identified. Val, Validation.

Figure 4.. A comparison of the most stringent validation strategies implemented between the studies using traditional and newly developed algorithms.

A. Cluster reproducibility validation strategies. B. Cluster utility validation strategies