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Review
. 2023 Aug;39(8):1309-1326.
doi: 10.1007/s12264-023-01057-2. Epub 2023 Apr 24.

Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning

Affiliations
Review

Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning

Qian Lv et al. Neurosci Bull. 2023 Aug.

Abstract

Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.

Keywords: Core region; Machine learning; Magnetic resonance imaging; Neuroimaging-based diagnosis; Obsessive-compulsive disorder; Psychiatric disorders.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
The application of neuroimaging-based diagnosis in OCD. A, B Comparison of traditional symptom-based diagnosis and neuroimaging-based diagnosis in OCD. Traditionally, patients with OCD are diagnosed by psychiatrists according to the DSM. In neuroimaging-based diagnosis, imaging datasets from subjects are acquired, and a classifier automatically distinguishes patients with OCD from HCs. C The pipeline of diagnostic classification based on neuroimaging data. DSM, the Diagnostic and Statistical Manual of Mental Disorders; OCD, obsessive-compulsive disorder; HC, healthy control; AUC, the area under the curve.
Fig. 2
Fig. 2
Evidence supporting the concept of core regions in OCD. A The Neurosynth automatically analyzed information from 81 studies investigating OCD (FDR corrected, P <0.01). The color bar denotes the z scores of the uniformity test. Please see the link (https://neurosynth.org/analyses/terms/ocd/) for further details. B Three types of neuromodulation therapies for OCD: stereotactic lesion (left), deep brain stimulation (middle), and transcranial magnetic stimulation (right). C Application of the core region-based strategy in schizophrenia, adapted from [101]. OCD, obsessive-compulsive disorder; ALIC, anterior limb of the internal capsule; NAc, nucleus accumbens; STN, subthalamic nucleus; dlPFC, dorsolateral prefrontal cortex; mPFC/ACC, medial prefrontal cortex/anterior cingulate cortex; FDR, false discovery rate; fALFF, fractional amplitude of low-frequency fluctuations.

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