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Review
. 2024 Oct 14;67(1):e69.
doi: 10.1192/j.eurpsy.2024.1785.

Specificity in the commonalities of inhibition control: using meta-analysis and regression analysis to identify the key brain regions in psychiatric disorders

Li Wan  1   2   3   4 , Pingting Pei  5 , Qinghui Zhang  1 , Wenxiang Gao  1
Affiliations
Review

Specificity in the commonalities of inhibition control: using meta-analysis and regression analysis to identify the key brain regions in psychiatric disorders

Li Wan et al. Eur Psychiatry. .

Abstract

Background: The differential diagnosis of psychiatric disorders is relatively challenging for several reasons. In this context, we believe that task-based magnetic resonance imaging (MRI) can serve as a tool for differential diagnosis. The aim of this study was to explore the commonalities in brain activities among individuals with psychiatric disorders and to identify the key brain regions that can distinguish between these disorders.

Methods: The PubMed, MEDLINE, EMBASE, Web of Science, Scopus, PsycINFO, and Google Scholar databases were searched for whole-brain functional MRI studies that compared psychiatric patients and normal controls. The psychiatric disorders included schizophrenia (SCZ), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder, attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Studies using go-nogo paradigms were selected, we then conducted activation likelihood estimation (ALE) meta-analysis, factor analysis, and regression analysis on these studies subsequently.

Results: A total of 152 studies (108 with patients) were selected and a consistent pattern was found, that is, decreased activities in the same brain regions across six disorders. Factor analysis clustered six disorders into three pairs: SCZ and ASD, MDD and BD, and ADHD and BD. Furthermore, the heterogeneity of SCZ and ASD was located in the left and right thalamus; and the heterogeneity of MDD and BD was located in the thalamus, insula, and superior frontal gyrus.

Conclusion: The results can lead to a new classification method for psychiatric disorders, benefit the differential diagnosis at an early stage, and help to understand the biobasis of psychiatric disorders.

Keywords: ALE meta-analysis; clustering center; factor analysis; inhibition control; psychiatric disorders.

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Conflict of interest statement

The authors report no biomedical or financial interests or potential conflicts of interest.

Figures

Figure 1.
Figure 1.
PRISMA flow diagram.
Figure 2.
Figure 2.
A consistent pattern across six disorders – the decreased activities in patients versus normal controls. The decreased activities were consistently found in the bilateral cingulate gyri, bilateral inferior frontal gyri, bilateral medial frontal gyri, bilateral superior frontal gyri, bilateral precentral gyri, and bilateral insula across all six disorders. Moreover, in the right hemisphere, the decreased activities were consistently found in the right inferior parietal lobule, right superior parietal lobule, and right thalamus in patients. (1. Medial frontal gyrus; 2. Insula; 3. Thalamus; 4. Precentral gyrus; 5. Superior frontal gyrus; 6. Inferior frontal gyrus) (2. A, anterior; L, left; R, right) (formula image ALE score range: 0.02–0.07)
Figure 3.
Figure 3.
The factor loadings on each disorder. *The largest loadings showing that the two disorders in the same category. ADHD, attention-deficit/hyperactivity disorder; ASD, autism spectrum disorder; BD, bipolar disorder; MDD, major depressive disorder; OCD, obsessive–compulsive disorder; SCZ, schizophrenia.
Figure 4.
Figure 4.
The residual of each brain region in regression analysis. *The largest residuals.
See this image and copyright information in PMC

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