Transdiagnostic structural neuroimaging features in depression and psychosis: A systematic review
Paris Alexandros Lalousis1, Stephen Wood2, Renate Reniers1
1Institute for Mental Health, University of Birmingham, Birmingham B15 2SA, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham B15 2SA, United Kingdom.
Background:
Previous research suggests that there may be similarities in structural brain changes seen in patients with depression and psychosis compared to healthy controls. However, there is yet no systematic review collating studies comparing structural brain changes in depression and psychosis. Establishing shared and specific neuroanatomical features could aid the investigation of underlying biological processes.
Aims:
To identify structural neuroimaging similarities and differences between patients with depression and psychosis.
Method:
We searched PubMed, PsychInfo, Embase, NICE Evidence, Medline and the Cochrane Library were searched from inception to 30/06/2021 using relevant subject headings (controlled vocabularies) and search syntax. Papers were assessed for quality using the Newcastle-Ottawa Scale.
Results:
Five-hundred and twenty papers were retrieved, seven met inclusion criteria. In narrative collation of results, grey matter volume (GMV) reductions were found in the medial frontal gyrus (MFG), hippocampus and left-sided posterior subgenual prefrontal cortex in both psychosis and depression. GMV reductions affected more brain regions in psychosis, including in the insula and thalamus. White matter volume (WMV) decline was found in both depression and psychosis. Reduced fractional anisotropy (FA) was more commonly seen in depression.
Conclusions:
Our results suggest potential transdiagnostic patterns of GMV and WMV reductions in areas including the MFG, hippocampus, and left-sided posterior subgenual prefrontal cortex. These could be investigated as a future biomarker of transdiagnostic signature across mental illnesses. However, due to the limited number and poor quality of studies future research in large samples and harmonised imaging data is first needed.
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