Cortical gyrification pattern of depression in Parkinson's disease: a neuroimaging marker for disease severity?

Front Aging Neurosci. 2023 Nov 14:15:1241516. doi: 10.3389/fnagi.2023.1241516. eCollection 2023.

Abstract

Background: Although the study of the neuroanatomical correlates of depression in Parkinson's Disease (PD) is gaining increasing interest, up to now the cortical gyrification pattern of PD-related depression has not been reported. This study was conducted to investigate the local gyrification index (LGI) in PD patients with depression, and its associations with the severity of depression.

Methods: LGI values, as measured using FreeSurfer software, were compared between 59 depressed PD (dPD), 27 non-depressed PD (ndPD) patients and 43 healthy controls. The values were also compared between ndPD and mild-depressed PD (mi-dPD), moderate-depressed PD (mo-dPD) and severe-depressed PD (se-dPD) patients as sub-group analyses. Furthermore, we evaluated the correlation between LGI values and depressive symptom scores within dPD group.

Results: Compared to ndPD, the dPD patients exhibited decreased LGI in the left parietal, the right superior-frontal, posterior cingulate and paracentral regions, and the LGI values within these areas negatively correlated with the severity of depression. Specially, reduced gyrification was observed in mo-dPD and involving a larger region in se-dPD, but not in mi-dPD group.

Conclusion: The present study demonstrated that cortical gyrification is decreased within specific brain regions among PD patients with versus without depression, and those changes were associated with the severity of depression. Our findings suggested that cortical gyrification might be a potential neuroimaging marker for the severity of depression in patients with PD.

Keywords: Parkinson’s disease; cortical gyrification; depression; magnetic resonance imaging; severity of depression.

Grants and funding

This study was supported by grants from National Key R&D Program of China (grant nos. 2022YFC2009904 and 2022YFC2009900), the Natural Science Foundation of Hunan Province (grant nos. 2022JJ30818 and 2021JJ40860), the Science and Technology Innovation Program of Hunan Province (grant no. 2021SK53502), and the Natural Science Foundation of Changsha (grant no. kq2202416).