Mapping brain structural differences and neuroreceptor correlates in Parkinson's disease visual hallucinations: a mega-analysis

Parkinson's psychosis (PDP) describes a spectrum of symptoms that may arise in Parkinson's disease (PD) including visual hallucinations (VH). Imaging studies investigating the neural correlates of PDP have been inconsistent in their findings, due to differences in study design and limitations of scale. Here we use empirical Bayes harmonisation to pool together structural imaging data from multiple research groups into a large-scale mega-analysis, allowing us to apply new methodological approaches to identify cortical regions and networks involved in VH and their relation to receptor binding. Differences of cortical thickness and surface area show a wider cortical involvement underlying VH than previously recognised, including primary visual cortex and its surrounds, and the hippocampus, independent of its role in cognitive decline. Structural covariance analyses point to a strong involvement of the attentional control networks in PD-VH, while associations with receptor density maps suggest neurotransmitter loss may drive the cortical changes.

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Mapping brain structural differences and neuroreceptor correlates in Parkinson’s disease visual hallucinations: a mega-analysis

10.21203/rs.3.rs-270425/v1 ◽

2021 ◽

Author(s):

Miriam Vignando ◽

Dominic ffytche ◽

Simon Lewis ◽

Phil Hyu Lee ◽

Seok Chung ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Empirical Bayes ◽

Large Scale ◽

Visual Hallucinations ◽

Imaging Data ◽

Control Networks ◽

Density Maps ◽

Cortical Regions ◽

Cortical Involvement

Abstract Parkinson’s psychosis (PDP) describes a spectrum of symptoms that may arise in Parkinson’s disease (PD) including visual hallucinations (VH). Imaging studies investigating the neural correlates of PDP have been inconsistent in their findings, due to differences in study design and limitations of scale. Here we use empirical Bayes harmonisation to pool together structural imaging data from multiple research groups into a large-scale mega-analysis, allowing us to apply new methodological approaches to identify cortical regions and networks involved in VH and their relation to receptor binding. Differences of cortical thickness and surface area show a wider cortical involvement underlying VH than previously recognised, including primary visual cortex and its surrounds, and the hippocampus, independent of its role in cognitive decline. Structural covariance analyses point to a strong involvement of the attentional control networks in PD-VH, while associations with receptor density maps suggest neurotransmitter loss may drive the cortical changes.

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Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis

Journal of Personalized Medicine ◽

10.3390/jpm12010089 ◽

2022 ◽

Vol 12 (1) ◽

pp. 89

Author(s):

Xuan Cao ◽

Fang Yang ◽

Jingyi Zheng ◽

Xiao Wang ◽

Qingling Huang

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Likelihood Estimation ◽

Imaging Data ◽

Mri Imaging ◽

New Family ◽

Tensor Regression ◽

Nonmotor Feature ◽

Cortical Regions ◽

The Individual

Background: Depression is a prominent and highly prevalent nonmotor feature in patients with Parkinson’s disease (PD). The neural and pathophysiologic mechanisms of PD with depression (DPD) remain unclear. The current diagnosis of DPD largely depends on clinical evaluation. Methods: We proposed a new family of multinomial tensor regressions that leveraged whole-brain structural magnetic resonance imaging (MRI) data to discriminate among 196 non-depressed PD (NDPD) patients, 84 DPD patients, 200 healthy controls (HC), and to assess the special brain microstructures in NDPD and DPD. The method of maximum likelihood estimation coupled with state-of-art gradient descent algorithms was used to predict the individual diagnosis of PD and the development of DPD in PD patients. Results: The results reveal that the proposed efficient approach not only achieved a high prediction accuracy (0.94) with a multi-class AUC (0.98) for distinguishing between NDPD, DPD, and HC on the testing set but also located the most discriminative regions for NDPD and DPD, including cortical regions, the cerebellum, the brainstem, the bilateral basal ganglia, and the thalamus and limbic regions. Conclusions: The proposed imaging technique based on tensor regression performs well without any prior feature information, facilitates a deeper understanding into the abnormalities in DPD and PD, and plays an essential role in the statistical analysis of high-dimensional complex MRI imaging data to support the radiological diagnosis of comorbidity of depression with PD.

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