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Parkinson's Disease: Treatment

Parkinson's Disease: Treatment

Neurodegenerative disorders, such as Parkinson's Disease (PD), involve the gradual and irreversible destruction of neurons in particular brain areas. These disorders exhibit standard features like proteinopathies, selective vulnerability of some neurons, and an interaction of intrinsic properties, genetics, and environmental influences in neural injury.
Parkinson's Disease is primarily a result of the loss of dopaminergic neurons in the substantia nigra pars compacta. The cornerstone of its...

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Identifying Depression in Parkinson's Disease by Using Combined Diffusion Tensor Imaging and Support Vector Machine.

Yunjun Yang¹, Yuelong Yang², Aizhen Pan¹

¹Department of Radiology, The First People's Hospital of Foshan, Foshan, China.

Frontiers in Neurology

|July 7, 2022

Summary

Parkinson's disease (PD) with depression shows white matter changes. Diffusion tensor imaging (DTI) and machine learning can help identify depressed PD patients.

Keywords:

Parkinson's disease depression diffusion tensor imaging machine learning support vector machine

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Area of Science:

Neuroimaging
Neurology
Machine Learning

Background:

Parkinson's disease (PD) is a neurodegenerative disorder.
Depression is a common comorbidity in PD patients.
Understanding the neurobiological underpinnings of depression in PD is crucial.

Purpose of the Study:

To investigate white matter microstructural alterations in PD patients with depression.
To explore the utility of diffusion tensor imaging (DTI) and machine learning for identifying depressed PD (dPD).

Main Methods:

Whole-brain DTI data were acquired from 37 dPD, 35 non-depressed PD (ndPD), and 25 healthy control (HC) subjects.
Atlas-based analysis compared fractional anisotropy (FA) and mean diffusivity (MD) between groups.
A support vector machine (SVM) model was trained to differentiate dPD from ndPD.

Main Results:

dPD patients showed significantly decreased FA in multiple white matter tracts (e.g., corticospinal tract, cingulum, longitudinal fasciculi) compared to ndPD.
Increased MD was observed in the right cingulum and left superior longitudinal fasciculus-temporal part in dPD.
The SVM model achieved 70% accuracy in the training set and 73% in the test set for dPD identification.

Conclusions:

Depression in PD is associated with distinct white matter microstructural alterations.
DTI-based machine learning models show promise for the individualized diagnosis of dPD.