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Parkinson's Disease: Overview

Parkinson's Disease: Overview

Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...

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Updated: Sep 15, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Towards Precision Functional Brain Network Mapping in Parkinson's Disease.

Jake Chernicky^1,2,3, Ally Dworetsky^4,3, Sarah Grossen⁵

¹Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois.

Biorxiv : the Preprint Server for Biology

|July 17, 2025

Summary

Precision resting-state functional connectivity (RSFC) is a reliable method for studying Parkinson's disease (PD) brain networks. This approach reveals individual differences in PD, offering potential for personalized diagnostics and biomarker discovery.

Keywords:

Parkinson’s Disease fMRI functional connectivity individual differences

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Last Updated: Sep 15, 2025

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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Area of Science:

Neuroscience
Medical Imaging
Systems Biology

Background:

Parkinson's disease (PD) involves complex neurodegeneration affecting brain networks with significant individual variability.
Current resting-state functional connectivity (RSFC) studies struggle with reliability and group-averaged network definitions, hindering the study of interindividual differences.
Precision RSFC, using extended data collection and individual network definition, offers a potential solution but needs validation in PD.

Purpose of the Study:

To assess the feasibility and reliability of precision RSFC in individuals with Parkinson's disease.
To compare the quality of precision RSFC measures against conventional methods in PD.
To explore the potential of individualized brain network measures in PD.

Main Methods:

Collected over 100 minutes of RSFC data from 20 PD patients and 6 healthy controls (HC).
Evaluated motion, reliability, and stability of RSFC measures, comparing PD and HC groups, and contrasting with conventional 5-minute RSFC data.
Developed individualized brain network maps for PD participants to test feasibility.

Main Results:

Precision RSFC yielded reliable and stable brain network measures in PD participants, comparable to HC.
Precision methods significantly outperformed conventional methods in data quality.
Individualized network maps in PD showed distinct patterns compared to group averages and among individuals, including motor systems.

Conclusions:

Precision RSFC is a feasible and reliable technique for use in individuals with Parkinson's disease.
This advanced imaging approach shows promise for developing personalized diagnostic tools.
Precision RSFC may help identify brain-based biomarkers that explain clinical variability in PD.