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Updated: May 21, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Imaging of Disease-Related Networks in Parkinson's Disease
Yoshikazu Nakano1, Martin Niethammer1,2, David Eidelberg3,2,4
1Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York 11030, USA.
Abstract:
Functional neuroimaging techniques are increasingly being used to advance the diagnosis and management of Parkinson's disease (PD). Methods such as [18F]-fluorodeoxyglucose positron emission tomography (FDG PET), resting-state functional magnetic resonance imaging (rs-fMRI), arterial spin labeling (ASL) MRI, and single-photon emission computed tomography (SPECT) enable the identification of disease-specific patterns like the PD-related pattern (PDRP) and PD cognition-related pattern (PDCP), which correlate with motor and cognitive symptoms. Network analysis using graph theory further elucidates the alterations in brain connectivity associated with PD, providing insights into disease progression and response to treatment. Moreover, these neuroimaging patterns assist in distinguishing PD from atypical parkinsonian syndromes, enhancing diagnostic accuracy. Understanding the impact of genetic variants like LRRK2 and GBA1 on functional connectivity highlights the potential for precision medicine in PD. As neuroimaging technologies evolve, their integration into clinical practice will be pivotal in the personalized management of PD, offering improved diagnostic precision and targeted therapeutic interventions.
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