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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Spatial Metabolic Covariance Networks in Progressive Supranuclear Palsy: Implications for Symptomatology and Their
Bo Wang1, Haotian Wang1, Yixin Kang1
1Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Background:
Progressive supranuclear palsy (PSP) is a clinically heterogeneous neurodegenerative disorder with unclear pathophysiology.
Objective:
This study aimed to uncover clinically relevant metabolic networks derived from 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in PSP.
Methods:
FDG and dopaminergic transporter PET data from 72 PSP patients and 70 healthy controls were analyzed, with an independent test set of 24 PSP patients. All patients underwent comprehensive neuropsychiatric assessments. Using spatial independent component analysis, the study identified independent metabolic networks and examined their correlations with clinical features and striatal dopaminergic binding.
Results:
Three distinct metabolic networks were identified in PSP: The first network demonstrated hypometabolism in dorsomedial thalamus (dmT), medial prefrontal cortex (mPFC) and midbrain, termed the dmT-mPFC network, negatively correlating with disease severity, functional disability and duration, and associating with gait/midline disturbances and ocular dysfunction. The second network displayed posterior cingulate cortex (PCC) and lateral prefrontal hypometabolism (LPFC), named the PCC-LPFC network, linking to disease severity, cognitive impairment, and parkinsonism. The third network exhibited preserved putamen metabolism with ventrolateral thalamus and sensorimotor cortex hypermetabolism, inversely relating to disease duration. Both dmT-mPFC and PCC-LPFC networks strongly correlated with striatal dopaminergic degeneration. The test set showed strong associations between cognitive impairment and the PCC-LPFC network, and between functional disability and the dmT-mPFC network, along with potential trends linking disease severity to these networks.
Conclusion:
The robust clinical and dopaminergic-related independent metabolic networks offer novel insights into disease pathophysiology, whereas their qualitative weighting offers a potential tool for staging disease severity. © 2025 International Parkinson and Movement Disorder Society.
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