Abstract
To explore the effects of PD pathology on brain connectivity, we characterized with an emergent computational approach the brain metabolic connectome using [18F]FDG-PET in early idiopathic PD patients. We applied whole-brain and pathology-based connectivity analyses, using sparse-inverse covariance estimation in thirty-four cognitively normal PD cases and thirty-four age-matched healthy subjects for comparisons. Further, we assessed high-order resting state networks by interregional correlation analysis. Whole-brain analysis revealed altered metabolic connectivity in PD, with local decreases in frontolateral cortex and cerebellum and increases in the basal ganglia. Widespread long-distance decreases were present within the frontolateral cortex as opposed to connectivity increases in posterior cortical regions, all suggestive of a global-scale connectivity reconfiguration. The pathology-based analyses revealed significant connectivity impairment in the nigrostriatal dopaminergic pathway and in the regions early affected by α-synuclein pathology. Notably, significant connectivity changes were present in several resting state networks especially in frontal regions. These findings expand previous imaging evidence of altered connectivity in cognitively stable PD patients by showing pathology-based connectivity changes and disease-specific metabolic architecture reconfiguration at multiple scale levels, from the earliest PD phases. These alterations go well beyond the known striato-cortical connectivity derangement supporting in vivo an extended neural vulnerability in the PD synucleinopathy.
Highlights
Parkinson’s disease (PD) is a neurodegenerative disease predominantly characterized by abnormal intracellular accumulations of insoluble α-synuclein into fibrils[1]
A unique tool to capture in vivo the pathological events that contribute to synaptic dysfunction is [18F]fluorodeoxyglucose with positron emission tomography ([18F]FDG-PET). [18F]FDG-PET is considered as an effective measure of energy consumption in neurons[13], and its signal has been associated with synaptic density and function[14], altered intracellular signalling cascades, impaired neurotransmitter release, spreading of proteinopathies, and long distance disconnection
Our study applied for the first time an alternative approach, namely Sparse Inverse Covariance Estimation (SICE) in combination with seed-based inter-correlation analysis for a multiscale assessment of metabolic connectivity in PD, by exploiting the unique sensitivity of [18F]FDG-PET in capturing the pathological events affecting brain functionality
Summary
Parkinson’s disease (PD) is a neurodegenerative disease predominantly characterized by abnormal intracellular accumulations of insoluble α-synuclein into fibrils[1]. [18F]FDG-PET is considered as an effective measure of energy consumption in neurons ( in synapses)[13], and its signal has been associated with synaptic density and function[14], altered intracellular signalling cascades, impaired neurotransmitter release, spreading of proteinopathies, and long distance disconnection Based on the assumption that regions whose metabolism is correlated are functionally interconnected[21], seed-based voxel wise analysis[20] and Sparse Inverse Covariance Estimation (SICE) method[19] represent suitable approaches to measure functional interconnections between brain regions These methods provide results comparable to those derived by rs-fMRI images analyses[22], allowing the application of graph-theoretical analysis to [18F]FDG-PET data[19]. We assessed the possible dysfunctional changes in the high-order resting state networks, namely the attentional, anterior and posterior DMN, executive and motor networks in the same PD patients
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