Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson's disease

Abstract

In neurodegenerative disorders, a clearer understanding of the underlying aberrant networks facilitates the search for effective therapeutic targets and potential cures. [ 18 F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging data of brain metabolism reflects the distribution of glucose consumption known to be directly related to neural activity. In FDG PET resting-state metabolic data, characteristic disease-related patterns have been identified in group analysis of various neurodegenerative conditions using principal component analysis of multivariate spatial covariance. Notably, among several parkinsonian syndromes, the identified Parkinson’s disease-related pattern (PDRP) has been repeatedly validated as an imaging biomarker of PD in independent groups worldwide. Although the primary nodal associations of this network are known, its connectivity is not fully understood. Here, we describe a novel approach to elucidate functional principal component (PC) network connections by performing graph theoretical sparse network derivation directly within the disease relevant PC partition layer of the whole brain data rather than by searching for associations retrospectively in whole brain sparse representations. Using sparse inverse covariance estimation of each overlapping PC partition layer separately, a single coherent network is detected for each layer in contrast to more spatially modular segmentation in whole brain data analysis. Using this approach, the major nodal hubs of the PD disease network are identified and their characteristic functional pathways are clearly distinguished within the basal ganglia, midbrain and parietal areas. Network associations are further clarified using Laplacian spectral analysis of the adjacency matrices. In addition, the innate discriminative capacity of the eigenvector centrality of the graph derived networks in differentiating PD versus healthy external data provides evidence of their validity.

Stimulation of the Subthalamic Nucleus Changes Cortical-Subcortical Blood Flow Patterns During Speech: A Positron Emission Tomography Study

“…The cortical-subcortical rCBF patterns reported in this paper represent state-like, acute rCBF changes identified by virtue of their relationships with actual symptomatic behaviors engaged in during scanning (40). This approach contrasts with more trait-like, chronic patterns of metabolic abnormality that better represent the presence of disease (41)(42)(43), its progression and response to treatment (44)(45)(46), its components (47), and variants (48,49). The state-like approach has advantages for understanding functional neuropathology underlying specific neurologic signs (e.g., Parkinsonian speech) whereas the traitlike approach represents a more stable, chronic condition that is advantageous in establishing diagnoses and severity.…”

Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm

“…There are studies in the literature that show how glucose hypo-metabolism occurs prior to localized atrophy of brain matter and that the conversion time is correlated with this hypo-metabolism. The use of FDG-PET images [15] has become an effective tool for this information.…”

Single Photon Emission Computed Tomography/Positron Emission Tomography Molecular Imaging for Parkinsonism: A Fast‐Developing Field

“…46 The 18 F-FDG metabolic connectivity is based on voxelwise or region of interest methods with or without any a priori assumptions about the specific neurodegenerative disease, including PD. 46,47 When applied to resting-state 18 F-FDG PET scans of PD patients, this method identifies abnormal disease-related spatial covariance patterns (PD-related metabolic patterns [PDRPs]). PDRPs include numerous corticostriatopallidothalamocortical pathway components and are characterized by increased pallidal, thalamic, and pontine metabolic activity, coupled with relative reductions in the premotor cortex, supplemental motor area, and parietal association areas.…”

“…Analyzing the metabolic connectivity of 18 F‐FDG PET imaging in brain pathologies represents a significant shift from evaluating an underlying pathology of local neuronal function to improving the understanding of long‐distance effects on interconnected neural systems 46 . The 18 F‐FDG metabolic connectivity is based on voxelwise or region of interest methods with or without any a priori assumptions about the specific neurodegenerative disease, including PD 46,47 . When applied to resting‐state 18 F‐FDG PET scans of PD patients, this method identifies abnormal disease‐related spatial covariance patterns (PD‐related metabolic patterns [PDRPs]).…”