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Multiview Diffusion Map Improves Prediction of Fluid Intelligence with Two Paradigms of fMRI Analysis.

Sat, 01/02/2021 - 00:41
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Multiview Diffusion Map Improves Prediction of Fluid Intelligence with Two Paradigms of fMRI Analysis.

IEEE Trans Biomed Eng. 2020 Dec 31;PP:

Authors: Pan G, Xiao L, Bai Y, Wilson TW, Stephen JM, Calhoun VD, Wang YP

Abstract
OBJECTIVE: To understand the association betweenbrain networks and behaviors of an individual, most studiesbuild predictive models based on functional connectivity (FC) from a single dataset with linear analysis techniques. Such approaches may fail to capture the nonlinear structure of brainnetworks and neglect the complementary information contained in FC networks (FCNs) from multiple datasets. To address this challenging issue, we use multiview dimensionality reduction to extract a coherent low-dimensional representation of the FCNs from resting-state and emotion identification task-based functional magnetic resonance imaging (fMRI) datasets.
METHODS: We propose a scheme based on multiview diffusion map to extract intrinsic features while preserving the underlying geometric structure of high dimensional datasets. This method is robust to noise and small variations in the data.
RESULTS: After validation on the Philadelphia Neurodevelopmental Cohort data, the predictive model built from both resting-state and emotion identification task-based fMRI datasets outperforms the one using each individual fMRI dataset. In addition, the proposed model achieves better prediction performance than principal component analysis (PCA) and three other competingdata fusion methods.
CONCLUSION AND SIGNIFICANCE: Our framework for combing multiple FCNs in one predictive model exhibits improved prediction performance. To our knowledge, we demonstrate a first application of multiview diffusion map to successfully fuse differenttypes of fMRI data for fluid intelligence (gF).

PMID: 33382644 [PubMed - as supplied by publisher]

Distinct Changes in Global Brain Synchronization in Early-Onset vs. Late-Onset Parkinson Disease.

Sat, 01/02/2021 - 00:41
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Distinct Changes in Global Brain Synchronization in Early-Onset vs. Late-Onset Parkinson Disease.

Front Aging Neurosci. 2020;12:604995

Authors: Wang T, Liao H, Zi Y, Wang M, Mao Z, Xiang Y, Zhang L, Li J, Shen Q, Cai S, Tan C

Abstract
Early- and late-onset Parkinson's disease (EOPD and LOPD, respectively) have different risk factors, clinical features, and disease course; however, the functional outcome of these differences have not been well characterized. This study investigated differences in global brain synchronization changes and their clinical significance in EOPD and LOPD patients. Patients with idiopathic PD including 25 EOPD and 24 LOPD patients, and age- and sex-matched healthy control (HC) subjects including 27 younger and 26 older controls (YCs and OCs, respectively) were enrolled. Voxel-based degree centrality (DC) was calculated as a measure of global synchronization and compared between PD patients and HC groups matched in terms of disease onset and severity. DC was decreased in bilateral Rolandic operculum and left insula and increased in the left superior frontal gyrus (SFG) and precuneus of EOPD patients compared to YCs. DC was decreased in the right putamen, mid-cingulate cortex, bilateral Rolandic operculum, and left insula and increased in the right cerebellum-crus1 of LOPD patients compared to OCs. Correlation analyses showed that DC in the right cerebellum-crus1 was inversely associated with the Hamilton Depression Scale (HDS) score in LOPD patients. Thus, EOPD and LOPD patients show distinct alterations in global synchronization relative to HCs. Furthermore, our results suggest that the left SFG and right cerebellum-crus1 play important roles in the compensation for corticostriatal-thalamocortical loop injury in EOPD and LOPD patients, whereas the cerebellum is a key hub in the neural mechanisms underlying LOPD with depression. These findings provide new insight into the clinical heterogeneity of the two PD subtypes.

PMID: 33381021 [PubMed]