Weighted Permutation Entropy Reveals Neural Dynamic Alterations in the Alzheimer's Disease
Poster No:
122
Submission Type:
Abstract Submission
Authors:
Kexin Gao1, Pan Wang1, Bharat Biswal2
Institutions:
1University of Electronic Science and Technology of China, Sichuan, China, 2New Jersey Institute of Technology, Newark, NJ
First Author:
Co-Author(s):
Introduction:
Alzheimer's disease (AD) is an irreversible, progressive neurodegenerative disorder that leads to the impairment of memory and cognitive functions. A previous study found that abnormalities in dynamic functional connectivity strength may serve as potential imaging markers for the early diagnosis of AD (Zhao et al., 2022). However, the changes in local neural dynamics across the AD continuum remain unclear. Weighted permutation entropy (WPE), an information-theoretic measure, quantifies the intrinsic variability of neural fluctuations and provides a time-resolved account of neural variability to capture the temporal dynamics within individual signals. In this study, we employed WPE to investigate the signal complexity of BOLD activity and explore its role across the AD continuum.
Methods:
This study used public data from the OASIS-3 dataset (https://central.xnat.org), which included 53 AD patients, 90 individuals with mild cognitive impairment (MCI), and 100 gender- and age-matched healthy controls (HCs). Disease severity was evaluated using the Clinical Dementia Rating (CDR) score. Resting-state functional data (TR = 2200 ms, TE = 27 ms) were acquired from all subjects using 3-T Siemens Trio Tim scanners. Functional imaging preprocessing included the following steps: remove the first 10 time points, realign, slice timing, normalize, nuisance covariates regression (24 head movement parameters, white matter signal, cerebrospinal fluid signal,time points with frame displacement> 0.2 mm), remove the linear trends, band-pass filter (0.01–0.1 Hz), smooth, z-score. WPE quantifies pattern irregularity by calculating the Shannon entropy of symbolic motifs within a time series, as expressed mathematically in Fig. 1(A). Based on methodological considerations (Fadlallah et al., 2013) and previous applications of WPE in functional MRI data analysis (Krohn et al., 2023; Liu et al., 2024), we used a motif length of m = 3 and a lag parameter of τ = 1 to calculate the motif distribution across the target signal vector. WPE was then calculated on the preprocessed BOLD signal for each voxel in grey matter and then normalized to the range [0, 1]. Finally, ANOVA was conducted to examine differences among the three groups, with age, gender, and education included as covariates. Gaussian Random Field (GRF) theory was applied to correct for cluster-level multiple comparisons (voxel p-value < 0.005; cluster p-value < 0.05). Partial correlation was performed to assess the relationship between WPE and cognitive function, measured by the Mini-Mental State Examination (MMSE) score, controlling for age, gender, and education.
Results:
The statistical results revealed that the WPE values of three clusters differed significantly among the three groups, including the left lobule X of the cerebellar hemisphere, the left and right pulvinar nuclei of the medial thalamus, and the left superior parietal gyrus. Post hoc and correlation analysis showed that the WPE values in the left lobule X of the cerebellar hemisphere and the left and right pulvinar nuclei of the medial thalamus decreased across the AD continuum and were positively associated with MMSE scores. However, the WPE value in the left superior parietal gyrus was higher in the MCI group compared with both HC and AD groups.
·Fig1. Mathematical Expression of Weighted Permutation Entropy (WPE) and ANOVA Results Across Three Groups
·Fig2. Post Hoc Analysis Results and Correlations Between Weighted Permutation Entropy (WPE) and Mini-Mental State Examination (MMSE) Scores
Conclusions:
This study enhances the understanding of neural dynamic deficits across the AD continuum. The observed decrease in neural dynamics in the thalamus and cerebellum, along with their association with cognitive impairment, highlights the potential role of sensory-motor regions as a key factor contributing to cognitive deficits in AD.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Keywords:
Degenerative Disease
FUNCTIONAL MRI
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Krohn, S. (2023). A spatiotemporal complexity architecture of human brain activity. SCIENCE ADVANCES.
Liu, L. (2024). Neuroimaging markers of aberrant brain activity and treatment response in schizophrenia patients based on brain complexity. Translational Psychiatry, 14(1), 1–12. https://doi.org/10.1038/s41398-024-03067-8
Zhao, C. (2022). Abnormal characterization of dynamic functional connectivity in Alzheimer's disease. Neural Regeneration Research, 17(9), 2014. https://doi.org/10.4103/1673-5374.332161