Altered intrinsic neural timescales in mild cognitive impairment and Alzheimer's disease
Poster No:
143
Submission Type:
Abstract Submission
Authors:
Aiying Zhang1,2, Seonjoo Lee2, Xi Zhu2, Kenneth Wengler2, Guillermo Horga2, Terry Goldberg3
Institutions:
1University of Virginia, Charlottesville, VA, 2New York State Psychiatric Institute, New York, NY, 3Columbia University Medical Center, New York, NY
First Author:
Aiying Zhang
University of Virginia|New York State Psychiatric Institute
Charlottesville, VA|New York, NY
University of Virginia|New York State Psychiatric Institute
Charlottesville, VA|New York, NY
Co-Author(s):
Introduction:
Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects millions of older adults in the US and worldwide. Resting-state functional magnetic resonance imaging (rs-fMRI) has become a widely used neuroimaging tool to study neurophysiology in AD and its prodromal condition, mild cognitive impairment (MCI). The intrinsic neural timescale (INT), which can be estimated through the magnitude of the autocorrelation of intrinsic neural signals using rs-fMRI, is thought to quantify the duration that neural information is stored in a local cortical circuit. The heterogeneity of the timescales is considered to be a basis of the functional hierarchy in the brain. In addition, INT captures an aspect of circuit dynamics relevant to excitation/inhibition (E/I) balance, which is thought to be broadly relevant for cognitive functions. Here we examined its relevance to AD.
Methods:
We used rs-fMRI data of 904 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The subjects were divided into 4 groups based on their baseline and end-visit clinical status, which were cognitively normal (CN), stable MCI, Converter, and AD groups. The rs-fMRI preprocessing was implemented through fMRIprep. Voxel-wise INT was estimated as the area under the curve of the autocorrelation function from the fMRI time-series during the initial positive period. ROI-specific INT were calculated as the mean of the voxel INTs within the ROI, where the Desikan-Killiany (DK) Atlas was used.
Linear Mixed Effect (LME) Model to Detect Altered Hierarchical Gradient Effects on INT: LME model was applied to predict the INT value in our ADNI sample using hierarchical level (HL) in the cerebral cortex, assuming different intercepts and slopes by diagnosis group. The HL of the ROIs is determined using the rs-fMRI of 100 unrelated young and healthy subjects from the Human Connectome Project WU-Minn Consortium. We considered age, sex, motion, and cortical thickness (CT) extracted from the T1 MRI as covariates (fixed effects), and allowed for variations of intercept and slope at the subject level (random effects).
Pairwise Comparison for Significant INT Differences among Groups: We averaged the INT values of the same ROIs on the left and right hemisphere, t-tests were conducted to compare the differences between each pair of the 4 groups. Multiple comparison correction was performed using the Tukey method for comparing a family of 4 estimates.
Linear Mixed Effect (LME) Model to Detect Altered Hierarchical Gradient Effects on INT: LME model was applied to predict the INT value in our ADNI sample using hierarchical level (HL) in the cerebral cortex, assuming different intercepts and slopes by diagnosis group. The HL of the ROIs is determined using the rs-fMRI of 100 unrelated young and healthy subjects from the Human Connectome Project WU-Minn Consortium. We considered age, sex, motion, and cortical thickness (CT) extracted from the T1 MRI as covariates (fixed effects), and allowed for variations of intercept and slope at the subject level (random effects).
Pairwise Comparison for Significant INT Differences among Groups: We averaged the INT values of the same ROIs on the left and right hemisphere, t-tests were conducted to compare the differences between each pair of the 4 groups. Multiple comparison correction was performed using the Tukey method for comparing a family of 4 estimates.
Results:
Hierarchical Gradient Effect on INTs among Various Groups: The parameters of fitted lines of INT as a function of HL of the 4 diagnosis groups are shown in Figure 1A. In terms of group differences (see Figure 1B), the MCI had a less pronounced hierarchical-gradient effect (i.e., slope) when compared to the CN; the AD showed longer INT values in lower HL areas than CN (i.e., the contrast of intercept); the AD and Converter had longer INT across all cortical areas compared to the MCI.
Significant ROIs in which AD and Converter Had Longer INT Values than CN and MCI: In Figure 2, 4 ROIs (entorhinal, fusiform, inferior temporal and temporal pole areas) were identified having higher INT in the AD than the CN. The pairwise comparison results indicate that INT alterations of these areas in the Converter are very similar to the AD at baseline, suggesting similar pathological changes prior to clinical presentation of AD.
Significant ROIs in which AD and Converter Had Longer INT Values than CN and MCI: In Figure 2, 4 ROIs (entorhinal, fusiform, inferior temporal and temporal pole areas) were identified having higher INT in the AD than the CN. The pairwise comparison results indicate that INT alterations of these areas in the Converter are very similar to the AD at baseline, suggesting similar pathological changes prior to clinical presentation of AD.
Conclusions:
We used rs-fMRI data to investigate INTs in CN, MCI, including those who convert to AD, and AD populations. Using established INTs as a functional index of hierarchy, we found that compared to CN, 1) AD and Converter are similar, as they both had longer INTs in low HL areas; and 2) stable MCI is distinct from AD and Converter, which had a less pronounced hierarchical-gradient effect with shorter INTs in high HL areas, suggesting distinct pathophysiological changes in the stable MCI and AD/Converter.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Aging
Data analysis
Degenerative Disease
FUNCTIONAL MRI
Other - Mild Cognitive Impairment
1|2Indicates the priority used for review
·Figure 1: The results of the LME fitting. A) The estimated intercept and slope of the hierarchical gradient effects by diagnosis groups. B) Pairwise comparisons.
·Figure 2: Cortical ROIs in which the INT values in AD group (and Converter group) are significantly longer than those in CN and MCI groups.
Provide references using author date format
Van Essen DC, Smith SM, Barch DM, et al. The WU-Minn human connectome project: an overview. Neuroimage. 2013;80:62–79.
Wengler K, Goldberg AT, Chahine G, Horga G. Distinct hierarchical alterations of intrinsic neural timescales account for different manifestations of psychosis. Elife. 2020;9:e56151.