Dynamic Functional Connectivity in Alzheimer's Patients
Poster No:
222
Submission Type:
Abstract Submission
Authors:
Abia Fazili1, Harrison Watters1, Shella Keilholz1
Institutions:
1Emory University, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
A resting state, quasi-periodic pattern (QPP) of anti-correlated brain activity between default mode (DMN) and task positive networks (TPN) has been implicated in attentional control (1).
We detected QPPs using complex principal components analysis (CPCA) to compare dynamic functional connectivity in Alzheimer's Disease (AD), Early Mild Cognitive Impairment (EMCI), and healthy control patients. We hypothesized that we would find decreased anti-correlated DMN and TPN activity in AD patients as a previous study has found such decreases in Alzheimer's mice (2). We found that AD patients displayed a largely positive correlation between the DMN and dorsal attention network (DAN), a primary subnetwork of the TPN, while the healthy patients' DMN and DAN were instead weakly correlated.
We detected QPPs using complex principal components analysis (CPCA) to compare dynamic functional connectivity in Alzheimer's Disease (AD), Early Mild Cognitive Impairment (EMCI), and healthy control patients. We hypothesized that we would find decreased anti-correlated DMN and TPN activity in AD patients as a previous study has found such decreases in Alzheimer's mice (2). We found that AD patients displayed a largely positive correlation between the DMN and dorsal attention network (DAN), a primary subnetwork of the TPN, while the healthy patients' DMN and DAN were instead weakly correlated.
Methods:
Resting-state functional scans were obtained from open-source Alzheimer's Disease Neuroimaging Initiative (ADNI). 60 subjects were classified by condition: 20 healthy controls, 20 early mild cognitive impairment (EMCI), and 20 Alzheimer's Disease (AD). The subjects' ages range from 55-90 years old. Each group consisted of 10 males and 10 females. For full details, see the ADNI-2 acquisition protocol.
Scans were obtained on 3T Philips scanners. Scans were T1-weighted; TR = 11.1 ms; TE = 4.6 ms; flip angle = 15° ; voxel size = 1.0 mm; slice thickness = 10.0 mm.Pre-processing and global signal regression was done with the CPAC pipeline (https://fcp-indi.github.io/) and Brainnetome atlas (3).
QPPs were detected using Complex Principal Component's Analysis (CPCA) as seen in Bolt et al, 2021 (4), then plotted as Yeo's 7 Networks (5). CPCA is a dimensionality reduction method where the first component of global-signal regressed time-series corresponds to the primary DMN-TPN anticorrelation pattern. QPP patterns obtained with CPCA were then visualized with FSLeyes (see Fig 1). CPCA-based waveforms were also plotted (Fig. 2).
Scans were obtained on 3T Philips scanners. Scans were T1-weighted; TR = 11.1 ms; TE = 4.6 ms; flip angle = 15° ; voxel size = 1.0 mm; slice thickness = 10.0 mm.Pre-processing and global signal regression was done with the CPAC pipeline (https://fcp-indi.github.io/) and Brainnetome atlas (3).
QPPs were detected using Complex Principal Component's Analysis (CPCA) as seen in Bolt et al, 2021 (4), then plotted as Yeo's 7 Networks (5). CPCA is a dimensionality reduction method where the first component of global-signal regressed time-series corresponds to the primary DMN-TPN anticorrelation pattern. QPP patterns obtained with CPCA were then visualized with FSLeyes (see Fig 1). CPCA-based waveforms were also plotted (Fig. 2).
Results:
We found the DMN and DAN to be highly correlated (r = 0.884) in AD patients. The healthy controls only had a slight positive correlation (r = 0.231), and the EMCI patients displayed the typical anti-correlation (r = -0.830).
Conclusions:
Using a CPCA-based approach to detect QPPs, we found decreased DMN-DAN anti-correlation, or increased correlation, in Alzheimer's patients. This finding of decreased anti-correlation in AD human subjects aligns with Belloy et al.'s results of decreased DMN-TPN anti-correlation in Alzheimer's mice (2). Given that the selective norepinephrine reuptake inhibitor (SNRI) has been successfully used to improve brain activity in Alzheimer's patients (6), the finding that AD progression may be classified by degree of DMN-DAN correlation points to future studies that dynamic functional analysis may be used to indicate successful neuroprotection in AD patients as well.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis
Perception, Attention and Motor Behavior:
Consciousness and Awareness
Perception and Attention Other
Keywords:
Aging
Degenerative Disease
FUNCTIONAL MRI
1|2Indicates the priority used for review
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2. Belloy M. E (2018), ‘Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer’s Disease in Mice’, Nature Scientific Reports, 8,10024.
3. Fan, L. (2016), 'The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture', Cerebral Cortex, 26(8), 3508-3526.
4. Bolt, T. (2022), 'A Parsiminious description of global functional brain organization in three spatiotemporal patterns', Nature, 25, 1093-1103.
5. Yeo, B. T. (2011), 'The organization of the human cerebral cortex estimated by intrinsic functional connectivity', Journal of Neurophysiology, 106(3),1125-1165.
6. Levey A. I, (2022), ‘A phase II study repurposing atomoxetine for neuroprotection in mild cognitive impairment’, Brain, 145, 1924-1938.