Altered Association of Dynamic Functional Connectivity and Cognitive Functions by Amyloid Deposition
Poster No:
1260
Submission Type:
Abstract Submission
Authors:
narae kim1,2, Bumhee Park2,3
Institutions:
1Department of Biomedical Sciences, Ajou University School of Medicine, Suwon, Korea, Republic of, 2Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea, Republic of, 3Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for innovative Medicine, Ajou University Medical Center, Suwon, Korea, Republic of
First Author:
narae kim
Department of Biomedical Sciences, Ajou University School of Medicine|Department of Biomedical Informatics, Ajou University School of Medicine
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Department of Biomedical Sciences, Ajou University School of Medicine|Department of Biomedical Informatics, Ajou University School of Medicine
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Co-Author:
Bumhee Park
Department of Biomedical Informatics, Ajou University School of Medicine|Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for innovative Medicine, Ajou University Medical Center
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Department of Biomedical Informatics, Ajou University School of Medicine|Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for innovative Medicine, Ajou University Medical Center
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Introduction:
Alzheimer's disease is characterized by the accumulation of amyloid-beta protein in the brain, which serves as a hallmark biomarker of the disease. Recent studies have highlighted alterations in dynamic state-switching during resting-state functional connectivity in patients with Alzheimer's disease. These state-switching patterns are thought to reflect disruptions in the brain's ability to maintain or transition between distinct functional connectivity states. Therefore, this study aims to explore the association between dynamic functional connectivity patterns, cognitive functions along to amyloid deposition.
Methods:
We acquired resting-state functional neuroimaging data of 184 cognitive impairment patients within Alzheimer's disease spectrum disorders (MCI = 136, AD = 48, Amyloid Positive: 67, Amyloid Negative: 117) from the Biobank Innovation for Chronic Cerebrovascular Disease with Alzheimer's Disease Study (BICWALZS) cohort (137 females, mean age = 71.58). The fMRI data were obtained using the following parameters and preprocessed followed by canonical pipelines: TR = 2000 ms, TE = 30 ms, and a total of 200 volumes. The regional BOLD signals were extracted according to the Schaefer 200 atlas.
To remove nuisance components of the signal, detrending, despiking, and bandpass filtering (0.01 Hz–0.1 Hz) were applied. For dynamic functional connectivity (FC) analysis, we extracted partial BOLD signals using a tapered Gaussian window (σ = 3) applied every 30 TRs with 1 TR steps. Pearson correlation was then calculated for each window. Subsequently, the upper triangular FC matrices from the dynamic windows were concatenated vertically to identify possible discrete FC patterns using k-means clustering algorithms, tested for k in the range of two to six. We determined that two states (k = 2) were optimal and calculated dwell times for each state.
To assess various cognitive functions, the Seoul Neuropsychological Screening Battery (SNSB), which encompasses multiple domains (memory, visuospatial abilities, language, executive functions, and attention/working memory), was used. The relationships between state-switching patterns and cognitive functions were then examined using partial correlation analyses, controlling for age, sex, and years of education in both amyloid-positive and amyloid-negative groups.
To remove nuisance components of the signal, detrending, despiking, and bandpass filtering (0.01 Hz–0.1 Hz) were applied. For dynamic functional connectivity (FC) analysis, we extracted partial BOLD signals using a tapered Gaussian window (σ = 3) applied every 30 TRs with 1 TR steps. Pearson correlation was then calculated for each window. Subsequently, the upper triangular FC matrices from the dynamic windows were concatenated vertically to identify possible discrete FC patterns using k-means clustering algorithms, tested for k in the range of two to six. We determined that two states (k = 2) were optimal and calculated dwell times for each state.
To assess various cognitive functions, the Seoul Neuropsychological Screening Battery (SNSB), which encompasses multiple domains (memory, visuospatial abilities, language, executive functions, and attention/working memory), was used. The relationships between state-switching patterns and cognitive functions were then examined using partial correlation analyses, controlling for age, sex, and years of education in both amyloid-positive and amyloid-negative groups.
Results:
The characteristics of the states were distinct. State 1 appeared more frequently and exhibited well-organized connectivity patterns. Otherwise, state 2 hardly appeared and poorly-organized(State 1 occurrence rate=0.77, Q=0.29, State 2 occurrence rate = 0.23, Q=0.08).
The dwell time of State 1 was positively correlated with multiple scales regarding to cognitive functions(FDR corrected P <0.05; Mini-Mental State Examination(MMSE): r=.23, p=0.004, Rey Complex Figure Test(RCFT): r=.23, p=0.001, Seoul Verbal Learning Test(SVLT): r=.25, p=0.0004, Controlled Oral Word Association Test(COWAT), r=.22, p=0.007), indicating that prolonged consecutive stays in the well-organized State 1 are associated with better-preserved cognitive functions. However, while these relationships between state dwell times and cognitive functions were maintained in the amyloid-negative group, many of them disappeared in the amyloid-positive group.
The dwell time of State 1 was positively correlated with multiple scales regarding to cognitive functions(FDR corrected P <0.05; Mini-Mental State Examination(MMSE): r=.23, p=0.004, Rey Complex Figure Test(RCFT): r=.23, p=0.001, Seoul Verbal Learning Test(SVLT): r=.25, p=0.0004, Controlled Oral Word Association Test(COWAT), r=.22, p=0.007), indicating that prolonged consecutive stays in the well-organized State 1 are associated with better-preserved cognitive functions. However, while these relationships between state dwell times and cognitive functions were maintained in the amyloid-negative group, many of them disappeared in the amyloid-positive group.
·Dynamic Functional Connectivity States
Conclusions:
Our findings indicate that dynamic functional connectivity state-switching patterns are strongly associated with cognitive function. Specifically, prolonged dwell times in well-organized states are linked to better-preserved cognition, while amyloid deposition appears to disrupt these relationships. These results underscore the potential of dynamic functional connectivity as a biomarker for Alzheimer's disease pathology. Future studies could further explore the utility of these measures in predicting disease progression and assessing the effectiveness of therapeutic interventions.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Aging
Degenerative Disease
FUNCTIONAL MRI
1|2Indicates the priority used for review
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2. Friston, K. J. (2003). Statistical parametric mapping. Neuroscience databases: a practical guide, 237-250.
3. Gu, Y., Lin, Y., Huang, L., Ma, J., Zhang, J., Xiao, Y., ... & Alzheimer's Disease Neuroimaging Initiative. (2020). Abnormal dynamic functional connectivity in Alzheimer’s disease. CNS neuroscience & therapeutics, 26(9), 962-971.
4. Roh, H. W., Kim, N. R., Lee, D. G., Cheong, J. Y., Seo, S. W., Choi, S. H., ... & Hong, C. H. (2022). Baseline clinical and biomarker characteristics of biobank innovations for chronic cerebrovascular disease with Alzheimer’s disease study: BICWALZS. Psychiatry Investigation, 19(2), 100.