Projection of Spatiotemporal Patterns of Functional Brain Networks in Alzheimer’s Disease
Poster No:
1448
Submission Type:
Abstract Submission
Authors:
Theodore LaGrow1, Vaibhavi Itkyal2, Vince Calhoun3, Shella Keilholz4
Institutions:
1Georgia Institute of Technology, Atlanta, GA, 2Emory University, Atlanta, GA, 3GSU/GATech/Emory, Atlanta, GA, 4Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
Alzheimer's Disease (AD) poses significant challenges due to its detrimental impact and the difficulty of early detection. One of the primary obstacles in diagnosing AD lies in the limited availability of non-invasive methods for assessing the disease's progression. This study investigates the role of Quasi-Periodic Patterns (QPPs) in the functional connectivity of individuals across a discrete spectrum of AD progression, including normal controls (NC), mild cognitive impairment (MCI), and Dementia of Alzheimer's Type (DAT). Previous research has shown diminished QPP involvement in functional connectivity for neuropsychiatric and neurodegenerative disorders, including ADHD (Abbas et al. 2018) and AD in rodent models (Belloy et al. 2018). Investigation into the projection of QPPs on clinical cohorts of AD from healthy controls is sparse, if any. Our findings highlight significant changes in detection rates when projecting normal control templates onto disease datasets while revealing conserved QPP detection patterns between group templates. These insights enhance our understanding of the underlying mechanisms of neurodegeneration and emphasize QPPs' potential as biomarkers for clinical exploration in neurodegenerative diseases.
Methods:
This study uses two datasets: (1) Human Connectome Project (HCP) (Van Essen et al. 2013) consisting of 1000+ subjects (4 scans/subject; 4122 scans total) at TR=0.72s; and (2) Alzheimer's disease neuroimaging initiative (ADNI) (Mueller et al. 2005) consisting of 517 NC subjects, 708 MCI subjects, and 201 DAT subject at TR=3.0s. For further demographic information, please see Iraji et al., 2023. Preprocessing follows the standard quality assurance (QA) for each dataset, preprocessing standardization, and group-level spatially constrained independent component analysis (scICA) in the GIFT toolbox (Iraji et al., 2023; Jensen et al., 2024) (Fig. 1A-B). QPPs were detected with the software package, QPPLab (Xu, N. et. al. 2023), and derived with a window length of 24 seconds (Fig. 1C). QPP templates were derived from each dataset and projected onto the other datasets (algorithm illustration, Fig. 1D). To address mismatched repetition times (TR), we resampled the time series from the HCP data by averaging non-overlapping windows of consecutive time points. This approach ensured that each resampled time point represented the mean of an equal partition of the original series, aligning the data to match the longer TR used in the analysis.
Results:
Fig. 2A presents the QPP templates derived from HCP and ADNI NC datasets. These templates (along with ANDI MCI and ADNI DAT) were projected into other datasets. The QPP templates reveal a consistent pattern of network engagement across datasets. As illustrated in Fig 2B, QPP occurrences per minute were calculated based on correlation thresholds greater than 0.2 (or less than -0.2 for reverse phase) as done in Abbas et al. 2018 and Belloy et al. 2018. These results are color-coded for each dataset: HCP (blue), NC (green), MCI (purple), and DAT (yellow). Across datasets, occurrences show strong fidelity within each dataset and a notable decline over disease progression. HCP and NC datasets exhibited the highest occurrences of QPPs, reflecting robust network dynamics in healthy populations. In MCI and DAT datasets, QPP occurrences diminished progressively, indicating disrupted network engagement and reduced dynamic interactions. This decline underscores the loss of network coherence and functional connectivity associated with disease progression.
Conclusions:
Our study replicates previous findings from rodents to clinical populations while leveraging a massive open-source corpus of neuroimaging data. This study reveals new insights into spatiotemporal dynamics in human rs-fMRI data as neurodegenerative diseases progress through projections of QPPs templates. This initial research emphasizes the need for further investigation into QPP analysis across clinical cohorts.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Keywords:
Other - Quasi-Periodic Patterns, rs-fMRI, Alzheimer’s Disease, Spatiotemporal Patterns, Open-Source Data, Brain States, NeuroMark, HCP, ADNI, Neurodegeneration
1|2Indicates the priority used for review
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Provide references using APA citation style.
Belloy, Michaël E., et al. "Quasi-periodic patterns of neural activity improve classification of Alzheimer’s disease in mice." Scientific reports 8.1 (2018): 10024.
Iraji, A., Fu, Z., Faghiri, A., Duda, M., Chen, J., Rachakonda, S., DeRamus, T., Kochunov, P., Adhikari, B. M., Belger, A., Ford, J. M., Mathalon, D. H., Pearlson, G. D., Potkin, S. G., Preda, A., Turner, J. A., van Erp, T. G. M., Bustillo, J. R., Yang, K., … Calhoun, V. D. (2023). Identifying canonical and replicable multi‐scale intrinsic connectivity networks in 100k+ resting‐state fMRI datasets. Human Brain Mapping, 44(17), 5729–5748. https://doi.org/10.1002/hbm.26472
Jensen, K. M., Turner, J. A., Calhoun, V. D., & Iraji, A. (2024). Addressing inconsistency in functional neuroimaging: A replicable data-driven multi-scale functional atlas for canonical brain networks. bioRxiv: The Preprint Server for Biology, 2024.09.09.612129. https://doi.org/10.1101/2024.09.09.612129
Majeed, W., Magnuson, M., Hasenkamp, W., Schwarb, H., Schumacher, E. H., Barsalou, L., & Keilholz, S. D. (2011). Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans. Neuroimage, 54(2), 1140-1150.
Mueller, S.G., Weiner, M.W., Thal, L.J., Petersen, R.C., Jack, C., Jagust, W., Trojanowski, J.Q., Toga, A.W. and Beckett, L., 2005. The Alzheimer's disease neuroimaging initiative. Neuroimaging Clinics, 15(4), pp.869-877.
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K. and Wu-Minn HCP Consortium, 2013. The WU-Minn human connectome project: an overview. Neuroimage, 80, pp.62-79.
Xu, N., Yousefi, B., Anumba, N., LaGrow, T. J., Zhang, X., & Keilholz, S. (2023). QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity. bioRxiv.