Poster No:
1531
Submission Type:
Abstract Submission
Authors:
Charly Billaud1, Junhong Yu1
Institutions:
1Nanyang Technological University, Singapore
First Author:
Co-Author:
Introduction:
Multimodal neuroimaging studies on neurocognitive disorders, such as Alzheimer's disease (AD) and mild cognitive impairment (MCI) have highlighted associations between gray matter structures and white matter integrity (Cauda et al., 2018; Sui et al., 2015). AD has been suggested to follow a degeneration model where pathology affects widespread brain structures through network propagation across axonal tracts (Brier, Thomas & Ances, 2014; Pandya et al., 2017; Raj & Powell, 2018). Across neurocognitive disorders including AD, co-alterations in grey matter volumes have been associated both with fMRI functional connectivity (FC) and diffusion tensor imaging (DTI) measured structural connectivity (SC, Cauda et al., 2018), which suggests disrupted connections in both modalities may explain widespread atrophy. The medial temporal lobe, in particular the hippocampal formation, has been identified as a potential epicentre of connectivity disruption as well as of distant alterations (Cauda et al., 2020, Mallio et al., 2015; Manuello et al., 2018). The present study tested the effect of SC and FC to the hippocampus on the cortical thickness (CT) of areas connected to the same hippocampus.
Methods:
T1, DTI and resting state fMRI images were obtained and preprocessed in 26 participants with AD, 150 with MCI, 15 with subjective memory complaint and 228 cognitively normal from the Alzheimer's Disease Neuroimaging Initiative (ADNI*) dataset (N=419; Age=73±8; 223F:196M). A composite memory score was defined using learning, immediate, delay, recognition scores from the Rey Auditory Verbal Learning Test (RAVLT) and used to define clusters where the score was significantly associated with CT in the sample. Two structural equation models (SEM, for SC and FC respectively) were fitted including cortical thickness of the clusters were identified previously; regressors were SC (shortest weighted path length with Dijkstra's algorithm) and FC (correlation) between the bilateral hippocampi and the parcellations.
Results:
The SEM for SC (CFI=.951, RMSEA = 0.042, SRMR = .092) showed that the weighted length of the shortest path to the left hippocampus negatively predicted CT in the temporal poles/parahippocampal cortices (left CT: β = -0.150, p = <.001), so did the shortest path length to the right hippocampus (left CT: β = 0.139, p = <.001; right: β = -0.191, p = <.001). Shortest path length to the left hippocampus also predicted CT in the right fusiform/parahippocampal cortex (β = -0.104, p = .011) and right intraparietal sulcus/superior parietal lobule (β = 0.101; p = .028). The SEM for FC (CFI=.996, RMSEA = 0.012, SRMR = .092) showed that the connectivity with the hippocampi predicted right fusiform/parahippocampal CT (left: β = -0.97; p = .023) and the left parietal operculum/inferior parietal lobule (right: β = 0.375; p = .042).
Conclusions:
The present findings show that both SC and FC from the bilateral hippocampi, especially from the left hippocampus, are associated with the grey matter thickness of temporal and parietal regions they connect to. The more "costly" it is for information to travel to these regions (longer path length, less white matter streamlines) from the hippocampus, the greater the atrophy can be observed in these regions. Association are also found between CT and FC from the left hippocampus. This gives support to the idea that the hippocampus is an epicentre for distant brain alterations and that its disrupted connections affect distant brain structures. Such network phenomenon may explain neurodegenerative spread processes in AD and MCI.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Lifespan Development:
Aging
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Diffusion MRI Modeling and Analysis
fMRI Connectivity and Network Modeling
Keywords:
Aging
FUNCTIONAL MRI
STRUCTURAL MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Alzheimer’s disease; mild cognitive impairment; network
1|2Indicates the priority used for review
Provide references using author date format
*Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative
(ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design
and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.
A complete listing of ADNI investigators can be found at:
http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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Cauda, F. (2020). Hubs of long‐distance co‐alteration characterize brain pathology. Human Brain Mapping, 41(14), 3878.
Cauda, F. (2018). Brain structural alterations are distributed following functional, anatomic and genetic connectivity. Brain : A Journal of Neurology, 141(11), 3211–3232.
Mallio, C. A. (2015). Epicentral Disruption of Structural Connectivity in Alzheimer’s Disease. CNS Neuroscience & Therapeutics, 21(10), 837.
Manuello, J. (2018). The pathoconnectivity profile of Alzheimer’s disease: A morphometric coalteration network analysis. Frontiers in Neurology, 8(JAN), 289568.
Pandya, S. (2017). The Brain’s Structural Connectome Mediates the Relationship between Regional Neuroimaging Biomarkers in Alzheimer’s Disease. Journal of Alzheimer’s Disease : JAD, 55(4), 1639–1657. https://doi.org/10.3233/JAD-160090
Raj, A. (2018). Models of Network Spread and Network Degeneration in Brain Disorders. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 3(9), 788–797.
Sui, X. (2015). Sparse canonical correlation analysis reveals correlated patterns of gray matter loss and white matter impairment in Alzheimer’s disease. Proceedings - International Symposium on Biomedical Imaging, 2015-July, 470–473. https://doi.org/10.1109/ISBI.2015.7163913