Poster No:
123
Submission Type:
Abstract Submission
Authors:
Samuel Maddox1, Justine Hansen2, Jacob Newman1, Michal Mackiewicz1, Saber Sami1
Institutions:
1University of East Anglia, Norwich, Norfolk, 2McGill University, Montreal, QC
First Author:
Co-Author(s):
Saber Sami
University of East Anglia
Norwich, Norfolk
Introduction:
Alzheimer's Disease (AD) is a progressive disorder where protein disfunction leads to cognitive decline, yet the driving mechanisms and systems facilitating this pathology is still not clear. Gaining insights into the multi-system interplay and whole-brain dynamics is helpful to fully understand how progressive neurodegenerative conditions like AD unfold across different stages.
Recent work has demonstrated the brainstem's extensive connectivity with the cerebral cortex and has identified distinct communities driving this connectivity [1]. Our study aims to evaluate brainstem-specific connections and investigate how interactions between regions could contribute to the changes in distinct AD stages. We focus on locations specific to AD and Mild Cognitive Impairment (MCI), looking to highlight potential drivers of functional alteration in AD. We extend this analysis to include additional systems, such as cell-type signatures, receptors densities, and genetic expression. Through this approach, we aim to provide new insights into the mechanisms underlying AD, and it's progression.
Methods:
To focus our analysis, specific regions of interest were identified and linked to the Schaefer400 atlas using cluster locations from a meta-analysis comparing functional alterations in MCI/AD to Healthy Controls (HC). This involved 31 MCI and 20 AD studies [2], and specifically focused on the default mode network, which shows dysconnectivity at early stages of neurodegeneration [3]. The analysis included seed-based, or independent component connectivity analysis methods.
We included contrasts of AD<HC and MCI<HC, as well as clusters more significantly affected in AD than MCI (against HC), ensuring results are within a p<0.0001 threshold. These regions of interest were further analysed across multiple brain systems, including functional connectivity to brainstem [1], receptors densities [4], brain gene expression [5][6][7] and cell type data [8]. Regions were ranked against all other network nodes with distinct results in the top or bottom 10% highlighted for further analysis.
Results:
For the MCI<HC contrast, the left prefrontal cortex and right precuneus were identified, while AD<HC had right prefrontal cortex and right precuneus. In AD<MCI (vs. HC), a single location was identified in the left precuneus.
Across MCI<HC and AD<HC, prefrontal locations showed high connectivity to many brainstem nodes, including the locus coeruleus. However, in MCI<HC, the precuneus had low brainstem connectivity, contrasting with AD<HC, where it showed high connectivity to brainstem nodes including the paramedian nucleus, microcellular tegmental nucleus, and median raphe. In AD<MCI (vs. HC), the left precuneus showed reduced connectivity with the inferior colliculus and increased connectivity with the median raphe.
Both MCI<HC and AD<HC had high receptors densities of MU, MGluR5, CB1, H3 in prefrontal regions, and GABA A/Bz in precuneus nodes. AD<MCI (vs. HC) had lower receptor densities in brainstem regions including MU, A4B2, NAT, mGluR5. Cell-type signatures differed as AD<HC had high VLMC, Vip, Lamp5 Lhx6 and Pax6 in the right precuneus, but both shared SST, L6b and Oligo in prefrontal cortex nodes. AD<MCI (vs. HC) showed PVALB signatures. MCI<HC showed high APOE expression in the prefrontal region, while AD<HC showed high RIN3, PSEN2 in prefrontal and ABI3, EPHA1 in precuneus regions. AD<MCI (vs. HC) showed high ABCA7 expression in the precuneus.
Conclusions:
A multisystem-level analysis of the brain regions affected in MCI and AD establishes an important pathway for pinpointing key molecular drivers and informing the development of targeted, more effective therapeutic interventions. Our findings highlight distinct molecular and cellular characteristics in cortical nodes implicated in MCI and AD. Elevated APOE expression in left prefrontal cortex and the unique receptors densities and cell-type profiles in these regions highlight their potential importance in early disease progression.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Genetics:
Genetics Other
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Receptors
Transmitter Systems
Keywords:
Aging
Brainstem
Cortex
Data analysis
Degenerative Disease
Systems
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Please indicate which methods were used in your research:
Functional MRI
For human MRI, what field strength scanner do you use?
3.0T
7T
Provide references using APA citation style.
[1] Hansen, J. Y., Cauzzo, S., Singh, K., García-Gomar, M. G., Shine, J. M., Bianciardi, M., & Misic, B. (2024). Integrating brainstem and cortical functional architectures. Nature Neuroscience, 1-12.
[2] Wang, Y., Li, Q., Yao, L., He, N., Tang, Y., Chen, L., ... & Li, F. (2024). Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer’s disease. Cerebral Cortex, 34(3), bhae094.
[3] Ereira, S., Waters, S., Razi, A., & Marshall, C. R. (2024). Early detection of dementia with default-mode network effective connectivity. Nature Mental Health, 1-14.
[4] Markello, R. D., Hansen, J. Y., Liu, Z. Q., Bazinet, V., Shafiei, G., Suárez, L. E., ... & Misic, B. (2022). Neuromaps: structural and functional interpretation of brain maps. Nature Methods, 19(11), 1472-1479.
[5] Markello, R. D., Arnatkeviciute, A., Poline, J. B., Fulcher, B. D., Fornito, A., & Misic, B. (2021). Standardizing workflows in imaging transcriptomics with the abagen toolbox. elife, 10, e72129.
[6] Novikova, G., Kapoor, M., Tcw, J., Abud, E. M., Efthymiou, A. G., Chen, S. X., ... & Goate, A. M. (2021). Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nature communications, 12(1), 1610.
[7] Kunkle, B. W., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., ... & Rotter, J. I. (2019). Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nature genetics, 51(3), 414-430.
[8] Zhang, X. H., Anderson, K. M., Dong, H. M., Chopra, S., Dhamala, E., Emani, P. S., ... & Holmes, A. J. (2024). The cell-type underpinnings of the human functional cortical connectome. Nature Neuroscience, 1-11.
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