Predicting Regional Neurodegeneration from the Healthy Brain Functional Connectome
Open accessAbstract
Neurodegenerative diseases target large-scale neural networks. Four competing mechanistic hypotheses have been proposed to explain network-based disease patterning: nodal stress, transneuronal spread, trophic failure, and shared vulnerability. Here, we used task-free fMRI to derive the healthy intrinsic connectivity patterns seeded by brain regions vulnerable to any of five distinct neurodegenerative diseases. These data enabled us to investigate how intrinsic connectivity in health predicts region-by-region vulnerability to disease. For each illness, specific regions emerged as critical network "epicenters" whose normal connectivity profiles most resembled the disease-associated atrophy pattern. Graph theoretical analyses in healthy subjects revealed that regions with higher total connectional flow and, more consistently, shorter functional paths to the epicenters, showed greater disease-related vulnerability. These findings best fit a transneuronal spread model of network-based vulnerability. Molecular pathological approaches may help clarify what makes each epicenter vulnerable to its targeting disease and how toxic protein species travel between networked brain structures.
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There is post-mortem evidence in AD for regional spread of tau pathology, and more recent neuroimaging studies in human AD suggest epicentres or nodes that may link to network dysfunction.
“There is post-mortem evidence in AD for this regional spread (Zhou Gennatas et al., 2012) and more recent neuroimaging studies in human AD suggest “epicentres” (Zhou et al., 2012) or “nodes” (Raj et al., 2012) that may link to network dysfunction.”
From In vivo imaging of tau pathology using multi-parametric quantitative MRI (2015) · cites this paper as Zhou et al., 2012
The Discussion (‘Exploring the target networks: epicenters and early disease spread’; page unavailable) proposes an angular-gyrus epicenter in AD and links healthy-brain connectivity to the pattern of neurodegeneration, supporting the neuroimaging portion of the claim. It also suspects that its AD findings largely reflect tau pathology, but does not present post-mortem evidence for regional spread of tau pathology in AD; that portion is not supported by this paper’s text. (LLM, conf=0.84)
Experimental — accuracy not yet measuredFunctional graph theory models captured node-degree differences in the left superior occipital area, left Heschl gyrus, and left frontal inferior pars triangularis.
“Moreover, the functional graph theory models captured node-degree differences in the left superior occipital area (Agosta et al., 2013; Reyes et al., 2018), left Heschl gyrus (Agosta et al., 2013), and left frontal inferior pars triangularis (Zhou et al., 2012).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Zhou et al., 2012
Many neurodegenerative diseases have known causes involving mutations in ubiquitously expressed proteins, yet they follow stereotyped degeneration patterns that selectively affect certain cell subsets, producing disease-specific spatial and temporal patterns.
“Many of these diseases have known causes that involve mutations in ubiquitously expressed proteins134, but they follow stereotyped patterns of degeneration that selectively affect certain cell subsets more severely, resulting in disease-specific spatial and temporal patterns of degeneration135–137 (BOX 1).”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [136]
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“Previous studies have suggested that AD pathogenesis may selectively target certain hub regions (Dai et al., 2015; Stam et al., 2009; Xie & He, 2011; Yao et al., 2010; Zhou et al., 2012).”
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Self-citation
- Authors
- Zhou, J., Gennatas, E. D., Kramer, J. H., Miller, B. L., & Seeley, W. W.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Seeley, William W.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2012-03
- Volume
- 73
- Issue
- 6
- Pages
- 1216-1227
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC3361461
- OpenAlex
- W2080816290
- Cited by
- 706
- References
- 45
- Citations by year
- 2026: 19 · 2025: 42 · 2024: 53 · 2023: 47 · 2022: 40 · 2021: 51 · 2020: 55 · 2019: 51 · 2018: 63 · 2017: 53 · 2016: 56 · 2015: 58 · 2014: 44 · 2013: 48 · 2012: 24
- FWCI
- 28.52
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging (AG-19724, AG1657303, P01AG019724, P50AG023501)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- neurodegenerative diseases, functional connectome, intrinsic functional connectivity, disease susceptibility, transneuronal spread, shared vulnerability, graph theory
- MeSH terms
- Aged, Atrophy, Brain, Brain Mapping, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Neural Pathways, Oxygen, Reference Values, Neurodegenerative Diseases
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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