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Neurodegenerative Diseases Target Large-Scale Human Brain Networks
Open accessAbstract
During development, the healthy human brain constructs a host of large-scale, distributed, function-critical neural networks. Neurodegenerative diseases have been thought to target these systems, but this hypothesis has not been systematically tested in living humans. We used network-sensitive neuroimaging methods to show that five different neurodegenerative syndromes cause circumscribed atrophy within five distinct, healthy, human intrinsic functional connectivity networks. We further discovered a direct link between intrinsic connectivity and gray matter structure. Across healthy individuals, nodes within each functional network exhibited tightly correlated gray matter volumes. The findings suggest that human neural networks can be defined by synchronous baseline activity, a unified corticotrophic fate, and selective vulnerability to neurodegenerative illness. Future studies may clarify how these complex systems are assembled during development and undermined by disease.
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“Instead, they rely on atrophy patterns or relatively low-resolution positron emission tomography (PET) imaging of tau deposition, and histopathological analysis of end-stage disease (Seeley et al., 2009).”
From Spontaneous pathology in PS19 tauopathy mice progresses via brain networks (2025) · cites this paper as Seeley et al., 2009
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Human studies based on functional connectivity and tractography have suggested brain networks could play a role in tauopathy progression, consistent with the prion hypothesis (Braak and Braak, 1991; Seeley et al., 2009; Drzezga, 2018; Frontzkowski et al., 2022).”
From Spontaneous pathology in PS19 tauopathy mice progresses via brain networks (2025) · cites this paper as Seeley et al., 2009
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In humans, magnetic resonance imaging- (MRI) based studies using functional MRI (fMRI) and tractography cannot precisely map pathology at the cellular level.”
From Spontaneous pathology in PS19 tauopathy mice progresses via brain networks (2025) · cites this paper as Seeley et al., 2009
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“In humans, studies of the role of network involvement have combined functional connectivity or tractography, with imaging of tau pathology based on positron emission tomography, or, as a surrogate, brain atrophy (Seeley et al., 2009).”
From Spontaneous pathology in PS19 tauopathy mice progresses via brain networks (2025) · cites this paper as Seeley et al., 2009
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“Additionally, our classification of interest was clinical diagnosis, as clinical syndromes are known to correspond more closely to neuroanatomical lesions as compared to neuropathology (Seeley et al., 2009).”
From Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI (2024) · cites this paper as Seeley et al., 2009
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“the spatial atrophy pattern of neurodegenerative diseases resembles a distinct intrinsic functional connectivity (Buckner et al., 2005; Seeley et al., 2009)”
From Effects of Alzheimer’s and Vascular Pathologies on Structural Connectivity in Early- and Late-Onset Alzheimer’s Disease (2021) · cites this paper as Seeley et al., 2009
In the citing paper
“The pathology of AD primarily affects the PCC and the medial temporal subsystem.191,192,194”
From The default network and self-generated thought: component processes, dynamic control, and clinical relevance (2014) · cites this paper as [192]
In the citing paper
“Alzheimer’s disease (AD) and forms of frontotemporal lobar degeneration (FTLD), including semantic dementia (SD) and behavioral variant frontotemporal dementia (bv-FTD), are neurodegenerative disorders that target relatively distinct regions within and outside the DN.190–193”
From The default network and self-generated thought: component processes, dynamic control, and clinical relevance (2014) · cites this paper as [192]
In the citing paper
“In contrast to AD, FTLD more specifically affects the dorsal medial subsystem, the amPFC, and the salience network, with degeneration extending from dorsal to ventral prefrontal regions and into the lateral temporal cortex.43,192”
From The default network and self-generated thought: component processes, dynamic control, and clinical relevance (2014) · cites this paper as [192]
In the citing paper
“The subjects with bvFTD showed reduced connectivity in the prefrontal, insular, and temporal regions in terms of the functional (Agosta et al., 2013; Jalilianhasanpour et al., 2019; Seeley et al., 2009; Whitwell, 2019) and structural (Dopper et al., 2014; Mahoney et al., 2015; Tovar-Moll et al., 2014; Whitwell, 2019) networks”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Seeley et al., 2009
In the citing paper
“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 [135]
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Metadata
- Authors
- Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L., & Greicius, M. D.
- Affiliations
- University of California, San Francisco (US)
- Stanford Medicine (US)
- Corresponding author
- Seeley, William W.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2009-04
- Volume
- 62
- Issue
- 1
- Pages
- 42-52
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC2691647
- OpenAlex
- W2103717170
- Cited by
- 2,350
- References
- 68
- Citations by year
- 2026: 135 · 2025: 143 · 2024: 108 · 2023: 109 · 2022: 143 · 2021: 148 · 2020: 149 · 2019: 177 · 2018: 174 · 2017: 144 · 2016: 139 · 2015: 151 · 2014: 154 · 2013: 147 · 2012: 111
- FWCI
- 52.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1568 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (K23NS048302)
- Larry L. Hillblom Foundation
- National Institute on Aging (K08 AG027086, P01 AG19724, P50 AG1657303-75271)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- neurodegenerative diseases, large-scale brain networks, intrinsic functional connectivity, gray matter atrophy, gray matter volume, human brain development
- MeSH terms
- Aged, Brain, Brain Mapping, Female, Humans, Image Processing, Computer-Assisted, Functional Laterality, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Neural Pathways, Oxygen, Case-Control Studies, 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.
What counts as a claim ›How we measure confidence ›How we check this ›
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