Early axonal degeneration linked to clinical decline in Alzheimer’s disease progression revealed with diffusion MRI
Open accessAbstract
BACKGROUNDAxonal degeneration is believed to be an early hallmark of Alzheimer's disease (AD). This study investigated the temporal trajectory of axonal loss and its association with cognitive and functional decline using a dMRI-derived axonal density index (ADI).METHODSLongitudinal dMRI, CSF, and PET data from the ADNI study were analyzed, including 117 subjects that were cognitively normal (CN) and 88 that were cognitively impaired (CI), consisting of 74 individuals with mild cognitive impairment (MCI) and 14 with AD. Linear mixed-effects models examined group differences and associations between baseline and longitudinal changes in ADI, CSF, or PET biomarkers and clinical outcomes. Results derived from larger CSF (n = 527) and PET (tau-PET: n = 870; amyloid-PET: n = 1,581) datasets are also presented.RESULTSCompared with the CN group, the CI group exhibited significantly lower baseline ADI values and steeper longitudinal decline (P < 10-6). Lower baseline ADI predicted faster cognitive and functional decline in the CI group (MMSE: P = 0.03; CDR-SB: P < 10-4), and longitudinal decreases in ADI were associated with worsening clinical outcomes (MMSE: P = 0.001; CDR-SB: P < 10-12). Compared with CSF and PET biomarkers, ADI demonstrated superior sensitivity in tracking disease progression and matched these biomarkers in predicting future cognitive and functional decline. Furthermore, decreases in ADI were significantly associated with declines in clinical outcomes; this association was observed only with amyloid-PET, but not CSF, biomarkers.CONCLUSIONAxonal degeneration is an early and clinically meaningful feature of AD. ADI is a promising noninvasive biomarker for early detection, prognosis, and disease monitoring.TRIAL REGISTRATIONClinicalTrials.gov NCT00106899.FUNDINGThis work was supported by the National Institute on Aging Intramural Research Program.
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Recent longitudinal work suggests that diffusion-derived axonal-density measures may detect early, spatially selective degeneration related to clinical decline in Alzheimer's disease.
“Recent longitudinal work further suggests that diffusion-derived axonal-density measures may detect early, spatially selective degeneration related to clinical decline in Alzheimer’s disease (Gong et al., 2026; Wang et al., 2023).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Gong et al., 2026
The Discussion’s summary of main findings reports that longitudinal diffusion-derived axonal-density measures declined more steeply in cognitively impaired participants and were associated with cognitive and functional decline. However, the Strengths and limitations section says the analysis considered only whole-brain white matter; it describes mapping the spatial distribution of degeneration as a capability for future study, not an established spatially selective finding. (LLM, conf=0.84)
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“By probing 1 specific component of WM, our work revealed lower axonal density in patients with cognitive impairment, in agreement with previous cross section–based work that used NODDI (39–42).”
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“Additionally, clinical studies in genetic forms of AD, including autosomal dominant AD and Down’s syndrome, highlight impairments in WM integrity decades before the onset of symptoms (9, 24, 25).”
Gong, Z., Faulkner, M. E., Akhonda, M. A. B. S., Guo, A., Bae, J., Laporte, J. P., Church, S., D’Agostino, J., Bergeron, J., Bergeron, C. M., et al. (2024) · GeroScience · doi:10.1007/s11357-024-01392-w · Cited by 12 · cited 1× · see citing works
“WM degeneration disrupts neural pathways, impairing the communication between brain regions whose integrity is essential against cognitive decline and mobility and autonomic dysfunction (26–28).”
Self-citationNAD+-dependent mechanism of pathological axon degeneration
Cao, Y., Wang, Y., & Yang, J. H. (2022) · Cell Insight · doi:10.1016/j.cellin.2022.100019 · Cited by 11 · cited 1× · see citing works
“Axonal degeneration is a complex, multistep process that is primarily driven by disruptions in cellular homeostasis, energy deficits, and altered intracellular signaling pathways (43–45).”
The Dementia SomaSignal Test (dSST): A plasma proteomic predictor of 20‐year dementia risk
Duggan, M. R., Paterson, C., Lü, Y., Biegel, H., Dark, H. E., Cordon, J., Bilgel, M., Kaneko, N., Shibayama, M., Kato, S., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14549 · Cited by 8 · see citing works
Self-citationGuo, A. Y., Laporte, J. P., Singh, K., Bae, J., Bergeron, K., de Rouen, A., Fox, N., Zhang, N., Carino, I., Faulkner, M. E., et al. (2025) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70182 · Cited by 5 · cited 1× · see citing works
“Further work is needed to validate ADI in multicohort data with diverse populations and to explore its integration with PET and fluid biomarkers to enhance early diagnosis, disease staging and phenotyping, and treatment monitoring in AD (32, 39, 55, 61).”
Self-citationParker, D. M., Adams, J. N., Kim, S., McMillan, L., & Yassa, M. (2023) · bioRxiv · doi:10.1101/2023.10.11.561946 · Cited by 5
Xiao, B., & Tan, E.-K. (2024) · Clinical and Translational Medicine · doi:10.1002/ctm2.1559 · Cited by 4 · cited 4× · see citing works
“Amyloid-PET data were obtained from the UC Berkeley – Amyloid PET 6mm Res analysis [ADNI1,GO,2,3,4] file, and tau-PET data were obtained from the UC Berkeley – Tau PET PVC 6mm Res analysis [ADNI2,3,4] file.”
InfluentialDark, H. E., Shafer, A. T., Cordon, J., An, Y., Lewis, A., Moghekar, A., Landman, B. A., Resnick, S., & Walker, K. A. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.092515 · Cited by 1 · see citing works
Guo, A., Laporte, J., Singh, K., Bae, J., Bergeron, K., de Rouen, A., Fox, N., Zhang, N., Faulkner, M., Carino, I., et al. (2025) · medRxiv · doi:10.1101/2025.02.27.25322792 · Cited by 1 · cited 2×
“Further work is needed to validate ADI in multicohort data with diverse populations and to explore its integration with PET and fluid biomarkers to enhance early diagnosis, disease staging and phenotyping, and treatment monitoring in AD (32, 39, 55, 61).”
Self-citation
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Le Bihan, D. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1858655 · Cited by 1 · see citing works
“…diffusion MRI closer to a mechanistically interpretable bio-marker of axonal structural failure while retaining the practical advantages of MRI: it is non-invasive, repeatable, radiation-free and more scalable than PET for longitudinal stratification and intervention studies (Gong et al., 2026).”
Li, K., Wei, L.-L., Yin, R.-L., Xu, M., Zhao, Q.-B., Ke, J., & Yang, L.-Y. (2026) · BMC Endocrine Disorders · doi:10.1186/s12902-026-02412-w · Cited by 1
“The model form was [17]: Here β 1 represents the rate of change (slope) of metabolic indicator before DKD onset (Refer to cluster1), and β 2 represents the rate of change (slope) after DKD onset (Refer to cluster4).”
Flaherty, R., & Masurkar, A. V. (2026) · Journal of Clinical Investigation · doi:10.1172/jci202411 · Cited by 1 · see citing works
“In this issue of the JCI , Gong et al. (12) leveraged the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to conduct a study of whole white matter ADI derived from a free water–corrected implementation of NODDI called constrained NOD-DI (C-NODDI), which they propose to be a more physiologically relevant mea…”
Influential40 Years of diffusion MRI in the brain: From history to emerging frontiers
Le Bihan, D. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1365
“Recent longitudinal work further suggests that diffusion-derived axonal-density measures may detect early, spatially selective degeneration related to clinical decline in Alzheimer’s disease ( Gong et al., 2026; Wang et al., 2023).”
Zhao, J., Nie, C., Gao, R., Hu, Z., & Lin, Y. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1964029
“…may contribute to disease progression, and diffusion MRI-derived axonal density indices have demonstrated superior sensitivity in tracking disease progression compared to CSF and PET biomarkers, matching these biomarkers in predicting future cognitive and functional decline (Gong et al., 2026).”
Zhang, Y.-Y., Zhao, K., Liu, G., Huang, Q., Gu, P.-F., Kim, D., Enriquez, E., Leow, A. D., Thompson, P. M., Huang, H., et al. (2026) · doi:10.48550/arxiv.2608.19436
“All three groups exhibited positive average longitudinal changes, with relatively modest rates in CN and MCI and a substantially greater rate in AD [Gong et al., 2026, Nowrangi et al., 2013, Jack Jr et al., 2000].”
Radomski, K. L., Zi, X.-M., Sullivan, G. M., & Armstrong, R. C. (2026) · Neurotherapeutics · doi:10.1016/j.neurot.2026.e00975
“These findings may also have broader relevance to other neurodegenerative diseases, such as multiple sclerosis and Alzheimer's disease, that involve early axonal degeneration [33,34].”
Baseline neuroimaging profiles and predictive modelling for early Alzheimer disease prognosis
Martí-Navia, A., López-Bellido, A., Balaguer, Á., Ballesta-Martínez, S., Álvarez-Sánchez, L., Peretó, M., García-Vallés, L., Raga, L., Baquero, M., & Cháfer-Pericás, C. (2026) · Scientific Reports · doi:10.1038/s41598-026-60061-4
Frailty and Brain Myelin Across Adulthood: Multimodal MRI Insights From the BLSA
Bouhrara, M., Gong, Z., Tanaka, T., & Ferrucci, L. (2026) · Aging Cell · doi:10.1111/acel.70489
Self-citation
- Authors
- Gong, Z., Laporte, J. P., Guo, A. Y., Bilgel, M., Bae, J., Fox, N. Y., de Rouen, A., Zhang, N., Taranath, A., de Cabo, R., Egan, J. M., Ferrucci, L., Bouhrara, M., & Initiative, A. D. N.
- Affiliations
- National Institutes of Health (US)
- National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA.
- Alzheimer’s Disease Neuroimaging Initiative (US)
- Corresponding author
- Gong, Zhaoyuan
- ORCID
- Venue
- Journal of Clinical Investigation
- Published
- 2025-11-27
- Volume
- 136
- Issue
- 3
- ISSN
- 0021-9738 (ISSN-L)
- 1558-8238
- PMC
- PMC12867157
- OpenAlex
- W4416757106
- Cited by
- 7
- References
- 60
- Citations by year
- 2026: 7
- FWCI
- 4.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Clinical Investigation (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institute on Aging (N/A)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- Alzheimer's disease, axonal degeneration, diffusion MRI, disease progression, cognitive decline, functional decline, mild cognitive impairment, amyloid PET, tau PET, cerebrospinal fluid biomarkers, early detection, disease surveillance
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Axons, Female, Humans, Male, Middle Aged, Nerve Degeneration, Biomarkers, Disease Progression, Diffusion Magnetic Resonance Imaging, Positron-Emission Tomography, Cognitive Dysfunction
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