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Neuroimaging biomarkers for Alzheimer’s disease.
Open accessAbstract
Currently, over five million Americans suffer with Alzheimer's disease (AD). In the absence of a cure, this number could increase to 13.8 million by 2050. A critical goal of biomedical research is to establish indicators of AD during the preclinical stage (i.e. biomarkers) allowing for early diagnosis and intervention. Numerous advances have been made in developing biomarkers for AD using neuroimaging approaches. These approaches offer tremendous versatility in terms of targeting distinct age-related and pathophysiological mechanisms such as structural decline (e.g. volumetry, cortical thinning), functional decline (e.g. fMRI activity, network correlations), connectivity decline (e.g. diffusion anisotropy), and pathological aggregates (e.g. amyloid and tau PET). In this review, we survey the state of the literature on neuroimaging approaches to developing novel biomarkers for the amnestic form of AD, with an emphasis on combining approaches into multimodal biomarkers. We also discuss emerging methods including imaging epigenetics, neuroinflammation, and synaptic integrity using PET tracers. Finally, we review the complementary information that neuroimaging biomarkers provide, which highlights the potential utility of composite biomarkers as suitable outcome measures for proof-of-concept clinical trials with experimental therapeutics.
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“patients with preclinical AD have widespread white matter abnormalities at a stage similar to those reported in AD (Marquez and Yassa, 2019; Stone et al., 2021).”
From Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging (2022) · cites this paper as Marquez and Yassa, 2019
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An overview on Alzheimer’s disease, diagnosis and treatment approach
Nivetha, S. R., Shivani, K., Vigneshwaran, R., Prasanna, G. L., & Veeramani, M. (2023) · International Journal of Allied Medical Sciences and Clinical Research · doi:10.61096/ijamscr.v11.iss4.2023.379-391 · Cited by 1
Graph neural network based heterogeneous propagation scheme for classifying alzheimer’s diseasePreprint
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Progress of Nanotechnology-Based Detection and Treatment of Alzheimer’s Disease Biomarkers
Singh, Y. R., Shukla, A., & Pattanayek, S. K. (2022) · Springer eBooks · doi:10.1007/978-981-16-3667-7_3
Zhang, Z., Wu, J., Pulgar, I. M. R., Chang, E. H. T., Chai, K., Wang, P., Jiang, S., Li, X., Oishi, K., Smith, G. S., et al. (2026) · Neurology International · doi:10.3390/neurolint18090177
Mitchell, B. A., Hausle, I., Smith, S., Thropp, P., Sirota, M., Tosun, D., & Initiative, A. D. N. (2026) · npj Dementia · doi:10.1038/s44400-026-00100-z
“…our ability to detect structural atrophy (cortical and subcortical gray matter tissue loss), synaptic dysfunction, and pathological protein aggregation (A β and tau) 4 , signi fi cant gaps remain in understanding the , progression trajectories, and patient-speci fi c phenotypic manifestations 5,6 .”
Perez, A., Hammer, H. L., Andersson, V., Saarinen, T., Alfonsín, S., Giuffrè, G. M., Caraglia, N., Martellacci, N., Ramírez‐Toraño, F., Tveitstøl, T., et al. (2026) · GeroScience · doi:10.1007/s11357-026-02466-7
Jaganathan, R., Vijayakumar, S., Chen, Y., Ye, J., Bakthavatchalam, P., Wankhar, D., Yang, C., Iyaswamy, A., & Li, M. (2026) · Neurobiology of Disease · doi:10.1016/j.nbd.2026.107555
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Imaging of Central Nervous System Disorders of Protein Folding
Saini, J. S., Johnson, A. C., Prasad, S., & Soni, N. (2026) · Seminars in Roentgenology · doi:10.1016/j.ro.2026.150989
Brain-Derived Extracellular Vesicle Subpopulations: from Bulk Measurements to Single-Entity Assays
Bai, S., Chen, L., Zhang, W., Li, M., Liu, H., Huang, X., Zhou, L., Xin, W., Jiang, C., & Li, C. (2026) · JACS Au · doi:10.1021/jacsau.6c00300
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“30,31 However, FDG-PET is highly expensive and involves exposure to radiation, which is unrealistic in community setting.”
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“29 The Month 9 MRI endpoint was selected because structural GMV changes consolidate more slowly than cognitive effects.”
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Identification of Biomarkers in Alzheimer Disease using Artificial Intelligence
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“…measurements of MTL subregions, including volumes of hippocampal subfields and thickness of MTL cortical subregions, serve as effective structural imaging biomarkers in magnetic resonance imaging (MRI) (Backhausen et al., 2022; Benzinger et al., 2013; Hampel et al., 2008; Márquez and Yassa, 2019).”
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Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans
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BackgroundInformation Flow Pattern in Early Mild Cognitive Impairment Patients
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“, amyloid aggregates) and cognitive decline in patients with MCI and AD (10, 15, 16).”
BackgroundAn overview of Alzheimer's disease and its diagnosis using conventional and novel methods
Zhao, X. (2021) · International Conference on Bioinformatics and Biomedical Science (ICBBS) · doi:10.1145/3498731.3498761
“Other preliminary imaging techniques include assessing for neuroinflammation/microglia activation through translocator protein (TSPO)-PET [44], epigenetic modifications and synapses density/loss [45].”
MethodValidation of Diffusion Kurtosis as an Early-Stage Biomarker of Parkinson’s in Animal Models
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Multimodal Neuroimaging Game Theoretic Data Fusion in Adversarial Conditions
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“The selected case study is represented by the Alzheimer’s Disease classification, which is a very largely investigate degenerative pathology by using neuroimaging of various kinds [22, 33].”
BackgroundDementia, A General Medical Practitioner’s Concern: A Review
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Constraining Disease Progression Models Using Subject Specific Connectivity Priors
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Proteomics Principles and Clinical Applications
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Optimizing Alzheimer’s Disease diagnosis with MRI and clinical data via lightweight transformers
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Metadata
- Authors
- Márquez, F., & Yassa, M. A.
- Affiliations
- University of California, Irvine (US)
- Corresponding author
- Márquez, Freddie
- Yassa, Michael A.
- Venue
- Molecular Neurodegeneration
- Published
- 2019-06-07
- Volume
- 14
- Issue
- 1
- Pages
- 21-21
- ISSN
- 1750-1326 (ISSN-L)
- PMC
- PMC6555939
- OpenAlex
- W2955418937
- Cited by
- 289
- References
- 196
- Citations by year
- 2026: 31 · 2025: 51 · 2024: 53 · 2023: 36 · 2022: 49 · 2021: 46 · 2020: 18 · 2019: 3
- FWCI
- 4.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Molecular Neurodegeneration (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Division of Molecular and Cellular Biosciences (GRFP DGE-1321846, B2D 1612490)
- National Institute on Aging (P50AG05146, R21AG049220, R01AG053555)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- Alzheimer's disease, neuroimaging biomarkers, preclinical Alzheimer's disease, early diagnosis, multimodal biomarkers, amyloid PET, tau PET, cortical thinning, brain volumetry, functional connectivity, diffusion anisotropy, neuroinflammation, synaptic integrity, composite biomarker, clinical trial outcome measures
- MeSH terms
- Alzheimer Disease, Brain, Humans, Biomarkers, Early Diagnosis, Amyloidogenic Proteins, Neuroimaging
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