Cited by 289Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsFunctional Brain Connectivity Studies

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Neuroimaging biomarkers for Alzheimer’s disease.

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Márquez, Freddie, Yassa, Michael A.·2019·Molecular Neurodegeneration

DOI 10.1186/s13024-019-0325-5PMC PMC6555939

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Abstract

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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Background
Quantitative Gradient Echo MRI Identifies Dark Matter as a New Imaging Biomarker of Neurodegeneration that Precedes Tissue Atrophy in Early Alzheimer’s Disease
Kothapalli, S. V. V. N., Benzinger, T. L. S., Aschenbrenner, A. J., Perrin, R. J., Hildebolt, C. F., Goyal, M. S., Fagan, A. M., Raichle, M. E., Morris, J. C., & Yablonskiy, D. A. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210503 · Cited by 15
“37/2000 ms , ﬂip angle = 10 ◦ , acquisition time = 6 min, and resolution 1 × 1 × 1 mm 3 ) were acquired for image segmentation.”
Aging-dependent loss of functional connectivity in a mouse model of Alzheimer’s disease and reversal by mGluR5 modulator
Mandino, F., Shen, X., Desrosiers-Grégoire, G., O’Connor, D. A., Mukherjee, B., Owens, A., Qu, A., Onofrey, J. A., Papademetris, X., Chakravarty, M. M., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02779-z · Cited by 19
“However, despite tremendous effort, and investment, human fMRI research has yet to produce actionable neuroimaging markers of AD [4].”
Demystifying the Role of Neuroinflammatory Mediators as Biomarkers for Diagnosis, Prognosis, and Treatment of Alzheimer’s Disease: A Review
Doke, R. R., Lamkhade, G., Vinchurkar, K., & Singh, S. K. (2024) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.4c00457 · Cited by 24
Personalized Management and Treatment of Alzheimer’s Disease
Cacabelos, R., Naidoo, V., Martínez-Iglesias, O. A., Corzo, L., Cacabelos, N., Pego, R., & Carril, J. C. (2022) · Life · doi:10.3390/life12030460 · Cited by 19
Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models
Gurdon, B., & Kaczorowski, C. C. (2021) · Neurobiology of Disease · doi:10.1016/j.nbd.2021.105558 · Cited by 16
An AI‐first framework for multimodal data in Alzheimer's disease and related dementias
Jasodanand, V. H., Bellitti, M., & Kolachalama, V. B. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70719 · Cited by 19
“13 16 The result is deeply phenotyped datasets that enable precise modeling of disease mechanisms.”
Quantification of perforant path fibers for early detection of Alzheimer's disease
Uchida, Y., Hou, Z., Gómez-Isaza, L., Luongo, M., Troncoso, J. C., Miller, M. I., Mori, S., & Oishi, K. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70142 · Cited by 16
Which neuroimaging and fluid biomarkers method is better in theranostic of Alzheimer’s disease? An umbrella review
Mohammadi, H., Ariaei, A., Ghobadi, Z., Gorgich, E. A. C., & Rustamzadeh, A. (2024) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2024.02.007 · Cited by 18
The Role of H3K9 Acetylation and Gene Expression in Different Brain Regions of Alzheimer’s Disease Patients
Santana, D. A., Bedrat, A., Puga, R., Turecki, G., Mechawar, N., Faria, T. C., Gigek, C. O., Payão, S. L. M., Smith, M. D. A. C., Lemos, B., et al. (2022) · Epigenomics · doi:10.2217/epi-2022-0096 · Cited by 19
Multi-channel spatio-temporal graph attention contrastive network for brain disease diagnosis
Li, C., Ma, K., Li, S.-R., Meng, X.-S., Wang, R., Zhang, D.-Q., & Zhu, Q. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121013 · Cited by 15
Longitudinal MRI analysis using a hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction
Jomeiri, A., Navin, A. H., & Shamsi, M. (2024) · Behavioural Brain Research · doi:10.1016/j.bbr.2024.114900 · Cited by 18
Deep learning application for the classification of Alzheimer’s disease using 18F-flortaucipir (AV-1451) tau positron emission tomography
Park, S., Yeo, N. Y., Kim, Y., Byeon, G., & Jang, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-35389-w · Cited by 16
“Previous studies using an ADNI database-driven approach have determined that the principal regions of tau pathology mainly overlap with the Braak stage III regions of interest (ROIs) (i.e., the amygdala, para-hippocampal gyrus, and spindle) 6,17,20 .”
Result
Science disconnected: the translational gap between basic science, clinical trials, and patient care in Alzheimer's disease
Gregory, S., Saunders, S., & Ritchie, C. W. (2022) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(22)00219-7 · Cited by 16
“Currently, a PET tracer compound is being developed to detect synaptic loss, 2 which is a crucial event in Alzheimer’s disease pathology thought to correspond with the start of observable cognitive impairment and other behavioural and neuropsychiatric problems.”
Background
Impact of physiological factors on longitudinal structural MRI measures of the brain
Zahid, U., Hedges, E. P., Dimitrov, M., Murray, R. M., Barker, G. J., & Kempton, M. J. (2022) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2022.111446 · Cited by 14
Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer’s disease neuropathology
Antoniades, M., Srinivasan, D., Wen, J., Erus, G., Abdulkadir, A., Mamourian, E., Melhem, R., Hwang, G., Cui, Y., Govindarajan, S. T., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105399 · Cited by 13
Astrogliosis, neuritic microstructure, and sex effects: GFAP is an indicator of neuritic orientation in women
Thaker, A. A., McConnell, B. V., Rogers, D., Carlson, N. E., Coughlan, C. M., Jensen, A. M., Lopez‐Paniagua, D., Holden, S. K., Pressman, P., Pelak, V. S., et al. (2023) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2023.06.026 · Cited by 16
Retinal Alterations as Potential Biomarkers of Structural Brain Changes in Alzheimer’s Disease Spectrum Patients
Hu, Z., Wang, L., Zhu, D., Qin, R., Sheng, X., Ke, Z., Shao, P., Zhao, H., Xu, Y., & Bai, F. (2023) · Brain Sciences · doi:10.3390/brainsci13030460 · Cited by 32
Single‐value scores of memory‐related brain activity reflect dissociable neuropsychological and anatomical signatures of neurocognitive aging
Richter, A., Soch, J., Kizilirmak, J. M., Fischer, L., Schütze, H., Assmann, A., Behnisch, G., Feldhoff, H., Knopf, L., Raschick, M., et al. (2023) · Human Brain Mapping · doi:10.1002/hbm.26281 · Cited by 15
“However, thus far, task-based fMRI has largely focused on dysfunctional hippocampal activity (Marquez & Yassa, 2019).”
Background
Synthesizing Multi-tracer PET Images for Alzheimer’s Disease Patients Using a 3D Unified Anatomy-Aware Cyclic Adversarial Network
Zhou, B., Wang, R., Chen, M.-K., Mecca, A. P., O’Dell, R. S., van Dyck, C. H., Carson, R. E., Duncan, J. S., & Liu, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87231-1_4 · Cited by 16
White Matter Differences in Networks in Elders with Mild Cognitive Impairment and Alzheimer's Disease
Yang, F., Bal, S. S., Lee, J.-F., & Chen, C. (2020) · Brain Connectivity · doi:10.1089/brain.2020.0767 · Cited by 18
“Functional connectivity results suggest difference in DMN connectivity along with hyperconnectivity in the anterior DMN and hypoconnectivity in the posterior DMN as biomarkers to diagnose AD (Marquez and Yassa, 2019).”
Coordination chemistry of ligands: Insights into the design of amyloid beta/tau-PET imaging probes and nanoparticles-based therapies for Alzheimer’s disease
Selvan, S. T., Ravichandar, R., Ghosh, K. K., Mohan, A., Mahalakshmi, P., Gulyás, B. Z., & Padmanabhan, P. (2020) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2020.213659 · Cited by 16
EEG biomarkers for Alzheimer's disease: A novel automated pipeline for detecting and monitoring disease progression
Simmatis, L. E. R., Russo, E. E., Steininger, T., Riddell, H., Chen, E., Chiu, Q., Lin, M., Oh, D., Taheri, P., Harmsen, I. E., et al. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251327754 · Cited by 8
“10 Functional analysis of brain activity using electroencephalography (EEG) is another promising avenue for AD biomarker development.”
Unraveling the mystery of citrate transporters in Alzheimer’s disease: An updated review
Mukherjee, A. G., Mishra, S., Gopalakrishnan, A. V., Kannampuzha, S., Murali, R., Wanjari, U. R., Stany, B. K., Vellingiri, B., Madhyastha, H., Deepankumar, K., et al. (2025) · Ageing Research Reviews · doi:10.1016/j.arr.2025.102726 · Cited by 11
Blood-derived APLP1 + extracellular vesicles are potential biomarkers for the early diagnosis of brain diseases
Choi, Y., Park, J. H., Jo, A., Lim, C.-W., Park, J.-M., Hwang, J. W., Lee, K. S., Kim, Y., Lee, H., & Moon, J. (2025) · Science Advances · doi:10.1126/sciadv.ado6894 · Cited by 16
A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer’s Disease
Xia, H., Luan, X., Bao, Z., Zhu, Q., Wen, C., Wang, M., & Song, W. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02836-9 · Cited by 13
Layer‐specific microstructural patterns of anterior hippocampus in Alzheimer's disease with ex vivo diffusion MRI at 14.1 T
Zhao, Z., Zhang, L., Luo, W., Cao, Z., Zhu, Q., Kong, X., Zhu, K., Zhang, J., & Wu, D. (2022) · Human Brain Mapping · doi:10.1002/hbm.26062 · Cited by 11
“Moreover, the lesion of intra-hippocampal fiber connection, such as Perforant path, results in neuronal loss of hippocampal layer, which is one of the earliest hallmark features of AD (Márquez & Yassa, 2019).”
Background
Relationship between cerebrospinal fluid neurodegeneration biomarkers and temporal brain atrophy in cognitively healthy older adults
Dídac Vidal-piñeiro, A., Sørensen, Ø., Blennow, K., Capogna, E., Halaas, N. B., Idland, A., Mowinckel, A. M., Pereira, J. B., Watne, L. O., Zetterberg, H., et al. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.04.010 · Cited by 11
“hypothesis being that it mostly reflects neuropil loss (Márquez and Yassa, 2019).”
Clinically oriented Alzheimer's biosensors: expanding the horizons towards point-of-care diagnostics and beyond
Murti, B. T., Putri, A. D., Huang, Y., Wei, S.-M., Peng, C., & Yang, P. (2021) · RSC Advances · doi:10.1039/d1ra01553b · Cited by 28
Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging
Maharjan, S., Tsai, A. P., Lin, P. B., Ingraham, C., Jewett, M. R., Landreth, G. E., Oblak, A. L., & Wang, N. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.964654 · Cited by 15
“Advanced neuroimaging studies demonstrate that 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).”
Background
Longitudinal biomarker studies in human neuroimaging: capturing biological change of Alzheimer’s pathology
Fischer, L., Parker, D. M., Maboudian, S. A., Fonseca, C. S., Tato-Fernández, C., Annen, L., Arunachalam, P., Bacci, J. R., Barboure, M. C., Capelli, S., et al. (2025) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01920-6 · Cited by 13
“…biomarkers have been developed using positron emission tomography (PET) to assess Aβ and tau burden, as well as PET and magnetic resonance imaging (MRI) to evaluate nonspecific pathophysiological changes such as neurodegeneration, network dysfunction, vascular disease, and inflammation [2, 3].”
Mechanisms and Impact of Cognitive Reserve in Normal Aging and Alzheimer’s Disease
Simfukwe, C., An, S. S. A., & Youn, Y. C. (2025) · Diagnostics · doi:10.3390/diagnostics15233068 · Cited by 11
Nanomedicine-Driven Modulation of the Gut–Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer’s and Parkinson’s Disease
Kršek, A., Schleicher, L. M. S., Jagodic, A., & Batičić, L. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26189178 · Cited by 13
Deep normative modelling reveals insights into early-stage Alzheimer's disease using multi-modal neuroimaging data
Aguila, A. L., Lorenzini, L., Janahi, M., Barkhof, F., & Altmann, A. (2025) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01753-3 · Cited by 11
“As such, several studies using structural T1-weighted MRI have identified both macrostructural changes (e.g., general atrophy) and specific brain regions which exhibit neurodegeneration (e.g., the amygdala and hippocampus) [11].”
Artificial intelligence in drug development for delirium and Alzheimer’s disease
Ai, R., Xiao, X., Deng, S., Yang, N., Xing, X., Watne, L. O., Selbæk, G., Wedatilake, Y., Xie, C., Rubinsztein, D. C., et al. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.04.026 · Cited by 11
“Neuroimaging biomarkers, such as MRI or PET scans, can reveal structural or functional changes in the brain, while protein-based biomarkers in blood or cerebrospinal ﬂuid can detect abnormal levels of amyloid or tau associated with neurodegenerative conditions 259,262,263 .”
Poly-Adenine Assisted Signaling Displaced Probe Ratiometric Electrochemical Aptasensor for Accurate Detection of Alzheimer's Disease Aβ Biomarkers.
Tong, J.-J., Li, C., Zhao, J., Wang, K.-M., Zhao, Z.-J., Liu, Y.-W., Qing, T.-P., & Liu, X.-F. (2024) · ACS Applied Materials and Interfaces · doi:10.1021/acsami.4c14877 · Cited by 11
Local functional connectivity abnormalities in mild cognitive impairment and Alzheimer's disease: A meta-analytic investigation using minimum Bayes factor activation likelihood estimation
Costa, T., Premi, E., Borroni, B., Manuello, J., Cauda, F., Duca, S., & Liloia, D. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120798 · Cited by 11
“Considering the growing field of potential disease-modifying treatments, it is mandatory to elucidate the neurobiology of MCI, to obtain the greatest likelihood to modify the natural history of the disease with pharmacological (or non-pharmacological) interventions (M ´ arquez and Yassa, 2019).”
Background
ER ‐stress response in retinal Müller glia occurs significantly earlier than amyloid pathology in the Alzheimer's mouse brain and retina
Palko, S. I., Benoit, M., Yao, A. Y., Mohan, R., & Yan, R. (2024) · Glia · doi:10.1002/glia.24514 · Cited by 12
“Cognitive tests, brain PET scans, and various biomarkers such as A β levels are currently aiding in earlier diagnosis of AD (Elmaleh et al., 2019; Márquez & Yassa, 2019; West et al., 2021; Zetterberg & Blennow, 2021).”
Method
A novel cascade machine learning pipeline for Alzheimer’s disease identification and prediction
Zhou, K., Piao, S., Liu, X., Luo, X., Chen, H., Xiang, R., & Geng, D. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1073909 · Cited by 14
“Neuroimaging biomarkers may give more predictive information and make the diagnosis more reliable (Márquez and Yassa, 2019).”
Background
Discovery of Novel Drug Candidates for Alzheimer’s Disease by Molecular Network Modeling
Zhou, J., Li, Q., Wu, W., Zhang, X., Zuo, Z., Lu, Y., Zhao, H., & Wang, Z. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.850217 · Cited by 12

Authors: Márquez, F., & Yassa, M. A.
Affiliations: University of California, Irvine (US)
Corresponding author: Márquez, Freddie
Yassa, Michael A.
ORCID: Márquez, Freddie
Yassa, Michael A.
Venue: Molecular Neurodegeneration
Publisher: Springer Science and Business Media LLC
Published: 2019-06-07
Volume: 14
Issue: 1
Pages: 21-21
ISSN: 1750-1326 (ISSN-L)
DOI: 10.1186/s13024-019-0325-5
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
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
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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Neuroimaging biomarkers for Alzheimer’s disease.

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