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.
“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
The “Reduced white matter integrity – Diffusion MRI” section reports widespread declines in white matter integrity in MCI and AD, and the Summary and Conclusions repeats that finding. Neither passage establishes widespread white matter abnormalities in patients with preclinical AD or says their abnormalities are similar to those in AD. (LLM, conf=0.88)
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Self-citationAtrophy and dysfunction of parahippocampal white matter in mild Alzheimer’s disease
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Hypometabolism of the posterior cingulate cortex is not restricted to Alzheimer's disease
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Efficacy of acetylcholinesterase inhibitors in Alzheimer’s disease.
Marucci, G., Buccioni, M., Ben, D. D., Lambertucci, C., Volpini, R., & Amenta, F. (2021) · Neuropharmacology · doi:10.1016/j.neuropharm.2020.108352 · Cited by 1,147 · see citing works
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Developing the ATX(N) classification for use across the Alzheimer disease continuum
Hampel, H. J., Cummings, J. L., Blennow, K., Gao, P., Jack, C. R., & Vergallo, A. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00520-w · Cited by 331 · see citing works
Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease
Wang, X., Huang, W., Su, L., Xing, Y., Jessen, F., Sun, Y., Shu, N., & Han, Y. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00395-3 · Cited by 265 · see citing works
“The development of selective tau tracers, such as the most extensively used tracer (18)FFlortaucipir [47], allows researchers to investigate regional distribution of tau pathology in the living human brain [46, 48, 49].”
BackgroundYen, C., Lin, C.-L., & Chiang, M.-C. (2023) · Life · doi:10.3390/life13071472 · Cited by 266 · see citing works
Extracellular protein components of amyloid plaques and their roles in Alzheimer’s disease pathology
Rahman, M. M., & Lendel, C. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00465-0 · Cited by 245 · see citing works
Diagnostic biomarkers of Alzheimer’s disease: A state-of-the-art review
Khoury, R., & Ghossoub, E. (2019) · Biomarkers in Neuropsychiatry · doi:10.1016/j.bionps.2019.100005 · Cited by 157 · see citing works
Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia
Winchester, L. M., Harshfield, E. L., Shi, L., Badhwar, A., Khleifat, A. A., Clarke, N., Dehsarvi, A., Lengyel, I., Lourida, I., Madan, C. R., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13390 · Cited by 130 · see citing works
Odusami, M., Maskeliūnas, R., Damaševičius, R., & Misra, S. (2023) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-023-00801-3 · Cited by 116 · see citing works
“By evaluating the brain images captured from PET and MRI, volumetric consolidation in parts of the brain atrophy can be used as an essential biomarker for AD [11, 12].”
MethodNeuroimaging Modalities in Alzheimer’s Disease: Diagnosis and Clinical Features
Kim, J., Jeong, M.-J., Stiles, W. R., & Choi, H.-S. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23116079 · Cited by 106 · see citing works
Local Sleep and Alzheimer’s Disease Pathophysiology
Mander, B. A. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.525970 · Cited by 97 · see citing works
Yang, D.-L., Huang, R., Yoo, S.-H., Shin, M., Yoon, J. A., Shin, Y., & Hong, K. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00141 · Cited by 91 · see citing works
“However, because medical imaging can identify the different stages of the AD temporally and anatomically, some researchers claim that the superiority of medical imaging over the biofluid biomarkers mentioned above (Márquez and Yassa, 2019).”
BackgroundEarly diagnosis and treatment of Alzheimer’s disease: new definitions and challenges
Pais, M. V., Martinez, L. D., Ribeiro, O. G., Loureiro, J. C., Fernandez, R., Valiengo, L. D. C. L., Canineu, P. R., Stella, F., Talib, L. L., Radanovic, M., et al. (2020) · Brazilian Journal of Psychiatry · doi:10.1590/1516-4446-2019-0735 · Cited by 118 · see citing works
Wang, Z., Ye, J.-R., Zhang, K., Ding, L., Granzier-Nakajima, T., Ranasinghe, J. C., Xue, Y., Sharma, S., Biase, I., Terrones, M., et al. (2021) · bioRxiv · doi:10.1101/2021.06.03.446929 · Cited by 72
Choroid plexus volume as a novel candidate neuroimaging marker of the Alzheimer’s continuum
Jiang, J., Zhuo, Z., Wang, A., Li, W., Jiang, S., Duan, Y., Ren, Q., Zhao, M., Wang, L., Yang, S., et al. (2024) · Alzheimer s Research & Therapy · doi:10.1186/s13195-024-01520-w · Cited by 67 · see citing works
“…ventricular volume (LVV), hippocampal volume, white matter hypointensity (WMH) volume, and estimated total intracranial volume (eTIV), and the average whole cortical thickness, entorhinal cortical thickness (ECT), middle temporal cortical thickness, and parahip-pocampal cortical thickness [27–29].”
BackgroundOlajide, O. J., Suvanto, M. E., & Chapman, C. A. (2021) · Biology Open · doi:10.1242/bio.056796 · Cited by 71 · see citing works
Fang, Z., Tang, Y., Ying, J., Tang, C., & Wang, Q. (2020) · Chinese Medicine · doi:10.1186/s13020-020-00359-1 · Cited by 72 · see citing works
Therapy for Alzheimer’s disease: Missing targets and functional markers?
Stoiljković, M., Horváth, T. L., & Hajós, M. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101318 · Cited by 72 · see citing works
Sex is a defining feature of neuroimaging phenotypes in major brain disorders
Salminen, L. E., Tubi, M. A., Bright, J. K., Thomopoulos, S. I., Wieand, A., & Thompson, P. M. (2021) · Human Brain Mapping · doi:10.1002/hbm.25438 · Cited by 61 · see citing works
Distinct network topology in Alzheimer’s disease and behavioral variant frontotemporal dementia
Ng, A. S. L., Wang, J., Ng, K. K., Chong, J. S. X., Qian, X., Lim, J. K. W., Tan, Y. J., Yong, A. C. W., Chander, R. J., Hameed, S., et al. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-00752-w · Cited by 61 · see citing works
Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers
Dang, C., Wang, Y., Li, Q., & Lu, Y. (2023) · Psychoradiology · doi:10.1093/psyrad/kkad009 · Cited by 53 · see citing works
Deep learning for Alzheimer prediction using brain biomarkers
Goenka, N., & Tiwari, S. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10016-0 · Cited by 60 · see citing works
“However, some studies suggest that neuroimaging biomarkers provides many more prediction points as compared to other biomarkers (Márquez and Yassa 2019).”
MethodResultInfluentialEarly affective changes and increased connectivity in preclinical Alzheimer's disease
Fredericks, C. A., Sturm, V. E., Brown, J. A., Hua, A. Y., Bilgel, M., Wong, D. F., Resnick, S. M., & Seeley, W. W. (2018) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.06.002 · Cited by 59 · see citing works
Early changes of fecal short‐chain fatty acid levels in patients with mild cognitive impairments
Gao, C., Li, B., He, Y., Huang, P., Du, J., He, G., Zhang, P., Tang, H., & Chen, S. (2023) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14252 · Cited by 50 · see citing works
“22– 26 In fact, stool is more accessible and noninvasive sample source compared with above samples.”
BackgroundOmics-based biomarkers discovery for Alzheimer's disease
Aerqin, Q., Wang, Z.-T., Wu, K., He, X., Dong, Q., & Yu, J. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04614-6 · Cited by 54 · see citing works
“Potential imaging biomarkers that have been approved can be used to observe structural and functional changes, as well as synapses and cell degeneration through positron emission tomography (PET) or functional magnetic resonance imaging (fMRI) [13].”
BackgroundMirkin, S., & Albensi, B. C. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1094233 · Cited by 46 · see citing works
“Neuroimaging has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage allowing for earlier diagnosis and intervention (Márquez and Yassa, 2019).”
MethodSaggu, S., Bai, A., Aida, M., Rehman, H., Pless, A., Ware, D., Deák, F., Jiao, K., & Wang, Q. (2024) · GeroScience · doi:10.1007/s11357-024-01359-x · Cited by 47 · see citing works
“Neuroimaging techniques, such as MRI and functional MRI, detect structural and functional changes in MA-ergic pathways [195, 196].”
Gupta, Y., Kim, J., Kim, B. C., & Kwon, G. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00238 · Cited by 45 · see citing works
“Nevertheless, some authors argue for the perfection of imaging above fluid biomarkers, given that imaging modalities can distinguish the different phases of the disease both anatomically and temporally (Khoury and Ghossoub, 2019; Márquez and Yassa, 2019).”
BackgroundLivingston, N. R., Calsolaro, V., Hinz, R., Nowell, J., Raza, S., Gentleman, S., Tyacke, R. J., Myers, J., Venkataraman, A. V., Perneczky, R., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01429-y · Cited by 44 · see citing works
“Glucose hypometabolism, measured using (18)F-fluorodeoxyglucose ((18)F-FDG) PET, and brain atrophy, measured using MRI, are two of the earliest neuroimaging markers of neurodegeneration developed for AD [7].”
BackgroundMethodOdusami, M., Maskeliūnas, R., & Damaševičius, R. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13101496 · Cited by 42 · see citing works
Rehiman, S. H., Lim, S. M., Neoh, C. F., Majeed, A. B. A., Chin, A., Tan, M. P., Kamaruzzaman, S. B., & Ramasamy, K. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101066 · Cited by 52 · see citing works
Bridging the Gap Between Fluid Biomarkers for Alzheimer’s Disease, Model Systems, and Patients
Bjorkli, C., Sandvig, A., & Sandvig, I. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00272 · Cited by 41 · see citing works
Kim, S. E., Kim, H. J., Jang, H., Weiner, M. W., DeCarli, C. S., Na, D. L., & Seo, S. W. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231810490 · Cited by 42 · see citing works
Costanzo, M., Cutrona, C., Leodori, G., Malimpensa, L., D’Antonio, F., Conte, A., & Belvisi, D. (2024) · Alzheimer s Research & Therapy · doi:10.1186/s13195-024-01607-4 · Cited by 39 · see citing works
“Among the measures of disease progression that we have considered, there are well-known CSF (Aβ 1–42, phos-pho-tau 181; total-tau) [15, 16, 34, 35], neuroimaging biomarkers (hippocampal volumetry, cortical thinning, amyloid and tau PET) [15, 16, 35, 36] [37, 38].”
BackgroundFodor, Z., Horváth, A., Hidasi, Z., Gouw, A., Stam, C., & Csukly, G. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.680200 · Cited by 37 · see citing works
Hou, L., Zhang, L., Hong, J., Zhang, D., Zhao, J., & Wang, Q. (2020) · Antioxidants and Redox Signaling · doi:10.1089/ars.2019.8014 · Cited by 39 · see citing works
“AD is one of the most common neurodegenerative diseases of the CNS, characterized by progressive cognitive dysfunction, memory impairment, and decreased cognitive function (112).”
CoQ10 and Mitochondrial Dysfunction in Alzheimer’s Disease
Fišar, Z., & Hroudová, J. (2024) · Antioxidants · doi:10.3390/antiox13020191 · Cited by 55 · see citing works
“Neuroimaging biomarkers [44–47], proteomic and metabolomic markers [48], and biomarkers of oxidative stress [49], mitochondrial dysfunction [50,51], and neuroinflammation [52] are currently being sought and studied in AD.”
BackgroundGao, Y., Liu, J., Wang, J., Liu, Y., Zeng, L., Ge, W., & Ma, C. (2022) · Brain Pathology · doi:10.1111/bpa.13047 · Cited by 35 · see citing works
“Since AD was firstly defined by Alois Alzheimer in 1984, numerous studies have been performed on the pathology [24], neuroimaging [25, 26], and neural circuits [27] to clarify the pathogenesis of AD and provide new therapeutic strategies.”
BackgroundPuranik, N., & Song, M. (2024) · Molecules · doi:10.3390/molecules29235744 · Cited by 45 · see citing works
Fluid Biomarkers for the Diagnosis of Neurodegenerative Diseases
Luebke, M., Parulekar, M., & Thomas, F. (2023) · Biomarkers in Neuropsychiatry · doi:10.1016/j.bionps.2023.100062 · Cited by 33 · see citing works
Carpi, M., Fernandes, M., Mercuri, N. B., & Liguori, C. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230933 · Cited by 36 · see citing works
Mhm, N., Chakravarthi, S., Aung, T., Htoo, P. M., Shwe, W. H., Gupalo, S., Udayah, M. W., Singh, H., Kabir, M. S., Thangarajan, R., et al. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26157212 · Cited by 33 · see citing works
Biclustering fMRI time series: a comparative study
Castanho, E. N., Aidos, H., & Madeira, S. C. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04733-8 · Cited by 31 · see citing works
“These biomarkers are a set of characteristics (features) that allows early disease detection and prognostic prediction [106–110].”
BackgroundThe diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine
Katwaroo, A. R., Adesh, V. S., Lowtan, A., & Umakanthan, S. (2023) · Postgraduate Medical Journal · doi:10.1093/postmj/qgad135 · Cited by 40 · see citing works
Herpes simplex virus, early neuroimaging markers and incidence of Alzheimer’s disease
Linard, M., Baillet, M., Letenneur, L., Garrigue, I., Catheline, G., Dartigues, J., Pérès, K., & Helmer, C. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01532-2 · Cited by 32 · see citing works
Ghafoori, S., & Shalbaf, A. (2022) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-022-02620-4 · Cited by 28 · see citing works
Lanabecestat: Neuroimaging results in early symptomatic Alzheimer’s disease
Zimmer, J. A., Shcherbinin, S., Devous, M. D., Bragg, S. M., Selzler, K. J., Wessels, A. M., Shering, C., Mullen, J., Landry, J., Andersen, S. W., et al. (2021) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1002/trc2.12123 · Cited by 27 · see citing works
Khan, A. F., & Iturria‐Medina, Y. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03073-w · Cited by 28 · see citing works
“Although imaging and fl uid measures have shown promise in research settings, we still lack de fi nitive clinical biomarkers across neurodegenerative diseases, particularly in the early prodromal or preclinical stages [317 – 319].”
BackgroundMarawi, T., Zhukovsky, P., Rashidi‐Ranjbar, N., Bowie, C. R., Brooks, H., Fischer, C. E., Flint, A. J., Herrmann, N., Mah, L., Pollock, B. G., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.05.018 · Cited by 29 · see citing works
Mohanty, R., Ferreira, D., Nordberg, A., & Westman, E. (2023) · Alzheimer's Research & Therapy · doi:10.1186/s13195-023-01173-1 · Cited by 26 · see citing works
Tanaka, M., Battaglia, S., & Liloia, D. (2025) · Biomedicines · doi:10.3390/biomedicines13051045 · Cited by 24 · see citing works
Prediction Models for Early Detection of Alzheimer: Recent Trends and Future Prospects
Kaur, I., & Sachdeva, R. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10246-3 · Cited by 28 · see citing works
“On the other hand, some research indicates that neuroimaging biomarkers have a wider range of predictive capacities than other biomarkers [94].”
Frontostriatal and limbic contributions to cognitive decline in Parkinson's disease
Brown, G., Hakun, J. G., Lewis, M. M., Jesus, S. D., Du, G., Eslinger, P. J., Kong, L., & Huang, X. (2022) · Journal of Neuroimaging · doi:10.1111/jon.13045 · Cited by 28 · see citing works
Derby, C. A. (2020) · Current Epidemiology Reports · doi:10.1007/s40471-020-00231-8 · Cited by 26 · see citing works
Frye, B. M., Craft, S., Register, T. C., Kim, J., Whitlow, C. T., Barcus, R. A., Lockhart, S. N., Sai, K. K. S., & Shively, C. A. (2022) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1002/trc2.12284 · Cited by 24 · see citing works
Sheng, J., Wang, B., Zhang, Q., Zhou, R., Wang, L., & Yu, X. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e07287 · Cited by 29 · see citing works
Chen, L., Song, J., Cheng, R., Wang, K., Liu, X., He, M., & Luo, T. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.614833 · Cited by 21 · see citing works
Basanta-Torres, S., Rivas-Fernández, M. A., & Galdo-Álvarez, S. (2025) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2025.111028 · Cited by 20 · see citing works
Shi, D., Xie, S., Li, A., Wang, Q., Guo, H., Han, Y., Xu, H., Gan, W., Zhang, L., Guo, T., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-01899-w · Cited by 25 · see citing works
Heneghan, A., Deng, F., Wells, K., Ritchie, K., Muniz-Terrera, G., Ritchie, C. W., Lawlor, B., & Naci, L. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-220267 · Cited by 18 · see citing works
A Spatiotemporal Brain Network Analysis of Alzheimer’s Disease Based on Persistent Homology
Xing, J., Jia, J., Wu, X., & Kuang, L.-Q. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.788571 · Cited by 18 · see citing works
“A lot of early brain functional alterations related to AD pathophysiology have been reported using various brain network analysis methods (Hallquist and Hillary, 2018; Marquez and Yassa, 2019; Mill et al., 2020).”
BackgroundKothapalli, 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 14 · see citing works
“37/2000 ms , flip angle = 10 ◦ , acquisition time = 6 min, and resolution 1 × 1 × 1 mm 3 ) were acquired for image segmentation.”
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 18 · see citing works
“However, despite tremendous effort, and investment, human fMRI research has yet to produce actionable neuroimaging markers of AD [4].”
Doke, R. R., Lamkhade, G., Vinchurkar, K., & Singh, S. K. (2024) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.4c00457 · Cited by 23 · see citing works
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 · see citing works
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 · see citing works
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 16 · see citing works
“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 · see citing works
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“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.”
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“However, thus far, task-based fMRI has largely focused on dysfunctional hippocampal activity (Marquez & Yassa, 2019).”
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“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).”
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“10 Functional analysis of brain activity using electroencephalography (EEG) is another promising avenue for AD biomarker development.”
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“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).”
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“hypothesis being that it mostly reflects neuropil loss (Márquez and Yassa, 2019).”
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“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).”
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“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).”
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“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).”
MethodA novel cascade machine learning pipeline for Alzheimer’s disease identification and prediction
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“Neuroimaging biomarkers may give more predictive information and make the diagnosis more reliable (Márquez and Yassa, 2019).”
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- 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 · 2018: 1
- 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
- Health Sciences › Medicine › Physiology › 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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