The Brain Chart of Aging: Machine‐learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans

N4ITK: Improved N3 Bias Correction

Tustison, N. J., Avants, B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2046908 · Cited by 6,413

Cardia: study design, recruitment, and some characteristics of the examined subjects

FRIEDMAN, G. D., Cutter, G., Donahue, R. P., Hughes, G. H., Hulley, S. B., Jacobs, D. R., Liu, K., & Savage, P. J. (1988) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(88)90080-7 · Cited by 1,689

Harmonization of cortical thickness measurements across scanners and sites

Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., Adams, P., Cooper, C., Fava, M., McGrath, P. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.024 · Cited by 1,580

Memory and Executive Function in Aging and AD

Buckner, R. L. (2004) · Neuron · doi:10.1016/j.neuron.2004.09.006 · Cited by 1,508

Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain

Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-08-03295.2003 · Cited by 1,425

Generalized additive models for medical research

Hastie, T. J., & Tibshirani, R. J. (1995) · Statistical Methods in Medical Research · doi:10.1177/096228029500400302 · Cited by 1,287

Building better biomarkers: brain models in translational neuroimaging

Woo, C.-W., Chang, L. J., Lindquist, M. A., & Wager, T. D. (2017) · Nature Neuroscience · doi:10.1038/nn.4478 · Cited by 1,056

Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers

Cole, J. H., & Franke, K. (2017) · Trends in Neurosciences · doi:10.1016/j.tins.2017.10.001 · Cited by 939

Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline

Debette, S., Seshadri, S., Beiser, A., Au, R., Himali, J. J., Palumbo, C., Wolf, P. A., & DeCarli, C. (2011) · Neurology · doi:10.1212/wnl.0b013e318227b227 · Cited by 772

Trajectories of brain aging in middle-aged and older adults: Regional and individual differences

Raz, N., Ghisletta, P., Rodrigue, K. M., Kennedy, K. M., & Lindenberger, U. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.020 · Cited by 626

Brain charts for the human lifespan

Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., et al. (2022) · Nature · doi:10.1038/s41586-022-04554-y · Cited by 1,794

Senolytic therapy to modulate the progression of Alzheimer’s Disease (SToMP-AD) – Outcomes from the first clinical trial of senolytic therapy for Alzheimer's disease

Orr, M., Gonzales, M., Garbarino, V., Kautz, T., Palavicini, J., Lopez-Cruzan, M., Dehkordi, S. K., Mathews, J., Zare, H., Xu, P., et al. (2023) · doi:10.21203/rs.3.rs-2809973/v1 · Cited by 319

Senolytic therapy in mild Alzheimer's disease: a phase 1 feasibility trial

Gonzales, M. M., Garbarino, V. R., Kautz, T. F., Palavicini, J. P., Lopez-Cruzan, M., Dehkordi, S. K., Mathews, J. J., Zare, H., Xu, P., Zhang, B., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02543-w · Cited by 334

Antiageing strategy for neurodegenerative diseases: from mechanisms to clinical advances

Jiang, Q., Liu, J., Huang, S., Wang, X.-Y., Chen, X., Liu, G.-H., Ye, K., Song, W., Masters, C. L., Wang, J., et al. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02145-7 · Cited by 165

Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression

Wen, J., Fu, C. H. Y., Tosun, D., Veturi, Y., Yang, Z., Abdulkadir, A., Mamourian, E., Srinivasan, D., Skampardoni, I., Singh, A., et al. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.0020 · Cited by 142

Warped Bayesian linear regression for normative modelling of big data

Fraza, C., Dinga, R., Beckmann, C. F., & Marquand, A. F. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118715 · Cited by 134

Applications of generative adversarial networks in neuroimaging and clinical neuroscience

Wang, R., Bashyam, V., Yang, Z., Yu, F., Tassopoulou, V., Chintapalli, S. S., Skampardoni, I., Sreepada, L. P., Sahoo, D., Nikita, K. S., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119898 · Cited by 121

Beyond the average patient: how neuroimaging models can address heterogeneity in dementia

Verdi, S., Marquand, A. F., Schott, J. M., & Cole, J. H. (2021) · Brain · doi:10.1093/brain/awab165 · Cited by 106

Dementia with Lewy bodies: Impact of co‐pathologies and implications for clinical trial design

Toledo, J. B., Ruiz, C. A., Weil, R. S., Ferreira, D., Rodriguez‐Porcel, F. E., Pilotto, A., Wyman‐Chick, K. A., Grothe, M. J., Kane, J. P. M., Taylor, A., et al. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12814 · Cited by 112

A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure

Yang, Z., Nasrallah, I. M., Shou, H., Wen, J., Doshi, J. J., Habes, M., Erus, G., Abdulkadir, A., Resnick, S. M., Albert, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26703-z · Cited by 112

Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses

Bayer, J., Thompson, P. M., Ching, C. R. K., Liu, M., Chen, A. A., Panzenhagen, A. C., Jahanshad, N., Marquand, A. F., Schmaal, L., & Sämann, P. G. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.923988 · Cited by 112

Genetics of common cerebral small vessel disease

Bordes, C., Sargurupremraj, M., Mishra, A., & Debette, S. (2022) · Nature Reviews Neurology · doi:10.1038/s41582-021-00592-8 · Cited by 108

Brain aging patterns in a large and diverse cohort of 49,482 individuals

Yang, Z., Wen, J., Erus, G., Govindarajan, S. T., Melhem, R., Mamourian, E., Cui, Y., Srinivasan, D., Abdulkadir, A., Parmpi, P., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03144-x · Cited by 102

Bias in machine learning models can be significantly mitigated by careful training: Evidence from neuroimaging studies

Wang, R., Chaudhari, P., & Davatzikos, C. A. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2211613120 · Cited by 84

Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression

Kia, S. M., Huijsdens, H., Rutherford, S. E., de Boer, A. A. A., Dinga, R., Wolfers, T., Berthet, P., Mennes, M., Andreassen, O. A., Westlye, L. T., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0278776 · Cited by 95

Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry

Cong, S., Wang, H., Zhou, Y., Wang, Z., Yao, X., & Yang, C. (2024) · Brain‐X · doi:10.1002/brx2.57 · Cited by 75

Association Between Obstructive Sleep Apnea and Brain White Matter Hyperintensities in a Population-Based Cohort in Germany

Zacharias, H. U., Weihs, A., Habes, M., Wittfeld, K., Frenzel, S., Rashid, T., Stubbe, B., Obst, A., Szentkirályi, A., Bülow, R., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.28225 · Cited by 63

Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation

Wang, R., Chaudhari, P., & Davatzikos, C. A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102309 · Cited by 69

A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity

Jiang, R., Scheinost, D., Zuo, N., Wu, J., Qi, S., Liang, Q., Zhi, D., Luo, N., Chung, Y., Liu, S., et al. (2022) · Advanced Science · doi:10.1002/advs.202201621 · Cited by 62

A scoping review of fair machine learning techniques when using real-world data

Huang, Y., Guo, J., Chen, W.-H., Lin, H.-Y., Tang, H., Wang, F., Xu, H., & Bian, J. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104622 · Cited by 61

Slow-Wave Sleep and MRI Markers of Brain Aging in a Community-Based Sample

Baril, A., Beiser, A., Mysliwiec, V., Sanchez, E., DeCarli, C. S., Redline, S. S., Gottlieb, D. J., Maillard, P., Romero, J. R., Satizábal, C. L., et al. (2020) · Neurology · doi:10.1212/wnl.0000000000011377 · Cited by 59

Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Yang, Z., Wen, J., Abdulkadir, A., Cui, Y., Erus, G., Mamourian, E., Melhem, R., Srinivasan, D., Govindarajan, S. T., Chen, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44271-2 · Cited by 55

A framework of biomarkers for brain aging: a consensus statement by the Aging Biomarker Consortium

Jia, Y., Wang, J., Ren, J.-R., Chan, P., Chen, S., Chen, X., Chhetri, J. K., Guo, J., Guo, Q., Jin, L., et al. (2023) · Life Medicine · doi:10.1093/lifemedi/lnad017 · Cited by 52

The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research

Pati, S., Baid, U., Edwards, B., Sheller, M. J., Foley, P., Anthony Reina, G., Thakur, S., Sako, C., Bilello, M., Davatzikos, C., et al. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac9449 · Cited by 51

The heterogeneity of microglial activation and its epigenetic and non-coding RNA regulations in the immunopathogenesis of neurodegenerative diseases

Li, C., Ren, J., Zhang, M., Wang, H., Fang, Y., Wu, J., & Tang, Y. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04536-3 · Cited by 52

White matter-associated microglia: New players in brain aging and neurodegenerative diseases

Ahn, K., Lee, S., & Mook‐Jung, I. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101574 · Cited by 50

Brain health in diverse settings: How age, demographics and cognition shape brain function

Hernandez, H., Báez, S., Medel, V., Moguilner, S., Cuadros, J., Santamaría‐García, H. A., Tagliazucchi, E., Valdes-Sosa, P. A., Lopera, F. J., Ochoa-Gómez, J. F., et al. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120636 · Cited by 42

Vascular burden and cognition: Mediating roles of neurodegeneration and amyloid PET

Ottoy, J., Ozzoude, M., Zukotynski, K., Adamo, S. M., Scott, C. J. M., Gaudet, V., Ramirez, J., Swardfager, W., Cogo‐Moreira, H., Lam, B., et al. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12750 · Cited by 48

DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI

Rashid, T., Abdulkadir, A., Nasrallah, I. M., Ware, J. B., Liu, H., Spincemaille, P., Romero, J. R., Bryan, R. N., Heckbert, S. R., & Habes, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-93427-x · Cited by 46

Mid-life epigenetic age, neuroimaging brain age, and cognitive function: coronary artery risk development in young adults (CARDIA) study

Zheng, Y., Habes, M., Gonzales, M. M., Pomponio, R., Nasrallah, I. M., Khan, S. S., Vaughan, D. E., Davatzikos, C. A., Seshadri, S. S., Launer, L. J., et al. (2022) · Aging · doi:10.18632/aging.203918 · Cited by 42

Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry

Sone, D., & Beheshti, I. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12111850 · Cited by 41

ComBat Harmonization: Empirical Bayes versus fully Bayes approaches

Reynolds, M. J., Chaudhary, T., Torbati, M. E., Tudorascu, D., Batmanghelich, K. N., & Initiative, T. A. D. N. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103472 · Cited by 40

Harmonizing functional connectivity reduces scanner effects in community detection

Chen, A. A., Srinivasan, D., Pomponio, R., Yong Fan, A., Nasrallah, I. M., Resnick, S. M., Beason‐Held, L. L., Davatzikos, C. A., Satterthwaite, T. D., Bassett, D. S., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119198 · Cited by 39

Association of Intensive vs Standard Blood Pressure Control With Regional Changes in Cerebral Small Vessel Disease Biomarkers

Rashid, T., Li, K., Toledo, J. B., Nasrallah, I. M., Pajewski, N. M., Dolui, S., Detre, J. A., Wolk, D. A., Liu, H., Heckbert, S. R., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.1055 · Cited by 37

Accelerated brain aging in major depressive disorder and antidepressant treatment response: A CAN-BIND report

Ballester, P. L., Suh, J. S., Nogovitsyn, N., Hassel, S., Strother, S. C., Arnott, S. R., Minuzzi, L., Sassi, R. B., Lam, R. W. M., Milev, R. V., et al. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102864 · Cited by 35

Deep learning based detection of enlarged perivascular spaces on brain MRI

Rashid, T., Liu, H., Ware, J. B., Li, K., Romero, J. R., Fadaee, E., Nasrallah, I. M., Hilal, S., Bryan, R. N., Hughes, T. M., et al. (2023) · Neuroimage Reports · doi:10.1016/j.ynirp.2023.100162 · Cited by 35

Federated Multi-Site Normative Modeling using Hierarchical Bayesian RegressionPreprint

Kia, S. M., Huijsdens, H., Rutherford, S., Dinga, R., Wolfers, T., Mennes, M., Andreassen, O., Westlye, L., Beckmann, C., & Marquand, A. (2021) · bioRxiv · doi:10.1101/2021.05.28.446120 · Cited by 32

18F-florbetapir PET as a marker of myelin integrity across the Alzheimer’s disease spectrum

Moscoso, A., Silva‐Rodríguez, J., Aldrey, J. M., Cortés, J., Peleteiro, J. M. P., Ruibal, Á., Aguiar, P., & Initiative, A. D. N. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05493-y · Cited by 37

Patterns of Regional Brain Atrophy and Brain Aging in Middle- and Older-Aged Adults With Type 1 Diabetes

Habes, M., Jacobson, A. M., Braffett, B. H., Rashid, T., Ryan, C. M., Shou, H., Cui, Y., Davatzikos, C. A., Luchsinger, J. A., Biessels, G. J., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.16182 · Cited by 34

Brain charts for the human lifespanPreprint

Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K., Adamson, C. L., Adler, S., Alexopoulos, G. S., Anagnostou, E. A., Areces-González, A., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.08.447489 · Cited by 32

Regional rather than global brain age mediates cognitive function in cerebral small vessel disease

Lee, P., Kuo, C.-Y., Wang, P., Chen, L., Lin, C., Chou, K., & Chung, C. (2022) · Brain Communications · doi:10.1093/braincomms/fcac233 · Cited by 30

The adverse effect of modifiable dementia risk factors on cognition amplifies across the adult lifespan

LaPlume, A. A., McKetton, L., Levine, B., Troyer, A. K., & Anderson, N. D. (2022) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1002/dad2.12337 · Cited by 29

Genetic and clinical correlates of AI-based brain aging patterns in cognitively unimpaired individuals

Skampardoni, I., Nasrallah, I. M., Abdulkadir, A., Wen, J., Melhem, R., Mamourian, E., Erus, G., Doshi, J., Singh, A., Yang, Z., et al. (2024) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.5599 · Cited by 28

Convolutional Autoencoder based Deep Learning Approach for Alzheimer's Disease Diagnosis using Brain MRI

Yağış, E., de Herrera, A. G. S., & Citi, L. (2021) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms52027.2021.00097 · Cited by 27

Unsupervised machine learning model to predict cognitive impairment in subcortical ischemic vascular disease

Qin, Q., Qu, J., Yin, Y., Liang, Y., Wang, Y., Xie, B., Liu, Q., Wang, X., Xia, X., Wang, M., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.12971 · Cited by 24

Using normative models pre-trained on cross-sectional data to evaluate intra-individual longitudinal changes in neuroimaging data

Bučková, B. R., Fraza, C., Rehák, R., Kolenič, M., Beckmann, C. F., Španiel, F., Marquand, A. F., & Hlinka, J. (2025) · eLife · doi:10.7554/elife.95823.4 · Cited by 24

Critical analysis of translational potential of rodent models of white matter pathology across a wide spectrum of human diseases

Zhou, W., Xia, S., Wang, C., Yang, Q., Verkhratsky, A., & Niu, J. (2025) · Cell Death and Disease · doi:10.1038/s41419-025-07893-6 · Cited by 31

Cross-Sectional Association Between Blood Cell Phenotypes, Cognitive Function, and Brain Imaging Measures in the Community-Based Framingham Heart Study

Fang, Y., Doyle, M. F., Alosco, M. L., Mez, J., Satizábal, C. L., Qiu, W. Q., Lunetta, K. L., & Murabito, J. M. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-215533 · Cited by 25

Disentangling Alzheimer’s disease neurodegeneration from typical brain ageing using machine learning

Hwang, G., Abdulkadir, A., Erus, G., Habes, M., Pomponio, R., Shou, H., Doshi, J. J., Mamourian, E., Rashid, T., Bilgel, M., et al. (2022) · Brain Communications · doi:10.1093/braincomms/fcac117 · Cited by 31

Impact of weight loss on brain age: Improved brain health following bariatric surgery

Zeighami, Y., Dadar, M., Daoust, J., Pelletier, M., Biertho, L. D., Bouvet-Bouchard, L., Fulton, S., Tchernof, A., Dagher, A., Richard, D., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119415 · Cited by 29

Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

Zhou, Z., Li, H., Srinivasan, D., Abdulkadir, A., Nasrallah, I. M., Wen, J., Doshi, J. J., Erus, G., Mamourian, E., Bryan, R. N., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119911 · Cited by 26

Assessment of Risk Factors and Clinical Importance of Enlarged Perivascular Spaces by Whole-Brain Investigation in the Multi-Ethnic Study of Atherosclerosis

Charisis, S., Rashid, T., Liu, H., Ware, J. B., Jensen, P. N., Austin, T. R., Li, K., Fadaee, E., Hilal, S., Chen, C., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.9196 · Cited by 26

Extensive T1-weighted MRI preprocessing improves generalizability of deep brain age prediction models

Dular, L., Pernuš, F., & Špiclin, Ž. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108320 · Cited by 25

Deep learning reveals pathology-confirmed neuroimaging signatures in Alzheimer’s, vascular and Lewy body dementias

Wang, D., Honnorat, N., Toledo, J. B., Li, K., Charisis, S., Rashid, T., Nirmala, A. B., Brandigampala, S. R., Mojtabai, M., Seshadri, S. S., et al. (2024) · Brain · doi:10.1093/brain/awae388 · Cited by 26

Regional age-related atrophy after screening for preclinical alzheimer disease

Koenig, L. N., LaMontagne, P., Glasser, M. F., Bateman, R. J., Holtzman, D. M., Yakushev, I. Y., Chhatwal, J. P., Day, G. S., Jack, C. R., Mummery, C. J., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.09.010 · Cited by 23

Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer’s disease continuum

Wen, J., Yang, Z., Nasrallah, I. M., Cui, Y., Erus, G., Srinivasan, D., Abdulkadir, A., Mamourian, E., Skampardoni, I., Hwang, G., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03121-5 · Cited by 31

Structural and Functional MRI Data Differentially Predict Chronological Age and Behavioral Memory Performance

Soch, J., Richter, A., Kizilirmak, J. M., Schütze, H., Feldhoff, H., Fischer, L., Knopf, L., Raschick, M., Schult, A., Düzel, E., et al. (2022) · eNeuro · doi:10.1523/eneuro.0212-22.2022 · Cited by 23

The Spectrum of Alzheimer-Type Pathology in Cognitively Normal Individuals

Walker, J. M., Dehkordi, S. K., Schaffert, J., Goette, W. F., White, C. L., Richardson, T. E., & Zare, H. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220898 · Cited by 26

Postmortem Brain Imaging in Alzheimer’s Disease and Related Dementias: The South Texas Alzheimer’s Disease Research Center Repository

Li, K., Rashid, T., Li, J., Honnorat, N., Nirmala, A. B., Fadaee, E., Wang, D., Charisis, S., Liu, H., Franklin, C. G., et al. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230389 · Cited by 25

Race, sex, and mid‐life changes in brain health: Cardia MRI substudy

Moonen, J. E. F., Nasrallah, I. M., Detre, J. A., Dolui, S., Erus, G., Davatzikos, C. A., Meirelles, O., Bryan, R. N., & Launer, L. J. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12560 · Cited by 23

Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction

Ren, B., Wu, Y., Huang, L., Zhang, Z., Huang, B., Zhang, H., Ma, J., Li, B., Liu, X., Wu, G., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25748 · Cited by 21

Microglial Transforming Growth Factor-β Signaling in Alzheimer’s Disease

Vidovic, N., & Spittau, B. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25063090 · Cited by 22

Harmonization with Flow-Based Causal Inference

Wang, R., Chaudhari, P., & Davatzikos, C. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_17 · Cited by 20

Elevated blood pressure is associated with advanced brain aging in mid‐life: A 30‐year follow‐up of The CARDIA Study

Dintica, C. S., Habes, M., Erus, G., Vittinghoff, E., Davatzikos, C. A., Nasrallah, I. M., Launer, L. J., Sidney, S. S., & Yaffe, K. S. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12725 · Cited by 20

Self- and Partner-Reported Subjective Memory Complaints: Association with Objective Cognitive Impairment and Risk of Decline

Zuroff, L. R., Wisse, L. E. M., Glenn, T. W., Xie, S. X., Nasrallah, I. M., Habes, M., Dubroff, J. G., de Florès, R., Xie, L., Yushkevich, P. A., et al. (2022) · Journal of Alzheimer s Disease Reports · doi:10.3233/adr-220013 · Cited by 22

Disentangling tau and brain atrophy cluster heterogeneity across the Alzheimer's disease continuum

Toledo, J. B., Liu, H., Grothe, M. J., Rashid, T., Launer, L. J., Shaw, L. M. J., Snoussi, H., Heckbert, S. R., Weiner, M. W., Trojanwoski, J. Q., et al. (2022) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12305 · Cited by 19

Alzheimer’s disease: an axonal injury disease?

Liang, D., & Zhang, Z. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1264448 · Cited by 21

The effect of Alzheimer’s disease risk factors on brain aging in normal Chineses: Cognitive aging and cognitive reserve

Wang, M., Ren, Q., Shi, Y., Shu, H., Liu, D., Gu, L., Xie, C., Zhang, Z., Wu, T., & Wang, Z. (2021) · Neuroscience Letters · doi:10.1016/j.neulet.2021.136398 · Cited by 18

Sex differences in the trajectories of plasma biomarkers, brain atrophy, and cognitive decline relative to amyloid onset

Joynes, C. M., Bilgel, M., An, Y., Moghekar, A., Ashton, N. J., Kac, P. R., Karikari, T. K., Blennow, K., Zetterberg, H., Thambisetty, M., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14405 · Cited by 18

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Sex differences in plasma lipid profiles of accelerated brain aging

Tian, Q., Mitchell, B. A., Erus, G., Davatzikos, C. A., Moaddel, R., Resnick, S. M., & Ferrucci, L. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.05.013 · Cited by 17

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The cross-sectional association between amyloid burden and white matter hyperintensities in older adults without cognitive impairment: A systematic review and meta-analysis

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Wen, J., Yang, Z., Nasrallah, I. M., Cui, Y., Erus, G., Srinivasan, D., Abdulkadir, A., Mamourian, E., Skampardoni, I., Hwang, G., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.16.508329 · Cited by 12

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Tian, Q., Greig, E. E., Davatzikos, C. A., Landman, B. A., Resnick, S. M., & Ferrucci, L. (2024) · Nature Communications · doi:10.1038/s41467-024-55009-z · Cited by 10

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Radiomic and proteomic signatures of body mass index on brain ageing and Alzheimer's-like patterns of brain atrophy

Anagnostakis, F., Kokkorakis, M., Walker, K. A., Skampardoni, I., Wen, J., Erus, G., Tosun, D., Tassopoulou, V., Cui, Y., Govindarajan, S. T., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105763 · Cited by 9

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Rowsthorn, E., Cribb, L., Sinclair, B., Pham, W., Chong, T. T. ‐., Yiallourou, S. R., Cavuoto, M. G., Vivash, L. E., O'Brien, T. J., Shao, X., et al. (2025) · Fluids and Barriers of the CNS · doi:10.1186/s12987-025-00664-7 · Cited by 9

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Alameen, A. A. M., Al-Kuraishy, H. M., Al-Gareeb, A. I., Alexiou, A. T., Papadakis, M., Faheem, S. A., & Batiha, G. E. (2026) · NeuroMolecular Medicine · doi:10.1007/s12017-026-08923-y · Cited by 11

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Toward a statistical validation of brain signatures as robust measures of behavioral substrates

Fletcher, E. M., Farias, S. T., DeCarli, C. S., Gavett, B. E., Widaman, K. F., Leon, F. S. D., & Mungas, D. (2023) · Human Brain Mapping · doi:10.1002/hbm.26265 · Cited by 9

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Liu, M., Chen, B., Zhong, X., Zhang, M., Wang, Q., Zhou, H., Wu, Z., Hou, L., Peng, Q., Zhang, S., et al. (2022) · Brain Sciences · doi:10.3390/brainsci12020276 · Cited by 9

Green tea consumption and cerebral white matter lesions in community-dwelling older adults without dementia

Shibata, S., Noguchi‐Shinohara, M., Shima, A., Ozaki, T., Usui, Y., Taki, Y., Uchida, K., Honda, T., Hata, J., Ohara, T., et al. (2025) · npj Science of Food · doi:10.1038/s41538-024-00364-w · Cited by 8

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Toledo, J. B., Rashid, T., Liu, H., Launer, L. J., Shaw, L. M. J., Heckbert, S. R., Weiner, M. W., Seshadri, S. S., Habes, M., & Initiative, A. D. N. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276392 · Cited by 8

IC‐P‐108: WHITE MATTER HYPERINTENSITIES IN RELATION TO PATTERNS OF ACCELERATED BRAIN AGING, AD‐LIKE ATROPHY AND AMYLOID BURDEN: RESULTS FROM THE ISTAGING CONSORTIUM ON MACHINE LEARNING AND LARGE‐SCALE IMAGING ANALYTICS

Habes, M., Pomponio, R., Shou, H., Doshi, J. J., Sotiras, A., Erus, G., Launer, L. J., Mamourian, E., Bilgel, M., Yaffe, K. S., et al. (2019) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2019.06.4222

P2‐400: IMAGING SIGNATURES OF AD, BRAIN AGING AND WHITE MATTER HYPERINTENSITIES SHOW DISSOCIABLE ASSOCIATIONS WITH COGNITIVE DECLINE IN HEALTHY SUBJECTS LONGITUDINALLY FOLLOWED IN ADNI

Habes, M., Erus, G., Sotiras, A., Doshi, J. J., Yong Fan, A., Nasrallah, I. M., Wolk, D. A., & Davatzikos, C. A. (2018) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2018.06.1091

Longitudinal change of white matter-specific brain age is associated with Alzheimer's disease-related regional atrophy

Chen, C., Li, J., Wang, L., Schweitzer, N., Tudorascu, D., Aizenstein, H. J., & Wu, M. (2024) · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · doi:10.58530/2023/2785

A computer approach to assess age-related changes of the brain white matter in Alzheimer's disease

Karami, V., Ricci, G., Pesel, G., & Nittari, G. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e37836 · Cited by 3

Magnitude and kinetics of a set of neuroanatomic volume and thickness together with white matter hyperintensity is definitive of cognitive status and brain age

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