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Description
Learn more:
vmacdata.org/adsp-phc
The Alzheimer’s Disease (AD) Sequencing Project (ADSP) was initiated in 2012 with a focus on identifying novel genes driving risk and resilience in AD and related dementias (ADRD). To date, the ADSP has curated sequencing data from 20,000+ individuals from 39 cohorts, which will increase to 100,000 participants across 70+ cohorts by 2023. The ADSP has focused primarily on AD case/control phenotypes derived from clinical data; however, advancements in our understanding of AD and movement toward a biological definition that integrates pathological and neurodegenerative aspects of the disease, many of which precede clinical symptoms by decades, has presented an opportunity and pressing need to integrate rich endophenotypic data and characterize the genetic architecture of these complex biological cascades in ADRD.
The ADSP Phenotype Harmonization Consortium (ADSP-PHC, U24-AG074855) was established in response to PAR-20-099 to harmonize the rich endophenotype data across cohort studies to enable modern genomic analyses of ADRD. The mission of the ADSP-PHC is to work in coordination with other ADSP workgroups and initiatives to streamline access to endophenotype data, provide high quality phenotype harmonization across domains, and provide comprehensive documentation of both data availability and harmonization procedures, with the goal of generating harmonized data that will become a “legacy” dataset perpetually curated and shared through NIAGADS.
The aims of the ADSP-PHC are below:
- Procure, collate and curate endophenotype data from ADSP cohort studies.
- Harmonize data from ADSP cohort studies leveraging advanced statistical approaches.
- Disseminate harmonized phenotypes and harmonization protocols to the research community.
- Educate the research community on available harmonized resources and best practices.
Current Documentation:
- ADSP-PHC Read Me
- ADSP-PHC Cognitive Harmonization Read Me and Cohort White Papers
- ADSP-PHC Fluid Biomarker Harmonization Read Me and White Paper
- ADSP-PHC Neuropathology Harmonization Read Me
- ADSP-PHC Cardiovascular Risk Factors Harmonization Read Me
- ADSP-PHC DTI Harmonization Read Me
- ADSP-PHC FLAIR Harmonization Read Me and White Paper
- ADSP-PHC MRI Harmonization Read Me
- ADSP-PHC PET Harmonization Read Me and White Paper
PIs
MPI:
- Cuccaro, Michael
- Hohman, Timothy J.
- Toga, Arthur
Domain Leads:
- Beecham, Gary
- Brickman, Adam
- Crane, Paul
- Cruchaga, Carlos
- Davatzikos, Christos
- Habes, Mohamad
- Landman, Bennett
- Mayeux, Richard
- Mez, Jesse
- Montine, Thomas
- Mormino, Elizabeth
- Risacher, Shannon
- Saykin, Andrew
- Thompson, Paul
- Tosun-Turgut, Duygu
Cohorts
Cohort | Autopsy | Vascular Risk Factors | Cognition | Fluid Biomarker | Diffusion Tensor Imaging (DTI) | Fluid Attenuated Inversion Recovery (FLAIR) | Magnetic Resonance Imaging (MRI) FreeSurfer | Magnetic Resonance Imaging (MRI) MUSE | Positron Emission Tomography (PET) Amyloid | Positron Emission Tomography (PET) Tau |
---|---|---|---|---|---|---|---|---|---|---|
TOTAL | 6,910 | 25,971 | 33,630 | 2,860 | 2,939 | 7,276 | 7,781 | 7,034 | 6,631 | 2,253 |
A4 | 0 | 0 | 3,345 | 0 | 0 | 949 | 1,386 | 0 | 3,340 | 338 |
ACT | 475 | 0 | 1,337 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
ADNI | 0 | 2,164 | 2,166 | 1,210 | 987 | 1,530 | 2,109 | 2,158 | 1,648 | 808 |
EFIGA | 0 | 4,296 | 4,563 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
HABS-HD | 0 | 1,319 | 0 | 0 | 1,211 | 1,312 | 1,276 | 1,293 | 922 | 629 |
KBASE | 0 | 0 | 603 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Knight ADRC | 0 | 0 | 842 | 543 | 155 | 267 | 406 | 413 | 0 | 0 |
MAP – Rush | 500 | 570 | 586 | 0 | 48 | 119 | 88 | 119 | 0 | 0 |
MARS | 9 | 42 | 44 | 0 | 16 | 18 | 16 | 18 | 0 | 0 |
NACC | 4,997 | 11,715 | 11,956 | 836 | 242 | 1,495 | 1,430 | 1,754 | 385 | 88 |
NIA-AD FBS | 393 | 0 | 1,651 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
ROS | 536 | 576 | 581 | 0 | 5 | 13 | 4 | 13 | 0 | 0 |
TARCC | 0 | 1,161 | 1,161 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
WHICAP | 0 | 3,142 | 3,795 | 0 | 0 | 1,306 | 667 | 767 | 0 | 0 |
WRAP | 0 | 986 | 1,000 | 271 | 275 | 267 | 399 | 499 | 335 | 390 |
Anti-Amyloid Treatment in Asymptomatic Alzheimer’s study (A4 Study)
Adult Changes in Thought (ACT)
Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Estudio Familiar de Influencia Genetica en Alzheimer (EFIGA)
Health and Aging Brain Study – Health Disparities (HABS-HD)
Korean Brain Aging Study for the Early Diagnosis and Prediction of AD (KBASE)
Memory & Aging Project at Knight Alzheimer’s Disease Research Center (MAP at Knight ADRC)
Minority Aging Research Study (MARS)
National Alzheimer’s Coordinating Center (NACC)
National Institute on Aging Alzheimer’s Disease Family Based Study (NIA-AD FBS)
Religious Orders Study/Memory and Aging Project (ROS, MAP – Rush)
Texas Alzheimer’s Research and Care Consortium (TARCC)
Washington Heights/Inwood Columbia Aging Project (WHICAP)
Wisconsin Registry for Alzheimer’s Prevention (WRAP)
Grants
PAR-20-099 Harmonization of Alzheimer’s Disease and Related Dementias (AD/ADRD) Genetic, Epidemiologic, and Clinical Data to Enhance Therapeutic Target Discovery (U24 Clinical Trial Not Allowed)
- Alzheimer’s Disease Sequencing Project Phenotype Harmonization Consortium (ADSP-PHC), U24-AG074855; MPI: Timothy J. Hohman, Michael Cuccaro, Arthur Toga; Project Period: September 30, 2021 – August 31, 2026
PAR-22-110 NIA Renewal and Revision Cooperative Agreements in AD/ADRD Research (U24 Clinical Trial Not Allowed)
- MVP Data Integration into the ADSP Phenotype Harmonization Consortium (ADSP-PHC), U24-AG074855; PI: Timothy J. Hohman; Project Period: May 15, 2024 – August 31, 2026
Acknowledgment
The ADSP Phenotype Harmonization Consortium (ADSP-PHC) is funded by NIA (U24 AG074855, U01 AG068057 and R01 AG059716). The harmonized cohorts within the ADSP-PHC include: the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s study (A4 Study), a secondary prevention trial in preclinical Alzheimer’s disease, aiming to slow cognitive decline associated with brain amyloid accumulation in clinically normal older individuals. The A4 Study is funded by a public-private-philanthropic partnership, including funding from the National Institutes of Health-National Institute on Aging, Eli Lilly and Company, Alzheimer’s Association, Accelerating Medicines Partnership, GHR Foundation, an anonymous foundation and additional private donors, with in-kind support from Avid and Cogstate. The companion observational Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study is funded by the Alzheimer’s Association and GHR Foundation. The A4 and LEARN Studies are led by Dr. Reisa Sperling at Brigham and Women’s Hospital, Harvard Medical School and Dr. Paul Aisen at the Alzheimer’s Therapeutic Research Institute (ATRI), University of Southern California. The A4 and LEARN Studies are coordinated by ATRI at the University of Southern California, and the data are made available through the Laboratory for Neuro Imaging at the University of Southern California. The participants screening for the A4 Study provided permission to share their de-identified data in order to advance the quest to find a successful treatment for Alzheimer’s disease. We would like to acknowledge the dedication of all the participants, the site personnel, and all of the partnership team members who continue to make the A4 and LEARN Studies possible. The complete A4 Study Team list is available on: a4study.org/a4-study-team.; the Adult Changes in Thought study (ACT), U01 AG006781, U19 AG066567; Alzheimer’s Disease Neuroimaging Initiative (ADNI): Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.;Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.;Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California; Estudio Familiar de Influencia Genetica en Alzheimer (EFIGA): 5R37AG015473, RF1AG015473, R56AG051876; the Health & Aging Brain Study – Health Disparities (HABS-HD), supported by the National Institute on Aging of the National Institutes of Health under Award Numbers R01AG054073, R01AG058533, R01AG070862, P41EB015922, and U19AG078109;the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s disease (KBASE), which was supported by a grant from Ministry of Science, ICT and Future Planning (Grant No: NRF-2014M3C7A1046042);Memory & Aging Project at Knight Alzheimer’s Disease Research Center (MAP at Knight ADRC): The Memory and Aging Project at the Knight-ADRC (Knight-ADRC). This work was supported by the National Institutes of Health (NIH) grants R01AG064614, R01AG044546, RF1AG053303, RF1AG058501, U01AG058922 and R01AG064877 to Carlos Cruchaga. The recruitment and clinical characterization of research participants at Washington University was supported by NIH grants P30AG066444, P01AG03991, and P01AG026276. Data collection and sharing for this project was supported by NIH grants RF1AG054080, P30AG066462, R01AG064614 and U01AG052410. We thank the contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/) and the Departments of Neurology and Psychiatry at Washington University School of Medicine; National Alzheimer’s Coordinating Center (NACC): The NACC database is funded by NIA/NIH Grant U24 AG072122. SCAN is a multi-institutional project that was funded as a U24 grant (AG067418) by the National Institute on Aging in May 2020. Data collected by SCAN and shared by NACC are contributed by the NIA-funded ADRCs as follows: P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Neil Kowall, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Todd Golde, MD, PhD), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Justin Miller, PhD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD); National Institute on Aging Alzheimer’s Disease Family Based Study (NIA-AD FBS): U24 AG056270; Religious Orders Study (ROS): P30AG10161,R01AG15819, R01AG42210; Memory and Aging Project (MAP – Rush): R01AG017917, R01AG42210; Minority Aging Research Study (MARS): R01AG22018, R01AG42210; the Texas Alzheimer’s Research and Care Consortium (TARCC), funded by the Darrell K Royal Texas Alzheimer’s Initiative, directed by the Texas Council on Alzheimer’s Disease and Related Disorders; Washington Heights/Inwood Columbia Aging Project (WHICAP): RF1 AG054023;and Wisconsin Registry for Alzheimer’s Prevention (WRAP): R01AG027161 and R01AG054047. Additional acknowledgments include the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS, U24AG041689) at the University of Pennsylvania, funded by NIA.
Related Publications
- Brenowitz, W. D., Fornage, M., Launer, L. J., Habes, M., Davatzikos, C., & Yaffe, K. (2023). Alzheimer’s Disease Genetic Risk, Cognition, and Brain Aging in Midlife. Annals of Neurology, 93(3), 629–634. https://doi.org/10.1002/ana.26569
- Charisis, S., Rashid, T., Liu, H., Ware, J. B., Jensen, P. N., Austin, T. R., Li, K., Fadaee, E., Hilal, S., Chen, C., Hughes, T. M., Romero, J. R., Toledo, J. B., Longstreth, W. T., Hohman, T. J., Nasrallah, I., Bryan, R. N., Launer, L. J., Davatzikos, C., … Habes, M. (2023). Assessment of Risk Factors and Clinical Importance of Enlarged Perivascular Spaces by Whole-Brain Investigation in the Multi-Ethnic Study of Atherosclerosis. JAMA Network Open, 6(4), e239196. https://doi.org/10.1001/jamanetworkopen.2023.9196
- Eissman, J. M., Wells, G., Khan, O. A., Liu, D., Petyuk, V. A., Gifford, K. A., Dumitrescu, L., Jefferson, A. L., & Hohman, T. J. (2023). Polygenic resilience score may be sensitive to preclinical Alzheimer’s disease changes. Pacific Symposium on Biocomputing, 28, 449–460
- Evans, T. E., Knol, M. J., Schwingenschuh, P., Wittfeld, K., Hilal, S., Ikram, M. A., Dubost, F., van Wijnen, K. M. H., Katschnig, P., Yilmaz, P., de Bruijne, M., Habes, M., Chen, C., Langer, S., Völzke, H., Ikram, M. K., Grabe, H. J., Schmidt, R., Adams, H. H. H., & Vernooij, M. W. (2023). Determinants of Perivascular Spaces in the General Population: A Pooled Cohort Analysis of Individual Participant Data. Neurology, 100(2), e107–e122. https://doi.org/10.1212/WNL.0000000000201349
- Hampton, O. L., Mukherjee, S., Properzi, M. J., Schultz, A. P., Crane, P. K., Gibbons, L. E., Hohman, T. J., Maruff, P., Lim, Y. Y., Amariglio, R. E., Papp, K. V., Johnson, K. A., Rentz, D. M., Sperling, R. A., & Buckley, R. F. (2023). Harmonizing the preclinical Alzheimer cognitive composite for multicohort studies. Neuropsychology, 37(4), 436–449. https://doi.org/10.1037/neu0000833
- Kang, M., Ang, T. F. A., Devine, S. A., Sherva, R., Mukherjee, S., Trittschuh, E. H., Gibbons, L. E., Scollard, P., Lee, M., Choi, S.-E., Klinedinst, B., Nakano, C., Dumitrescu, L. C., Durant, A., Hohman, T. J., Cuccaro, M. L., Saykin, A. J., Kukull, W. A., Bennett, D. A., … Farrer, L. A. (2023). A genome-wide search for pleiotropy in more than 100,000 harmonized longitudinal cognitive domain scores. Molecular Neurodegeneration, 18(1), 40. https://doi.org/10.1186/s13024-023-00633-4
- Klingenberg, M., Stark, D., Eitel, F., Budding, C., Habes, M., Ritter, K., & Alzheimer’s Disease Neuroimaging Initiative. (2023). Higher performance for women than men in MRI-based Alzheimer’s disease detection. Alzheimer’s Research & Therapy, 15(1), 84. https://doi.org/10.1186/s13195-023-01225-6
- Mukherjee, S., Choi, S.-E., Lee, M. L., Scollard, P., Trittschuh, E. H., Mez, J., Saykin, A. J., Gibbons, L. E., Sanders, R. E., Zaman, A. F., Teylan, M. A., Kukull, W. A., Barnes, L. L., Bennett, D. A., Lacroix, A. Z., Larson, E. B., Cuccaro, M., Mercado, S., Dumitrescu, L., … Crane, P. K. (2023). Cognitive domain harmonization and cocalibration in studies of older adults. Neuropsychology, 37(4), 409–423. https://doi.org/10.1037/neu0000835
- Rashid, T., Li, K., Toledo, J. B., Nasrallah, I., Pajewski, N. M., Dolui, S., Detre, J., Wolk, D. A., Liu, H., Heckbert, S. R., Bryan, R. N., Williamson, J., Davatzikos, C., Seshadri, S., Launer, L. J., & Habes, M. (2023). Association of Intensive vs Standard Blood Pressure Control With Regional Changes in Cerebral Small Vessel Disease Biomarkers: Post Hoc Secondary Analysis of the SPRINT MIND Randomized Clinical Trial. JAMA Network Open, 6(3), e231055. https://doi.org/10.1001/jamanetworkopen.2023.1055
- Toledo, J. B., Rashid, T., Liu, H., Launer, L., Shaw, L. M., Heckbert, S. R., Weiner, M., Seshadri, S., Habes, M., & Alzheimer’s Disease Neuroimaging Initiative. (2022). SPARE-Tau: A flortaucipir machine-learning derived early predictor of cognitive decline. PloS One, 17(11), e0276392. https://doi.org/10.1371/journal.pone.0276392
- Tosun, D., Thropp, P., Southekal, S., Spottiswoode, B., Fahmi, R., & Alzheimer’s Disease Neuroimaging Initiative. (2023). Profiling and predicting distinct tau progression patterns: An unsupervised data-driven approach to flortaucipir positron emission tomography. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. https://doi.org/10.1002/alz.13164
- Wang, D., Honnorat, N., Fox, P. T., Ritter, K., Eickhoff, S. B., Seshadri, S., Alzheimer’s Disease Neuroimaging Initiative, & Habes, M. (2023). Deep neural network heatmaps capture Alzheimer’s disease patterns reported in a large meta-analysis of neuroimaging studies. NeuroImage, 269, 119929. https://doi.org/10.1016/j.neuroimage.2023.119929
- Wortha, S. M., Frenzel, S., Bahls, M., Habes, M., Wittfeld, K., Van der Auwera, S., Bülow, R., Zylla, S., Friedrich, N., Nauck, M., Völzke, H., Grabe, H. J., Schwarz, C., & Flöel, A. (2023). Association of spermidine plasma levels with brain aging in a population-based study. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 19(5), 1832–1840. https://doi.org/10.1002/alz.12815
- Younes, K., & Mormino, E. C. (2023). The pathotome and precision health. Brain: A Journal of Neurology, 146(6), 2208–2210. https://doi.org/10.1093/brain/awad154
- Archer DB, Eissman JM, Mukherjee S, Lee ML, Choi S, Scollard P, Trittschuh EH, Mez JB, Bush WS, Kunkle BW, Naj AC, Gifford KA, The Alzheimer’s Disease Neuroimaging Initiative (ADNI) Alzheimer’s Disease Genetics Consortium (ADGC), The Alzheimer’s Disease Sequencing Project (ADSP), Cuccaro ML, Pericak-Vance MA, Farrer LA, Wang L, Schellenberg GD, Mayeux RP, Haines JL, Jefferson AL, Kukull WA, Keene C, Saykin AJ, Thompson PA, Martin ER, Bennett DA, Barnes LL, Schneider JA, Crane PK, Dumitrescu L, Hohman TJ (2023). Longitudinal change in memory performance as a strong endophenotype for Alzheimer’s disease. Alzheimer’s & Dementia. PMC in process.
- Eissman JM, Mukherjee S, Lee ML, Choi S, Scollard P, Trittschuh EH, Mez JB, Bush WS, Kunkle BW, Naj AC, Gifford KA, The Alzheimer’s Disease Neuroimaging Initiative (ADNI) Alzheimer’s Disease Genetics Consortium (ADGC), The Alzheimer’s Disease Sequencing Project (ADSP), Cuccaro ML, Cruchaga C, Pericak-Vance MA, Farrer LA, Wang L, Schellenberg GD, Mayeux RP, Haines JL, Jefferson AL, Kukull WA, Keene C, Saykin AJ, Thompson PA, Martin ER, Bennett DA, Barnes LL, Schneider JA, Crane PK, Hohman TJ, Dumitrescu L (2023). Sex-specific genetic architecture of late-life memory performance. Alzheimer’s & Dementia. PMC in Process.
- Young CB, Johns E, Kennedy G, Belloy ME, Insel PS, Greicius MD, Sperling RA, Johnson KA, Poston KL, Mormino EC, Alzheimer’s Disease Neuroimaging Initiatives; A4 Study Team (2023). APOE effects on regional tau in preclinical Alzheimer’s disease. Molecular Neurodegeneration, 18(1): 1 . https://doi.org/10.1186/s13024-022-00590-4
- Chemparathy A, Guen YL, Chen S, Lee EG, Leong L, Gorztnski JE, Jensen TD, Ferrasse A, Xu G, Xiang H, Belloy ME, Kasireddy N, Peña-Tauber A, Williams K, Stewart I, Talozzi L, Wingo TS, Lah JJ, Jayadev S, Hales CM, Peskind E, Child DD, Roeber S, Keene CD, Cong L, Ashley EA, Yu CE, Greicius MD (2024). APOE loss-of-function variants: Compatible with longevity and associated with resistance to Alzheimer’s disease pathology. Neuron, 112(7): 1110-1116.e5. https://doi.org/10.1016/j.neuron.2024.01.008
- Yang Y, Sathe A, Schilling K, Shashikumar N, Moore E, Dumitrescu L, Pechman KR, Landman BA, Gifford KA, Hohman TJ, Jefferson AL, Archer DB (2024). A deep neural network estimation of brain age is sensitive to cognitive impairment and decline. Pacific Symposium on Biocomputing, 29: 148-162. PMC10764074.
- Walters S, Contreras AG, Eissman JM, Mukherjee S, Lee ML, Choi SE, Scollard P, Trittschuh EH, Mez JB, Bush WS, Kunkle BW, Naj AC, Peterson A, Gifford KA, Cuccaro ML, Cruchaga C, Pericak-Vance MA, Farrer LA, Wang LS, Haines JL, Jefferson AL, Kukull WA, Keene CD, Saykin AJ, Thompson PM, Martin ER, Bennett DA, Barnes LL, Schneider JA, Crane PK, Hohman TJ, Dumitrescu L, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Genetics Consortium, and Alzheimer’s Disease Sequencing Project (2023). Associations of Sex, Race, and Apolipoprotein E Alleles with Multiple Domains of Cognition Among Older Adults. JAMA Neurology, 80(9): 929-939. https://doi.org/10.1001/jamaneurol.2023.2169
- Rashid T, Liu H, Ware JB, Li K, Romero JR, Fadaee E, Nasrallah IM, Hilal S, Bryan RN, Hughes TM, Davatzikos C, Launer L, Seshadri S, Heckbert SR, Habes M (2023). Deep learning based detection of enlarged perivascular spaces on brain MRI. NeuroImage: Reports, 3(1): 100162. https://doi.org/10.1016/j.ynirp.2023.100162
- Kanakaraj P, Yao T, Cai LY, Lee HH, Newlin NR, Kim ME, Gao C, Pechman KR, Archer D, Hohman TJ, Jefferson AL, Beason-Held LL, Resnick SM, Alzheimer’s Disease Neuroimaging Initiative (ADNI); BIOCARD Study Team; Garyfallidis E, Anderson A, Schilling KG, Landman BA, Moyer D (2024). DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images. Neuroinformatics, 22(2): 193-205. https://doi.org/10.1007/s12021-024-09655-9
- Tejeda M, Farrell J, Zhu C, Wetzler L, Lunetta KL, Bush WS, Martin ER, Wang LS, Schellenberg GD, Pericak-Vance MA, Haines JL, Farrer LA, Sherva R (2024). DNA from multiple viral species is associated with Alzheimer’s disease risk. Alzheimer’s Dementia, 20(1): 253-265. https://doi.org/10.1002/alz.13414
- Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, Melhelm R, Srinivasan D, Govindarajan ST, Chen J, Habes M, Masters CL, Maruff P, Fripp J, Ferrucci L, Albert MS, Johnson SC, Morris JC, LaMontagne P, Marcus DS, Benzinger TL, Wolk DA, Shen L, Bao J, Resnick SM, Shou H, Nasrallah IM, Davatzikos C (2024). Gene-SCAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. Nature Communications, 15(1): 354. https://doi.org/10.1038/s41467-023-44271-2
- Tosun D, Yardibi O, Benzinger TL, Kukull WA, Masters CL, Perrin RJ, Weiner MW, Simen A, Schwarz AJ, Alzheimer’s Disease Neuroimaging Initiative (2024). Identifying individuals with non-Alzheimer’s disease co-pathologies: A precision medicine approach to clinical trials in sporadic Alzheimer’s disease. Alzheimer’s Dementia, 20(1): 421-436. https://doi.org/10.1002/alz.13447
- Archer DB, Schilling K, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason-Held LL, An Y, Shafer A, Ferrucci L, Risacher SL, Gifford KA, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ; Alzheimer’s Disease Neuroimaging Initiative (2023). Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging. Alzheimer’s Dementia (Amsterdam), 15(4): e12468. https://doi.org/10.1002/dad2.12468
- Wells LF, Risacher SL, McDonald BC, Farlow MR, Brosch J, Gao S, Apostolova LG, Saykin AJ, Alzheimer’s Disease Neuroimaging Initiative (2022). Measuring Subjective Decline in Older Adults: Harmonization Between the Cognitive Change Index and the Measurement of Everyday Cognition Instruments. Journal of Alzheimer’s Disease, 87(2): 761-769. https://doi.org/10.3233/jad-215388
- Newlin NR, Kim ME, Kanakaraj P, Yao T, Hohman TJ, Pechman KR, Beason-Held LL, Resnick SM, Archer DB, Jefferson AL, Landman BA, Moyer D (2024). MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal. Magnetic Resonance Imaging, 111: 113-119. https://doi.org/10.1016/j.mri.2024.03.033
- Li K, Rashid T, Li J, Honnorat N, Nirmala AB, Fadaee E, Wang D, Charisis S, Liu H, Franklin C, Maybrier M, Katragadda H, Abazid L, Ganapathy V, Valaparla VL, Bagadu P, Vasquez E, Solano L, Clarke G, Maestre G, Richardson T, Walker J, Fox PT, Bieniek K, Seshadri S, Habes M (2023). Postmortem Brain Imaging in Alzheimer’s Disease and Related Dementias: The South Texas Alzheimer’s Disease Research Center Repository. Journal of Alzheimer’s Disease, 96(3): 1267-1283. https://doi.org/10.3233/jad-230389
- Lee AJ, Sanchez D, Reyes-Dumeyer D, Brickman AM, Lantigua RA, Vardarajan BN, Mayeux R (2023). Reliability and Validity of Self-Reported Vascular Risk Factors: Hypertension, Diabetes, and Heart Disease in a Multi-Ethnic Community Based Study of Aging and Dementia. Journal of Alzheimer’s Disease, 95(1): 275-285. https://doi.org/10.3233/jad-230374
- Honnorat N, Seshadri S, Killiany R, Blangero J, Glahn DC, Fox P, Habes M (2024). Riemannian frameworks for the harmonization of resting-state functional MRI scans. Medical Image Analysis, 91: 103043. https://doi.org/10.1016/j.media.2023.103043
- Eissman JM, Dumitrecu L, Mahoney ER, Smith AN, Mukherjee S, Lee ML, Scollard P, Choi SE, Bush WS, Engelman CD, Lu Q, Fardo DW, Trittschuh EH, Mez J, Kaczorowski CC, Saucedo HH, Widaman KF, Buckley RF, Properzi MJ, Mormino EC, Yang HS, Harrison TM, Hedden T, Nho K, Andrews SJ, Tommet D, Hadad N, Sanders RE, Ruderfer DM, Gifford KA, Zhong X, Raghavan NS, Vardarajan BN; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Alzheimer’s Disease Genetics Consortium (ADGC); A4 Study Team; Pericak-Vance MA, Farrer LA, Wang LS, Cruchaga C, Schellenberg GD, Cox NJ, Haines JL, Keene CD, Saykin AJ, Larson EB, Sperling RA, Mayeux R, Cuccaro ML, Bennett DA, Schneider JA, Crane PK, Jefferson AL, Hohman TJ (2022). Sex differences in the genetic architecture of cognitive resilience to Alzheimer’s disease. Brain, 145(7): 2541-2554. https://doi.org/10.1093/brain/awac177
- Younes K, Smith V, Johns E, Carlson ML, Winer J, He Z, Henderson VW, Greicius MD, Young CB, Mormino EC; Alzheimer’s Disease Neuroimaging Initiative Researchers (2024). Temporal tau asymmetry spectrum influences divergent behavior and language patterns in Alzheimer’s disease. Brain, Behavior, and Immunity, 119:807-817. https://doi.org/10.1016/j.bbi.2024.05.002
- Hirschfeld LR, Deardorff R, Chumin EJ, Wu YC, McDonald BC, Cao S, Risacher SL, Yi D, Byun MS, Lee JY, Kim YK, Kang KM, Sohn CH, Nho K, Saykin AJ, Lee DY; KBASE Research Group (2023). White matter integrity is associated with cognition and amyloid burden in older adult Koreans along the Alzheimer’s disease continuum. Alzheimer’s Research & Therapy, 15(1): 218. https://doi.org/10.1186/s13195-023-01369-5