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1. Molecular neuroimaging in dominantly inherited versus sporadic early-onset Alzheimer’s disease

Author

Iaccarino, Leonardo, Llibre-Guerra, Jorge J, McDade, Eric, Edwards, Lauren, Gordon, Brian, Benzinger, Tammie, Hassenstab, Jason, Kramer, Joel H, Li, Yan, Miller, Bruce L, Miller, Zachary, Morris, John C, Mundada, Nidhi, Perrin, Richard J, Rosen, Howard J, Soleimani-Meigooni, David, Strom, Amelia, Tsoy, Elena, Wang, Guoqiao, Xiong, Chengjie, Allegri, Ricardo, Chrem, Patricio, Vazquez, Silvia, Berman, Sarah B, Chhatwal, Jasmeer, Masters, Colin L, Farlow, Martin R, Jucker, Mathias, Levin, Johannes, Salloway, Stephen, Fox, Nick C, Day, Gregory S, Gorno-Tempini, Maria Luisa, Boxer, Adam L, La Joie, Renaud, Bateman, Randall, and Rabinovici, Gil D

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Acquired Cognitive Impairment, Brain Disorders, Dementia, Biomedical Imaging, Clinical Research, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aging, Alzheimer's Disease, 2.1 Biological and endogenous factors, Aetiology, Neurological, Good Health and Well Being, Alzheimer's disease, amyloid-PET, brain glucose metabolism, FDG-PET, Alzheimer’s disease, Clinical sciences, Biological psychology
Abstract

Approximately 5% of Alzheimer's disease patients develop symptoms before age 65 (early-onset Alzheimer's disease), with either sporadic (sporadic early-onset Alzheimer's disease) or dominantly inherited (dominantly inherited Alzheimer's disease) presentations. Both sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease are characterized by brain amyloid-β accumulation, tau tangles, hypometabolism and neurodegeneration, but differences in topography and magnitude of these pathological changes are not fully elucidated. In this study, we directly compared patterns of amyloid-β plaque deposition and glucose hypometabolism in sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease individuals. Our analysis included 134 symptomatic sporadic early-onset Alzheimer's disease amyloid-Positron Emission Tomography (PET)-positive cases from the University of California, San Francisco, Alzheimer's Disease Research Center (mean ± SD age 59.7 ± 5.6 years), 89 symptomatic dominantly inherited Alzheimer's disease cases (age 45.8 ± 9.3 years) and 102 cognitively unimpaired non-mutation carriers from the Dominantly Inherited Alzheimer Network study (age 44.9 ± 9.2). Each group underwent clinical and cognitive examinations, 11C-labelled Pittsburgh Compound B-PET and structural MRI. 18F-Fluorodeoxyglucose-PET was also available for most participants. Positron Emission Tomography scans from both studies were uniformly processed to obtain a standardized uptake value ratio (PIB50-70 cerebellar grey reference and FDG30-60 pons reference) images. Statistical analyses included pairwise global and voxelwise group comparisons and group-independent component analyses. Analyses were performed also adjusting for covariates including age, sex, Mini-Mental State Examination, apolipoprotein ε4 status and average composite cortical of standardized uptake value ratio. Compared with dominantly inherited Alzheimer's disease, sporadic early-onset Alzheimer's disease participants were older at age of onset (mean ± SD, 54.8 ± 8.2 versus 41.9 ± 8.2, Cohen's d = 1.91), with more years of education (16.4 ± 2.8 versus 13.5 ± 3.2, d = 1) and more likely to be apolipoprotein ε4 carriers (54.6% ε4 versus 28.1%, Cramer's V = 0.26), but similar Mini-Mental State Examination (20.6 ± 6.1 versus 21.2 ± 7.4, d = 0.08). Sporadic early-onset Alzheimer's disease had higher global cortical Pittsburgh Compound B-PET binding (mean ± SD standardized uptake value ratio, 1.92 ± 0.29 versus 1.58 ± 0.44, d = 0.96) and greater global cortical 18F-fluorodeoxyglucose-PET hypometabolism (mean ± SD standardized uptake value ratio, 1.32 ± 0.1 versus 1.39 ± 0.19, d = 0.48) compared with dominantly inherited Alzheimer's disease. Fully adjusted comparisons demonstrated relatively higher Pittsburgh Compound B-PET standardized uptake value ratio in the medial occipital, thalami, basal ganglia and medial/dorsal frontal regions in dominantly inherited Alzheimer's disease versus sporadic early-onset Alzheimer's disease. Sporadic early-onset Alzheimer's disease showed relatively greater 18F-fluorodeoxyglucose-PET hypometabolism in Alzheimer's disease signature temporoparietal regions and caudate nuclei, whereas dominantly inherited Alzheimer's disease showed relatively greater hypometabolism in frontal white matter and pericentral regions. Independent component analyses largely replicated these findings by highlighting common and unique Pittsburgh Compound B-PET and 18F-fluorodeoxyglucose-PET binding patterns. In summary, our findings suggest both common and distinct patterns of amyloid and glucose hypometabolism in sporadic and dominantly inherited early-onset Alzheimer's disease.

Published
2024

2. Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers

Author

Kumar, Sayantan, Earnest, Tom, Yang, Braden, Kothapalli, Deydeep, Aschenbrenner, Andrew J., Hassenstab, Jason, Xiong, Chengie, Ances, Beau, Morris, John, Benzinger, Tammie L. S., Gordon, Brian A., Payne, Philip, and Sotiras, Aristeidis

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Machine Learning
Abstract

INTRODUCTION: Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. METHODS: We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN. RESULTS: Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression. DISCUSSION: Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain., Comment: Under review in Alzheimer's & Dementia

Published
2024

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

Author

Yang, Zhijian, Wen, Junhao, Erus, Guray, Govindarajan, Sindhuja T., Melhem, Randa, Mamourian, Elizabeth, Cui, Yuhan, Srinivasan, Dhivya, Abdulkadir, Ahmed, Parmpi, Paraskevi, Wittfeld, Katharina, Grabe, Hans J., Bülow, Robin, Frenzel, Stefan, Tosun, Duygu, Bilgel, Murat, An, Yang, Yi, Dahyun, Marcus, Daniel S., LaMontagne, Pamela, Benzinger, Tammie L. S., Heckbert, Susan R., Austin, Thomas R., Waldstein, Shari R., Evans, Michele K., Zonderman, Alan B., Launer, Lenore J., Sotiras, Aristeidis, Espeland, Mark A., Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Toga, Arthur W., O’Bryant, Sid, Chakravarty, Mallar M., Villeneuve, Sylvia, Johnson, Sterling C., Morris, John C., Albert, Marilyn S., Yaffe, Kristine, Völzke, Henry, Ferrucci, Luigi, Nick Bryan, R., Shinohara, Russell T., Fan, Yong, Habes, Mohamad, Lalousis, Paris Alexandros, Koutsouleris, Nikolaos, Wolk, David A., Resnick, Susan M., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos

Published
2024
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4. Highly accurate blood test for Alzheimer’s disease is similar or superior to clinical cerebrospinal fluid tests

Author

Barthélemy, Nicolas R, Salvadó, Gemma, Schindler, Suzanne E, He, Yingxin, Janelidze, Shorena, Collij, Lyduine E, Saef, Benjamin, Henson, Rachel L, Chen, Charles D, Gordon, Brian A, Li, Yan, La Joie, Renaud, Benzinger, Tammie LS, Morris, John C, Mattsson-Carlgren, Niklas, Palmqvist, Sebastian, Ossenkoppele, Rik, Rabinovici, Gil D, Stomrud, Erik, Bateman, Randall J, and Hansson, Oskar

Subjects
Biomedical and Clinical Sciences, Health Sciences, Neurodegenerative, Dementia, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Biomedical Imaging, Aging, Neurosciences, Brain Disorders, Alzheimer's Disease, 4.2 Evaluation of markers and technologies, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Neurological, Humans, Alzheimer Disease, tau Proteins, Biomarkers, Amyloid beta-Peptides, Cognitive Dysfunction, Hematologic Tests, Positron-Emission Tomography, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

With the emergence of Alzheimer's disease (AD) disease-modifying therapies, identifying patients who could benefit from these treatments becomes critical. In this study, we evaluated whether a precise blood test could perform as well as established cerebrospinal fluid (CSF) tests in detecting amyloid-β (Aβ) plaques and tau tangles. Plasma %p-tau217 (ratio of phosporylated-tau217 to non-phosphorylated tau) was analyzed by mass spectrometry in the Swedish BioFINDER-2 cohort (n = 1,422) and the US Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC) cohort (n = 337). Matched CSF samples were analyzed with clinically used and FDA-approved automated immunoassays for Aβ42/40 and p-tau181/Aβ42. The primary and secondary outcomes were detection of brain Aβ or tau pathology, respectively, using positron emission tomography (PET) imaging as the reference standard. Main analyses were focused on individuals with cognitive impairment (mild cognitive impairment and mild dementia), which is the target population for available disease-modifying treatments. Plasma %p-tau217 was clinically equivalent to FDA-approved CSF tests in classifying Aβ PET status, with an area under the curve (AUC) for both between 0.95 and 0.97. Plasma %p-tau217 was generally superior to CSF tests in classification of tau-PET with AUCs of 0.95-0.98. In cognitively impaired subcohorts (BioFINDER-2: n = 720; Knight ADRC: n = 50), plasma %p-tau217 had an accuracy, a positive predictive value and a negative predictive value of 89-90% for Aβ PET and 87-88% for tau PET status, which was clinically equivalent to CSF tests, further improving to 95% using a two-cutoffs approach. Blood plasma %p-tau217 demonstrated performance that was clinically equivalent or superior to clinically used FDA-approved CSF tests in the detection of AD pathology. Use of high-performance blood tests in clinical practice can improve access to accurate AD diagnosis and AD-specific treatments.

Published
2024

5. Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning

Author

Rahmani, Maryam, Dierker, Donna, Yaeger, Lauren, Saykin, Andrew, Luckett, Patrick H., Vlassenko, Andrei G., Owens, Christopher, Jafri, Hussain, Womack, Kyle, Fripp, Jurgen, Xia, Ying, Tosun, Duygu, Benzinger, Tammie L. S., Masters, Colin L., Lee, Jin-Moo, Morris, John C., Goyal, Manu S., Strain, Jeremy F., Kukull, Walter, Weiner, Michael, Burnham, Samantha, CoxDoecke, Tim James, Fedyashov, Victor, Fripp, Jurgen, Shishegar, Rosita, Xiong, Chengjie, Marcus, Daniel, Raniga, Parnesh, Li, Shenpeng, Aschenbrenner, Andrew, Hassenstab, Jason, Lim, Yen Ying, Maruff, Paul, Sohrabi, Hamid, Robertson, Jo, Markovic, Shaun, Bourgeat, Pierrick, Doré, Vincent, Mayo, Clifford Jack, Mussoumzadeh, Parinaz, Rowe, Chris, Villemagne, Victor, Bateman, Randy, Fowler, Chris, Li, Qiao-Xin, Martins, Ralph, Schindler, Suzanne, Shaw, Les, Cruchaga, Carlos, Harari, Oscar, Laws, Simon, Porter, Tenielle, O’Brien, Eleanor, Perrin, Richard, Kukull, Walter, Bateman, Randy, McDade, Eric, Jack, Clifford, Morris, John, Yassi, Nawaf, Bourgeat, Pierrick, Perrin, Richard, Roberts, Blaine, Villemagne, Victor, Fedyashov, Victor, and Goudey, Benjamin

Published
2024
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6. Disease staging of Alzheimer’s disease using a CSF-based biomarker model

Author

Salvadó, Gemma, Horie, Kanta, Barthélemy, Nicolas R., Vogel, Jacob W., Pichet Binette, Alexa, Chen, Charles D., Aschenbrenner, Andrew J., Gordon, Brian A., Benzinger, Tammie L. S., Holtzman, David M., Morris, John C., Palmqvist, Sebastian, Stomrud, Erik, Janelidze, Shorena, Ossenkoppele, Rik, Schindler, Suzanne E., Bateman, Randall J., and Hansson, Oskar

Published
2024
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7. Longitudinal clinical, cognitive and biomarker profiles in dominantly inherited versus sporadic early-onset Alzheimer’s disease

Author

Llibre-Guerra, Jorge J, Iaccarino, Leonardo, Coble, Dean, Edwards, Lauren, Li, Yan, McDade, Eric, Strom, Amelia, Gordon, Brian, Mundada, Nidhi, Schindler, Suzanne E, Tsoy, Elena, Ma, Yinjiao, Lu, Ruijin, Fagan, Anne M, Benzinger, Tammie LS, Soleimani-Meigooni, David, Aschenbrenner, Andrew J, Miller, Zachary, Wang, Guoqiao, Kramer, Joel H, Hassenstab, Jason, Rosen, Howard J, Morris, John C, Miller, Bruce L, Xiong, Chengjie, Perrin, Richard J, Allegri, Ricardo, Chrem, Patricio, Surace, Ezequiel, Berman, Sarah B, Chhatwal, Jasmeer, Masters, Colin L, Farlow, Martin R, Jucker, Mathias, Levin, Johannes, Fox, Nick C, Day, Gregory, Gorno-Tempini, Maria Luisa, Boxer, Adam L, La Joie, Renaud, Rabinovici, Gil D, and Bateman, Randall

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Acquired Cognitive Impairment, Clinical Research, Clinical Trials and Supportive Activities, Alzheimer's Disease, Neurodegenerative, Genetics, Aging, Dementia, Brain Disorders, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), 2.1 Biological and endogenous factors, Aetiology, Neurological, early-onset Alzheimer's disease, sporadic, dominantly inherited, early-onset Alzheimer’s disease, Clinical sciences, Biological psychology
Abstract

Approximately 5% of Alzheimer's disease cases have an early age at onset (

Published
2023

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

Author

Wen, Junhao, Yang, Zhijian, Nasrallah, Ilya M., Cui, Yuhan, Erus, Guray, Srinivasan, Dhivya, Abdulkadir, Ahmed, Mamourian, Elizabeth, Hwang, Gyujoon, Singh, Ashish, Bergman, Mark, Bao, Jingxuan, Varol, Erdem, Zhou, Zhen, Boquet-Pujadas, Aleix, Chen, Jiong, Toga, Arthur W., Saykin, Andrew J., Hohman, Timothy J., Thompson, Paul M., Villeneuve, Sylvia, Gollub, Randy, Sotiras, Aristeidis, Wittfeld, Katharina, Grabe, Hans J., Tosun, Duygu, Bilgel, Murat, An, Yang, Marcus, Daniel S., LaMontagne, Pamela, Benzinger, Tammie L., Heckbert, Susan R., Austin, Thomas R., Launer, Lenore J., Espeland, Mark, Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Johnson, Sterling C., Morris, John C., Albert, Marilyn S., Bryan, R. Nick, Resnick, Susan M., Ferrucci, Luigi, Fan, Yong, Habes, Mohamad, Wolk, David, Shen, Li, Shou, Haochang, and Davatzikos, Christos

Published
2024
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9. CYP1B1-RMDN2 Alzheimer’s disease endophenotype locus identified for cerebral tau PET

Author

Nho, Kwangsik, Risacher, Shannon L., Apostolova, Liana G., Bice, Paula J., Brosch, Jared R., Deardorff, Rachael, Faber, Kelley, Farlow, Martin R., Foroud, Tatiana, Gao, Sujuan, Rosewood, Thea, Kim, Jun Pyo, Nudelman, Kelly, Yu, Meichen, Aisen, Paul, Sperling, Reisa, Hooli, Basavaraj, Shcherbinin, Sergey, Svaldi, Diana, Jack, Jr., Clifford R., Jagust, William J., Landau, Susan, Vasanthakumar, Aparna, Waring, Jeffrey F., Doré, Vincent, Laws, Simon M., Masters, Colin L., Porter, Tenielle, Rowe, Christopher C., Villemagne, Victor L., Dumitrescu, Logan, Hohman, Timothy J., Libby, Julia B., Mormino, Elizabeth, Buckley, Rachel F., Johnson, Keith, Yang, Hyun-Sik, Petersen, Ronald C., Ramanan, Vijay K., Ertekin-Taner, Nilüfer, Vemuri, Prashanthi, Cohen, Ann D., Fan, Kang-Hsien, Kamboh, M. Ilyas, Lopez, Oscar L., Bennett, David A., Ali, Muhammad, Benzinger, Tammie, Cruchaga, Carlos, Hobbs, Diana, De Jager, Philip L., Fujita, Masashi, Jadhav, Vaishnavi, Lamb, Bruce T., Tsai, Andy P., Castanho, Isabel, Mill, Jonathan, Weiner, Michael W., and Saykin, Andrew J.

Published
2024
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10. Genetic and multi-omic resources for Alzheimer disease and related dementia from the Knight Alzheimer Disease Research Center

Author

Fernandez, Maria Victoria, Liu, Menghan, Beric, Aleksandra, Johnson, Matt, Cetin, Arda, Patel, Maulik, Budde, John, Kohlfeld, Pat, Bergmann, Kristy, Lowery, Joseph, Flynn, Allison, Brock, William, Sanchez Montejo, Brenda, Gentsch, Jen, Sykora, Nicholas, Norton, Joanne, Gentsch, Jen, Valdez, Olga, Gorijala, Priyanka, Sanford, Jessie, Sun, Yichen, Wang, Ciyang, Western, Dan, Timsina, Jigyasha, Mangetti Goncalves, Tassia, Do, Anh N., Sung, Yun Ju, Zhao, Guoyan, Morris, John C., Moulder, Krista, Holtzman, David M., Bateman, Randall J., Karch, Celeste, Hassenstab, Jason, Xiong, Chengjie, Schindler, Suzanne E., Balls-Berry, Joyce (Joy), Benzinger, Tammie L. S., Perrin, Richard J., Denny, Andrea, Snider, B. Joy, Stark, Susan L., Ibanez, Laura, and Cruchaga, Carlos

Published
2024
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11. Baseline levels and longitudinal changes in plasma Aβ42/40 among Black and white individuals

Author

Xiong, Chengjie, Luo, Jingqin, Wolk, David A., Shaw, Leslie M., Roberson, Erik D., Murchison, Charles F., Henson, Rachel L., Benzinger, Tammie L. S., Bui, Quoc, Agboola, Folasade, Grant, Elizabeth, Gremminger, Emily N., Moulder, Krista L., Geldmacher, David S., Clay, Olivio J., Babulal, Ganesh, Cruchaga, Carlos, Holtzman, David M., Bateman, Randall J., Morris, John C., and Schindler, Suzanne E.

Published
2024
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12. Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Author

Yang, Zhijian, Wen, Junhao, Abdulkadir, Ahmed, Cui, Yuhan, Erus, Guray, Mamourian, Elizabeth, Melhem, Randa, Srinivasan, Dhivya, Govindarajan, Sindhuja T., Chen, Jiong, Habes, Mohamad, Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Ferrucci, Luigi, Albert, Marilyn S., Johnson, Sterling C., Morris, John C., LaMontagne, Pamela, Marcus, Daniel S., Benzinger, Tammie L. S., Wolk, David A., Shen, Li, Bao, Jingxuan, Resnick, Susan M., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos

Published
2024
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13. A universal neocortical mask for Centiloid quantification

Author

Bourgeat, Pierrick, Doré, Vincent, Rowe, Christopher C, Benzinger, Tammie, Tosun, Duygu, Goyal, Manu S, LaMontagne, Pamela, Jin, Liang, Weiner, Michael W, Masters, Colin L, Fripp, Jurgen, Villemagne, Victor L, Initiative, for the Alzheimer's Disease Neuroimaging, and OASIS3, and the AIBL research group

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Acquired Cognitive Impairment, Neurosciences, Dementia, Brain Disorders, Alzheimer's Disease, Biomedical Imaging, Neurodegenerative, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, amyloid positron emission tomography, Centiloid, Florbetaben, Florbetapir, Flutemetamol, NAV4694, PiB, Alzheimer's Disease Neuroimaging Initiative, OASIS3, and the AIBL research group, Genetics, Biological psychology
Abstract

IntroductionThe Centiloid (CL) project was developed to harmonize the quantification of amyloid beta (Aβ) positron emission tomography (PET) scans to a unified scale. The CL neocortical mask was defined using 11C Pittsburgh compound B (PiB), overlooking potential differences in regional distribution among Aβ tracers. We created a universal mask using an independent dataset of five Aβ tracers, and investigated its impact on inter-tracer agreement, tracer variability, and group separation.MethodsUsing data from the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) study (Australian Imaging Biomarkers and Lifestyle + Alzheimer's Disease Neuroimaging Initiative + Open Access Series of Imaging Studies), age-matched pairs of mild Alzheimer's disease (AD) and healthy controls (HC) were selected: 18F-florbetapir (N = 147 pairs), 18F-florbetaben (N = 22), 18F-flutemetamol (N = 10), 18F-NAV (N = 42), 11C-PiB (N = 63). The images were spatially and standardized uptake value ratio normalized. For each tracer, the mean AD-HC difference image was thresholded to maximize the overlap with the standard neocortical mask. The universal mask was defined as the intersection of all five masks. It was evaluated on the Global Alzheimer's Association Interactive Network (GAAIN) head-to-head datasets in terms of inter-tracer agreement and variance in the young controls (YC) and on the ADOPIC dataset comparing separation between HC/AD and HC/mild cognitive impairment (MCI).ResultsIn the GAAIN dataset, the universal mask led to a small reduction in the variance of the YC, and a small increase in the inter-tracer agreement. In the ADOPIC dataset, it led to a better separation between HC/AD and HC/MCI at baseline.DiscussionThe universal CL mask led to an increase in inter-tracer agreement and group separation. Those increases were, however, very small, and do not provide sufficient benefits to support departing from the existing standard CL mask, which is suitable for the quantification of all Aβ tracers.HighlightsThis study built an amyloid universal mask using a matched cohort for the five most commonly used amyloid positron emission tomography tracers.There was a high overlap between each tracer-specific mask.Differences in quantification and group separation between the standard and universal mask were small.The existing standard Centiloid mask is suitable for the quantification of all amyloid beta tracers.

Published
2023

14. Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Author

Yang, Zhijian, Wen, Junhao, Abdulkadir, Ahmed, Cui, Yuhan, Erus, Guray, Mamourian, Elizabeth, Melhem, Randa, Srinivasan, Dhivya, Govindarajan, Sindhuja T., Chen, Jiong, Habes, Mohamad, Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Ferrucci, Luigi, Albert, Marilyn S., Johnson, Sterling C., Morris, John C., LaMontagne, Pamela, Marcus, Daniel S., Benzinger, Tammie L. S., Wolk, David A., Shen, Li, Bao, Jingxuan, Resnick, Susan M., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.

Published
2023

15. Sex-Specific Patterns of Body Mass Index Relationship with White Matter Connectivity

Author

Rahmani, Farzaneh, primary, Wang, Qing, additional, McKay, Nicole S., additional, Keefe, Sarah, additional, Hantler, Nancy, additional, Hornbeck, Russ, additional, Wang, Yong, additional, Hassenstab, Jason, additional, Schindler, Suzanne, additional, Xiong, Chengjie, additional, Morris, John C., additional, Benzinger, Tammie L.S., additional, and Raji, Cyrus A., additional

Published
2024
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16. Comparison of tau spread in people with Down syndrome versus autosomal-dominant Alzheimer's disease: a cross-sectional study

Author

Aizenstein, Howard J., Andrews, Howard F., Bell, Karen, Birn, Rasmus M., Bulova, Peter, Cheema, Amrita, Chen, Kewei, Clare, Isabel, Clark, Lorraine, Cohen, Ann D., Constantino, John N., Doran, Eric W., Feingold, Eleanor, Foroud, Tatiana M., Hartley, Sigan L., Hom, Christy, Honig, Lawrence, Ikonomovic, Milos D., Johnson, Sterling C., Jordan, Courtney, Kamboh, M. Ilyas, Keator, David, Klunk, William E., Kofler, Julia K., Kreisl, William C., Krinsky-McHale, Sharon J., Lao, Patrick, Laymon, Charles, Lott, Ira T., Lupson, Victoria, Mathis, Chester A., Minhas, Davneet S., Nadkarni, Neelesh, Pang, Deborah, Petersen, Melissa, Price, Julie C., Pulsifer, Margaret, Reiman, Eric, Rizvi, Batool, Sabbagh, Marwan N., Schupf, Nicole, Tudorascu, Dana L., Tumuluru, Rameshwari, Tycko, Benjamin, Varadarajan, Badri, White, Desiree A., Yassa, Michael A., Zaman, Shahid, Zhang, Fan, Bateman, Randall, Daniels, Alisha J., Courtney, Laura, McDade, Eric, Llibre-Guerra, Jorge J., Supnet-Bell, Charlene, Xiong, Chengie, Xu, Xiong, Lu, Ruijin, Wang, Guoqiao, Li, Yan, Gremminger, Emily, Perrin, Richard J., Franklin, Erin, Ibanez, Laura, Jerome, Gina, Herries, Elizabeth, Stauber, Jennifer, Baker, Bryce, Minton, Matthew, Cruchaga, Carlos, Goate, Alison M., Renton, Alan E., Picarello, Danielle M., Benzinger, Tammie, Gordon, Brian A., Hornbeck, Russall, Hassenstab, Jason, Smith, Jennifer, Stout, Sarah, Aschenbrenner, Andrew J., Karch, Celeste M., Marsh, Jacob, Morris, John C., Holtzman, David M., Barthelemy, Nicolas, Xu, Jinbin, Noble, James M., Berman, Sarah B., Ikonomovic, Snezana, Nadkarni, Neelesh K., Day, Gregory, Graff-Radford, Neill R., Farlow, Martin, Chhatwal, Jasmeer P., Ikeuchi, Takeshi, Kasuga, Kensaku, Niimi, Yoshiki, Huey, Edward D., Salloway, Stephen, Schofield, Peter R., Brooks, William S., Bechara, Jacob A., Martins, Ralph, Fox, Nick C., Cash, David M., Ryan, Natalie S., Jucker, Mathias, Laske, Christoph, Hofmann, Anna, Kuder-Buletta, Elke, Graber-Sultan, Susanne, Obermueller, Ulrike, Levin, Johannes, Roedenbeck, Yvonne, Vöglein, Jonathan, Lee, Jae-Hong, Roh, Jee Hoon, Sanchez-Valle, Raquel, Rosa-Neto, Pedro, Allegri, Ricardo F., Chrem Mendez, Patricio, Surace, Ezequiel, Vazquez, Silvia, Lopera, Francisco, Leon, Yudy Milena, Ramirez, Laura, Aguillon, David, Levey, Allan I., Johnson, Erik C.B, Seyfried, Nicholas T., Ringman, John, Mori, Hiroshi, Wisch, Julie K, McKay, Nicole S, Boerwinkle, Anna H, Kennedy, James, Flores, Shaney, Handen, Benjamin L, Christian, Bradley T, Head, Elizabeth, Mapstone, Mark, Rafii, Michael S, O’Bryant, Sid E, Price, Julie C, Laymon, Charles M, Krinsky-McHale, Sharon J, Lai, Florence, Rosas, H Diana, Hartley, Sigan L, Lott, Ira T, Tudorascu, Dana, Zammit, Matthew, Brickman, Adam M, Lee, Joseph H, Bird, Thomas D, Cohen, Annie, Chrem, Patricio, Daniels, Alisha, Chhatwal, Jasmeer P, Karch, Celeste M, Day, Gregory S, Llibre-Guerra, Jorge, Ringman, John M, van Dyck, Christopher H, Xiong, Chengjie, Morris, John C, Bateman, Randall J, Benzinger, Tammie L S, Gordon, Brian A, and Ances, Beau M

Published
2024
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17. Deconstructing pathological tau by biological process in early stages of Alzheimer disease: a method for quantifying tau spatial spread in neuroimaging

Author

Noble, James M., Day, Gregory S., Graff-Radford, Neill R., Voglein, Jonathan, Levin, Johannes, Allegri, Ricardo F., Mendez, Patricio Chrem, Surace, Ezequiel, Berman, Sarah B., Ikonomovic, Snezana, Nadkarni, Neelesh K., Lopera, Francisco, Ramirez, Laura, Aguillon, David, Leon, Yudy, Ramos, Claudia, Alzate, Diana, Baena, Ana, Londono, Natalia, Moreno, Sonia, Jucker, Mathias, Laske, Christoph, Kuder-Buletta, Elke, Graber-Sultan, Susanne, Preische, Oliver, Hofmann, Anna, Ikeuchi, Takeshi, Kasuga, Kensaku, Niimi, Yoshiki, Ishii, Kenji, Senda, Michio, Sanchez-Valle, Raquel, Rosa-Neto, Pedro, Fox, Nick C., Cash, Dave, Lee, Jae-Hong, Roh, Jee Hoon, Salloway, Stephen, Riddle, Meghan C., Menard, William, Bodge, Courtney, Surti, Mustafa, Takada, Leonel Tadao, Farlow, Martin, Chhatwal, Jasmeer P., Sanchez-Gonzalez, V.J., Orozco-Barajas, Maribel, Goate, Alison M., Renton, Alan E., Esposito, Bianca T., Karch, Celeste M., Marsh, Jacob, Cruchaga, Carlos, Fernanadez, Victoria, Gordon, Brian A., Fagan, Anne M., Jerome, Gina, Herries, Elizabeth, Llibre-Guerra, Jorge, Levey, Allan I., Johnson, Erik C.B., Seyfried, Nicholas T., Schofield, Peter R., Brooks, William S., Bechara, Jacob A., Bateman, Randall, McDade, Eric, Hassenstab, Jason, Perrin, Richard J., Franklin, Erin E., Benzinger, Tammie, Chen, Allison, Chen, Charles, Flores, Shaney, Friedrichsen, Nelly, Gordon, Brian, Hantler, Nancy, Hornbeck, Russ, Jarman, Steve, Keefe, Sarah, Koudelis, Deborah, Massoumzadeh, Parinaz, McCullough, Austin, McKay, Nicole, Nicklaus, Joyce, Pulizos, Christine, Wang, Qing, Mishall, Sheetal, Sabaredzovic, Edita, Deng, Emily, Candela, Madison, Smith, Hunter, Hobbs, Diana, Scott, Jalen, Xiong, Chengjie, Wang, Peter, Xu, Xiong, Li, Yan, Gremminger, Emily, Ma, Yinjiao, Bui, Ryan, Lu, Ruijin, Martins, Ralph, Sosa Ortiz, Ana Luisa, Daniels, Alisha, Courtney, Laura, Mori, Hiroshi, Supnet-Bell, Charlene, Xu, Jinbin, Ringman, John, Barthelemy, Nicolas, Morris, John, Smith, Jennifer, Doering, Stephanie, Chen, Charles D., Jarman, Stephen, Jackson, Kelley, Hornbeck, Russ C., Ances, Beau M., Aschenbrenner, Andrew J., Bateman, Randall J., Morris, John C., and Benzinger, Tammie L.S.

Published
2024
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18. Biomarker clustering in autosomal dominant Alzheimers disease.

Author

Luckett, Patrick, Chen, Charlie, Gordon, Brian, Wisch, Julie, Berman, Sarah, Chhatwal, Jasmeer, Cruchaga, Carlos, Fagan, Anne, Farlow, Martin, Fox, Nick, Jucker, Mathias, Levin, Johannes, Masters, Colin, Mori, Hiroshi, Noble, James, Salloway, Stephen, Schofield, Peter, Brickman, Adam, Brooks, William, Cash, David, Fulham, Michael, Ghetti, Bernardino, Jack, Clifford, Vöglein, Jonathan, Klunk, William, Koeppe, Robert, Su, Yi, Weiner, Michael, Wang, Qing, Marcus, Daniel, Koudelis, Deborah, Joseph-Mathurin, Nelly, Cash, Lisa, Hornbeck, Russ, Xiong, Chengjie, Perrin, Richard, Karch, Celeste, Hassenstab, Jason, McDade, Eric, Morris, John, Benzinger, Tammie, Bateman, Randall, and Ances, Beau

Subjects
Autosomal dominant Alzheimers disease, biomarkers, machine learning, Humans, Alzheimer Disease, Amyloid beta-Peptides, Amyloidogenic Proteins, Biomarkers, Cross-Sectional Studies, Inflammation, tau Proteins
Abstract

INTRODUCTION: As the number of biomarkers used to study Alzheimers disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others. METHODS: We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation. RESULTS: Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors. DISCUSSION: These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.

Published
2023

19. Comparison of amyloid burden in individuals with Down syndrome versus autosomal dominant Alzheimer's disease: a cross-sectional study

Author

Boerwinkle, Anna H, Gordon, Brian A, Wisch, Julie, Flores, Shaney, Henson, Rachel L, Butt, Omar H, McKay, Nicole, Chen, Charles D, Benzinger, Tammie LS, Fagan, Anne M, Handen, Benjamin L, Christian, Bradley T, Head, Elizabeth, Mapstone, Mark, Rafii, Michael S, O'Bryant, Sid, Lai, Florence, Rosas, H Diana, Lee, Joseph H, Silverman, Wayne, Brickman, Adam M, Chhatwal, Jasmeer P, Cruchaga, Carlos, Perrin, Richard J, Xiong, Chengjie, Hassenstab, Jason, McDade, Eric, Bateman, Randall J, Ances, Beau M, Syndrome, Alzheimer's Biomarker Consortium-Down, Aizenstein, Howard J, Andrews, Howard F, Bell, Karen, Birn, Rasmus M, Bulova, Peter, Cheema, Amrita, Chen, Kewei, Clare, Isabel, Clark, Lorraine, Cohen, Ann D, Constantino, John N, Doran, Eric W, Feingold, Eleanor, Foroud, Tatiana M, Hartley, Sigan L, Hom, Christy, Honig, Lawrence, Ikonomovic, Milos D, Johnson, Sterling C, Jordan, Courtney, Kamboh, M Ilyas, Keator, David, Klunk, William E, Kofler, Julia K, Kreisl, William C, McHale, Sharon J Krinsky-, Lao, Patrick, Laymon, Charles, Lott, Ira T, Lupson, Victoria, Mathis, Chester A, Minhas, Davneet S, Nadkarni, Neelesh, Pang, Deborah, Petersen, Melissa, Price, Julie C, Pulsifer, Margaret, Reiman, Eric, Rizvi, Batool, Sabbagh, Marwan N, Schupf, Nicole, Tudorascu, Dana L, Tumuluru, Rameshwari, Tycko, Benjamin, Varadarajan, Badri, White, Desiree A, Yassa, Michael A, Zaman, Shahid, Zhang, Fan, Network, Dominantly Inherited Alzheimer, Adams, Sarah, Allegri, Ricardo, Araki, Aki, Barthelemy, Nicolas, Bechara, Jacob, Berman, Sarah, Bodge, Courtney, Brandon, Susan, Brooks, William, Brosch, Jared, Buck, Jill, Buckles, Virginia, Carter, Kathleen, Cash, Lisa, Mendez, Patricio C, Chua, Jasmin, Chui, Helena, Courtney, Laura, Day, Gregory, and DeLaCruz, Chrismary

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Biomedical Imaging, Aging, Alzheimer's Disease, Clinical Research, Neurodegenerative, Brain Disorders, Dementia, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Intellectual and Developmental Disabilities (IDD), Genetics, Acquired Cognitive Impairment, 2.1 Biological and endogenous factors, Aetiology, Neurological, Adult, Aged, Humans, Middle Aged, Alzheimer Disease, Amyloid beta-Peptides, Apolipoproteins E, Biomarkers, Cross-Sectional Studies, Down Syndrome, Positron-Emission Tomography, Cerebral Cortex, Alzheimer's Biomarker Consortium-Down Syndrome, Dominantly Inherited Alzheimer Network, Neurology & Neurosurgery, Clinical sciences
Abstract

BackgroundImportant insights into the early pathogenesis of Alzheimer's disease can be provided by studies of autosomal dominant Alzheimer's disease and Down syndrome. However, it is unclear whether the timing and spatial distribution of amyloid accumulation differs between people with autosomal dominant Alzheimer's disease and those with Down syndrome. We aimed to directly compare amyloid changes between these two groups of people.MethodsIn this cross-sectional study, we included participants (aged ≥25 years) with Down syndrome and sibling controls who had MRI and amyloid PET scans in the first data release (January, 2020) of the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) study. We also included carriers of autosomal dominant Alzheimer's disease genetic mutations and non-carrier familial controls who were within a similar age range to ABC-DS participants (25-73 years) and had MRI and amyloid PET scans at the time of a data freeze (December, 2020) of the Dominantly Inherited Alzheimer Network (DIAN) study. Controls from the two studies were combined into a single group. All DIAN study participants had genetic testing to determine PSEN1, PSEN2, or APP mutation status. APOE genotype was determined from blood samples. CSF samples were collected in a subset of ABC-DS and DIAN participants and the ratio of amyloid β42 (Aβ42) to Aβ40 (Aβ42/40) was measured to evaluate its Spearman's correlation with amyloid PET. Global PET amyloid burden was compared with regards to cognitive status, APOE ɛ4 status, sex, age, and estimated years to symptom onset. We further analysed amyloid PET deposition by autosomal dominant mutation type. We also assessed regional patterns of amyloid accumulation by estimated number of years to symptom onset. Within a subset of participants the relationship between amyloid PET and CSF Aβ42/40 was evaluated.Findings192 individuals with Down syndrome and 33 sibling controls from the ABC-DS study and 265 carriers of autosomal dominant Alzheimer's disease mutations and 169 non-carrier familial controls from the DIAN study were included in our analyses. PET amyloid centiloid and CSF Aβ42/40 were negatively correlated in carriers of autosomal dominant Alzheimer's disease mutations (n=216; r=-0·565; p

Published
2023

20. β-amyloid PET harmonisation across longitudinal studies: Application to AIBL, ADNI and OASIS3

Author

Bourgeat, Pierrick, Doré, Vincent, Burnham, Samantha C, Benzinger, Tammie, Tosun, Duygu, Li, Shenpeng, Goyal, Manu, LaMontagne, Pamela, Jin, Liang, Rowe, Christopher C, Weiner, Michael W, Morris, John C, Masters, Colin L, Fripp, Jurgen, Villemagne, Victor L, and Alzheimer's Disease Neuroimaging Initiative, OASIS3

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Neurosciences, Alzheimer Disease, Amyloid beta-Peptides, Aniline Compounds, Cross-Sectional Studies, Humans, Longitudinal Studies, Positron-Emission Tomography, Amyloid PET, Centiloid, Harmonisation, Alzheimer's Disease Neuroimaging Initiative, OASIS3, and the AIBL research group, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

IntroductionThe Centiloid scale was developed to harmonise the quantification of β-amyloid (Aβ) PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences across tracers and scanners even after centiloid conversion. In this study, we aim to evaluate the impact of different pre and post-processing harmonisation steps on the robustness of longitudinal Centiloid data across three large international cohort studies.MethodsAll Aβ PET data in AIBL (N = 3315), ADNI (N = 3442) and OASIS3 (N = 1398) were quantified using the MRI-based Centiloid standard SPM pipeline and the PET-only pipeline CapAIBL. SUVR were converted into Centiloids using each tracer's respective transform. Global Aβ burden from pre-defined target cortical regions in Centiloid units were quantified for both raw PET scans and PET scans smoothed to a uniform 8 mm full width half maximum (FWHM) effective smoothness. For Florbetapir, we assessed the performance of using both the standard Whole Cerebellum (WCb) and a composite white matter (WM)+WCb reference region. Additionally, our recently proposed quantification based on Non-negative Matrix Factorisation (NMF) was applied to all spatially and SUVR normalised images. Correlation with clinical severity measured by the Mini-Mental State Examination (MMSE) and effect size, as well as tracer agreement in 11C-PiB-18F-Florbetapir pairs and longitudinal consistency were evaluated.ResultsThe smoothing to a uniform resolution partially reduced longitudinal variability, but did not improve inter-tracer agreement, effect size or correlation with MMSE. Using a Composite reference region for 18F-Florbetapir improved inter-tracer agreement, effect size, correlation with MMSE, and longitudinal consistency. The best results were however obtained when using the NMF method which outperformed all other quantification approaches in all metrics used.ConclusionsFWHM smoothing has limited impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for computing both cross-sectional and longitudinal Florbetapir Centiloid. NMF improves Centiloid quantification on all metrics examined.

Published
2022

21. Autosomal dominant and sporadic late onset Alzheimer's disease share a common in vivo pathophysiology

Author

Morris, John C, Weiner, Michael, Xiong, Chengjie, Beckett, Laurel, Coble, Dean, Saito, Naomi, Aisen, Paul S, Allegri, Ricardo, Benzinger, Tammie LS, Berman, Sarah B, Cairns, Nigel J, Carrillo, Maria C, Chui, Helena C, Chhatwal, Jasmeer P, Cruchaga, Carlos, Fagan, Anne M, Farlow, Martin, Fox, Nick C, Ghetti, Bernardino, Goate, Alison M, Gordon, Brian A, Graff-Radford, Neill, Day, Gregory S, Hassenstab, Jason, Ikeuchi, Takeshi, Jack, Clifford R, Jagust, William J, Jucker, Mathias, Levin, Johannes, Massoumzadeh, Parinaz, Masters, Colin L, Martins, Ralph, McDade, Eric, Mori, Hiroshi, Noble, James M, Petersen, Ronald C, Ringman, John M, Salloway, Stephen, Saykin, Andrew J, Schofield, Peter R, Shaw, Leslie M, Toga, Arthur W, Trojanowski, John Q, Vöglein, Jonathan, Weninger, Stacie, Bateman, Randall J, and Buckles, Virginia D

Subjects
Biomedical and Clinical Sciences, Health Sciences, Psychology, Brain Disorders, Clinical Trials and Supportive Activities, Alzheimer's Disease, Clinical Research, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Biomedical Imaging, Genetics, Aging, Acquired Cognitive Impairment, Neurosciences, Neurodegenerative, Dementia, 2.1 Biological and endogenous factors, Aetiology, Neurological, Humans, Alzheimer Disease, Amyloid beta-Peptides, Magnetic Resonance Imaging, Amyloidosis, Biomarkers, Alzheimer pathophysiology, biomarkers, rates of change, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

The extent to which the pathophysiology of autosomal dominant Alzheimer's disease corresponds to the pathophysiology of 'sporadic' late onset Alzheimer's disease is unknown, thus limiting the extrapolation of study findings and clinical trial results in autosomal dominant Alzheimer's disease to late onset Alzheimer's disease. We compared brain MRI and amyloid PET data, as well as CSF concentrations of amyloid-β42, amyloid-β40, tau and tau phosphorylated at position 181, in 292 carriers of pathogenic variants for Alzheimer's disease from the Dominantly Inherited Alzheimer Network, with corresponding data from 559 participants from the Alzheimer's Disease Neuroimaging Initiative. Imaging data and CSF samples were reprocessed as appropriate to guarantee uniform pipelines and assays. Data analyses yielded rates of change before and after symptomatic onset of Alzheimer's disease, allowing the alignment of the ∼30-year age difference between the cohorts on a clinically meaningful anchor point, namely the participant age at symptomatic onset. Biomarker profiles were similar for both autosomal dominant Alzheimer's disease and late onset Alzheimer's disease. Both groups demonstrated accelerated rates of decline in cognitive performance and in regional brain volume loss after symptomatic onset. Although amyloid burden accumulation as determined by PET was greater after symptomatic onset in autosomal dominant Alzheimer's disease than in late onset Alzheimer's disease participants, CSF assays of amyloid-β42, amyloid-β40, tau and p-tau181 were largely overlapping in both groups. Rates of change in cognitive performance and hippocampal volume loss after symptomatic onset were more aggressive for autosomal dominant Alzheimer's disease participants. These findings suggest a similar pathophysiology of autosomal dominant Alzheimer's disease and late onset Alzheimer's disease, supporting a shared pathobiological construct.

Published
2022

22. γ-Secretase activity, clinical features, and biomarkers of autosomal dominant Alzheimer's disease: cross-sectional and longitudinal analysis of the Dominantly Inherited Alzheimer Network observational study (DIAN-OBS)

Author

Aguillon, David, Allegri, Ricardo F., Aschenbrenner, Andrew J., Baker, Bryce, Barthelemy, Nicolas, Bechara, Jacob A., Berman, Sarah B., Brooks, William S., Cash, David M., Chen, Allison, Chrem Mendez, Patricio, Courtney, Laura, Cruchaga, Carlos, Daniels, Alisha J., Fagan, Anne M., Flores, Shaney, Fox, Nick C., Franklin, Erin, Goate, Alison M., Graber-Sultan, Susanne, Graff-Radford, Neill R., Gremminger, Emily, Herries, Elizabeth, Hofmann, Anna, Holtzman, David M., Hornbeck, Russ, Huey, Edward D., Ibanez, Laura, Ikeuchi, Takeshi, Ikonomovic, Snezana, Jackson, Kelley, Jarman, Steve, Jerome, Gina, Johnson, Erik C.B, Kasuga, Kensaku, Keefe, Sarah, Koudelis, Deborah, Kuder-Buletta, Elke, Laske, Christoph, Leon, Yudy Milena, Levey, Allan I., Li, Yan, Llibre-Guerra, Jorge J., Lopera, Francisco, Lu, Ruijin, Marsh, Jacob, Martins, Ralph, Massoumzadeh, Parinaz, Masters, Colin, McCullough, Austin, McKay, Nicole, Minton, Matthew, Mori, Hiroshi, Morris, John C., Nadkarni, Neelesh K., Nicklaus, Joyce, Niimi, Yoshiki, Noble, James M., Obermueller, Ulrike, Picarello, Danielle M., Pulizos, Christine, Ramirez, Laura, Renton, Alan E., Ringman, John, Rizzo, Jacqueline, Roedenbeck, Yvonne, Roh, Jee Hoon, Rosa-Neto, Pedro, Ryan, Natalie S., Sabaredzovic, Edita, Salloway, Stephen, Sanchez-Valle, Raquel, Scott, Jalen, Seyfried, Nicholas T., Simmons, Ashlee, Smith, Jennifer, Smith, Hunter, Stauber, Jennifer, Stout, Sarah, Supnet-Bell, Charlene, Surace, Ezequiel, Vazquez, Silvia, Vöglein, Jonathan, Wang, Guoqiao, Wang, Qing, Xu, Xiong, Xu, Jinbin, Schultz, Stephanie A, Liu, Lei, Schultz, Aaron P, Fitzpatrick, Colleen D, Levin, Raina, Bellier, Jean-Pierre, Shirzadi, Zahra, Joseph-Mathurin, Nelly, Chen, Charles D, Benzinger, Tammie L S, Day, Gregory S, Farlow, Martin R, Gordon, Brian A, Hassenstab, Jason J, Jack, Clifford R, Jr, Jucker, Mathias, Karch, Celeste M, Lee, Jae-Hong, Levin, Johannes, Perrin, Richard J, Schofield, Peter R, Xiong, Chengjie, Johnson, Keith A, McDade, Eric, Bateman, Randall J, Sperling, Reisa A, Selkoe, Dennis J, and Chhatwal, Jasmeer P

Published
2024
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23. Cerebrospinal fluid proteomics define the natural history of autosomal dominant Alzheimer’s disease

Author

Johnson, Erik C. B., Bian, Shijia, Haque, Rafi U., Carter, E. Kathleen, Watson, Caroline M., Gordon, Brian A., Ping, Lingyan, Duong, Duc M., Epstein, Michael P., McDade, Eric, Barthélemy, Nicolas R., Karch, Celeste M., Xiong, Chengjie, Cruchaga, Carlos, Perrin, Richard J., Wingo, Aliza P., Wingo, Thomas S., Chhatwal, Jasmeer P., Day, Gregory S., Noble, James M., Berman, Sarah B., Martins, Ralph, Graff-Radford, Neill R., Schofield, Peter R., Ikeuchi, Takeshi, Mori, Hiroshi, Levin, Johannes, Farlow, Martin, Lah, James J., Haass, Christian, Jucker, Mathias, Morris, John C., Benzinger, Tammie L. S., Roberts, Blaine R., Bateman, Randall J., Fagan, Anne M., Seyfried, Nicholas T., and Levey, Allan I.

Published
2023
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24. CSF MTBR-tau243 is a specific biomarker of tau tangle pathology in Alzheimer’s disease

Author

Horie, Kanta, Salvadó, Gemma, Barthélemy, Nicolas R., Janelidze, Shorena, Li, Yan, He, Yingxin, Saef, Benjamin, Chen, Charles D., Jiang, Hong, Strandberg, Olof, Pichet Binette, Alexa, Palmqvist, Sebastian, Sato, Chihiro, Sachdev, Pallavi, Koyama, Akihiko, Gordon, Brian A., Benzinger, Tammie L. S., Holtzman, David M., Morris, John C., Mattsson-Carlgren, Niklas, Stomrud, Erik, Ossenkoppele, Rik, Schindler, Suzanne E., Hansson, Oskar, and Bateman, Randall J.

Published
2023
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25. Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN)

Author

McKay, Nicole S., Gordon, Brian A., Hornbeck, Russ C., Dincer, Aylin, Flores, Shaney, Keefe, Sarah J., Joseph-Mathurin, Nelly, Jack, Clifford R., Koeppe, Robert, Millar, Peter R., Ances, Beau M., Chen, Charles D., Daniels, Alisha, Hobbs, Diana A., Jackson, Kelley, Koudelis, Deborah, Massoumzadeh, Parinaz, McCullough, Austin, Nickels, Michael L., Rahmani, Farzaneh, Swisher, Laura, Wang, Qing, Allegri, Ricardo F., Berman, Sarah B., Brickman, Adam M., Brooks, William S., Cash, David M., Chhatwal, Jasmeer P., Day, Gregory S., Farlow, Martin R., la Fougère, Christian, Fox, Nick C., Fulham, Michael, Ghetti, Bernardino, Graff-Radford, Neill, Ikeuchi, Takeshi, Klunk, William, Lee, Jae-Hong, Levin, Johannes, Martins, Ralph, Masters, Colin L., McConathy, Jonathan, Mori, Hiroshi, Noble, James M., Reischl, Gerald, Rowe, Christopher, Salloway, Stephen, Sanchez-Valle, Raquel, Schofield, Peter R., Shimada, Hiroyuki, Shoji, Mikio, Su, Yi, Suzuki, Kazushi, Vöglein, Jonathan, Yakushev, Igor, Cruchaga, Carlos, Hassenstab, Jason, Karch, Celeste, McDade, Eric, Perrin, Richard J., Xiong, Chengjie, Morris, John C., Bateman, Randall J., and Benzinger, Tammie L. S.

Published
2023
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26. Advanced structural brain aging in preclinical autosomal dominant Alzheimer disease

Author

Millar, Peter R, Gordon, Brian A, Wisch, Julie K, Schultz, Stephanie A, Benzinger, Tammie LS, Cruchaga, Carlos, Hassenstab, Jason J, Ibanez, Laura, Karch, Celeste, Llibre-Guerra, Jorge J, Morris, John C, Perrin, Richard J, Supnet-Bell, Charlene, Xiong, Chengjie, Allegri, Ricardo F, Berman, Sarah B, Chhatwal, Jasmeer P, Chrem Mendez, Patricio A, Day, Gregory S, Hofmann, Anna, Ikeuchi, Takeshi, Jucker, Mathias, Lee, Jae-Hong, Levin, Johannes, Lopera, Francisco, Niimi, Yoshiki, Sánchez-González, Victor J, Schofield, Peter R, Sosa-Ortiz, Ana Luisa, Vöglein, Jonathan, Bateman, Randall J, Ances, Beau M, and McDade, Eric M

Published
2023
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28. Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease

Author

Ali, Muhammad, Archer, Derek B., Gorijala, Priyanka, Western, Daniel, Timsina, Jigyasha, Fernández, Maria V., Wang, Ting-Chen, Satizabal, Claudia L., Yang, Qiong, Beiser, Alexa S., Wang, Ruiqi, Chen, Gengsheng, Gordon, Brian, Benzinger, Tammie L. S., Xiong, Chengjie, Morris, John C., Bateman, Randall J., Karch, Celeste M., McDade, Eric, Goate, Alison, Seshadri, Sudha, Mayeux, Richard P., Sperling, Reisa A., Buckley, Rachel F., Johnson, Keith A., Won, Hong-Hee, Jung, Sang-Hyuk, Kim, Hang-Rai, Seo, Sang Won, Kim, Hee Jin, Mormino, Elizabeth, Laws, Simon M., Fan, Kang-Hsien, Kamboh, M. Ilyas, Vemuri, Prashanthi, Ramanan, Vijay K., Yang, Hyun-Sik, Wenzel, Allen, Rajula, Hema Sekhar Reddy, Mishra, Aniket, Dufouil, Carole, Debette, Stephanie, Lopez, Oscar L., DeKosky, Steven T., Tao, Feifei, Nagle, Michael W., Hohman, Timothy J., Sung, Yun Ju, Dumitrescu, Logan, and Cruchaga, Carlos

Published
2023
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29. Longitudinal head-to-head comparison of 11C-PiB and 18F-florbetapir PET in a Phase 2/3 clinical trial of anti-amyloid-β monoclonal antibodies in dominantly inherited Alzheimer’s disease

Author

Chen, Charles D., McCullough, Austin, Gordon, Brian, Joseph-Mathurin, Nelly, Flores, Shaney, McKay, Nicole S., Hobbs, Diana A., Hornbeck, Russ, Fagan, Anne M., Cruchaga, Carlos, Goate, Alison M., Perrin, Richard J., Wang, Guoqiao, Li, Yan, Shi, Xinyu, Xiong, Chengjie, Pontecorvo, Michael J., Klein, Gregory, Su, Yi, Klunk, William E., Jack, Clifford, Koeppe, Robert, Snider, B. Joy, Berman, Sarah B., Roberson, Erik D., Brosch, Jared, Surti, Ghulam, Jiménez-Velázquez, Ivonne Z., Galasko, Douglas, Honig, Lawrence S., Brooks, William S., Clarnette, Roger, Wallon, David, Dubois, Bruno, Pariente, Jérémie, Pasquier, Florence, Sanchez-Valle, Raquel, Shcherbinin, Sergey, Higgins, Ixavier, Tunali, Ilke, Masters, Colin L., van Dyck, Christopher H., Masellis, Mario, Hsiung, Robin, Gauthier, Serge, Salloway, Steve, Clifford, David B., Mills, Susan, Supnet-Bell, Charlene, McDade, Eric, Bateman, Randall J., and Benzinger, Tammie L. S.

Published
2023
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31. Accelerated functional brain aging in pre-clinical familial Alzheimer’s disease

Author

Gonneaud, Julie, Baria, Alex T, Pichet Binette, Alexa, Gordon, Brian A, Chhatwal, Jasmeer P, Cruchaga, Carlos, Jucker, Mathias, Levin, Johannes, Salloway, Stephen, Farlow, Martin, Gauthier, Serge, Benzinger, Tammie LS, Morris, John C, Bateman, Randall J, Breitner, John CS, Poirier, Judes, Vachon-Presseau, Etienne, and Villeneuve, Sylvia

Subjects
Biological Psychology, Psychology, Clinical Research, Neurodegenerative, Aging, Brain Disorders, Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Acquired Cognitive Impairment, Alzheimer's Disease, Dementia, Neurological, Adult, Aged, Aged, 80 and over, Alzheimer Disease, Amyloid beta-Peptides, Brain, Brain Mapping, Cognitive Dysfunction, Female, Genetic Predisposition to Disease, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Mutation, Positron-Emission Tomography, Young Adult, Alzheimer’s Disease Neuroimaging Initiative, Dominantly Inherited Alzheimer Network (DIAN) Study Group, Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer’s Disease (PREVENT-AD) Research Group
Abstract

Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer's disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18-94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology.

Published
2021

34. CSF tau phosphorylation occupancies at T217 and T205 represent improved biomarkers of amyloid and tau pathology in Alzheimer’s disease

Author

Barthélemy, Nicolas R., Saef, Benjamin, Li, Yan, Gordon, Brian A., He, Yingxin, Horie, Kanta, Stomrud, Erik, Salvadó, Gemma, Janelidze, Shorena, Sato, Chihiro, Ovod, Vitaliy, Henson, Rachel L., Fagan, Anne M., Benzinger, Tammie L. S., Xiong, Chengjie, Morris, John C., Hansson, Oskar, Bateman, Randall J., and Schindler, Suzanne E.

Published
2023
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36. Sustained Preservation of Cognition and Prevention of Patient-Reported Symptoms With Hippocampal Avoidance During Whole-Brain Radiation Therapy for Brain Metastases: Final Results of NRG Oncology CC001

Author

Gondi, Vinai, Deshmukh, Snehal, Brown, Paul D., Wefel, Jeffrey S., Armstrong, Terri S., Tome, Wolfgang A., Gilbert, Mark R., Konski, Andre, Robinson, Clifford G., Bovi, Joseph A., Benzinger, Tammie L.S., Roberge, David, Kundapur, Vijayananda, Kaufman, Isaac, Shah, Sunjay, Usuki, Kenneth Y., Baschnagel, Andrew M., Mehta, Minesh P., and Kachnic, Lisa A.

Published
2023
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37. Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease

Author

Adams, Sarah, Allegri, Ricardo, Araki, Aki, Barthelemy, Nicolas, Bateman, Randall, Bechara, Jacob, Benzinger, Tammie, Berman, Sarah, Bodge, Courtney, Brandon, Susan, Brooks, William (Bill), Brosch, Jared, Buck, Jill, Buckles, Virginia, Carter, Kathleen, Cash, Lisa, Chen, Charlie, Chhatwal, Jasmeer, Mendez, Patricio Chrem, Chua, Jasmin, Chui, Helena, Courtney, Laura, Cruchaga, Carlos, Day, Gregory S, DeLaCruz, Chrismary, Denner, Darcy, Diffenbacher, Anna, Dincer, Aylin, Donahue, Tamara, Douglas, Jane, Duong, Duc, Egido, Noelia, Esposito, Bianca, Fagan, Anne, Farlow, Marty, Feldman, Becca, Fitzpatrick, Colleen, Flores, Shaney, Fox, Nick, Franklin, Erin, Joseph-Mathurin, Nelly, Fujii, Hisako, Gardener, Samantha, Ghetti, Bernardino, Goate, Alison, Goldberg, Sarah, Goldman, Jill, Gonzalez, Alyssa, Gordon, Brian, Gräber-Sultan, Susanne, Graff-Radford, Neill, Graham, Morgan, Gray, Julia, Gremminger, Emily, Grilo, Miguel, Groves, Alex, Haass, Christian, Häsler, Lisa, Hassenstab, Jason, Hellm, Cortaiga, Herries, Elizabeth, Hoechst-Swisher, Laura, Hofmann, Anna, Holtzman, David, Hornbeck, Russ, Igor, Yakushev, Ihara, Ryoko, Ikeuchi, Takeshi, Ikonomovic, Snezana, Ishii, Kenji, Jack, Clifford, Jerome, Gina, Johnson, Erik, Jucker, Mathias, Karch, Celeste, Käser, Stephan, Kasuga, Kensaku, Keefe, Sarah, Klunk, William, Koeppe, Robert, Koudelis, Deb, Kuder-Buletta, Elke, Laske, Christoph, Levey, Allan, Levin, Johannes, Li, Yan, Lopez, Oscar, Marsh, Jacob, Martins, Ralph, Mason, Neal Scott, Masters, Colin, Mawuenyega, Kwasi, McCullough, Austin, McDade, Eric, Mejia, Arlene, Morenas-Rodriguez, Estrella, Morris, John, Mountz, James, Mummery, Cath, Nadkarni, N eelesh, Nagamatsu, Akemi, Neimeyer, Katie, Niimi, Yoshiki, Noble, James, Norton, Joanne, Nuscher, Brigitte, Obermüller, Ulricke, O'Connor, Antoinette, Patira, Riddhi, Perrin, Richard, Ping, Lingyan, Preische, Oliver, Renton, Alan, Ringman, John, Salloway, Stephen, Schofield, Peter, Senda, Michio, Seyfried, Nicholas T, Shady, Kristine, Shimada, Hiroyuki, Sigurdson, Wendy, Smith, Jennifer, Smith, Lori, Snitz, Beth, Sohrabi, Hamid, Stephens, Sochenda, Taddei, Kevin, Thompson, Sarah, Vöglein, Jonathan, Wang, Peter, Wang, Qing, Weamer, Elise, Xiong, Chengjie, Xu, Jinbin, Xu, Xiong, Millar, Peter R., Luckett, Patrick H., Gordon, Brian A., Benzinger, Tammie L.S., Schindler, Suzanne E., Fagan, Anne M., Bateman, Randall J., Lee, Jae-Hong, Mori, Hiroshi, Salloway, Stephen P, Yakushev, Igor, Morris, John C., and Ances, Beau M.

Published
2022
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38. Deconstructing pathological tau by biological process in early stages of Alzheimer disease: a method for quantifying tau spatial spread in neuroimaging

Author

Doering, Stephanie, primary, McCullough, Austin, additional, Gordon, Brian A., additional, Chen, Charles D., additional, McKay, Nicole, additional, Hobbs, Diana, additional, Keefe, Sarah, additional, Flores, Shaney, additional, Scott, Jalen, additional, Smith, Hunter, additional, Jarman, Stephen, additional, Jackson, Kelley, additional, Hornbeck, Russ C., additional, Ances, Beau M., additional, Xiong, Chengjie, additional, Aschenbrenner, Andrew J., additional, Hassenstab, Jason, additional, Cruchaga, Carlos, additional, Daniels, Alisha, additional, Bateman, Randall J., additional, Noble, James M., additional, Day, Gregory S., additional, Graff-Radford, Neill R., additional, Voglein, Jonathan, additional, Levin, Johannes, additional, Allegri, Ricardo F., additional, Mendez, Patricio Chrem, additional, Surace, Ezequiel, additional, Berman, Sarah B., additional, Ikonomovic, Snezana, additional, Nadkarni, Neelesh K., additional, Lopera, Francisco, additional, Ramirez, Laura, additional, Aguillon, David, additional, Leon, Yudy, additional, Ramos, Claudia, additional, Alzate, Diana, additional, Baena, Ana, additional, Londono, Natalia, additional, Moreno, Sonia, additional, Jucker, Mathias, additional, Laske, Christoph, additional, Kuder-Buletta, Elke, additional, Graber-Sultan, Susanne, additional, Preische, Oliver, additional, Hofmann, Anna, additional, Ikeuchi, Takeshi, additional, Kasuga, Kensaku, additional, Niimi, Yoshiki, additional, Ishii, Kenji, additional, Senda, Michio, additional, Sanchez-Valle, Raquel, additional, Rosa-Neto, Pedro, additional, Fox, Nick C., additional, Cash, Dave, additional, Lee, Jae-Hong, additional, Roh, Jee Hoon, additional, Salloway, Stephen, additional, Riddle, Meghan C., additional, Menard, William, additional, Bodge, Courtney, additional, Surti, Mustafa, additional, Takada, Leonel Tadao, additional, Farlow, Martin, additional, Chhatwal, Jasmeer P., additional, Sanchez-Gonzalez, V.J., additional, Orozco-Barajas, Maribel, additional, Goate, Alison M., additional, Renton, Alan E., additional, Esposito, Bianca T., additional, Karch, Celeste M., additional, Marsh, Jacob, additional, Fernanadez, Victoria, additional, Fagan, Anne M., additional, Jerome, Gina, additional, Herries, Elizabeth, additional, Llibre-Guerra, Jorge, additional, Levey, Allan I., additional, Johnson, Erik C.B., additional, Seyfried, Nicholas T., additional, Schofield, Peter R., additional, Brooks, William S., additional, Bechara, Jacob A., additional, Bateman, Randall, additional, McDade, Eric, additional, Perrin, Richard J., additional, Franklin, Erin E., additional, Benzinger, Tammie, additional, Chen, Allison, additional, Chen, Charles, additional, Friedrichsen, Nelly, additional, Gordon, Brian, additional, Hantler, Nancy, additional, Hornbeck, Russ, additional, Jarman, Steve, additional, Koudelis, Deborah, additional, Massoumzadeh, Parinaz, additional, Nicklaus, Joyce, additional, Pulizos, Christine, additional, Wang, Qing, additional, Mishall, Sheetal, additional, Sabaredzovic, Edita, additional, Deng, Emily, additional, Candela, Madison, additional, Wang, Peter, additional, Xu, Xiong, additional, Li, Yan, additional, Gremminger, Emily, additional, Ma, Yinjiao, additional, Bui, Ryan, additional, Lu, Ruijin, additional, Martins, Ralph, additional, Sosa Ortiz, Ana Luisa, additional, Courtney, Laura, additional, Mori, Hiroshi, additional, Supnet-Bell, Charlene, additional, Xu, Jinbin, additional, Ringman, John, additional, Barthelemy, Nicolas, additional, Morris, John, additional, Smith, Jennifer, additional, Morris, John C., additional, and Benzinger, Tammie L.S., additional

Published
2024
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39. Differential impacts of road diets on driving behavior among older adults with and without preclinical Alzheimer’s pathology

Author

Wisch, Julie K., Kianfar, Jalil, Carr, David B., Dickerson, Anne D., Vivoda, Jonathon, Harmon, Annie, Trani, Jean Francois, Johnson, Ann M., Doherty, Jason M., Murphy, Samantha A., Domash, Hailee, Ashraf, Shahida, Aschenbrenner, Andrew J., Schindler, Suzanne E., Benzinger, Tammie L.S., Morris, John C., Ances, Beau M., and Babulal, Ganesh M.

Published
2023
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41. Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer's disease

Author

Keret, Ophir, Staffaroni, Adam M, Ringman, John M, Cobigo, Yann, Goh, Sheng‐Yang M, Wolf, Amy, Allen, Isabel Elaine, Salloway, Stephen, Chhatwal, Jasmeer, Brickman, Adam M, Reyes‐Dumeyer, Dolly, Bateman, Randal J, Benzinger, Tammie LS, Morris, John C, Ances, Beau M, Joseph‐Mathurin, Nelly, Perrin, Richard J, Gordon, Brian A, Levin, Johannes, Vöglein, Jonathan, Jucker, Mathias, la Fougère, Christian, Martins, Ralph N, Sohrabi, Hamid R, Taddei, Kevin, Villemagne, Victor L, Schofield, Peter R, Brooks, William S, Fulham, Michael, Masters, Colin L, Ghetti, Bernardino, Saykin, Andrew J, Jack, Clifford R, Graff‐Radford, Neill R, Weiner, Michael, Cash, David M, Allegri, Ricardo F, Chrem, Patricio, Yi, Su, Miller, Bruce L, Rabinovici, Gil D, Rosen, Howard J, and Network, Dominantly Inherited Alzheimer

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, Aging, Prevention, Neurodegenerative, Alzheimer's Disease, Dementia, Brain Disorders, Acquired Cognitive Impairment, Genetics, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Patient Safety, Neurological, Good Health and Well Being, autosomal dominant Alzheimer's disease, brain atrophy, Dominantly Inherited Alzheimer Network, preclinical Alzheimer's disease, Biological psychology
Abstract

IntroductionAsymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment.MethodsWe created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally.ResultsOur risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%-98.2%]) and improved prediction beyond established methods based on familial age of onset.DiscussionIndividualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials.

Published
2021

43. Cortical atrophy and leukoaraiosis, imaging markers of cerebrovascular small vessel disease, are associated with driving behavior changes among cognitively normal older adults

Author

Babulal, Ganesh M., Chen, Ling, Carr, David B., Johnson, Ann M., Shimony, Joshua S., Doherty, Jason, Murphy, Samantha, Walker, Alexis, Domash, Hailee, Hornbeck, Russ, Keefe, Sarah, Flores, Shaney, Raji, Cyrus A., Morris, John C., Ances, Beau M., and Benzinger, Tammie L.S.

Published
2023
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44. Comparison of [11C]-PBR28 Binding Between Persons Living With HIV and HIV-Uninfected Individuals.

Author

Boerwinkle, Anna H, Strain, Jeremy F, Burdo, Tricia, Doyle, John, Christensen, Jon, Su, Yi, Wisch, Julie K, Cooley, Sarah A, Vaida, Florin, Smith, Mandy D, Jafri, Hussain, Paul, Robert H, Benzinger, Tammie LS, and Ances, Beau M

Subjects
Neurosciences, Mental Health, Brain Disorders, Acquired Cognitive Impairment, Infectious Diseases, Clinical Research, HIV/AIDS, Behavioral and Social Science, Biomedical Imaging, Acetamides, Aged, Anti-Retroviral Agents, Carbon Radioisotopes, Cognition Disorders, Female, HIV Infections, Humans, Inflammation, Male, Middle Aged, Positron-Emission Tomography, Pyridines, Receptors, GABA, HIV, neuroinflammation, positron emission tomography, kDa translocator protein, cognition, Clinical Sciences, Public Health and Health Services, Virology
Abstract

ObjectiveDespite combined antiretroviral therapy, neuroinflammation may persist in persons living with HIV (PLWH) and contribute to cognitive impairment in this population. Positron emission tomography (PET) imaging targeting 18 kDa translocator protein (TSPO) has been used to localize neuroinflammation. We aimed to use TSPO-PET imaging to evaluate neuroinflammation in PLWH.DesignTwenty-four virologically suppressed PLWH on combined antiretroviral therapy and 13 HIV-negative (HIV-) controls completed TSPO-PET imaging using the radiotracer [C]PBR28. Because of tracer complexity and differing procedures used in previous studies, we employed an expansive methodological approach, using binding potential (BP) and standard uptake value ratio and multiple different reference regions to estimate [C]PBR28 binding.Methods[C]PBR28 binding was measured in 30 cortical and subcortical regions and compared between PLWH and HIV- controls. Pearson correlation evaluated the association between [C]PBR28 binding and cognition and clinical measures of HIV.ResultsAnalyses conducted using multiple reference regions and measures of tracer uptake revealed no significant differences between [C]PBR28 binding in PLWH compared with HIV- controls. In addition, [C]PBR28 binding in PLWH was not significantly associated with clinical measures of HIV or plasma biomarkers of inflammation. [C]PBR28 binding was not significantly elevated in cognitively impaired PLWH compared with unimpaired PLWH, but there were inverse relationships between cognitive performance (executive and global function) and [C]PBR28 binding in PLWH.ConclusionsOur results suggest that neuroinflammation may play a role in cognitive deficits, but overall neuroinflammatory levels as measured by TSPO-PET imaging in PLWH are not significantly different from those seen in HIV- controls.

Published
2020

45. APOE4 leads to blood–brain barrier dysfunction predicting cognitive decline

Author

Montagne, Axel, Nation, Daniel A, Sagare, Abhay P, Barisano, Giuseppe, Sweeney, Melanie D, Chakhoyan, Ararat, Pachicano, Maricarmen, Joe, Elizabeth, Nelson, Amy R, D’Orazio, Lina M, Buennagel, David P, Harrington, Michael G, Benzinger, Tammie LS, Fagan, Anne M, Ringman, John M, Schneider, Lon S, Morris, John C, Reiman, Eric M, Caselli, Richard J, Chui, Helena C, TCW, Julia, Chen, Yining, Pa, Judy, Conti, Peter S, Law, Meng, Toga, Arthur W, and Zlokovic, Berislav V

Subjects
Aging, Brain Disorders, Neurosciences, Dementia, Acquired Cognitive Impairment, Alzheimer's Disease Related Dementias (ADRD), Alzheimer's Disease, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aetiology, 2.1 Biological and endogenous factors, Neurological, Alleles, Alzheimer Disease, Amyloid beta-Peptides, Apolipoprotein E4, Blood-Brain Barrier, Capillaries, Cognitive Dysfunction, Cyclophilin A, Female, Heterozygote, Hippocampus, Humans, Male, Matrix Metalloproteinase 9, Parahippocampal Gyrus, Pericytes, Positron-Emission Tomography, Receptor, Platelet-Derived Growth Factor beta, Temporal Lobe, tau Proteins, General Science & Technology
Abstract

Vascular contributions to dementia and Alzheimer's disease are increasingly recognized1-6. Recent studies have suggested that breakdown of the blood-brain barrier (BBB) is an early biomarker of human cognitive dysfunction7, including the early clinical stages of Alzheimer's disease5,8-10. The E4 variant of apolipoprotein E (APOE4), the main susceptibility gene for Alzheimer's disease11-14, leads to accelerated breakdown of the BBB and degeneration of brain capillary pericytes15-19, which maintain BBB integrity20-22. It is unclear, however, whether the cerebrovascular effects of APOE4 contribute to cognitive impairment. Here we show that individuals bearing APOE4 (with the ε3/ε4 or ε4/ε4 alleles) are distinguished from those without APOE4 (ε3/ε3) by breakdown of the BBB in the hippocampus and medial temporal lobe. This finding is apparent in cognitively unimpaired APOE4 carriers and more severe in those with cognitive impairment, but is not related to amyloid-β or tau pathology measured in cerebrospinal fluid or by positron emission tomography23. High baseline levels of the BBB pericyte injury biomarker soluble PDGFRβ7,8 in the cerebrospinal fluid predicted future cognitive decline in APOE4 carriers but not in non-carriers, even after controlling for amyloid-β and tau status, and were correlated with increased activity of the BBB-degrading cyclophilin A-matrix metalloproteinase-9 pathway19 in cerebrospinal fluid. Our findings suggest that breakdown of the BBB contributes to APOE4-associated cognitive decline independently of Alzheimer's disease pathology, and might be a therapeutic target in APOE4 carriers.

Published
2020

46. Comparing cortical signatures of atrophy between late-onset and autosomal dominant Alzheimer disease

Author

Dincer, Aylin, Gordon, Brian A, Hari-Raj, Amrita, Keefe, Sarah J, Flores, Shaney, McKay, Nicole S, Paulick, Angela M, Lewis, Kristine E Shady, Feldman, Rebecca L, Hornbeck, Russ C, Allegri, Ricardo, Ances, Beau M, Berman, Sarah B, Brickman, Adam M, Brooks, William S, Cash, David M, Chhatwal, Jasmeer P, Farlow, Martin R, la Fougère, Christian, Fox, Nick C, Fulham, Michael J, Jack, Clifford R, Joseph-Mathurin, Nelly, Karch, Celeste M, Lee, Athene, Levin, Johannes, Masters, Colin L, McDade, Eric M, Oh, Hwamee, Perrin, Richard J, Raji, Cyrus, Salloway, Stephen P, Schofield, Peter R, Su, Yi, Villemagne, Victor L, Wang, Qing, Weiner, Michael W, Xiong, Chengjie, Yakushev, Igor, Morris, John C, Bateman, Randall J, Benzinger, Tammie LS, and DIAN, for the Dominantly Inherited Alzheimer Network

Subjects
Neurodegenerative, Dementia, Clinical Research, Brain Disorders, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurosciences, Aging, Acquired Cognitive Impairment, Alzheimer's Disease, 2.1 Biological and endogenous factors, Aetiology, Neurological, Alzheimer Disease, Amyloid beta-Peptides, Atrophy, Hippocampus, Humans, Magnetic Resonance Imaging, Positron-Emission Tomography, Alzheimer disease, Autosomal dominant Alzheimer disease, Preclinical, Cortical signature, Amyloid, Cortical thickness, Dominantly Inherited Alzheimer Network DIAN
Abstract

Defining a signature of cortical regions of interest preferentially affected by Alzheimer disease (AD) pathology may offer improved sensitivity to early AD compared to hippocampal volume or mesial temporal lobe alone. Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals. We defined and analyzed the LOAD cortical maps of cortical thickness in 588 participants from the Knight Alzheimer Disease Research Center (Knight ADRC) and the ADAD cortical maps in 269 participants from the Dominantly Inherited Alzheimer Network (DIAN) observational study. Both cohorts were divided into three groups: cognitively normal controls (nADRC = 381; nDIAN = 145), preclinical (nADRC = 153; nDIAN = 76), and cognitively impaired (nADRC = 54; nDIAN = 48). Both cohorts underwent clinical assessments, 3T MRI, and amyloid PET imaging with either 11C-Pittsburgh compound B or 18F-florbetapir. To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume. We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.

Published
2020

47. Mapping language function with task-based vs. resting-state functional MRI

Author

Park, Ki Yun, Lee, John J, Dierker, Donna, Marple, Laura M, Hacker, Carl D, Roland, Jarod L, Marcus, Daniel S, Milchenko, Mikhail, Miller-Thomas, Michelle M, Benzinger, Tammie L, Shimony, Joshua S, Snyder, Abraham Z, and Leuthardt, Eric C

Subjects
Biological Psychology, Psychology, Brain Disorders, Behavioral and Social Science, Bioengineering, Basic Behavioral and Social Science, Biomedical Imaging, Neurosciences, Clinical Research, Neurological, Adult, Aged, Attention, Brain, Brain Mapping, Broca Area, Female, Frontal Lobe, Functional Laterality, Glioblastoma, Humans, Language, Magnetic Resonance Imaging, Male, Middle Aged, Rest, Temporal Lobe, Young Adult, General Science & Technology
Abstract

BackgroundUse of functional MRI (fMRI) in pre-surgical planning is a non-invasive method for pre-operative functional mapping for patients with brain tumors, especially tumors located near eloquent cortex. Currently, this practice predominantly involves task-based fMRI (T-fMRI). Resting state fMRI (RS-fMRI) offers an alternative with several methodological advantages. Here, we compare group-level analyses of RS-fMRI vs. T-fMRI as methods for language localization.PurposeTo contrast RS-fMRI vs. T-fMRI as techniques for localization of language function.MethodsWe analyzed data obtained in 35 patients who had both T-fMRI and RS-fMRI scans during the course of pre-surgical evaluation. The RS-fMRI data were analyzed using a previously trained resting-state network classifier. The T-fMRI data were analyzed using conventional techniques. Group-level results obtained by both methods were evaluated in terms of two outcome measures: (1) inter-subject variability of response magnitude and (2) sensitivity/specificity analysis of response topography, taking as ground truth previously reported maps of the language system based on intraoperative cortical mapping as well as meta-analytic maps of language task fMRI responses.ResultsBoth fMRI methods localized major components of the language system (areas of Broca and Wernicke) although not with equal inter-subject consistency. Word-stem completion T-fMRI strongly activated Broca's area but also several task-general areas not specific to language. RS-fMRI provided a more specific representation of the language system.ConclusionWe demonstrate several advantages of classifier-based mapping of language representation in the brain. Language T-fMRI activated task-general (i.e., not language-specific) functional systems in addition to areas of Broca and Wernicke. In contrast, classifier-based analysis of RS-fMRI data generated maps confined to language-specific regions of the brain.

Published
2020

48. Comparison of Pittsburgh compound B and florbetapir in cross‐sectional and longitudinal studies

Author

Su, Yi, Flores, Shaney, Wang, Guoqiao, Hornbeck, Russ C, Speidel, Benjamin, Joseph‐Mathurin, Nelly, Vlassenko, Andrei G, Gordon, Brian A, Koeppe, Robert A, Klunk, William E, Jack, Clifford R, Farlow, Martin R, Salloway, Stephen, Snider, Barbara J, Berman, Sarah B, Roberson, Erik D, Brosch, Jared, Jimenez‐Velazques, Ivonne, Dyck, Christopher H, Galasko, Douglas, Yuan, Shauna H, Jayadev, Suman, Honig, Lawrence S, Gauthier, Serge, Hsiung, Ging‐Yuek R, Masellis, Mario, Brooks, William S, Fulham, Michael, Clarnette, Roger, Masters, Colin L, Wallon, David, Hannequin, Didier, Dubois, Bruno, Pariente, Jeremie, Sanchez‐Valle, Raquel, Mummery, Catherine, Ringman, John M, Bottlaender, Michel, Klein, Gregory, Milosavljevic‐Ristic, Smiljana, McDade, Eric, Xiong, Chengjie, Morris, John C, Bateman, Randall J, and Benzinger, Tammie LS

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Bioengineering, Aging, Alzheimer's Disease, Clinical Research, Brain Disorders, Neurodegenerative, Dementia, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Biomedical Imaging, Neurosciences, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, PiB, Florbetapir, Amyloid imaging, Centiloid, Positron emission tomography, Genetics, Biological psychology
Abstract

IntroductionQuantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B-based and florbetapir-based amyloid imaging in the same participants from two independent cohorts using a crossover design.MethodsPittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter-individual variability of the two tracers were compared using multivariate linear models both cross-sectionally and longitudinally.ResultsGlobal amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers.DiscussionAlthough the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers.

Published
2019

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