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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. 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

3. 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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4. 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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5. 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

6. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Author: Jo, Taeho, Kim, Junpyo, Bice, Paula, Huynh, Kevin, Wang, Tingting, Arnold, Matthias, Meikle, Peter J, Giles, Corey, Kaddurah-Daouk, Rima, Saykin, Andrew J, Nho, Kwangsik, Kueider-Paisley, Alexandra, Doraiswamy, P Murali, Blach, Colette, Moseley, Arthur, Thompson, Will, St John-Williams, Lisa, Mahmoudiandehkhordi, Siamak, Tenenbaum, Jessica, Welsh-Balmer, Kathleen, Plassman, Brenda, Risacher, Shannon L, Kastenmüller, Gabi, Han, Xianlin, Baillie, Rebecca, Knight, Rob, Dorrestein, Pieter, Brewer, James, Mayer, Emeran, Labus, Jennifer, Baldi, Pierre, Gupta, Arpana, Fiehn, Oliver, Barupal, Dinesh, Meikle, Peter, Mazmanian, Sarkis, Rader, Dan, Kling, Mitchel, Shaw, Leslie, Trojanowski, John, van Duijin, Cornelia, Nevado-Holgado, Alejo, Bennett, David, Krishnan, Ranga, Keshavarzian, Ali, Vogt, Robin, Ikram, Arfan, Hankemeier, Thomas, Thiele, Ines, Price, Nathan, Funk, Cory, Baloni, Priyanka, Jia, Wei, Wishart, David, Brinton, Roberta, Chang, Rui, Farrer, Lindsay, Au, Rhoda, Qiu, Wendy, Würtz, Peter, Koal, Therese, Mangravite, Lara, Krumsiek, Jan, Suhre, Karsten, Newman, John, Moreno, Herman, Foroud, Tatania, Sacks, Frank, Jansson, Janet, Weiner, Michael W, Aisen, Paul, Petersen, Ronald, Jack, Clifford R, Jagust, William, Trojanowki, John Q, Toga, Arthur W, Beckett, Laurel, Green, Robert C, Morris, John C, Perrin, Richard J, Shaw, Leslie M, Khachaturian, Zaven, Carrillo, Maria, Potter, William, Barnes, Lisa, Bernard, Marie, Gonzalez, Hector, Ho, Carole, Hsiao, John K, Jackson, Jonathan, Masliah, Eliezer, Masterman, Donna, Okonkwo, Ozioma, Perrin, Richard, and Ryan, Laurie
Subjects: Medical Biochemistry and Metabolomics, Biomedical and Clinical Sciences, Alzheimer's Disease, Aging, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Acquired Cognitive Impairment, Neurosciences, Dementia, Neurodegenerative, Neurological, Humans, Aged, Magnetic Resonance Imaging, Deep Learning, Alzheimer Disease, Neuroimaging, Metabolome, Lipids, Cognitive Dysfunction, Alzheimer's Disease Metabolomics Consortium, Alzheimer's Disease Neuroimaging Initiative, Alzheimer's disease, Deep learning, Lipidomics, Machine learning, Metabolomics, Clinical Sciences, Public Health and Health Services, Clinical sciences, Epidemiology
Abstract: BackgroundDeep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.MethodsThe c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification.FindingsThe application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6).InterpretationOur results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation.FundingThe specific funding of this article is provided in the acknowledgements section.
Published: 2023

7. Determinants of cognitive and brain resilience to tau pathology: a longitudinal analysis

Author: Bocancea, Diana I, Svenningsson, Anna L, van Loenhoud, Anna C, Groot, Colin, Barkhof, Frederik, Strandberg, Olof, Smith, Ruben, Weiner, Michael W, Aisen, Paul, Petersen, Ronald, Jack, Clifford R, Jagust, William, Trojanowki, John Q, Toga, Arthur W, Beckett, Laurel, Green, Robert C, Saykin, Andrew J, Morris, John C, Perrin, Richard J, Shaw, Leslie M, Khachaturian, Zaven, Carrillo, Maria, Potter, William, Barnes, Lisa, Bernard, Marie, González, Hector, Ho, Carole, Hsiao, John K, Jackson, Jonathan, Masliah, Eliezer, Masterman, Donna, Okonkwo, Ozioma, Ryan, Laurie, Silverberg, Nina, Fleisher, Adam, Sacrey, Diana Truran, Fockler, Juliet, Conti, Cat, Veitch, Dallas, Neuhaus, John, Jin, Chengshi, Nosheny, Rachel, Ashford, Miriam, Flenniken, Derek, Kormo, Adrienne, Montine, Tom, Conti, Cat B, Rafii, Michael, Raman, Rema, Jimenez, Gustavo, Donohue, Michael, Gessert, Devon, Salazar, Jennifer, Zimmerman, Caileigh, Cabrera, Yuliana, Walter, Sarah, Miller, Garrett, Coker, Godfrey, Clanton, Taylor, Hergesheimer, Lindsey, Smith, Stephanie, Adegoke, Olusegun, Mahboubi, Payam, Moore, Shelley, Pizzola, Jeremy, Shaffer, Elizabeth, Harvey, Danielle, Forghanian-Arani, Arvin, Borowski, Bret, Ward, Chad, Schwarz, Christopher, Jones, David, Gunter, Jeff, Kantarci, Kejal, Senjem, Matthew, Vemuri, Prashanthi, Reid, Robert, Fox, Nick C, Malone, Ian, Thompson, Paul, Thomopoulos, Sophia I, Nir, Talia M, Jahanshad, Neda, DeCarli, Charles, Knaack, Alexander, Fletcher, Evan, Tosun-Turgut, Duygu, Chen, Stephanie Rossi, Choe, Mark, and Crawfor, Karen
Subjects: Health Sciences, Dementia, Vascular Cognitive Impairment/Dementia, Biomedical Imaging, Aging, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Behavioral and Social Science, Alzheimer's Disease, Neurodegenerative, Cerebrovascular, Alzheimer's Disease Related Dementias (ADRD), Clinical Research, Brain Disorders, Neurosciences, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Neurological, Mental health, Humans, Alzheimer Disease, Longitudinal Studies, tau Proteins, Cross-Sectional Studies, Cerebral Cortical Thinning, Positron-Emission Tomography, Brain, Cognition, Apolipoproteins E, Alzheimer's disease, tau, resilience, cognition, PET, MRI, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s disease, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences, Psychology
Abstract: Mechanisms of resilience against tau pathology in individuals across the Alzheimer's disease spectrum are insufficiently understood. Longitudinal data are necessary to reveal which factors relate to preserved cognition (i.e. cognitive resilience) and brain structure (i.e. brain resilience) despite abundant tau pathology, and to clarify whether these associations are cross-sectional or longitudinal. We used a longitudinal study design to investigate the role of several demographic, biological and brain structural factors in yielding cognitive and brain resilience to tau pathology as measured with PET. In this multicentre study, we included 366 amyloid-β-positive individuals with mild cognitive impairment or Alzheimer's disease dementia with baseline 18F-flortaucipir-PET and longitudinal cognitive assessments. A subset (n = 200) additionally underwent longitudinal structural MRI. We used linear mixed-effects models with global cognition and cortical thickness as dependent variables to investigate determinants of cognitive resilience and brain resilience, respectively. Models assessed whether age, sex, years of education, APOE-ε4 status, intracranial volume (and cortical thickness for cognitive resilience models) modified the association of tau pathology with cognitive decline or cortical thinning. We found that the association between higher baseline tau-PET levels (quantified in a temporal meta-region of interest) and rate of cognitive decline (measured with repeated Mini-Mental State Examination) was adversely modified by older age (Stβinteraction = -0.062, P = 0.032), higher education level (Stβinteraction = -0.072, P = 0.011) and higher intracranial volume (Stβinteraction = -0.07, P = 0.016). Younger age, higher education and greater cortical thickness were associated with better cognitive performance at baseline. Greater cortical thickness was furthermore associated with slower cognitive decline independent of tau burden. Higher education also modified the negative impact of tau-PET on cortical thinning, while older age was associated with higher baseline cortical thickness and slower rate of cortical thinning independent of tau. Our analyses revealed no (cross-sectional or longitudinal) associations for sex and APOE-ε4 status on cognition and cortical thickness. In this longitudinal study of clinically impaired individuals with underlying Alzheimer's disease neuropathological changes, we identified education as the most robust determinant of both cognitive and brain resilience against tau pathology. The observed interaction with tau burden on cognitive decline suggests that education may be protective against cognitive decline and brain atrophy at lower levels of tau pathology, with a potential depletion of resilience resources with advancing pathology. Finally, we did not find major contributions of sex to brain nor cognitive resilience, suggesting that previous links between sex and resilience might be mainly driven by cross-sectional differences.
Published: 2023

8. 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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9. 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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10. 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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11. 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

12. Outcome measures for Alzheimer's disease: A global inter‐societal Delphi consensus

Author: Ellison, Tim S, Cappa, Stefano F, Garrett, Dawne, Georges, Jean, Iwatsubo, Takeshi, Kramer, Joel H, Lehmann, Maryna, Lyketsos, Constantine, Maier, Andrea B, Merrilees, Jennifer, Morris, John C, Naismith, Sharon L, Nobili, Flavio, Pahor, Marco, Pond, Dimity, Robinson, Louise, Soysal, Pinar, Vandenbulcke, Mathieu, Weber, Christopher J, Visser, Pieter Jelle, Weiner, Michael, and Frisoni, Giovanni B
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Neurodegenerative, Alzheimer's Disease, Aging, Brain Disorders, Behavioral and Social Science, Acquired Cognitive Impairment, Clinical Research, Health Services, Neurosciences, Neurological, Humans, Alzheimer Disease, Quality of Life, Consensus, Delphi Technique, Outcome Assessment, Health Care, Alzheimer's disease, consensus, Delphi, dementia, measures, outcomes, Geriatrics, Clinical sciences, Biological psychology
Abstract: IntroductionWe aim to provide guidance on outcomes and measures for use in patients with Alzheimer's clinical syndrome.MethodsA consensus group of 20 voting members nominated by 10 professional societies, and a non-voting chair, used a Delphi approach and modified GRADE criteria.ResultsConsensus was reached on priority outcomes (n = 66), measures (n = 49) and statements (n = 37) across nine domains. A number of outcomes and measurement instruments were ranked for: Cognitive abilities; Functional abilities/dependency; Behavioural and neuropsychiatric symptoms; Patient quality of life (QoL); Caregiver QoL; Healthcare and treatment-related outcomes; Medical investigations; Disease-related life events; and Global outcomes.DiscussionThis work provides indications on the domains and ideal pertinent measurement instruments that clinicians may wish to use to follow patients with cognitive impairment. More work is needed to develop instruments that are more feasible in the context of the constraints of clinical routine.
Published: 2023

13. Association of Stages of Objective Memory Impairment With Incident Symptomatic Cognitive Impairment in Cognitively Normal Individuals

Author: Grober, Ellen, Petersen, Kellen K, Lipton, Richard B, Hassenstab, Jason, Morris, John C, Gordon, Brian A, and Ezzati, Ali
Subjects: Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Brain Disorders, Aging, Neurodegenerative, Behavioral and Social Science, Mental Health, Alzheimer's Disease, Acquired Cognitive Impairment, Prevention, 2.1 Biological and endogenous factors, 4.2 Evaluation of markers and technologies, 4.1 Discovery and preclinical testing of markers and technologies, Neurological, Humans, Female, Aged, Male, tau Proteins, Alzheimer Disease, Cognitive Dysfunction, Amyloid beta-Peptides, Memory Disorders, Biomarkers, Disease Progression, Cognitive Sciences, Neurology & Neurosurgery, Clinical sciences
Abstract: Background and objectivesIncreasing evidence indicates that a subset of cognitively normal individuals has subtle cognitive impairment at baseline. We sought to identify them using the Stages of Objective Memory Impairment (SOMI) system. Symptomatic cognitive impairment was operationalized by a Clinical Dementia Rating (CDR) ≥0.5. We hypothesized that incident impairment would be higher for participants with subtle retrieval impairment (SOMI-1), higher still for those with moderate retrieval impairment (SOMI-2), and highest for those with storage impairment (SOMI-3/4) after adjusting for demographics and APOE ε4 status. A secondary objective was to determine whether including biomarkers of β-amyloid, tau pathology, and neurodegeneration in the models affect prediction. We hypothesized that even after adjusting for in vivo biomarkers, SOMI would remain a significant predictor of time to incident symptomatic cognitive impairment.MethodsAmong 969 cognitively normal participants, defined by a CDR = 0, from the Knight Alzheimer Disease Research Center, SOMI stage was determined from their baseline Free and Cued Selective Reminding Test scores, 555 had CSF and structural MRI measures and comprised the biomarker subgroup, and 144 of them were amyloid positive. Cox proportional hazards models tested associations of SOMI stages at baseline and biomarkers with time to incident cognitive impairment defined as the transition to CDR ≥0.5.ResultsAmong all participants, the mean age was 69.35 years, 59.6% were female, and mean follow-up was 6.36 years. Participants in SOMI-1-4 had elevated hazard ratios for the transition from normal to impaired cognition in comparison with those who were SOMI-0 (no memory impairment). Individuals in SOMI-1 (mildly impaired retrieval) and SOMI-2 (moderately impaired retrieval) were at nearly double the risk of clinical progression compared with persons with no memory problems. When memory storage impairment emerges (SOMI-3/4), the hazard ratio for clinical progression increased approximately 3 times. SOMI stage remained an independent predictor of incident cognitive impairment after adjusting for all biomarkers.DiscussionSOMI predicts the transition from normal cognition to incident symptomatic cognitive impairment (CDR ≥0.5). The results support the use of SOMI to identify those cognitively normal participants most likely to develop incident cognitive impairment who can then be referred for biomarker screening.
Published: 2023

14. 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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15. 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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16. Increasing participant diversity in AD research: Plans for digital screening, blood testing, and a community‐engaged approach in the Alzheimer's Disease Neuroimaging Initiative 4

Author: Weiner, Michael W, Veitch, Dallas P, Miller, Melanie J, Aisen, Paul S, Albala, Bruce, Beckett, Laurel A, Green, Robert C, Harvey, Danielle, Jack, Clifford R, Jagust, William, Landau, Susan M, Morris, John C, Nosheny, Rachel, Okonkwo, Ozioma C, Perrin, Richard J, Petersen, Ronald C, Rivera‐Mindt, Monica, Saykin, Andrew J, Shaw, Leslie M, Toga, Arthur W, Tosun, Duygu, Trojanowski, John Q, and Initiative, Alzheimer's Disease Neuroimaging
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Acquired Cognitive Impairment, Minority Health, Clinical Trials and Supportive Activities, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurosciences, Prevention, Dementia, Brain Disorders, Clinical Research, Neurodegenerative, Aging, Alzheimer's Disease, Biomedical Imaging, Detection, screening and diagnosis, 4.5 Resources and infrastructure (detection), 4.1 Discovery and preclinical testing of markers and technologies, Neurological, Good Health and Well Being, Humans, Alzheimer Disease, Community Participation, Stakeholder Participation, Neuroimaging, Biomarkers, Cognitive Dysfunction, Amyloid beta-Peptides, Alzheimer's disease, amyloid, cerebrovascular disease, digital biomarkers, generalizability, mild cognitive impairment, plasma biomarkers, tau, underrepresented populations, Alzheimer's Disease Neuroimaging Initiative, Geriatrics, Clinical sciences, Biological psychology
Abstract: IntroductionThe Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to validate biomarkers for Alzheimer's disease (AD) clinical trials. To improve generalizability, ADNI4 aims to enroll 50-60% of its new participants from underrepresented populations (URPs) using new biofluid and digital technologies. ADNI4 has received funding from the National Institute on Aging beginning September 2022.MethodsADNI4 will recruit URPs using community-engaged approaches. An online portal will screen 20,000 participants, 4000 of whom (50-60% URPs) will be tested for plasma biomarkers and APOE. From this, 500 new participants will undergo in-clinic assessment joining 500 ADNI3 rollover participants. Remaining participants (∼3500) will undergo longitudinal plasma and digital cognitive testing. ADNI4 will add MRI sequences and new PET tracers. Project 1 will optimize biomarkers in AD clinical trials.Results and discussionADNI4 will improve generalizability of results, use remote digital and blood screening, and continue providing longitudinal clinical, biomarker, and autopsy data to investigators.
Published: 2023

17. A data‐driven examination of apathy and depressive symptoms in dementia with independent replication

Author: Da Silva, Miguel Vasconcelos, Melendez‐Torres, Gerardo Javier, Ismail, Zahinoor, Testad, Ingelin, Ballard, Clive, Creese, Byron, Weiner, Michael, Aisen, Paul, Petersen, Ronald, Jack, Clifford R, Jagust, William, Trojanowki, John Q, Toga, Arthur W, Beckett, Laurel, Green, Robert C, Saykin, Andrew J, Morris, John, Shaw, Leslie M, Liu, Enchi, Montine, Tom, Thomas, Ronald G, Donohue, Michael, Walter, Sarah, Gessert, Devon, Sather, Tamie, Jiminez, Gus, Harvey, Danielle, Bernstein, Matthew, Fox, Nick, Thompson, Paul, Schuff, Norbert, DeCArli, Charles, Borowski, Bret, Gunter, Jeff, Senjem, Matt, Vemuri, Prashanthi, Jones, David, Kantarci, Kejal, Ward, Chad, Koeppe, Robert A, Foster, Norm, Reiman, Eric M, Chen, Kewei, Mathis, Chet, Landau, Susan, Cairns, Nigel J, Householder, Erin, Reinwald, Lisa Taylor, Lee, Virginia, Korecka, Magdalena, Figurski, Michal, Crawford, Karen, Neu, Scott, Foroud, Tatiana M, Potkin, Steven, Shen, Li, Kelley, Faber, Kim, Sungeun, Nho, Kwangsik, Kachaturian, Zaven, Frank, Richard, Snyder, Peter J, Molchan, Susan, Kaye, Jeffrey, Quinn, Joseph, Lind, Betty, Carter, Raina, Dolen, Sara, Schneider, Lon S, Pawluczyk, Sonia, Beccera, Mauricio, Teodoro, Liberty, Spann, Bryan M, Brewer, James, Van der Swag, Helen, Fleisher, Adam, Heidebrink, Judith L, Lord, Joanne L, Mason, Sara S, Albers, Colleen S, Knopman, David, Johnson, Kris, Doody, Rachelle S, Meyer, Javier Villanueva, Chowdhury, Munir, Rountree, Susan, Dang, Mimi, Stern, Yaakov, Honig, Lawrence S, Bell, Karen L, Ances, Beau, Morris, John C, Carroll, Maria, Leon, Sue, Mintun, Mark A, Schneider, Stacy, and Oliver, Angela
Subjects: Clinical and Health Psychology, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Mental Health, Depression, Mental Illness, Mental health, Alzheimer's Disease Neuroimaging Initiative, apathy, dementia, depression, latent class analysis, Genetics, Neurosciences, Biological psychology
Abstract: Apathy is one of the most common neuropsychiatric symptoms (NPS) and is associated with poor clinical outcomes. Research that helps define the apathy phenotype is urgently needed, particularly for clinical and biomarker studies. We used latent class analysis (LCA) with two independent cohorts to understand how apathy and depression symptoms co-occur statistically. We further explored the relationship between latent class membership, demographics, and the presence of other NPS. The LCA identified a four-class solution (no symptoms, apathy, depression, and combined apathy/depression), reproducible over both cohorts, providing robust support for an apathy syndrome distinct from depression and confirming that an apathy/depression syndrome exists, supported by the model fit test with the four-class solution scores evidencing better fitting (Bayesian information criterion adjusted and entropy R 2). Using a data-driven method, we show distinct and statistically meaningful co-occurrence of apathy and depressive symptoms. There was evidence that these classes have different clinical associations, which may help inform diagnostic categories for research studies and clinical practice.HighlightsWe found four classes: no symptoms, apathy, depression and apathy/depression.Apathy conferred a higher probability for agitation.Apathy diagnostic criteria should include accompanying neuropsychiatric symptoms.
Published: 2023

18. 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

19. 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

20. 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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21. To BYOD or not: Are device latencies important for bring-your-own-device (BYOD) smartphone cognitive testing?

Author: Nicosia, Jessica, Wang, Benjamin, Aschenbrenner, Andrew J., Sliwinski, Martin J., Yabiku, Scott T., Roque, Nelson A., Germine, Laura T., Bateman, Randall J., Morris, John C., and Hassenstab, Jason
Published: 2023
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22. 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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23. 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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24. 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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25. Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

Author: Veitch, Dallas P, Weiner, Michael W, Aisen, Paul S, Beckett, Laurel A, DeCarli, Charles, Green, Robert C, Harvey, Danielle, Jack, Clifford R, Jagust, William, Landau, Susan M, Morris, John C, Okonkwo, Ozioma, Perrin, Richard J, Petersen, Ronald C, Rivera‐Mindt, Monica, Saykin, Andrew J, Shaw, Leslie M, Toga, Arthur W, Tosun, Duygu, Trojanowski, John Q, and Initiative, Alzheimer's Disease Neuroimaging
Subjects: Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Neurodegenerative, Alzheimer's Disease, Biomedical Imaging, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Prevention, Brain Disorders, Dementia, Acquired Cognitive Impairment, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Neurological, Alzheimer Disease, Amyloid beta-Peptides, Biomarkers, Cognitive Dysfunction, Disease Progression, Humans, Neuroimaging, tau Proteins, Alzheimer's disease, amyloid, AV1541 tau positron emission tomography, disease progression, mild cognitive impairment, plasma biomarker, tau, Alzheimer's Disease Neuroimaging Initiative, Geriatrics, Clinical sciences, Biological psychology
Abstract: IntroductionThe Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.MethodsWe identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.ResultsDisease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.DiscussionADNI has had a profound impact in improving clinical trials for AD.
Published: 2022

26. Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learning

Author: Hwang, Gyujoon, Abdulkadir, Ahmed, Erus, Guray, Habes, Mohamad, Pomponio, Raymond, Shou, Haochang, Doshi, Jimit, Mamourian, Elizabeth, Rashid, Tanweer, Bilgel, Murat, Fan, Yong, Sotiras, Aristeidis, Srinivasan, Dhivya, Morris, John C., Marcus, Daniel, Albert, Marilyn S., Bryan, Nick R., Resnick, Susan M., Nasrallah, Ilya M., Davatzikos, Christos, and Wolk, David A.
Subjects: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics, Quantitative Biology - Neurons and Cognition
Abstract: Neuroimaging biomarkers that distinguish between typical brain aging and Alzheimer's disease (AD) are valuable for determining how much each contributes to cognitive decline. Machine learning models can derive multi-variate brain change patterns related to the two processes, including the SPARE-AD (Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease) and SPARE-BA (of Brain Aging) investigated herein. However, substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology toward disentangling the two. T1-weighted MRI images of 4,054 participants (48-95 years) with AD, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the iSTAGING (Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases) consortium were analyzed. First, a subset of AD patients and CN adults were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus AD). Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2: amyloid-positive (A+) AD continuum group (consisting of A+AD, A+MCI, and A+ and tau-positive CN individuals) and amyloid-negative (A-) CN group. Finally, the combined group of the AD continuum and A-/CN individuals was used to train SPARE-BA3, with the intention to estimate brain age regardless of AD-related brain changes. Disentangled SPARE models derived brain patterns that were more specific to the two types of the brain changes. Correlation between the SPARE-BA and SPARE-AD was significantly reduced. Correlation of disentangled SPARE-AD was non-inferior to the molecular measurements and to the number of APOE4 alleles, but was less to AD-related psychometric test scores, suggesting contribution of advanced brain aging to these scores., Comment: 4 figures, 3 tables
Published: 2021

27. 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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28. 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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29. Single-nucleus RNA-sequencing of autosomal dominant Alzheimer disease and risk variant carriers

Author: Brase, Logan, You, Shih-Feng, D’Oliveira Albanus, Ricardo, Del-Aguila, Jorge L., Dai, Yaoyi, Novotny, Brenna C., Soriano-Tarraga, Carolina, Dykstra, Taitea, Fernandez, Maria Victoria, Budde, John P., Bergmann, Kristy, Morris, John C., Bateman, Randall J., Perrin, Richard J., McDade, Eric, Xiong, Chengjie, Goate, Alison M., Farlow, Martin, Sutherland, Greg T., Kipnis, Jonathan, Karch, Celeste M., Benitez, Bruno A., and Harari, Oscar
Published: 2023
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30. Alzheimer’s disease genetic risk and cognitive reserve in relationship to long-term cognitive trajectories among cognitively normal individuals

Author: Pettigrew, Corinne, Nazarovs, Jurijs, Soldan, Anja, Singh, Vikas, Wang, Jiangxia, Hohman, Timothy, Dumitrescu, Logan, Libby, Julia, Kunkle, Brian, Gross, Alden L., Johnson, Sterling, Lu, Qiongshi, Engelman, Corinne, Masters, Colin L., Maruff, Paul, Laws, Simon M., Morris, John C., Hassenstab, Jason, Cruchaga, Carlos, Resnick, Susan M., Kitner-Triolo, Melissa H., An, Yang, and Albert, Marilyn
Published: 2023
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31. 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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32. Development and implementation of an electronic Clinical Dementia Rating and Financial Capacity Instrument‐Short Form

Author: Howell, Taylor, Gummadi, Shilpa, Bui, Chau, Santhakumar, Jessica, Knight, Kristen, Roberson, Erik D, Marson, Daniel, Chambless, Carol, Gersteneker, Adam, Martin, Roy, Kennedy, Richard, Zhang, Yue, Morris, John C, Moulder, Krista L, Mayo, Connie, Carroll, Maria, Li, Yan, Petersen, Ronald C, Stricker, Nikki H, Nosheny, Rachel L, Mackin, Scott, and Weiner, Michael W
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, Acquired Cognitive Impairment, Aging, Clinical Research, Dementia, Clinical Trials and Supportive Activities, Brain Disorders, Behavioral and Social Science, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Alzheimer's Disease, Neurological, Good Health and Well Being, aging research, Alzheimer's disease, Brain Health Registry, internet, remote online instruments, Genetics, Biological psychology
Abstract: IntroductionTo address the need for remote assessments of cognitive decline and dementia, we developed and administered electronic versions of the Clinical Dementia Rating (CDR®) and the Financial Capacity Instrument-Short Form (FCI-SF) (F-CAP®), called the eCDR and eFCI, respectively.MethodsThe CDR and FCI-SF were adapted for remote, unsupervised, online use based on item response analysis of the standard instruments. Participants completed the eCDR and eFCI first in clinic, and then at home within 2 weeks.ResultsOf the 243 enrolled participants, 179 (73%) cognitively unimpaired (CU), 50 (21%) with mild cognitive impairment (MCI) or dementia, and 14 (6%) with an unknown diagnosis, 84% and 85% of them successfully completed the eCDR and eFCI, respectively, at home.DiscussionThese results show initial feasibility in developing and administering online instruments to remotely assess and monitor cognitive decline along the CU to MCI/very mild dementia continuum. Validation is an important next step.
Published: 2022

33. Case Study: Crowdfunding Local Government: Belmont's Bicycle Sunday Program

Author: Mayer, Martin K., primary and Morris, John C., additional
Published: 2023
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34. Solution or Trouble?

Author: Mayer, Martin K., primary and Morris, John C., additional
Published: 2023
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35. 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. Predicting cognitive decline: Which is more useful, baseline amyloid levels or longitudinal change?

Author: Chen, Gengsheng, McKay, Nicole S., Gordon, Brian A., Liu, Jingxia, Joseph-Mathurin, Nelly, Schindler, Suzanne E., Hassenstab, Jason, Aschenbrenner, Andrew J., Wang, Qing, Schultz, Stephanie A., Su, Yi, LaMontagne, Pamela J, Keefe, Sarah J., Massoumzadeh, Parinaz, Cruchaga, Carlos, Xiong, Chengjie, Morris, John C., and Benzinger, Tammie L.S.
Published: 2024
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37. Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study

Author: Strain, Jeremy F., Rahmani, Maryam, Dierker, Donna, Owen, Christopher, Jafri, Hussain, Vlassenko, Andrei G., Womack, Kyle, Fripp, Jurgen, Tosun, Duygu, Benzinger, Tammie L.S., Weiner, Michael, Masters, Colin, Lee, Jin-Moo, Morris, John C., and Goyal, Manu S.
Published: 2024
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38. A deep learning framework identifies dimensional representations of Alzheimer's Disease from brain structure.

Author: Yang, Zhijian, Nasrallah, Ilya M, Shou, Haochang, Wen, Junhao, Doshi, Jimit, Habes, Mohamad, Erus, Guray, Abdulkadir, Ahmed, Resnick, Susan M, Albert, Marilyn S, Maruff, Paul, Fripp, Jurgen, Morris, John C, Wolk, David A, Davatzikos, Christos, iSTAGING Consortium, Baltimore Longitudinal Study of Aging (BLSA), and Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Subjects: iSTAGING Consortium, Baltimore Longitudinal Study of Aging, Alzheimer’s Disease Neuroimaging Initiative, Brain, Humans, Alzheimer Disease, Magnetic Resonance Imaging, Cluster Analysis, Case-Control Studies, Longitudinal Studies, Image Processing, Computer-Assisted, Aged, Aged, 80 and over, Middle Aged, Female, Male, Neuroimaging, Healthy Volunteers, Cognitive Dysfunction, Deep Learning, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Acquired Cognitive Impairment, Biomedical Imaging, Brain Disorders, Alzheimer's Disease, Dementia, Clinical Research, Aging, Neurosciences, Neurodegenerative, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Neurological, Good Health and Well Being
Abstract: Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment.
Published: 2021

39. Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging

Author: Bashyam, Vishnu M., Doshi, Jimit, Erus, Guray, Srinivasan, Dhivya, Abdulkadir, Ahmed, Habes, Mohamad, Fan, Yong, Masters, Colin L., Maruff, Paul, Zhuo, Chuanjun, Völzke, Henry, Johnson, Sterling C., Fripp, Jurgen, Koutsouleris, Nikolaos, Satterthwaite, Theodore D., Wolf, Daniel H., Gur, Raquel E., Gur, Ruben C., Morris, John C., Albert, Marilyn S., Grabe, Hans J., Resnick, Susan M., Bryan, R. Nick, Wolk, David A., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine. However, these methods have yet to see widespread clinical adoption, in part due to limited generalization performance across various imaging devices, acquisition protocols, and patient populations. In this work, we propose a new paradigm in which data from a diverse range of acquisition conditions are "harmonized" to a common reference domain, where accurate model learning and prediction can take place. By learning an unsupervised image to image canonical mapping from diverse datasets to a reference domain using generative deep learning models, we aim to reduce confounding data variation while preserving semantic information, thereby rendering the learning task easier in the reference domain. We test this approach on two example problems, namely MRI-based brain age prediction and classification of schizophrenia, leveraging pooled cohorts of neuroimaging MRI data spanning 9 sites and 9701 subjects. Our results indicate a substantial improvement in these tasks in out-of-sample data, even when training is restricted to a single site.
Published: 2020

40. 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

41. Human striatal glia differentially contribute to AD- and PD-specific neurodegeneration

Author: Xu, Jinbin, Farsad, Huifangjie L., Hou, Yiran, Barclay, Kia, Lopez, Ben Anthony, Yamada, Shinnosuke, Saliu, Ibrahim Olabayode, Shi, Yiming, Knight, William C., Bateman, Randall J., Benzinger, Tammie L. S., Yi, Jason J., Li, Qingyun, Wang, Ting, Perlmutter, Joel S., Morris, John C., and Zhao, Guoyan
Published: 2023
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42. Cell-free RNA signatures predict Alzheimer’s disease

Author: Cisterna-García, Alejandro, Beric, Aleksandra, Ali, Muhammad, Pardo, Jose Adrian, Chen, Hsiang-Han, Fernandez, Maria Victoria, Norton, Joanne, Gentsch, Jen, Bergmann, Kristy, Budde, John, Perlmutter, Joel S., Morris, John C., Cruchaga, Carlos, Botia, Juan A., and Ibanez, Laura
Published: 2023
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43. Un‘covering’ blanket bonds: Assessing the adoption of blanket bonds across the states

Author: Naher, Nurun, Mayer, Adam P., Fisk, Jonathan M., and Morris, John C.
Published: 2023
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44. Neuropsychiatric Symptoms and Alzheimer Disease Biomarkers Independently Predict Progression to Incident Cognitive Impairment

Author: Babulal, Ganesh M., Chen, Ling, Murphy, Samantha A., Doherty, Jason M., Johnson, Ann M., and Morris, John C.
Published: 2023
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45. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Author: Kaddurah-Daouk, Rima, Kueider-Paisley, Alexandra, Doraiswamy, P. Murali, Blach, Colette, Moseley, Arthur, Thompson, Will, St John-Williams, Lisa, Mahmoudiandehkhordi, Siamak, Tenenbaum, Jessica, Welsh-Balmer, Kathleen, Plassman, Brenda, Saykin, Andrew J., Nho, Kwangsik, Risacher, Shannon L., Kastenmüller, Gabi, Arnold, Matthias, Han, Xianlin, Baillie, Rebecca, Knight, Rob, Dorrestein, Pieter, Brewer, James, Mayer, Emeran, Labus, Jennifer, Baldi, Pierre, Gupta, Arpana, Fiehn, Oliver, Barupal, Dinesh, Meikle, Peter, Mazmanian, Sarkis, Rader, Dan, Kling, Mitchel, Shaw, Leslie, Trojanowski, John, van Duijin, Cornelia, Nevado-Holgado, Alejo, Bennett, David, Krishnan, Ranga, Keshavarzian, Ali, Vogt, Robin, Ikram, Arfan, Hankemeier, Thomas, Thiele, Ines, Price, Nathan, Funk, Cory, Baloni, Priyanka, Jia, Wei, Wishart, David, Brinton, Roberta, Chang, Rui, Farrer, Lindsay, Au, Rhoda, Qiu, Wendy, Würtz, Peter, Koal, Therese, Mangravite, Lara, Krumsiek, Jan, Suhre, Karsten, Newman, John, Moreno, Herman, Foroud, Tatania, Sacks, Frank, Jansson, Janet, Weiner, Michael W., Aisen, Paul, Petersen, Ronald, Jack, Clifford R., Jr., Jagust, William, Trojanowki, John Q., Toga, Arthur W., Beckett, Laurel, Green, Robert C., Morris, John C., Perrin, Richard J., Shaw, Leslie M., Khachaturian, Zaven, Carrillo, Maria, Potter, William, Barnes, Lisa, Bernard, Marie, Gonzalez, Hector, Ho, Carole, Hsiao, John K., Jackson, Jonathan, Masliah, Eliezer, Masterman, Donna, Okonkwo, Ozioma, Perrin, Richard, Ryan, Laurie, Silverberg, Nina, Fleisher, Adam, Sacrey, Diana Truran, Fockler, Juliet, Conti, Cat, Veitch, Dallas, Neuhaus, John, Jin, Chengshi, Nosheny, Rachel, Ashford, Miriam, Flenniken, Derek, Kormos, Adrienne, Montine, Tom, Rafii, Michael, Raman, Rema, Jimenez, Gustavo, Donohue, Michael, Gessert, Devon, Salazar, Jennifer, Zimmerman, Caileigh, Cabrera, Yuliana, Walter, Sarah, Miller, Garrett, Coker, Godfrey, Clanton, Taylor, Hergesheimer, Lindsey, Smith, Stephanie, Adegoke, Olusegun, Mahboubi, Payam, Moore, Shelley, Pizzola, Jeremy, Shaffer, Elizabeth, Sloan, Brittany, Harvey, Danielle, Forghanian-Arani, Arvin, Borowski, Bret, Ward, Chad, Schwarz, Christopher, Jones, David, Gunter, Jeff, Kantarci, Kejal, Senjem, Matthew, Vemuri, Prashanthi, Reid, Robert, Fox, Nick C., Malone, Ian, Thompson, Paul, Thomopoulos, Sophia I., Nir, Talia M., Jahanshad, Neda, DeCarli, Charles, Knaack, Alexander, Fletcher, Evan, Tosun-Turgut, Duygu, Chen, Stephanie Rossi, Choe, Mark, Crawford, Karen, Yushkevich, Paul A., Das, Sandhitsu, Koeppe, Robert A., Reiman, Eric M., Chen, Kewei, Mathis, Chet, Landau, Susan, Cairns, Nigel J., Householder, Erin, Franklin, Erin, Bernhardt, Haley, Taylor-Reinwald, Lisa, Korecka, Magdalena, Figurski, Michal, Neu, Scott, Apostolova, Liana G., Shen, Li, Foroud, Tatiana M., Nudelman, Kelly, Faber, Kelley, Wilmes, Kristi, Thal, Leon, Silbert, Lisa C., Lind, Betty, Crissey, Rachel, Kaye, Jeffrey A., Carter, Raina, Dolen, Sara, Quinn, Joseph, Schneider, Lon S., Pawluczyk, Sonia, Becerra, Mauricio, Teodoro, Liberty, Dagerman, Karen, Spann, Bryan M., Vanderswag, Helen, Ziolkowski, Jaimie, Heidebrink, Judith L., Zbizek-Nulph, Lisa, Lord, Joanne L., Mason, Sara S., Albers, Colleen S., Knopman, David, Johnson, Kris, Villanueva-Meyer, Javier, Pavlik, Valory, Pacini, Nathaniel, Lamb, Ashley, Kass, Joseph S., Doody, Rachelle S., Shibley, Victoria, Chowdhury, Munir, Rountree, Susan, Dang, Mimi, Stern, Yaakov, Honig, Lawrence S., Mintz, Akiva, Ances, Beau, Winkfield, David, Carroll, Maria, Stobbs-Cucchi, Georgia, Oliver, Angela, Creech, Mary L., Mintun, Mark A., Schneider, Stacy, Geldmacher, David, Love, Marissa Natelson, Griffith, Randall, Clark, David, Brockington, John, Marson, Daniel, Grossman, Hillel, Goldstein, Martin A., Greenberg, Jonathan, Mitsis, Effie, Shah, Raj C., Lamar, Melissa, Samuels, Patricia, Duara, Ranjan, Greig-Custo, Maria T., Rodriguez, Rosemarie, Albert, Marilyn, Onyike, Chiadi, Farrington, Leonie, Rudow, Scott, Brichko, Rottislav, Kielb, Stephanie, Smith, Amanda, Raj, Balebail Ashok, Fargher, Kristin, Sadowski, Martin, Wisniewski, Thomas, Shulman, Melanie, Faustin, Arline, Rao, Julia, Castro, Karen M., Ulysse, Anaztasia, Chen, Shannon, Sheikh, Mohammed O., Singleton-Garvin, Jamika, Petrella, JeffreyR., James, Olga, Wong, Terence Z., Borges-Neto, Salvador, Karlawish, Jason H., Wolk, David A., Vaishnavi, Sanjeev, Clark, Christopher M., Arnold, Steven E., Smith, Charles D., Jicha, Gregory A., Raslau, Flavius D., Lopez, Oscar L., Oakley, MaryAnn, Simpson, Donna M., Porsteinsson, Anton P., Martin, Kim, Kowalski, Nancy, Keltz, Melanie, Goldstein, Bonnie S., Makino, Kelly M., Ismail, M. Saleem, Brand, Connie, Thai, Gaby, Pierce, Aimee, Yanez, Beatriz, Sosa, Elizabeth, Witbracht, Megan, Kelley, Brendan, Nguyen, Trung, Womack, Kyle, Mathews, Dana, Quiceno, Mary, Levey, Allan I., Lah, James J., Hajjar, Ihab, Cellar, Janet S., Burns, Jeffrey M., Swerdlow, Russell H., Brooks, William M., Silverman, Daniel H.S., Kremen, Sarah, Apostolova, Liana, Tingus, Kathleen, Lu, Po H., Bartzokis, George, Woo, Ellen, Teng, Edmond, Graff-Radford, Neill R., Parfitt, Francine, Poki-Walker, Kim, Farlow, Martin R., Hake, Ann Marie, Matthews, Brandy R., Brosch, Jared R., Herring, Scott, van, Christopher H., Mecca, Adam P., Good, Susan P., MacAvoy, Martha G., Carson, Richard E., Varma, Pradeep, Chertkow, Howard, Vaitekunis, Susan, Hosein, Chris, Black, Sandra, Stefanovic, Bojana, Heyn, Chris (Chinthaka), Robin Hsiung, Ging-Yuek, Kim, Ellen, Mudge, Benita, Sossi, Vesna, Feldman, Howard, Assaly, Michele, Finger, Elizabeth, Pasternak, Stephen, Rachinsky, Irina, Kertesz, Andrew, Drost, Dick, Rogers, John, Grant, Ian, Muse, Brittanie, Rogalski, Emily, Robson, Jordan, Mesulam, M.-Marsel, Kerwin, Diana, Wu, Chuang-Kuo, Johnson, Nancy, Lipowski, Kristine, Weintraub, Sandra, Bonakdarpour, Borna, Pomara, Nunzio, Hernando, Raymundo, Sarrael, Antero, Rosen, Howard J., Miller, Bruce L., Perry, David, Turner, Raymond Scott, Johnson, Kathleen, Reynolds, Brigid, MCCann, Kelly, Poe, Jessica, Sperling, Reisa A., Johnson, Keith A., Marshall, Gad A., Yesavage, Jerome, Taylor, Joy L., Chao, Steven, Coleman, Jaila, White, Jessica D., Lane, Barton, Rosen, Allyson, Tinklenberg, Jared, Belden, Christine M., Atri, Alireza, Clark, Kelly A., Zamrini, Edward, Sabbagh, Marwan, Killiany, Ronald, Stern, Robert, Mez, Jesse, Kowall, Neil, Budson, Andrew E., Obisesan, Thomas O., Ntekim, Oyonumo E., Wolday, Saba, Khan, Javed I., Nwulia, Evaristus, Nadarajah, Sheeba, Lerner, Alan, Ogrocki, Paula, Tatsuoka, Curtis, Fatica, Parianne, Maillard, Pauline, Olichney, John, Carmichael, Owen, Bates, Vernice, Capote, Horacio, Rainka, Michelle, Borrie, Michael, Lee, T.-Y., Bartha, Dr Rob, Johnson, Sterling, Asthana, Sanjay, Carlsson, Cynthia M., Perrin, Allison, Burke, Anna, Scharre, Douglas W., Kataki, Maria, Tarawneh, Rawan, Hart, David, Zimmerman, Earl A., Celmins, Dzintra, Miller, Delwyn D., BolesPonto, Laura L., Smith, Karen Ekstam, Koleva, Hristina, Shim, Hyungsub, Nam, Ki Won, Schultz, Susan K., Williamson, Jeff D., Craft, Suzanne, Cleveland, Jo, Yang, Mia, Sink, Kaycee M., Ott, Brian R., Drake, Jonathan, Tremont, Geoffrey, Daiello, Lori A., Drake, Jonathan D., Ritter, Aaron, Bernick, Charles, Munic, Donna, O'Connelll, Abigail, Mintzer, Jacobo, Wiliams, Arthur, Masdeu, Joseph, Shi, Jiong, Garcia, Angelica, Newhouse, Paul, Potkin, Steven, Salloway, Stephen, Malloy, Paul, Correia, Stephen, Kittur, Smita, Pearlson, Godfrey D., Blank, Karen, Anderson, Karen, Flashman, Laura A., Seltzer, Marc, Hynes, Mary L., Santulli, Robert B., Relkin, Norman, Chiang, Gloria, Lee, Athena, Lin, Michael, Ravdin, Lisa, Petersen, Ron, Neylan, Thomas, Grafman, Jordan, Danowski, Sarah, Nguyen-Barrera, Catherine, Hayes, Jacqueline, Finley, Shannon, Bernstein, Matthew, Senjem, Matt, Foster, Norm, Kim, Sungeun, Sood, Ajay, Blanchard, Kimberly S., Fleischman, Debra, Arfanakis, Konstantinos, Varon, Daniel, Greig, Maria T., Petrella, Jeffrey R., Goldstein, Bonnie, Martin, Kimberly S., Reist, Christopher, Sadowsky, Carl, Martinez, Walter, Villena, Teresa, Rosen, Howard, Marshall, Gad, Peskind, Elaine R., Petrie, Eric C., Li, Gail, Mackin, Scott, Jimenez-Maggiora, Gustavo, Drake, Erin, Donohue, Mike, Nelson, Craig, Bickford, David, Butters, Meryl, Zmuda, Michelle, Reyes, Denise, Faber, Kelley M., Nudelman, Kelly N., Au, Yiu Ho, Scherer, Kelly, Catalinotto, Daniel, Stark, Samuel, Ong, Elise, Fernandez, Dariella, Jo, Taeho, Kim, Junpyo, Bice, Paula, Huynh, Kevin, Wang, Tingting, Meikle, Peter J., and Giles, Corey
Published: 2023
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46. 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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47. Neuroinflammation: A Modifiable Pathway Linking Obesity, Alzheimer's disease, and Depression

Author: Ly, Maria, Yu, Gary Z., Mian, Ali, Cramer, Austin, Meysami, Somayeh, Merrill, David A., Samara, Amjad, Eisenstein, Sarah A., Hershey, Tamara, Babulal, Ganesh M., Lenze, Eric J., Morris, John C., Benzinger, Tammie L.S., and Raji, Cyrus A.
Published: 2023
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48. Item response theory analysis of the Clinical Dementia Rating.

Author: Li, Yan, Xiong, Chengjie, Aschenbrenner, Andrew J, Chang, Chih-Hung, Weiner, Michael W, Nosheny, Rachel L, Mungas, Dan, Bateman, Randall J, Hassenstab, Jason, Moulder, Krista L, and Morris, John C
Subjects: Humans, Alzheimer Disease, Psychiatric Status Rating Scales, Algorithms, Aged, Female, Male, Cognitive Dysfunction, Mental Status and Dementia Tests, Alzheimer's disease, Clinical Dementia Rating, bi-factor model, cognitive assessment, dementia severity, item response theory, Alzheimer's Disease, Brain Disorders, Neurosciences, Dementia, Acquired Cognitive Impairment, Neurodegenerative, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurological, Alzheimer&apos, s disease, bi&#8208, factor model, Clinical Sciences, Geriatrics
Abstract: IntroductionThe Clinical Dementia Rating (CDR) is widely used in Alzheimer's disease research studies and has well established reliability and validity. To facilitate the development of an online, electronic CDR (eCDR) for more efficient clinical applications, this study aims to produce a shortened version of the CDR, and to develop the statistical model for automatic scoring.MethodsItem response theory (IRT) was used for item evaluation and model development. An automatic scoring algorithm was validated using existing CDR global and domain box scores as the reference standard.ResultsMost CDR items discriminate well at mild and very mild levels of cognitive impairment. The bi-factor IRT model fits best and the shortened CDR still demonstrates very high classification accuracy (81%∼92%).DiscussionThe shortened version of the CDR and the automatic scoring algorithm has established a good foundation for developing an eCDR and will ultimately improve the efficiency of cognitive assessment.
Published: 2021

49. Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture

Author: Chia, Ruth, Sabir, Marya S, Bandres-Ciga, Sara, Saez-Atienzar, Sara, Reynolds, Regina H, Gustavsson, Emil, Walton, Ronald L, Ahmed, Sarah, Viollet, Coralie, Ding, Jinhui, Makarious, Mary B, Diez-Fairen, Monica, Portley, Makayla K, Shah, Zalak, Abramzon, Yevgeniya, Hernandez, Dena G, Blauwendraat, Cornelis, Stone, David J, Eicher, John, Parkkinen, Laura, Ansorge, Olaf, Clark, Lorraine, Honig, Lawrence S, Marder, Karen, Lemstra, Afina, St George-Hyslop, Peter, Londos, Elisabet, Morgan, Kevin, Lashley, Tammaryn, Warner, Thomas T, Jaunmuktane, Zane, Galasko, Douglas, Santana, Isabel, Tienari, Pentti J, Myllykangas, Liisa, Oinas, Minna, Cairns, Nigel J, Morris, John C, Halliday, Glenda M, Van Deerlin, Vivianna M, Trojanowski, John Q, Grassano, Maurizio, Calvo, Andrea, Mora, Gabriele, Canosa, Antonio, Floris, Gianluca, Bohannan, Ryan C, Brett, Francesca, Gan-Or, Ziv, Geiger, Joshua T, Moore, Anni, May, Patrick, Krüger, Rejko, Goldstein, David S, Lopez, Grisel, Tayebi, Nahid, Sidransky, Ellen, Norcliffe-Kaufmann, Lucy, Palma, Jose-Alberto, Kaufmann, Horacio, Shakkottai, Vikram G, Perkins, Matthew, Newell, Kathy L, Gasser, Thomas, Schulte, Claudia, Landi, Francesco, Salvi, Erika, Cusi, Daniele, Masliah, Eliezer, Kim, Ronald C, Caraway, Chad A, Monuki, Edwin S, Brunetti, Maura, Dawson, Ted M, Rosenthal, Liana S, Albert, Marilyn S, Pletnikova, Olga, Troncoso, Juan C, Flanagan, Margaret E, Mao, Qinwen, Bigio, Eileen H, Rodríguez-Rodríguez, Eloy, Infante, Jon, Lage, Carmen, González-Aramburu, Isabel, Sanchez-Juan, Pascual, Ghetti, Bernardino, Keith, Julia, Black, Sandra E, Masellis, Mario, Rogaeva, Ekaterina, Duyckaerts, Charles, Brice, Alexis, Lesage, Suzanne, Xiromerisiou, Georgia, Barrett, Matthew J, Tilley, Bension S, Gentleman, Steve, Logroscino, Giancarlo, and Serrano, Geidy E
Subjects: Biological Sciences, Genetics, Human Genome, Alzheimer's Disease Related Dementias (ADRD), Prevention, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Alzheimer's Disease, Aging, Dementia, Brain Disorders, Acquired Cognitive Impairment, Lewy Body Dementia, Biotechnology, Parkinson's Disease, Neurosciences, Neurodegenerative, 2.1 Biological and endogenous factors, Aetiology, Neurological, Adaptor Proteins, Signal Transducing, Alzheimer Disease, Case-Control Studies, Gene Expression Profiling, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Glucosylceramidase, Humans, Lewy Body Disease, Nuclear Proteins, Parkinson Disease, Polymorphism, Single Nucleotide, Tumor Suppressor Proteins, alpha-Synuclein, American Genome Center, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract: The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia, and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer's disease and Parkinson's disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition.
Published: 2021

50. The Worldwide Alzheimer's Disease Neuroimaging Initiative: ADNI‐3 updates and global perspectives

Author: Weber, Christopher J, Carrillo, Maria C, Jagust, William, Jack, Clifford R, Shaw, Leslie M, Trojanowski, John Q, Saykin, Andrew J, Beckett, Laurel A, Sur, Cyrille, Rao, Naren P, Mendez, Patricio Chrem, Black, Sandra E, Li, Kuncheng, Iwatsubo, Takeshi, Chang, Chiung‐Chih, Sosa, Ana Luisa, Rowe, Christopher C, Perrin, Richard J, Morris, John C, Healan, Amanda MB, Hall, Stephen E, and Weiner, Michael W
Subjects: Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Aging, Neurodegenerative, Alzheimer's Disease, Dementia, Brain Disorders, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Biomedical Imaging, Neurological, Alzheimer's disease, amyloid, biomarkers, cerebrospinal fluid, cognitive impairment, MRI, neuroimaging, PET, Tau, Clinical sciences, Biological psychology
Abstract: The Worldwide Alzheimer's Disease Neuroimaging Initiative (WW-ADNI) is a collaborative effort to investigate imaging and biofluid markers that can inform Alzheimer's disease treatment trials. It is a public-private partnership that spans North America, Argentina, Australia, Canada, China, Japan, Korea, Mexico, and Taiwan. In 2004, ADNI researchers began a naturalistic, longitudinal study that continues today around the globe. Through several successive phases (ADNI-1, ADNI-GO, ADNI-2, and ADNI-3), the study has fueled amyloid and tau phenotyping and refined neuroimaging methodologies. WW-ADNI researchers have successfully standardized analyses and openly share data without embargo, providing a rich data set for other investigators. On August 26, 2020, the Alzheimer's Association convened WW-ADNI researchers who shared updates from ADNI-3 and their vision for ADNI-4.
Published: 2021

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