Search

Your search keyword '"Saykin, A. J."' showing total 3,546 results
Start Over Author "Saykin, A. J."
3,546 results on '"Saykin, A. J."'

1. A cross‐sectional study of α‐synuclein seed amplification assay in Alzheimer's disease neuroimaging initiative: Prevalence and associations with Alzheimer's disease biomarkers and cognitive function

Author

Tosun, Duygu, Hausle, Zachary, Iwaki, Hirotaka, Thropp, Pamela, Lamoureux, Jennifer, Lee, Edward B, MacLeod, Karen, McEvoy, Sean, Nalls, Michael, Perrin, Richard J, Saykin, Andrew J, Shaw, Leslie M, Singleton, Andrew B, Lebovitz, Russ, Weiner, Michael W, Blauwendraat, Cornelis, and Initiative, for the Alzheimer's Disease Neuroimaging

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aging, Behavioral and Social Science, Brain Disorders, Dementia, Acquired Cognitive Impairment, Alzheimer's Disease Related Dementias (ADRD), Alzheimer's Disease, 2.1 Biological and endogenous factors, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Neurological, Humans, Alzheimer Disease, alpha-Synuclein, Biomarkers, Male, Female, Aged, Cross-Sectional Studies, tau Proteins, Amyloid beta-Peptides, Neuroimaging, Aged, 80 and over, Prevalence, Lewy Bodies, Cognition, Sensitivity and Specificity, Brain, Cognitive Dysfunction, Alzheimer's disease, co-pathology, Lewy body, SAA, Alzheimer's Disease Neuroimaging Initiative, co‐pathology, Geriatrics, Clinical sciences, Biological psychology
Abstract

IntroductionAlzheimer's disease (AD) pathology is defined by β-amyloid (Aβ) plaques and neurofibrillary tau, but Lewy bodies (LBs; ?-synuclein aggregates) are a common co-pathology for which effective biomarkers are needed.MethodsA validated α-synuclein Seed Amplification Assay (SAA) was used on recent cerebrospinal fluid (CSF) samples from 1638 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, 78 with LB-pathology confirmation at autopsy. We compared SAA outcomes with neuropathology, Aβ and tau biomarkers, risk-factors, genetics, and cognitive trajectories.ResultsSAA showed 79% sensitivity and 97% specificity for LB pathology, with superior performance in identifying neocortical (100%) compared to limbic (57%) and amygdala-predominant (60%) LB-pathology. SAA+ rate was 22%, increasing with disease stage and age. Higher Aβ burden but lower CSF p-tau181 associated with higher SAA+ rates, especially in dementia. SAA+ affected cognitive impairment in MCI and Early-AD who were already AD biomarker positive.DiscussionSAA is a sensitive, specific marker for LB-pathology. Its increase in prevalence with age and AD stages, and its association with AD biomarkers, highlights the clinical importance of α-synuclein co-pathology in understanding AD's nature and progression.HighlightsSAA shows 79% sensitivity, 97% specificity for LB-pathology detection in AD. SAA positivity prevalence increases with disease stage and age. Higher Aβ burden, lower CSF p-tau181 linked with higher SAA+ rates in dementia. SAA+ impacts cognitive impairment in early disease stages. Study underpins need for wider LB-pathology screening in AD treatment.

Published
2024

2. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Author

Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S, New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I, Breier, Alan, Brodaty, Henry, Buckner, Randy L, Buitelaar, Jan K, Cannon, Dara M, Caseras, Xavier, Clark, Vincent P, Conrod, Patricia J, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E, Franke, Barbara, Glahn, David C, Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hatton, Sean, Hickie, Ian B, Pol, Hilleke E Hulshoff, Jamieson, Alec J, Jernigan, Terry L, Jiang, Jiyang, Kalnin, Andrew J, Kang, Sim, Kochan, Nicole A, Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S, Satterthwaite, Theodore D, Saykin, Andrew J, Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W, Soares, Jair C, Spalletta, Gianfranco, Tamnes, Christian K, Trollor, Julian N, Ent, Dennis Van't, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M, Williams, Steven CR, Wu, Mon‐Ju, Zunta‐Soares, Giovana B, Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, and Group, ENIGMA‐Lifespan Working

Subjects
Biological Psychology, Psychology, Neurosciences, Clinical Research, Aging, Mental health, Neurological, Humans, Adolescent, Female, Aged, Adult, Child, Young Adult, Male, Brain, Aged, 80 and over, Child, Preschool, Middle Aged, Magnetic Resonance Imaging, Neuroimaging, Sample Size, benchmarking, brain aging, brainAGE, ENIGMA‐Lifespan Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Published
2024

4. GWAS of multiple neuropathology endophenotypes identifies new risk loci and provides insights into the genetic risk of dementia

Author

Shade, Lincoln M. P., Katsumata, Yuriko, Abner, Erin L., Aung, Khine Zin, Claas, Steven A., Qiao, Qi, Heberle, Bernardo Aguzzoli, Brandon, J. Anthony, Page, Madeline L., Hohman, Timothy J., Mukherjee, Shubhabrata, Mayeux, Richard P., Farrer, Lindsay A., Schellenberg, Gerard D., Haines, Jonathan L., Kukull, Walter A., Nho, Kwangsik, Saykin, Andrew J., Bennett, David A., Schneider, Julie A., Ebbert, Mark T. W., Nelson, Peter T., and Fardo, David W.

Published
2024
Full Text
View/download PDF

5. Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries

Author

García-Marín, Luis M., Campos, Adrian I., Diaz-Torres, Santiago, Rabinowitz, Jill A., Ceja, Zuriel, Mitchell, Brittany L., Grasby, Katrina L., Thorp, Jackson G., Agartz, Ingrid, Alhusaini, Saud, Ames, David, Amouyel, Philippe, Andreassen, Ole A., Arfanakis, Konstantinos, Arias-Vasquez, Alejandro, Armstrong, Nicola J., Athanasiu, Lavinia, Bastin, Mark E., Beiser, Alexa S., Bennett, David A., Bis, Joshua C., Boks, Marco P. M., Boomsma, Dorret I., Brodaty, Henry, Brouwer, Rachel M., Buitelaar, Jan K., Burkhardt, Ralph, Cahn, Wiepke, Calhoun, Vince D., Carmichael, Owen T., Chakravarty, Mallar, Chen, Qiang, Ching, Christopher R. K., Cichon, Sven, Crespo-Facorro, Benedicto, Crivello, Fabrice, Dale, Anders M., Smith, George Davey, de Geus, Eco J. C., De Jager, Philip L., de Zubicaray, Greig I., Debette, Stéphanie, DeCarli, Charles, Depondt, Chantal, Desrivières, Sylvane, Djurovic, Srdjan, Ehrlich, Stefan, Erk, Susanne, Espeseth, Thomas, Fernández, Guillén, Filippi, Irina, Fisher, Simon E., Fleischman, Debra A., Fletcher, Evan, Fornage, Myriam, Forstner, Andreas J., Francks, Clyde, Franke, Barbara, Ge, Tian, Goldman, Aaron L., Grabe, Hans J., Green, Robert C., Grimm, Oliver, Groenewold, Nynke A., Gruber, Oliver, Gudnason, Vilmundur, Håberg, Asta K., Haukvik, Unn K., Heinz, Andreas, Hibar, Derrek P., Hilal, Saima, Himali, Jayandra J., Ho, Beng-Choon, Hoehn, David F., Hoekstra, Pieter J., Hofer, Edith, Hoffmann, Wolfgang, Holmes, Avram J., Homuth, Georg, Hosten, Norbert, Ikram, M. Kamran, Ipser, Jonathan C., Jack Jr, Clifford R., Jahanshad, Neda, Jönsson, Erik G., Kahn, Rene S., Kanai, Ryota, Klein, Marieke, Knol, Maria J., Launer, Lenore J., Lawrie, Stephen M., Hellard, Stephanie Le, Lee, Phil H., Lemaître, Hervé, Li, Shuo, Liewald, David C. M., Lin, Honghuang, Longstreth, Jr, W. T., Lopez, Oscar L., Luciano, Michelle, Maillard, Pauline, Marquand, Andre F., Martin, Nicholas G., Martinot, Jean-Luc, Mather, Karen A., Mattay, Venkata S., McMahon, Katie L., Mecocci, Patrizia, Melle, Ingrid, Meyer-Lindenberg, Andreas, Mirza-Schreiber, Nazanin, Milaneschi, Yuri, Mosley, Thomas H., Mühleisen, Thomas W., Müller-Myhsok, Bertram, Maniega, Susana Muñoz, Nauck, Matthias, Nho, Kwangsik, Niessen, Wiro J., Nöthen, Markus M., Nyquist, Paul A., Oosterlaan, Jaap, Pandolfo, Massimo, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda W. J. H., Pike, G. Bruce, Psaty, Bruce M., Pütz, Benno, Reppermund, Simone, Rietschel, Marcella D., Risacher, Shannon L., Romanczuk-Seiferth, Nina, Romero-Garcia, Rafael, Roshchupkin, Gennady V., Rotter, Jerome I., Sachdev, Perminder S., Sämann, Philipp G., Saremi, Arvin, Sargurupremraj, Muralidharan, Saykin, Andrew J., Schmaal, Lianne, Schmidt, Helena, Schmidt, Reinhold, Schofield, Peter R., Scholz, Markus, Schumann, Gunter, Schwarz, Emanuel, Shen, Li, Shin, Jean, Sisodiya, Sanjay M., Smith, Albert V., Smoller, Jordan W., Soininen, Hilkka S., Steen, Vidar M., Stein, Dan J., Stein, Jason L., Thomopoulos, Sophia I., Toga, Arthur W., Tordesillas-Gutiérrez, Diana, Trollor, Julian N., Valdes-Hernandez, Maria C., van ′t Ent, Dennis, van Bokhoven, Hans, van der Meer, Dennis, van der Wee, Nic J. A., Vázquez-Bourgon, Javier, Veltman, Dick J., Vernooij, Meike W., Villringer, Arno, Vinke, Louis N., Völzke, Henry, Walter, Henrik, Wardlaw, Joanna M., Weinberger, Daniel R., Weiner, Michael W., Wen, Wei, Westlye, Lars T., Westman, Eric, White, Tonya, Witte, A. Veronica, Wolf, Christiane, Yang, Jingyun, Zwiers, Marcel P., Ikram, M. Arfan, Seshadri, Sudha, Thompson, Paul M., Satizabal, Claudia L., Medland, Sarah E., and Rentería, Miguel E.

Published
2024
Full Text
View/download PDF

6. Genetic associations with dementia‐related proteinopathy: Application of item response theory

Author

Katsumata, Yuriko, Fardo, David W, Shade, Lincoln MP, Wu, Xian, Karanth, Shama D, Hohman, Timothy J, Schneider, Julie A, Bennett, David A, Farfel, Jose M, Gauthreaux, Kathryn, Mock, Charles, Kukull, Walter A, Abner, Erin L, Nelson, Peter T, Carrillo, Maria, Reiman, Eric M, Chen, Kewei, Masterman, Donna, Green, Robert C, Ho, Carole, Fleisher, Adam, Saykin, Andrew J, Nho, Kwangsik, Apostolova, Liana G, Risacher, Shannon L, Jackson, Jonathan, Forghanian-Arani, Arvin, Borowski, Bret, Ward, Chad, Schwarz, Christopher, Jack, Clifford R, Jones, David, Gunter, Jeff, Kantarci, Kejal, Senjem, Matthew, Vemuri, Prashanthi, Reid, Robert, Petersen, Ronald, Hsiao, John K, Potter, William, Masliah, Eliezer, Ryan, Laurie, Bernard, Marie, Silverberg, Nina, Kormos, Adrienne, Conti, Cat, Veitch, Dallas, Flenniken, Derek, Sacrey, Diana Truran, Choe, Mark, Ashford, Miriam, Chen, Stephanie Rossi, Faber, Kelley, Nudelman, Kelly, Wilme, Kristi, Foroud, Tatiana M, Trojanowki, John Q, Shaw, Leslie M, Korecka, Magdalena, Figurski, Michal, Khachaturian, Zaven, Barnes, Lisa, Malone, Ian, Fox, Nick C, Beckett, Laurel, Weiner, Michael W, Jagust, William, Landau, Susan, Knaack, Alexander, DeCarli, Charles, Harvey, Danielle, Fletcher, Evan, González, Hector, Jin, Chengshi, Tosun‐Turgut, Duygu, Neuhaus, John, Fockler, Juliet, Nosheny, Rachel, Koeppe, Robert A, Yushkevich, Paul A, Das, Sandhitsu, Mathis, Chet, Toga, Arthur W, Zimmerman, Caileigh, Gessert, Devon, Shcrer, Elizabeth, Miller, Garrett, Coker, Godfrey, Jimenez, Gustavo, Salazar, Jennifer, Pizzola, Jeremy, Crawford, Karen, Hergesheimer, Lindsey, Donohue, Michael, and Rafii, Michael

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Acquired Cognitive Impairment, Neurodegenerative, Brain Disorders, Dementia, Genetics, Prevention, Aging, 2.1 Biological and endogenous factors, Aetiology, Neurological, Humans, alpha-Synuclein, TDP-43 Proteinopathies, Proteostasis Deficiencies, DNA-Binding Proteins, Biological Products, Alzheimer Disease, Membrane Proteins, Nerve Tissue Proteins, Alzheimer's Disease Neuroimaging Initiative, National Alzheimer's Coordinating Center, ARHGEF28, Alzheimer's Coordinating Center, Alzheimer's Disease Sequencing Project, Alzheimer's disease neuropathologic changes, Item response theory, Lewy, RGNEF, Religious Orders Study, Rush Memory and Aging Project, SDHAF1, TMEM68, neuropathology, Geriatrics, Clinical sciences, Biological psychology
Abstract

IntroductionAlthough dementia-related proteinopathy has a strong negative impact on public health, and is highly heritable, understanding of the related genetic architecture is incomplete.MethodsWe applied multidimensional generalized partial credit modeling (GPCM) to test genetic associations with dementia-related proteinopathies. Data were analyzed to identify candidate single nucleotide variants for the following proteinopathies: Aβ, tau, α-synuclein, and TDP-43.ResultsFinal included data comprised 966 participants with neuropathologic and WGS data. Three continuous latent outcomes were constructed, corresponding to TDP-43-, Aβ/Tau-, and α-synuclein-related neuropathology endophenotype scores. This approach helped validate known genotype/phenotype associations: for example, TMEM106B and GRN were risk alleles for TDP-43 pathology; and GBA for α-synuclein/Lewy bodies. Novel suggestive proteinopathy-linked alleles were also discovered, including several (SDHAF1, TMEM68, and ARHGEF28) with colocalization analyses and/or high degrees of biologic credibility.DiscussionA novel methodology using GPCM enabled insights into gene candidates for driving misfolded proteinopathies.HighlightsLatent factor scores for proteinopathies were estimated using a generalized partial credit model. The three latent continuous scores corresponded well with proteinopathy severity. Novel genes associated with proteinopathies were identified. Several genes had high degrees of biologic credibility for dementia risk factors.

Published
2024

7. Referral Sources Across Racial and Ethnic Groups at Alzheimer’s Disease Research Centers

Author

Chan, Carol K, Lane, Kathleen A, Gao, Sujuan, Adeoye-Olatunde, Omolola A, Biber, Sarah, Glover, Crystal M, Johnson, David K, Risacher, Shannon L, Saykin, Andrew J, and Wang, Sophia

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Neurodegenerative, Clinical Research, Dementia, Alzheimer's Disease, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aging, Acquired Cognitive Impairment, Minority Health, Brain Disorders, Neurological, Quality Education, Aged, Aged, 80 and over, Female, Humans, Male, Middle Aged, Alzheimer Disease, Cross-Sectional Studies, Ethnicity, Racial Groups, Referral and Consultation, Alzheimer’s disease, diversity, language, race and ethnicity, recruitment, Cognitive Sciences, Neurology & Neurosurgery, Clinical sciences, Biological psychology
Abstract

BackgroundDespite the need to increase engagement of underrepresented groups (URG) in Alzheimer's disease and related dementias (ADRD) studies, enrollment remains low.ObjectiveCompare referral sources across racial and ethnic groups among participants enrolled in ADRC studies.MethodsData for this cross-sectional secondary analysis were extracted from the National Alzheimer's Coordinating Center Uniform Data Set. We performed mixed effects logistic regression models using generalized estimating equations for professional referral versus non-professional referral by racial and ethnic group, adjusted for age, gender, education, visit year, and Clinical Dementia Rating scale (CDR) with a random effect for study site.ResultsIncluded in the analysis were 48,330 participants across 46 ADRCs (mean [SD] age, 71.3 [10.5] years; 20,767 female [57%]; 4,138 Hispanic [8.6%]; 1,392 non-Hispanic Asian [2.9%]; 6,766 non-Hispanic Black [14%] individuals; and 676 individuals [1.4%] of other races. Non-Hispanic Black and Asian participants had lower odds of being referred by a professional contact compared to non-Hispanic White participants (Black: adjusted OR = 0.61, 95% CI = 0.44-0.86, p = 0.005; Asian: adjusted OR = 0.65, 95% CI, p = 0.004). In participants who had completed an MRI, there was no significant difference in referral source across ethnic and racial groups.ConclusionsFurther studies are needed to better understand the systemic and structural factors that contribute to differences in referral sources and disparities in recruitment of URG into ADRD studies.

Published
2024

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

Author

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

Published
2024
Full Text
View/download PDF

10. CYP1B1-RMDN2 Alzheimer’s disease endophenotype locus identified for cerebral tau PET

Author

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

Published
2024
Full Text
View/download PDF

12. Gliovascular transcriptional perturbations in Alzheimer’s disease reveal molecular mechanisms of blood brain barrier dysfunction

Author

İş, Özkan, Wang, Xue, Reddy, Joseph S., Min, Yuhao, Yilmaz, Elanur, Bhattarai, Prabesh, Patel, Tulsi, Bergman, Jeremiah, Quicksall, Zachary, Heckman, Michael G., Tutor-New, Frederick Q., Can Demirdogen, Birsen, White, Launia, Koga, Shunsuke, Krause, Vincent, Inoue, Yasuteru, Kanekiyo, Takahisa, Cosacak, Mehmet Ilyas, Nelson, Nastasia, Lee, Annie J., Vardarajan, Badri, Mayeux, Richard, Kouri, Naomi, Deniz, Kaancan, Carnwath, Troy, Oatman, Stephanie R., Lewis-Tuffin, Laura J., Nguyen, Thuy, Carrasquillo, Minerva M., Graff-Radford, Jonathan, Petersen, Ronald C., Jr Jack, Clifford R., Kantarci, Kejal, Murray, Melissa E., Nho, Kwangsik, Saykin, Andrew J., Dickson, Dennis W., Kizil, Caghan, Allen, Mariet, and Ertekin-Taner, Nilüfer

Published
2024
Full Text
View/download PDF

14. 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 including Alzheimer's Disease Related Dementias (AD/ADRD), Acquired Cognitive Impairment, Neurodegenerative, Dementia, Brain Disorders, Aging, Neurosciences, Machine Learning and Artificial Intelligence, Alzheimer's Disease, Networking and Information Technology R&D (NITRD), 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

15. Gene Expression Alterations in Peripheral Blood Following Sport-Related Concussion in a Prospective Cohort of Collegiate Athletes: A Concussion Assessment, Research and Education (CARE) Consortium Study

Author

Simpson, Edward, Reiter, Jill L., Ren, Jie, Zhang, Zhiqi, Nudelman, Kelly N., Riggen Jr., Larry D., Menser, Michael D., Harezlak, Jaroslaw, Foroud, Tatiana M., Saykin, Andrew J., Brooks, Alison, Cameron, Kenneth L., Duma, Stefan M., McGinty, Gerald, Rowson, Steven, Svoboda, Steven J., Broglio, Steven P., McCrea, Michael A., Pasquina, Paul F., McAllister, Thomas W., and Liu, Yunlong

Published
2024
Full Text
View/download PDF

16. 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, Alzheimer's Disease Related Dementias (ADRD), Neurodegenerative, Behavioral and Social Science, Brain Disorders, Alzheimer's Disease, Dementia, Neurosciences, Clinical Research, Acquired Cognitive Impairment, Cerebrovascular, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Biomedical Imaging, Vascular Cognitive Impairment/Dementia, Aging, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Mental health, Neurological, 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

17. LATE-NC risk alleles (in TMEM106B, GRN, and ABCC9 genes) among persons with African ancestry.

Author

Katsumata, Yuriko, Fardo, David W, Shade, Lincoln MP, Bowen, James D, Crane, Paul K, Jarvik, Gail P, Keene, C Dirk, Larson, Eric B, McCormick, Wayne C, McCurry, Susan M, Mukherjee, Shubhabrata, Kowall, Neil W, McKee, Ann C, Honig, Robert A, Lawrence, S, Vonsattel, Jean Paul, Williamson, Jennifer, Small, Scott, Burke, James R, Hulette, Christine M, Welsh-Bohmer, Kathleen A, Gearing, Marla, Lah, James J, Levey, Allan I, Wingo, Thomas S, Apostolova, Liana G, Farlow, Martin R, Ghetti, Bernardino, Saykin, Andrew J, Spina, Salvatore, Albert, Marilyn S, Lyketsos, Constantine G, Troncoso, Juan C, Frosch, Matthew P, Green, Robert C, Growdon, John H, Hyman, Bradley T, Tanzi, Rudolph E, Potter, Huntington, Dickson, Dennis W, Ertekin-Taner, Nilufer, Graff-Radford, Neill R, Parisi, Joseph E, Petersen, Ronald C, Duara, Ranjan, Buxbaum, Joseph D, Goate, Alison M, Sano, Mary, Masurkar, Arjun V, Wisniewski, Thomas, Bigio, Eileen H, Mesulam, Marsel, Weintraub, Sandra, Vassar, Robert, Kaye, Jeffrey A, Quinn, Joseph F, Woltjer, Randall L, Barnes, Lisa L, Bennett, David A, Schneider, Julie A, Yu, Lei, Henderson, Victor, Fallon, Kenneth B, Harrell, Lindy E, Marson, Daniel C, Roberson, Erik D, DeCarli, Charles, Jin, Lee-Way, Olichney, John M, Kim, Ronald, LaFerla, Frank M, Monuki, Edwin, Head, Elizabeth, Sultzer, David, Geschwind, Daniel H, Vinters, Harry V, Chesselet, Marie-Francoise, Galasko, Douglas R, Brewer, James B, Boxer, Adam, Karydas, Anna, Kramer, Joel H, Miller, Bruce L, Rosen, Howard J, Seeley, William W, Burns, Jeffrey M, Swerdlow, Russell H, Abner, Erin, Van Eldik, Linda J, Albin, Roger L, Lieberman, Andrew P, Paulson, Henry L, Arnold, Steven E, Trojanowski, John Q, Van Deerlin, Vivianna M, Hamilton, Ronald L, Kamboh, M Ilyas, Lopez, Oscar L, and Becker, James T

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Brain Disorders, Neurodegenerative, Alzheimer's Disease, Acquired Cognitive Impairment, Prevention, Aging, Minority Health, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Health Disparities, Genetics, 2.1 Biological and endogenous factors, Aetiology, Humans, Alleles, Polymorphism, Single Nucleotide, Alzheimer Disease, TDP-43 Proteinopathies, Progranulins, Membrane Proteins, Nerve Tissue Proteins, Sulfonylurea Receptors, Alzheimer’s Disease Genetics Consortium, KCNMB2, Diversity, Epidemiology, FTLD, Genome-Wide Association Studies, KATP, Neurology & Neurosurgery, Clinical sciences
Abstract

Limbic-predominant age-related TDP-43 encephalopathy (LATE) affects approximately one-third of older individuals and is associated with cognitive impairment. However, there is a highly incomplete understanding of the genetic determinants of LATE neuropathologic changes (LATE-NC) in diverse populations. The defining neuropathologic feature of LATE-NC is TDP-43 proteinopathy, often with comorbid hippocampal sclerosis (HS). In terms of genetic risk factors, LATE-NC and/or HS are associated with single nucleotide variants (SNVs) in 3 genes-TMEM106B (rs1990622), GRN (rs5848), and ABCC9 (rs1914361 and rs701478). We evaluated these 3 genes in convenience samples of individuals of African ancestry. The allele frequencies of the LATE-associated alleles were significantly different between persons of primarily African (versus European) ancestry: In persons of African ancestry, the risk-associated alleles for TMEM106B and ABCC9 were less frequent, whereas the risk allele in GRN was more frequent. We performed an exploratory analysis of data from African-American subjects processed by the Alzheimer's Disease Genomics Consortium, with a subset of African-American participants (n = 166) having corroborating neuropathologic data through the National Alzheimer's Coordinating Center (NACC). In this limited-size sample, the ABCC9/rs1914361 SNV was associated with HS pathology. More work is required concerning the genetic factors influencing non-Alzheimer disease pathology such as LATE-NC in diverse cohorts.

Published
2023

18. Integrative Analysis of Amyloid Imaging and Genetics Reveals Subtypes of Alzheimer Progression in Early Stage

Author

He, Bing, Sangani, Neel, Wu, Ruiming, Varathan, Pradeep, Patania, Alice, Risacher, Shannon L., Nho, Kwangsik, Apostolova, Liana G., Saykin, Andrew J., Shen, Li, Yan, Jingwen, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Finkelstein, Joseph, editor, Moskovitch, Robert, editor, and Parimbelli, Enea, editor

Published
2024
Full Text
View/download PDF

19. Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection

Author

Tong, Boning, Zhou, Zhuoping, Tarzanagh, Davoud Ataee, Hou, Bojian, Saykin, Andrew J., Moore, Jason, Ritchie, Marylyn, Shen, Li, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Xiaohuan, editor, Xu, Xuanang, editor, Rekik, Islem, editor, Cui, Zhiming, editor, and Ouyang, Xi, editor

Published
2024
Full Text
View/download PDF

20. Measurement Precision Across Cognitive Domains in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Data Set

Author

Crane, Paul K, Choi, Seo-Eun, Lee, Michael, Scollard, Phoebe, Sanders, R Elizabeth, Klinedinst, Brandon, Nakano, Connie, Trittschuh, Emily H, Mez, Jesse, Saykin, Andrew J, Gibbons, Laura E, Wang, Chun, Mungas, Dan, Zhu, Ruoyi, Foldi, Nancy S, Lamar, Melissa, Jutten, Roos, Sikkes, Sietske AM, Grandoit, Evan, Rabin, Laura A, Jones, Richard N, Tommet, Doug, and Mukherjee, Shubhabrata

Subjects
Cognitive and Computational Psychology, Psychology, Brain Disorders, Dementia, Neurodegenerative, Acquired Cognitive Impairment, Aging, Alzheimer's Disease, Neurosciences, Behavioral and Social Science, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurological, Good Health and Well Being, Humans, Alzheimer Disease, Cognitive Dysfunction, Executive Function, Cognition, Neuroimaging, cognition, measurement precision, psychometrics, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

ObjectiveTo demonstrate measurement precision of cognitive domains in the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set.MethodParticipants with normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD) were included from all ADNI waves. We used data from each person's last study visit to calibrate scores for memory, executive function, language, and visuospatial functioning. We extracted item information functions for each domain and used these to calculate standard errors of measurement. We derived scores for each domain for each diagnostic group and plotted standard errors of measurement for the observed range of scores.ResultsAcross all waves, there were 961 people with NC, 825 people with MCI, and 694 people with AD at their most recent study visit (data pulled February 25, 2019). Across ADNI's battery there were 34 memory items, 18 executive function items, 20 language items, and seven visuospatial items. Scores for each domain were highest on average for people with NC, intermediate for people with MCI, and lowest for people with AD, with most scores across all groups in the range of -1 to +1. Standard error of measurement in the range from -1 to +1 was highest for memory, intermediate for language and executive functioning, and lowest for visuospatial.ConclusionModern psychometric approaches provide tools to help understand measurement precision of the scales used in studies. In ADNI, there are important differences in measurement precision across cognitive domains. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Published
2023

22. 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

23. 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

26. 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

27. Multi-Omic analyses characterize the ceramide/sphingomyelin pathway as a therapeutic target in Alzheimer’s disease

Author

Baloni, Priyanka, Arnold, Matthias, Buitrago, Luna, Nho, Kwangsik, Moreno, Herman, Huynh, Kevin, Brauner, Barbara, Louie, Gregory, Kueider-Paisley, Alexandra, Suhre, Karsten, Saykin, Andrew J, Ekroos, Kim, Meikle, Peter J, Hood, Leroy, Price, Nathan D, Doraiswamy, P Murali, Funk, Cory C, Hernández, A Iván, Kastenmüller, Gabi, Baillie, Rebecca, Han, Xianlin, and Kaddurah-Daouk, Rima

Subjects
Medical Biochemistry and Metabolomics, Biological Sciences, Biomedical and Clinical Sciences, Genetics, Human Genome, Neurosciences, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Dementia, Neurodegenerative, Pediatric Research Initiative, Alzheimer's Disease, Aging, Aetiology, 2.1 Biological and endogenous factors, Neurological, Alzheimer Disease, Animals, Ceramides, Fingolimod Hydrochloride, Genome-Wide Association Study, Humans, Mice, Sphingolipids, Sphingomyelins, Alzheimer’s Disease Metabolomics Consortium, Biological sciences, Biomedical and clinical sciences
Abstract

Dysregulation of sphingomyelin and ceramide metabolism have been implicated in Alzheimer's disease. Genome-wide and transcriptome-wide association studies have identified various genes and genetic variants in lipid metabolism that are associated with Alzheimer's disease. However, the molecular mechanisms of sphingomyelin and ceramide disruption remain to be determined. We focus on the sphingolipid pathway and carry out multi-omics analyses to identify central and peripheral metabolic changes in Alzheimer's patients, correlating them to imaging features. Our multi-omics approach is based on (a) 2114 human post-mortem brain transcriptomics to identify differentially expressed genes; (b) in silico metabolic flux analysis on context-specific metabolic networks identified differential reaction fluxes; (c) multimodal neuroimaging analysis on 1576 participants to associate genetic variants in sphingomyelin pathway with Alzheimer's disease pathogenesis; (d) plasma metabolomic and lipidomic analysis to identify associations of lipid species with dysregulation in Alzheimer's; and (e) metabolite genome-wide association studies to define receptors within the pathway as a potential drug target. We validate our hypothesis in amyloidogenic APP/PS1 mice and show prolonged exposure to fingolimod alleviated synaptic plasticity and cognitive impairment in mice. Our integrative multi-omics approach identifies potential targets in the sphingomyelin pathway and suggests modulators of S1P metabolism as possible candidates for Alzheimer's disease treatment.

Published
2022

29. Depressive symptom trajectories in older breast cancer survivors: the Thinking and Living with Cancer Study

Author

Nakamura, Zev M., Small, Brent J., Zhai, Wanting, Ahles, Tim A., Ahn, Jaeil, Artese, Ashley L., Bethea, Traci N., Breen, Elizabeth C., Cohen, Harvey J., Extermann, Martine, Graham, Deena, Irwin, Michael R., Isaacs, Claudine, Jim, Heather S.L., Kuhlman, Kate R., McDonald, Brenna C., Patel, Sunita K., Rentscher, Kelly E., Root, James C., Saykin, Andrew J., Tometich, Danielle B., Van Dyk, Kathleen, Zhou, Xingtao, Mandelblatt, Jeanne S., and Carroll, Judith E.

Published
2023
Full Text
View/download PDF

30. Brain gray matter reduction and premature brain aging after breast cancer chemotherapy: a longitudinal multicenter data pooling analysis

Author

de Ruiter, Michiel B., Deardorff, Rachael L., Blommaert, Jeroen, Chen, Bihong T., Dumas, Julie A., Schagen, Sanne B., Sunaert, Stefan, Wang, Lei, Cimprich, Bernadine, Peltier, Scott, Dittus, Kim, Newhouse, Paul A., Silverman, Daniel H., Schroyen, Gwen, Deprez, Sabine, Saykin, Andrew J., and McDonald, Brenna C.

Published
2023
Full Text
View/download PDF

31. Associations between longitudinal changes in sleep disturbance and depressive and anxiety symptoms during the COVID‐19 virus pandemic among older women with and without breast cancer in the thinking and living with breast cancer study

Author

Bethea, Traci N, Zhai, Wanting, Zhou, Xingtao, Ahles, Tim A, Ahn, Jaeil, Cohen, Harvey J, Dilawari, Asma A, Graham, Deena MA, Jim, Heather SL, McDonald, Brenna C, Nakamura, Zev M, Patel, Sunita K, Rentscher, Kelly E, Root, James, Saykin, Andrew J, Small, Brent J, Van Dyk, Kathleen M, Mandelblatt, Jeanne S, and Carroll, Judith E

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Prevention, Rehabilitation, Breast Cancer, Clinical Research, Sleep Research, Mental Health, Behavioral and Social Science, Aging, Depression, Mental health, Good Health and Well Being, Aged, Anxiety, Breast Neoplasms, COVID-19, Female, Humans, Longitudinal Studies, Pandemics, SARS-CoV-2, Sleep, Sleep Wake Disorders, behavioral science, breast cancer, epidemiology, psychosocial studies, Biochemistry and Cell Biology, Oncology and carcinogenesis
Abstract

PurposeSeveral studies have reported sleep disturbances during the COVID-19 virus pandemic. Little data exist about the impact of the pandemic on sleep and mental health among older women with breast cancer. We sought to examine whether women with and without breast cancer who experienced new sleep problems during the pandemic had worsening depression and anxiety.MethodsBreast cancer survivors aged ≥60 years with a history of nonmetastatic breast cancer (n = 242) and frequency-matched noncancer controls (n = 158) active in a longitudinal cohort study completed a COVID-19 virus pandemic survey from May to September 2020 (response rate 83%). Incident sleep disturbance was measured using the restless sleep item from the Center for Epidemiological Studies-Depression Scale (CES-D). CES-D score (minus the sleep item) captured depressive symptoms; the State-Anxiety subscale of the State Trait Anxiety Inventory measured anxiety symptoms. Multivariable linear regression models examined how the development of sleep disturbance affected changes in depressive or anxiety symptoms from the most recent prepandemic survey to the pandemic survey, controlling for covariates.ResultsThe prevalence of sleep disturbance during the pandemic was 22.3%, with incident sleep disturbance in 10% and 13.5% of survivors and controls, respectively. Depressive and anxiety symptoms significantly increased during the pandemic among women with incident sleep disturbance (vs. no disturbance) (β = 8.16, p

Published
2022

32. Contribution of Alzheimer's biomarkers and risk factors to cognitive impairment and decline across the Alzheimer's disease continuum

Author

Tosun, Duygu, Demir, Zeynep, Veitch, Dallas P, Weintraub, Daniel, Aisen, Paul, Jack, Clifford R, Jagust, William J, Petersen, Ronald C, Saykin, Andrew J, Shaw, Leslie M, Trojanowski, John Q, Weiner, Michael W, and Initiative, for the Alzheimer's Disease Neuroimaging

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Clinical Trials and Supportive Activities, Dementia, Biomedical Imaging, Aging, Acquired Cognitive Impairment, Behavioral and Social Science, Brain Disorders, Clinical Research, Neurodegenerative, Alzheimer's Disease, Prevention, Neurological, Aged, Alzheimer Disease, Amyloid beta-Peptides, Atrophy, Biomarkers, Brain, Cognitive Dysfunction, Cross-Sectional Studies, Humans, Positron-Emission Tomography, Risk Factors, tau Proteins, Alzheimer's Disease Assessment Scale-Cognitive Subscale, amyloid beta, atrophy, cognition, magnetic resonance imaging, positron emission tomography, Preclinical Alzheimer Cognitive Composite, tau, white matter lesions, Alzheimer's Disease Neuroimaging Initiative, Geriatrics, Clinical sciences, Biological psychology
Abstract

IntroductionAmyloid beta (Aβ), tau, and neurodegeneration jointly with the Alzheimer's disease (AD) risk factors affect the severity of clinical symptoms and disease progression.MethodsWithin 248 Aβ-positive elderly with and without cognitive impairment and dementia, partial least squares structural equation pathway modeling was used to assess the direct and indirect effects of imaging biomarkers (global Aβ-positron emission tomography [PET] uptake, regional tau-PET uptake, and regional magnetic resonance imaging-based atrophy) and risk-factors (age, sex, education, apolipoprotein E [APOE], and white-matter lesions) on cross-sectional cognitive impairment and longitudinal cognitive decline.ResultsSixteen percent of variance in cross-sectional cognitive impairment was accounted for by Aβ, 46% to 47% by tau, and 25% to 29% by atrophy, although 53% to 58% of total variance in cognitive impairment was explained by incorporating mediated and direct effects of AD risk factors. The Aβ-tau-atrophy pathway accounted for 50% to 56% of variance in longitudinal cognitive decline while Aβ, tau, and atrophy independently explained 16%, 46% to 47%, and 25% to 29% of the variance, respectively.DiscussionThese findings emphasize that treatments that remove Aβ and completely stop downstream effects on tau and neurodegeneration would only be partially effective in slowing of cognitive decline or reversing cognitive impairment.

Published
2022

33. Multidimensional representations in late-life depression: convergence in neuroimaging, cognition, clinical symptomatology and genetics

Author

Wen, Junhao, Fu, Cynthia H. Y., Tosun, Duygu, Veturi, Yogasudha, Yang, Zhijian, Abdulkadir, Ahmed, Mamourian, Elizabeth, Srinivasan, Dhivya, Bao, Jingxuan, Erus, Guray, Shou, Haochang, Habes, Mohamad, Doshi, Jimit, Varol, Erdem, Mackin, Scott R, Sotiras, Aristeidis, Fan, Yong, Saykin, Andrew J., Sheline, Yvette I., Shen, Li, Ritchie, Marylyn D., Wolk, David A., Albert, Marilyn, Resnick, Susan M., and Davatzikos, Christos

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

Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity would aid in elucidating etiological mechanisms and pave the road to precision and individualized medicine. We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles. Multimodal data from a multicentre sample (N=996) were analyzed. A semi-supervised clustering method (HYDRA) was applied to regional grey matter (GM) brain volumes to derive dimensional representations. Two dimensions were identified, which accounted for the LLD-related heterogeneity in voxel-wise GM maps, white matter (WM) fractional anisotropy (FA), neurocognitive functioning, clinical phenotype, and genetics. Dimension one (Dim1) demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy controls. In contrast, dimension two (Dim2) showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, one de novo independent genetic variant (rs13120336) was significantly associated with Dim 1 but not with Dim 2. Notably, the two dimensions demonstrated significant SNP-based heritability of 18-27% within the general population (N=12,518 in UKBB). Lastly, in a subset of individuals having longitudinal measurements, Dim2 demonstrated a more rapid longitudinal decrease in GM and brain age, and was more likely to progress to Alzheimers disease, compared to Dim1 (N=1,413 participants and 7,225 scans from ADNI, BLSA, and BIOCARD datasets).

Published
2021

34. 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

36. Relationship of Hoarding and Depression Symptoms in Older Adults

Author

Nutley, Sara, Nguyen, Binh K., Mackin, Robert Scott, Insel, Philip S., Tosun, Duygu, Butters, Meryl, Aisen, Paul, Raman, Rema, Saykin, Andrew J., Toga, Arthur W., Jack, Clifford, Weiner, Michael W., Nelson, Craig, Kassel, Michelle, Kryza-Lacombe, Maria, Eichenbaum, Joseph, Nosheny, Rachel L., and Mathews, Carol A.

Published
2024
Full Text
View/download PDF

38. Identifying Shared Neuroanatomic Architecture Between Cognitive Traits Through Multiscale Morphometric Correlation Analysis

Author

Wen, Zixuan, Bao, Jingxuan, Yang, Shu, Risacher, Shannon L., Saykin, Andrew J., Thompson, Paul M., Davatzikos, Christos, Huang, Heng, Zhao, Yize, Shen, Li, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woo, Jonghye, editor, Hering, Alessa, editor, Silva, Wilson, editor, Li, Xiang, editor, Fu, Huazhu, editor, Liu, Xiaofeng, editor, Xing, Fangxu, editor, Purushotham, Sanjay, editor, Mathai, Tejas S., editor, Mukherjee, Pritam, editor, De Grauw, Max, editor, Beets Tan, Regina, editor, Corbetta, Valentina, editor, Kotter, Elmar, editor, Reyes, Mauricio, editor, Baumgartner, Christian F., editor, Li, Quanzheng, editor, Leahy, Richard, editor, Dong, Bin, editor, Chen, Hao, editor, Huo, Yuankai, editor, Lv, Jinglei, editor, Xu, Xinxing, editor, Li, Xiaomeng, editor, Mahapatra, Dwarikanath, editor, Cheng, Li, editor, Petitjean, Caroline, editor, and Presles, Benoît, editor

Published
2023
Full Text
View/download PDF

40. Contribution of Alzheimer's biomarkers and risk factors to cognitive impairment and decline across the Alzheimer's disease continuum.

Author

Tosun, Duygu, Demir, Zeynep, Veitch, Dallas P, Weintraub, Daniel, Aisen, Paul, Jack, Clifford R, Jagust, William J, Petersen, Ronald C, Saykin, Andrew J, Shaw, Leslie M, Trojanowski, John Q, Weiner, Michael W, and Alzheimer's Disease Neuroimaging Initiative

Subjects
Alzheimer's Disease Neuroimaging Initiative, Alzheimer's Disease Assessment Scale-Cognitive Subscale, Preclinical Alzheimer Cognitive Composite, amyloid beta, atrophy, cognition, magnetic resonance imaging, positron emission tomography, tau, white matter lesions, Brain Disorders, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Clinical Trials and Supportive Activities, Biomedical Imaging, Clinical Research, Alzheimer's Disease, Acquired Cognitive Impairment, Dementia, Aging, Prevention, Neurosciences, Behavioral and Social Science, Neurological, Geriatrics, Clinical Sciences
Abstract

IntroductionAmyloid beta (Aβ), tau, and neurodegeneration jointly with the Alzheimer's disease (AD) risk factors affect the severity of clinical symptoms and disease progression.MethodsWithin 248 Aβ-positive elderly with and without cognitive impairment and dementia, partial least squares structural equation pathway modeling was used to assess the direct and indirect effects of imaging biomarkers (global Aβ-positron emission tomography [PET] uptake, regional tau-PET uptake, and regional magnetic resonance imaging-based atrophy) and risk-factors (age, sex, education, apolipoprotein E [APOE], and white-matter lesions) on cross-sectional cognitive impairment and longitudinal cognitive decline.ResultsSixteen percent of variance in cross-sectional cognitive impairment was accounted for by Aβ, 46% to 47% by tau, and 25% to 29% by atrophy, although 53% to 58% of total variance in cognitive impairment was explained by incorporating mediated and direct effects of AD risk factors. The Aβ-tau-atrophy pathway accounted for 50% to 56% of variance in longitudinal cognitive decline while Aβ, tau, and atrophy independently explained 16%, 46% to 47%, and 25% to 29% of the variance, respectively.DiscussionThese findings emphasize that treatments that remove Aβ and completely stop downstream effects on tau and neurodegeneration would only be partially effective in slowing of cognitive decline or reversing cognitive impairment.

Published
2021

44. Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data

Author

Peng, Bo, Yao, Xiaohui, Risacher, Shannon L., Saykin, Andrew J., Shen, Li, and Ning, Xia

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

Background:Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer's Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method: We adapt a newly developed learning-to-rank approach PLTR to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list. We also extend PLTR to better separate the most effective cognitive assessments and the less effective ones. Results: Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers. We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1% and 19.7%, respectively, on prioritizing cognitive features. Conclusions: The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD., Comment: This paper has been accepted by BMC MIDM

Published
2020

46. A genome-wide search for pleiotropy in more than 100,000 harmonized longitudinal cognitive domain scores

Author

Kang, Moonil, Ang, Ting Fang Alvin, Devine, Sherral A., Sherva, Richard, Mukherjee, Shubhabrata, Trittschuh, Emily H., Gibbons, Laura E., Scollard, Phoebe, Lee, Michael, Choi, Seo-Eun, Klinedinst, Brandon, Nakano, Connie, Dumitrescu, Logan C., Durant, Alaina, Hohman, Timothy J., Cuccaro, Michael L., Saykin, Andrew J., Kukull, Walter A., Bennett, David A., Wang, Li-San, Mayeux, Richard P., Haines, Jonathan L., Pericak-Vance, Margaret A., Schellenberg, Gerard D., Crane, Paul K., Au, Rhoda, Lunetta, Kathryn L., Mez, Jesse B., and Farrer, Lindsay A.

Published
2023
Full Text
View/download PDF

47. Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer’s disease related proteins

Author

Oatman, Stephanie R., Reddy, Joseph S., Quicksall, Zachary, Carrasquillo, Minerva M., Wang, Xue, Liu, Chia-Chen, Yamazaki, Yu, Nguyen, Thuy T., Malphrus, Kimberly, Heckman, Michael, Biswas, Kristi, Nho, Kwangsik, Baker, Matthew, Martens, Yuka A., Zhao, Na, Kim, Jun Pyo, Risacher, Shannon L., Rademakers, Rosa, Saykin, Andrew J., DeTure, Michael, Murray, Melissa E., Kanekiyo, Takahisa, Dickson, Dennis W., Bu, Guojun, Allen, Mariet, and Ertekin-Taner, Nilüfer

Published
2023
Full Text
View/download PDF

48. Detection of β-amyloid positivity in Alzheimer’s Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers

Author

Tosun, Duygu, Veitch, Dallas, Aisen, Paul, Jack, Clifford R, Jagust, William J, Petersen, Ronald C, Saykin, Andrew J, Bollinger, James, Ovod, Vitaliy, Mawuenyega, Kwasi G, Bateman, Randall J, Shaw, Leslie M, Trojanowski, John Q, Blennow, Kaj, Zetterberg, Henrik, and Weiner, Michael W

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aging, Neurodegenerative, Clinical Research, Brain Disorders, Dementia, Biomedical Imaging, Acquired Cognitive Impairment, Alzheimer's Disease, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Neurological, Alzheimer's, beta-amyloid, MRI, PET, plasma, Alzheimer’s, β-amyloid, Clinical sciences, Biological psychology
Abstract

In vivo gold standard for the ante-mortem assessment of brain β-amyloid pathology is currently β-amyloid positron emission tomography or cerebrospinal fluid measures of β-amyloid42 or the β-amyloid42/β-amyloid40 ratio. The widespread acceptance of a biomarker classification scheme for the Alzheimer's disease continuum has ignited interest in more affordable and accessible approaches to detect Alzheimer's disease β-amyloid pathology, a process that often slows down the recruitment into, and adds to the cost of, clinical trials. Recently, there has been considerable excitement concerning the value of blood biomarkers. Leveraging multidisciplinary data from cognitively unimpaired participants and participants with mild cognitive impairment recruited by the multisite biomarker study of Alzheimer's Disease Neuroimaging Initiative, here we assessed to what extent plasma β-amyloid42/β-amyloid40, neurofilament light and phosphorylated-tau at threonine-181 biomarkers detect the presence of β-amyloid pathology, and to what extent the addition of clinical information such as demographic data, APOE genotype, cognitive assessments and MRI can assist plasma biomarkers in detecting β-amyloid-positivity. Our results confirm plasma β-amyloid42/β-amyloid40 as a robust biomarker of brain β-amyloid-positivity (area under curve, 0.80-0.87). Plasma phosphorylated-tau at threonine-181 detected β-amyloid-positivity only in the cognitively impaired with a moderate area under curve of 0.67, whereas plasma neurofilament light did not detect β-amyloid-positivity in either group of participants. Clinical information as well as MRI-score independently detected positron emission tomography β-amyloid-positivity in both cognitively unimpaired and impaired (area under curve, 0.69-0.81). Clinical information, particularly APOE ε4 status, enhanced the performance of plasma biomarkers in the detection of positron emission tomography β-amyloid-positivity by 0.06-0.14 units of area under curve for cognitively unimpaired, and by 0.21-0.25 units for cognitively impaired; and further enhancement of these models with an MRI-score of β-amyloid-positivity yielded an additional improvement of 0.04-0.11 units of area under curve for cognitively unimpaired and 0.05-0.09 units for cognitively impaired. Taken together, these multi-disciplinary results suggest that when combined with clinical information, plasma phosphorylated-tau at threonine-181 and neurofilament light biomarkers, and an MRI-score could effectively identify β-amyloid+ cognitively unimpaired and impaired (area under curve, 0.80-0.90). Yet, when the MRI-score is considered in combination with clinical information, plasma phosphorylated-tau at threonine-181 and plasma neurofilament light have minimal added value for detecting β-amyloid-positivity. Our systematic comparison of β-amyloid-positivity detection models identified effective combinations of demographics, APOE, global cognition, MRI and plasma biomarkers. Promising minimally invasive and low-cost predictors such as plasma biomarkers of β-amyloid42/β-amyloid40 may be improved by age and APOE genotype.

Published
2021

49. 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
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results