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1. Genetic variants for head size share genes and pathways with cancer

Author: Knol, Maria J, Poot, Raymond A, Evans, Tavia E, Satizabal, Claudia L, Mishra, Aniket, Sargurupremraj, Muralidharan, van der Auwera, Sandra, Duperron, Marie-Gabrielle, Jian, Xueqiu, Hostettler, Isabel C, van Dam-Nolen, Dianne HK, Lamballais, Sander, Pawlak, Mikolaj A, Lewis, Cora E, Carrion-Castillo, Amaia, van Erp, Theo GM, Reinbold, Céline S, Shin, Jean, Scholz, Markus, Håberg, Asta K, Kämpe, Anders, Li, Gloria HY, Avinun, Reut, Atkins, Joshua R, Hsu, Fang-Chi, Amod, Alyssa R, Lam, Max, Tsuchida, Ami, Teunissen, Mariël WA, Aygün, Nil, Patel, Yash, Liang, Dan, Beiser, Alexa S, Beyer, Frauke, Bis, Joshua C, Bos, Daniel, Bryan, R Nick, Bülow, Robin, Caspers, Svenja, Catheline, Gwenaëlle, Cecil, Charlotte AM, Dalvie, Shareefa, Dartigues, Jean-François, DeCarli, Charles, Enlund-Cerullo, Maria, Ford, Judith M, Franke, Barbara, Freedman, Barry I, Friedrich, Nele, Green, Melissa J, Haworth, Simon, Helmer, Catherine, Hoffmann, Per, Homuth, Georg, Ikram, M Kamran, Jack, Clifford R, Jahanshad, Neda, Jockwitz, Christiane, Kamatani, Yoichiro, Knodt, Annchen R, Li, Shuo, Lim, Keane, Longstreth, WT, Macciardi, Fabio, Consortium, The Cohorts for Heart and Aging Research in Genomic Epidemiology, Amouyel, Philippe, Arfanakis, Konstantinos, Aribisala, Benjamin S, Bastin, Mark E, Chauhan, Ganesh, Chen, Christopher, Cheng, Ching-Yu, de Jager, Philip L, Deary, Ian J, Fleischman, Debra A, Gottesman, Rebecca F, Gudnason, Vilmundur, Hilal, Saima, Hofer, Edith, Janowitz, Deborah, Jukema, J Wouter, Liewald, David CM, Lopez, Lorna M, Lopez, Oscar, Luciano, Michelle, Martinez, Oliver, Niessen, Wiro J, Nyquist, Paul, Rotter, Jerome I, Rundek, Tatjana, Sacco, Ralph L, Schmidt, Helena, Tiemeier, Henning, Trompet, Stella, van der Grond, Jeroen, Völzke, Henry, Wardlaw, Joanna M, Yanek, Lisa, Yang, Jingyun, and Consortium, The Enhancing NeuroImaging Genetics through Meta-Analysis
Subjects: Biomedical and Clinical Sciences, Biotechnology, Neurosciences, Stem Cell Research, Genetics, Cancer, Human Genome, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Neurological, Humans, Genome-Wide Association Study, Head, Neoplasms, Female, Male, Polymorphism, Single Nucleotide, Genetic Variation, Organ Size, Signal Transduction, Adult, Genetic Predisposition to Disease, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium, cancer, genetics, genome-wide association study, head circumference, head size, intracranial volume, meta-analysis, Biomedical and clinical sciences
Abstract: The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.
Published: 2024

2. 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

3. Cortical microstructural associations with CSF amyloid and pTau

Author: Nir, Talia M, Villalón-Reina, Julio E, Salminen, Lauren E, Haddad, Elizabeth, Zheng, Hong, Thomopoulos, Sophia I, Jack, Clifford R, Weiner, Michael W, Thompson, Paul M, and Jahanshad, Neda
Subjects: Biomedical and Clinical Sciences, Biological Psychology, Clinical and Health Psychology, Clinical Sciences, Psychology, Brain Disorders, Behavioral and Social Science, Clinical Research, Basic Behavioral and Social Science, Acquired Cognitive Impairment, Aging, Dementia, Neurodegenerative, Alzheimer's Disease, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurosciences, Biomedical Imaging, 2.1 Biological and endogenous factors, Aetiology, Neurological, Aged, Aged, 80 and over, Female, Humans, Male, Alzheimer Disease, Amyloid, Amyloid beta-Peptides, Biomarkers, Brain, Cerebral Cortex, Cognitive Dysfunction, Diffusion Magnetic Resonance Imaging, Gray Matter, Neuroimaging, Peptide Fragments, tau Proteins, Middle Aged, Alzheimer’s Disease Neuroimaging Initiative, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract: Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aβ1-42 than pTau181 and better distinguished Aβ+ from Aβ- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline.
Published: 2024

4. 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

5. 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

6. 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
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7. Role of GBA variants in Lewy body disease neuropathology

Author: Walton, Ronald L., Koga, Shunsuke, Beasley, Alexandra I., White, Launia J., Griesacker, Teresa, Murray, Melissa E., Kasanuki, Koji, Hou, Xu, Fiesel, Fabienne C., Springer, Wolfdieter, Uitti, Ryan J., Fields, Julie A., Botha, Hugo, Ramanan, Vijay K., Kantarci, Kejal, Lowe, Val J., Jack, Clifford R., Ertekin-Taner, Nilufer, Savica, Rodolfo, Graff-Radford, Jonathan, Petersen, Ronald C., Parisi, Joseph E., Reichard, R. Ross, Graff-Radford, Neill R., Ferman, Tanis J., Boeve, Bradley F., Wszolek, Zbigniew K., Dickson, Dennis W., Ross, Owen A., and Heckman, Michael G.
Published: 2024
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8. 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

9. 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

10. 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

11. 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

12. 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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13. Atypical Brain Aging and Its Association With Working Memory Performance in Major Depressive Disorder

Author: Adamson, Chris, Adler, Sophie, Alexander-Bloch, Aaron F., Anagnostou, Evdokia, Anderson, Kevin M., Areces-Gonzalez, Ariosky, Astle, Duncan E., Auyeung, Bonnie, Ayub, Muhammad, Bae, Jong Bin, Ball, Gareth, Baron-Cohen, Simon, Beare, Richard, Bedford, Saashi A., Benegal, Vivek, Bethlehem, Richard A.I., Beyer, Frauke, Blangero, John, Cábez, Manuel Blesa, Boardman, James P., Borzage, Matthew, Bosch-Bayard, Jorge F., Bourke, Niall, Bullmore, Edward T., Calhoun, Vince D., Chakravarty, Mallar M., Chen, Christina, Chertavian, Casey, Chetelat, Gaël, Chong, Yap S., Corvin, Aiden, Costantino, Manuela, Courchesne, Eric, Crivello, Fabrice, Cropley, Vanessa L., Crosbie, Jennifer, Crossley, Nicolas, Delarue, Marion, Delorme, Richard, Desrivieres, Sylvane, Devenyi, Gabriel, Di Biase, Maria A., Dolan, Ray, Donald, Kirsten A., Donohoe, Gary, Dorfschmidt, Lena, Dunlop, Katharine, Edwards, Anthony D., Elison, Jed T., Ellis, Cameron T., Elman, Jeremy A., Eyler, Lisa, Fair, Damien A., Fletcher, Paul C., Fonagy, Peter, Franz, Carol E., Galan-Garcia, Lidice, Gholipour, Ali, Giedd, Jay, Gilmore, John H., Glahn, David C., Goodyer, Ian M., Grant, P.E., Groenewold, Nynke A., Gudapati, Shreya, Gunning, Faith M., Gur, Raquel E., Gur, Ruben C., Hammill, Christopher F., Hansson, Oskar, Hedden, Trey, Heinz, Andreas, Henson, Richard N., Heuer, Katja, Hoare, Jacqueline, Holla, Bharath, Holmes, Avram J., Huang, Hao, Ipser, Jonathan, Jack, Clifford R., Jr., Jackowski, Andrea P., Jia, Tianye, Jones, David T., Jones, Peter B., Kahn, Rene S., Karlsson, Hasse, Karlsson, Linnea, Kawashima, Ryuta, Kelley, Elizabeth A., Kern, Silke, Kim, Ki-Woong, Kitzbichler, Manfred G., Kremen, William S., Lalonde, François, Landeau, Brigitte, Lerch, Jason, Lewis, John D., Li, Jiao, Liao, Wei, Liston, Conor, Lombardo, Michael V., Lv, Jinglei, Mallard, Travis T., Marcelis, Machteld, Mathias, Samuel R., Mazoyer, Bernard, McGuire, Philip, Meaney, Michael J., Mechelli, Andrea, Misic, Bratislav, Morgan, Sarah E., Mothersill, David, Ortinau, Cynthia, Ossenkoppele, Rik, Ouyang, Minhui, Palaniyappan, Lena, Paly, Leo, Pan, Pedro M., Pantelis, Christos, Park, Min Tae M., Paus, Tomas, Pausova, Zdenka, Paz-Linares, Deirel, Binette, Alexa Pichet, Pierce, Karen, Qian, Xing, Qiu, Anqi, Raznahan, Armin, Rittman, Timothy, Rodrigue, Amanda, Rollins, Caitlin K., Romero-Garcia, Rafael, Ronan, Lisa, Rosenberg, Monica D., Rowitch, David H., Salum, Giovanni A., Satterthwaite, Theodore D., Schaare, H. Lina, Schabdach, Jenna, Schachar, Russell J., Schöll, Michael, Schultz, Aaron P., Seidlitz, Jakob, Sharp, David, Shinohara, Russell T., Skoog, Ingmar, Smyser, Christopher D., Sperling, Reisa A., Stein, Dan J., Stolicyn, Aleks, Suckling, John, Sullivan, Gemma, Thyreau, Benjamin, Toro, Roberto, Traut, Nicolas, Tsvetanov, Kamen A., Turk-Browne, Nicholas B., Tuulari, Jetro J., Tzourio, Christophe, Vachon-Presseau, Étienne, Valdes-Sosa, Mitchell J., Valdes-Sosa, Pedro A., Valk, Sofie L., van Amelsvoort, Therese, Vandekar, Simon N., Vasung, Lana, Vértes, Petra E., Victoria, Lindsay W., Villeneuve, Sylvia, Villringer, Arno, Vogel, Jacob W., Wagstyl, Konrad, Wang, Yin-Shan S., Warfield, Simon K., Warrier, Varun, Westman, Eric, Westwater, Margaret L., Whalley, Heather C., White, Simon R., Witte, A. Veronica, Yang, Ning, Yeo, B.T. Thomas, Yun, Hyuk Jin, Zalesky, Andrew, Zar, Heather J., Zettergren, Anna, Zhou, Juan H., Ziauddeen, Hisham, Zimmerman, Dabriel, Zugman, Andre, Zuo, Xi-Nian N., Ho, Natalie C.W., Nogovitsyn, Nikita, Metzak, Paul, Ballester, Pedro L., Hassel, Stefanie, Rotzinger, Susan, Poppenk, Jordan, Lam, Raymond W., Taylor, Valerie H., Milev, Roumen, Frey, Benicio N., Harkness, Kate L., Addington, Jean, and Kennedy, Sidney H.
Published: 2024
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14. 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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15. 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

16. Altered structural and functional connectivity in Posterior Cortical Atrophy and Dementia with Lewy bodies

Author: Singh, Neha Atulkumar, Goodrich, Austin W., Graff-Radford, Jonathan, Machulda, Mary M., Sintini, Irene, Carlos, Arenn F., Robinson, Carling G., Reid, Robert I., Lowe, Val J., Jack, Clifford R., Jr, Petersen, Ronald C., Boeve, Bradley F., Josephs, Keith A., Kantarci, Kejal, and Whitwell, Jennifer L.
Published: 2024
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17. Patients with normal pressure hydrocephalus have fewer enlarged perivascular spaces in the centrum semiovale compared to cognitively unimpaired individuals

Author: Switzer, Aaron R., Graff-Radford, Jonathan, Gunter, Jeffery L., Elder, Benjamin D., Jones, David T., Huston, John, Jack, Clifford R., and Cogswell, Petrice M.
Published: 2024
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18. Neurite-based white matter alterations in MAPT mutation carriers: A multi-shell diffusion MRI study in the ALLFTD consortium

Author: Corriveau-Lecavalier, Nick, Tosakulwong, Nirubol, Lesnick, Timothy G., Fought, Angela J., Reid, Robert I., Schwarz, Christopher G., Senjem, Matthew L., Jack, Clifford R., Jr., Jones, David T., Vemuri, Prashanthi, Rademakers, Rosa, Ramos, Eliana Marisa, Geschwind, Daniel H., Knopman, David S., Botha, Hugo, Savica, Rodolfo, Graff-Radford, Jonathan, Ramanan, Vijay K., Fields, Julie A., Graff-Radford, Neill, Wszolek, Zbigniew, Forsberg, Leah K., Petersen, Ronald C., Heuer, Hilary W., Boxer, Adam L., Rosen, Howard J., Boeve, Bradley F., and Kantarci, Kejal
Published: 2024
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19. Speech-language within and between network disruptions in primary progressive aphasia variants

Author: Singh-Reilly, Neha, Botha, Hugo, Duffy, Joseph R., Clark, Heather M., Utianski, Rene L., Machulda, Mary M., Graff-Radford, Jonathan, Schwarz, Christopher G., Petersen, Ronald C., Lowe, Val J., Jack, Clifford R., Jr, Josephs, Keith A., and Whitwell, Jennifer L.
Published: 2024
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20. Complex relationships of socioeconomic status with vascular and Alzheimer’s pathways on cognition

Author: Shir, Dror, Graff-Radford, Jonathan, Fought, Angela J., Lesnick, Timothy G., Przybelski, Scott A., Vassilaki, Maria, Lowe, Val J., Knopman, David S., Machulda, Mary M., Petersen, Ronald C., Jack, Clifford R., Jr, Mielke, Michelle M., and Vemuri, Prashanthi
Published: 2024
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21. Assessing network degeneration and phenotypic heterogeneity in genetic frontotemporal lobar degeneration by decoding FDG-PET

Author: Corriveau-Lecavalier, Nick, Barnard, Leland R., Przybelski, Scott A., Gogineni, Venkatsampath, Botha, Hugo, Graff-Radford, Jonathan, Ramanan, Vijay K., Forsberg, Leah K., Fields, Julie A., Machulda, Mary M., Rademakers, Rosa, Gavrilova, Ralitza H., Lapid, Maria I., Boeve, Bradley F., Knopman, David S., Lowe, Val J., Petersen, Ronald C., Jack, Clifford R., Kantarci, Kejal, and Jones, David T.
Published: 2024
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22. The Longitudinal Early‐onset Alzheimer's Disease Study (LEADS): Framework and methodology

Author: Apostolova, Liana G, Aisen, Paul, Eloyan, Ani, Fagan, Anne, Fargo, Keith N, Foroud, Tatiana, Gatsonis, Constantine, Grinberg, Lea T, Jack, Clifford R, Kramer, Joel, Koeppe, Robert, Kukull, Walter A, Murray, Melissa E, Nudelman, Kelly, Rumbaugh, Malia, Toga, Arthur, Vemuri, Prashanthi, Trullinger, Amy, Iaccarino, Leonardo, Day, Gregory S, Graff‐Radford, Neill R, Honig, Lawrence S, Jones, David T, Masdeu, Joseph, Mendez, Mario, Musiek, Erik, Onyike, Chiadi U, Rogalski, Emily, Salloway, Steve, Wolk, David A, Wingo, Thomas S, Carrillo, Maria C, Dickerson, Bradford C, Rabinovici, Gil D, and Consortium, the LEADS
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Brain Disorders, Alzheimer's Disease, Clinical Research, Neurosciences, Dementia, Neurodegenerative, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aging, Biomedical Imaging, 4.1 Discovery and preclinical testing of markers and technologies, 2.1 Biological and endogenous factors, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Aetiology, Neurological, Alzheimer Disease, Aniline Compounds, Autopsy, Biomarkers, Brain, Cognitive Dysfunction, Early Diagnosis, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, National Institute on Aging (U.S.), Positron-Emission Tomography, Stilbenes, United States, Alzheimer&apos, s disease, early&#8208, onset, EOAD, LEADS, YOAD, young onset, LEADS Consortium, Alzheimer's disease, early-onset, Geriatrics, Clinical sciences, Biological psychology
Abstract: Patients with early-onset Alzheimer's disease (EOAD) are commonly excluded from large-scale observational and therapeutic studies due to their young age, atypical presentation, or absence of pathogenic mutations. The goals of the Longitudinal EOAD Study (LEADS) are to (1) define the clinical, imaging, and fluid biomarker characteristics of EOAD; (2) develop sensitive cognitive and biomarker measures for future clinical and research use; and (3) establish a trial-ready network. LEADS will follow 400 amyloid beta (Aβ)-positive EOAD, 200 Aβ-negative EOnonAD that meet National Institute on Aging-Alzheimer's Association (NIA-AA) criteria for mild cognitive impairment (MCI) or AD dementia, and 100 age-matched controls. Participants will undergo clinical and cognitive assessments, magnetic resonance imaging (MRI), [18 F]Florbetaben and [18 F]Flortaucipir positron emission tomography (PET), lumbar puncture, and blood draw for DNA, RNA, plasma, serum and peripheral blood mononuclear cells, and post-mortem assessment. To develop more effective AD treatments, scientists need to understand the genetic, biological, and clinical processes involved in EOAD. LEADS will develop a public resource that will enable future planning and implementation of EOAD clinical trials.
Published: 2021

23. Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia

Author: Ezzati, Ali, Abdulkadir, Ahmed, Jack, Clifford R, Thompson, Paul M, Harvey, Danielle J, Truelove‐Hill, Monica, Sreepada, Lasya P, Davatzikos, Christos, Initiative, the Alzheimer's Disease Neuroimaging, and Lipton, Richard B
Subjects: Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Prevention, Alzheimer's Disease Related Dementias (ADRD), Dementia, Neurodegenerative, Brain Disorders, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Alzheimer's Disease, Aging, Machine Learning and Artificial Intelligence, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Neurological, Aged, Alzheimer Disease, Amyloid beta-Peptides, Biomarkers, Cognitive Dysfunction, Data Collection, Disease Progression, Female, Humans, Machine Learning, Male, tau Proteins, Alzheimer's disease, amyloid, biomarker profile, machine learning, mild cognitive impairment, neurodegeneration, predictive analytics, tau, Alzheimer's Disease Neuroimaging Initiative, Geriatrics, Clinical sciences, Biological psychology
Abstract: We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 415) we assessed predictive performance of ATN classification using empirical knowledge-based cut-offs for each component of ATN and compared it to two data-driven approaches, logistic regression and RUSBoost machine learning classifiers, which used continuous clinical or biomarker scores. In data-driven approaches, we identified ATN features that distinguish normals from individuals with dementia and used them to classify persons with MCI into dementia-like and normal groups. Both data-driven classification methods performed better than the empirical cut-offs for ATN biomarkers in predicting conversion to dementia. Classifiers that used clinical features performed as well as classifiers that used ATN biomarkers for prediction of progression to dementia. We discuss that data-driven modeling approaches can improve our ability to predict disease progression and might have implications in future clinical trials.
Published: 2021

24. 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

25. 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

26. 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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27. Biomarkers of Alzheimer’s disease in Black and/or African American Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants

Author: Groechel, Renée C., Tripodis, Yorghos, Alosco, Michael L., Mez, Jesse, Qiao Qiu, Wei, Goldstein, Lee, Budson, Andrew E., Kowall, Neil W., Shaw, Leslie M., Weiner, Michael, Jack, Clifford R., Jr, and Killiany, Ronald J.
Published: 2023
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28. The diamagnetic component map from quantitative susceptibility mapping (QSM) source separation reveals pathological alteration in Alzheimer’s disease-driven neurodegeneration

Author: Ahmed, Maruf, Chen, Jingjia, Arani, Arvin, Senjem, Matthew L., Cogswell, Petrice M., Jack, Clifford R., Jr., and Liu, Chunlei
Published: 2023
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29. The Impact of Amyloid Burden and APOE on Rates of Cognitive Impairment in Late Life Depression.

Author: Rhodes, Emma, Insel, Philip S, Butters, Meryl A, Morin, Ruth, Bickford, David, Tosun, Duygu, Gessert, Devon, Rosen, Howie J, Aisen, Paul, Raman, Rema, Landau, Susan, Saykin, Andrew, Toga, Arthur, Jack, Clifford R, Weiner, Michael W, Nelson, Craig, and Mackin, Scott
Subjects: Biological Psychology, Psychology, Neurosciences, Acquired Cognitive Impairment, Neurodegenerative, Clinical Research, Brain Disorders, Behavioral and Social Science, Aging, Alzheimer's Disease, Depression, Mental Health, Dementia, Major Depressive Disorder, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aetiology, 2.1 Biological and endogenous factors, Neurological, Mental health, Aged, Alzheimer Disease, Amyloid beta-Peptides, Apolipoprotein E4, Cognitive Dysfunction, Depressive Disorder, Major, Female, Humans, Male, Middle Aged, Amyloid, apolipoprotein E, cognitive impairment, late life depression, Alzheimer’s Disease Neuroimaging Initiative, ADNI Depression Project, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery, Clinical sciences, Biological psychology
Abstract: BackgroundCognitive impairment (CI) is a key feature of late life depression (LLD), but the contribution of underlying neurodegenerative pathology remains unclear.ObjectiveTo evaluate cognitive dysfunction in LLD relative to a sample of nondepressed (ND) older adults with matched levels of memory impairment and amyloid-β (Aβ) burden.MethodsParticipants included 120 LLD and 240 ND older adults matched on age, education, sex, Mini-Mental State Exam, mild cognitive impairment diagnosis, and PET Aβ burden.ResultsLLD showed higher rates of impairment relative to ND with 54.6% of the LLD sample demonstrating impairment in at least one cognitive domain compared to 42.9% of controls (H = 7.13, p = 0.008). LLD had poorer performance and higher rates of impairment on Rey Auditory Verbal Learning Test learning and memory compared to controls. In the overall sample, Aβ positivity was associated with worse performance on Logical Memory I (p = 0.044), Logical Memory II (p = 0.011), and Trail Making Test -B (p = 0.032), and APOEɛ4 genotype was associated with worse performance on Logical Memory I (p = 0.022); these relationships did not differ between LLD and ND.ConclusionLLD showed higher rates of CI driven by focal deficits in verbal learning and memory. Alzheimer's disease (AD) biomarkers were associated with worse performance on timed set-shifting and story learning and memory, and these relationships were not impacted by depression status. These findings suggest that AD may account for a portion of previously reported multi-domain CI in LLD and highlight the potential for AD to confound studies of cognition in LLD.
Published: 2021

30. 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

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

Author: Keret, Ophir, Staffaroni, Adam M, Ringman, John M, Cobigo, Yann, Goh, Sheng‐Yang M, Wolf, Amy, Allen, Isabel Elaine, Salloway, Stephen, Chhatwal, Jasmeer, Brickman, Adam M, Reyes‐Dumeyer, Dolly, Bateman, Randal J, Benzinger, Tammie LS, Morris, John C, Ances, Beau M, Joseph‐Mathurin, Nelly, Perrin, Richard J, Gordon, Brian A, Levin, Johannes, Vöglein, Jonathan, Jucker, Mathias, la Fougère, Christian, Martins, Ralph N, Sohrabi, Hamid R, Taddei, Kevin, Villemagne, Victor L, Schofield, Peter R, Brooks, William S, Fulham, Michael, Masters, Colin L, Ghetti, Bernardino, Saykin, Andrew J, Jack, Clifford R, Graff‐Radford, Neill R, Weiner, Michael, Cash, David M, Allegri, Ricardo F, Chrem, Patricio, Yi, Su, Miller, Bruce L, Rabinovici, Gil D, Rosen, Howard J, and Network, Dominantly Inherited Alzheimer
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, Aging, Prevention, Neurodegenerative, Alzheimer's Disease, Dementia, Brain Disorders, Acquired Cognitive Impairment, Genetics, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Patient Safety, Neurological, Good Health and Well Being, autosomal dominant Alzheimer's disease, brain atrophy, Dominantly Inherited Alzheimer Network, preclinical Alzheimer's disease, Biological psychology
Abstract: IntroductionAsymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment.MethodsWe created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally.ResultsOur risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%-98.2%]) and improved prediction beyond established methods based on familial age of onset.DiscussionIndividualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials.
Published: 2021

32. A face-off of MRI research sequences by their need for de-facing

Author: Schwarz, Christopher G., Kremers, Walter K., Arani, Arvin, Savvides, Marios, Reid, Robert I., Gunter, Jeffrey L., Senjem, Matthew L., Cogswell, Petrice M., Vemuri, Prashanthi, Kantarci, Kejal, Knopman, David S., Petersen, Ronald C., and Jack, Clifford R., Jr.
Published: 2023
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33. Abstract 12181: Association of Left Atrial Function With Vascular Brain Injury: The Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS)

Author: Wang, Wendy, Inciardi, Riccardo M, Reyes, Jorge L, Mosley, Thomas, Johansen, Michelle C, Gottesman, Rebecca F, Alonso, Alvaro, Lutsey, Pamela L, Jack, Clifford R, Jr, Solomon, Scott, Shah, Amil M, Wasserman, Bruce A, and Chen, Lin Y
Published: 2023
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34. Association of common genetic variants with brain microbleeds: A Genome-wide Association Study

Author: Knol, Maria J, Lu, Dongwei, Traylor, Matthew, Adams, Hieab HH, Romero, José Rafael J, Smith, Albert V, Fornage, Myriam, Hofer, Edith, Liu, Junfeng, Hostettler, Isabel C, Luciano, Michelle, Trompet, Stella, Giese, Anne-Katrin, Hilal, Saima, van den Akker, Erik B, Vojinovic, Dina, Li, Shuo, Sigurdsson, Sigurdur, van der Lee, Sven J, Jack, Clifford R, Wilson, Duncan, Yilmaz, Pinar, Satizabal, Claudia L, Liewald, David CM, van der Grond, Jeroen, Chen, Christopher, Saba, Yasaman, van der Lugt, Aad, Bastin, Mark E, Windham, B Gwen, Cheng, Ching Yu, Pirpamer, Lukas, Kantarci, Kejal, Himali, Jayandra J, Yang, Qiong, Morris, Zoe, Beiser, Alexa S, Tozer, Daniel J, Vernooij, Meike W, Amin, Najaf, Beekman, Marian, Koh, Jia Yu, Stott, David J, Houlden, Henry, Schmidt, Reinhold, Gottesman, Rebecca F, MacKinnon, Andrew D, DeCarli, Charles, Gudnason, Vilmundur, Deary, Ian J, van Duijn, Cornelia M, Slagboom, P Eline, Wong, Tien Yin, Rost, Natalia S, Jukema, J Wouter, Mosley, Thomas H, Werring, David J, Schmidt, Helena, Wardlaw, Joanna M, Ikram, M Arfan, Seshadri, Sudha, Launer, Lenore J, Markus, Hugh S, and Initiative, for the Alzheimer’s Disease Neuroimaging
Subjects: Stroke, Human Genome, Neurosciences, Brain Disorders, Genetics, Prevention, Aetiology, 2.1 Biological and endogenous factors, Aged, Aged, 80 and over, Alleles, Apolipoprotein E2, Apolipoprotein E4, Apolipoproteins E, Case-Control Studies, Cerebral Hemorrhage, Cerebral Small Vessel Diseases, Cohort Studies, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Polymorphism, Single Nucleotide, Risk, White Matter, Alzheimer's Disease Neuroimaging Initiative, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery
Abstract: ObjectiveTo identify common genetic variants associated with the presence of brain microbleeds (BMBs).MethodsWe performed genome-wide association studies in 11 population-based cohort studies and 3 case-control or case-only stroke cohorts. Genotypes were imputed to the Haplotype Reference Consortium or 1000 Genomes reference panel. BMBs were rated on susceptibility-weighted or T2*-weighted gradient echo MRI sequences, and further classified as lobar or mixed (including strictly deep and infratentorial, possibly with lobar BMB). In a subset, we assessed the effects of APOE ε2 and ε4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMBs.ResultsBMBs were detected in 3,556 of the 25,862 participants, of which 2,179 were strictly lobar and 1,293 mixed. One locus in the APOE region reached genome-wide significance for its association with BMB (lead single nucleotide polymorphism rs769449; odds ratio [OR]any BMB [95% confidence interval (CI)] 1.33 [1.21-1.45]; p = 2.5 × 10-10). APOE ε4 alleles were associated with strictly lobar (OR [95% CI] 1.34 [1.19-1.50]; p = 1.0 × 10-6) but not with mixed BMB counts (OR [95% CI] 1.04 [0.86-1.25]; p = 0.68). APOE ε2 alleles did not show associations with BMB counts. Variants previously related to deep intracerebral hemorrhage and lacunar stroke, and a risk score of cerebral white matter hyperintensity variants, were associated with BMB.ConclusionsGenetic variants in the APOE region are associated with the presence of BMB, most likely due to the APOE ε4 allele count related to a higher number of strictly lobar BMBs. Genetic predisposition to small vessel disease confers risk of BMB, indicating genetic overlap with other cerebral small vessel disease markers.
Published: 2020

35. Cross–scanner harmonization methods for structural MRI may need further work: A comparison study

Author: Gebre, Robel K., Senjem, Matthew L., Raghavan, Sheelakumari, Schwarz, Christopher G., Gunter, Jeffery L., Hofrenning, Ekaterina I., Reid, Robert I., Kantarci, Kejal, Graff-Radford, Jonathan, Knopman, David S., Petersen, Ronald C., Jack, Clifford R., Jr, and Vemuri, Prashanthi
Published: 2023
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36. Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities

Author: Armstrong, Nicola J, Mather, Karen A, Sargurupremraj, Muralidharan, Knol, Maria J, Malik, Rainer, Satizabal, Claudia L, Yanek, Lisa R, Wen, Wei, Gudnason, Vilmundur G, Dueker, Nicole D, Elliott, Lloyd T, Hofer, Edith, Bis, Joshua, Jahanshad, Neda, Li, Shuo, Logue, Mark A, Luciano, Michelle, Scholz, Markus, Smith, Albert V, Trompet, Stella, Vojinovic, Dina, Xia, Rui, Alfaro-Almagro, Fidel, Ames, David, Amin, Najaf, Amouyel, Philippe, Beiser, Alexa S, Brodaty, Henry, Deary, Ian J, Fennema-Notestine, Christine, Gampawar, Piyush G, Gottesman, Rebecca, Griffanti, Ludovica, Jack, Clifford R, Jenkinson, Mark, Jiang, Jiyang, Kral, Brian G, Kwok, John B, Lampe, Leonie, C.M. Liewald, David, Maillard, Pauline, Marchini, Jonathan, Bastin, Mark E, Mazoyer, Bernard, Pirpamer, Lukas, Rafael Romero, José, Roshchupkin, Gennady V, Schofield, Peter R, Schroeter, Matthias L, Stott, David J, Thalamuthu, Anbupalam, Trollor, Julian, Tzourio, Christophe, van der Grond, Jeroen, Vernooij, Meike W, Witte, Veronica A, Wright, Margaret J, Yang, Qiong, Morris, Zoe, Siggurdsson, Siggi, Psaty, Bruce, Villringer, Arno, Schmidt, Helena, Haberg, Asta K, van Duijn, Cornelia M, Jukema, J Wouter, Dichgans, Martin, Sacco, Ralph L, Wright, Clinton B, Kremen, William S, Becker, Lewis C, Thompson, Paul M, Mosley, Thomas H, Wardlaw, Joanna M, Ikram, M Arfan, Adams, Hieab HH, Seshadri, Sudha, Sachdev, Perminder S, Smith, Stephen M, Launer, Lenore, Longstreth, William, DeCarli, Charles, Schmidt, Reinhold, Fornage, Myriam, Debette, Stephanie, and Nyquist, Paul A
Subjects: Epidemiology, Health Sciences, Dementia, Acquired Cognitive Impairment, Aging, Neurosciences, Genetics, Stroke, Brain Disorders, Biotechnology, Clinical Research, Human Genome, Prevention, Aetiology, 2.1 Biological and endogenous factors, Neurological, Aged, Brain, Cerebral Small Vessel Diseases, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Middle Aged, White Matter, brain, genome-wide association study, neuroimaging, risk factors, white matter, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Neurology & Neurosurgery, Clinical sciences, Allied health and rehabilitation science
Abstract: Background and purposePeriventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings.MethodsParticipants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC.ResultsIn the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 (NBEAL), 10q23.1 (TSPAN14/FAM231A), and 10q24.33 (SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 (NOS3) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype.ConclusionsOur study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.
Published: 2020

37. Trajectory of lobar atrophy in asymptomatic and symptomatic GRN mutation carriers: a longitudinal MRI study

Author: Chen, Qin, Boeve, Bradley F, Senjem, Matthew, Tosakulwong, Nirubol, Lesnick, Timothy, Brushaber, Danielle, Dheel, Christina, Fields, Julie, Forsberg, Leah, Gavrilova, Ralitza, Gearhart, Debra, Graff-Radford, Jonathan, Graff-Radford, Neill, Jack, Clifford R, Jones, David, Knopman, David, Kremers, Walter K, Lapid, Maria, Rademakers, Rosa, Ramos, Eliana Marisa, Syrjanen, Jeremy, Boxer, Adam L, Rosen, Howie, Wszolek, Zbigniew K, and Kantarci, Kejal
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Brain Disorders, Biomedical Imaging, Neurosciences, Alzheimer's Disease Related Dementias (ADRD), Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Frontotemporal Dementia (FTD), Dementia, Acquired Cognitive Impairment, Neurodegenerative, Neurological, Adult, Asymptomatic Diseases, Atrophy, Female, Frontotemporal Lobar Degeneration, Heterozygote, Humans, Loss of Function Mutation, Magnetic Resonance Imaging, Male, Middle Aged, Progranulins, Temporal Lobe, Young Adult, Magnetic resonance image, GRN, Asymptomatic, Frontotemporal dementia, Longitudinal, Neurology & Neurosurgery, Biological psychology
Abstract: Loss-of-function mutations in the progranulin gene (GRN) are one of the major causes of familial frontotemporal lobar degeneration. Our objective was to determine the rates and trajectories of lobar cortical atrophy from longitudinal structural magnetic resonance imaging in both asymptomatic and symptomatic GRN mutation carriers. Individuals in this study were from the ADRC and LEFFTDS studies at the Mayo Clinic. We identified 13 GRN mutation carriers (8 asymptomatic, 5 symptomatic) and noncarriers (n = 10) who had at least 2 serial T1-weighted structural magnetic resonance images and were followed annually with a median of 3 years (range 1.0-9.8 years). Longitudinal changes in lobar cortical volume were analyzed using the tensor-based morphometry with symmetric normalization (TBM-SyN) algorithm. Linear mixed-effect models were used to model cortical volume change over time among 3 groups. The annual rates of frontal (p < 0.05) and parietal (p < 0.01) lobe cortical atrophy were higher in asymptomatic GRN mutation carriers than noncarriers. The symptomatic GRN mutation carriers also had increased rates of atrophy in the frontal (p < 0.01) and parietal lobe (p < 0.01) cortices than noncarriers. In addition, greater rates of cortical atrophy were observed in the temporal lobe cortices of symptomatic GRN mutation carriers than noncarriers (p < 0.001). We found that a decline in frontal and parietal lobar cortical volume occurs in asymptomatic GRN mutation carriers and continues in the symptomatic GRN mutation carriers, whereas an increased rate of temporal lobe cortical atrophy is observed only in symptomatic GRN mutation carriers. This sequential pattern of cortical involvement in GRN mutation carriers has important implications for using imaging biomarkers of neurodegeneration as an outcome measure in potential treatment trials involving GRN mutation carriers.
Published: 2020

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

Author: Dincer, Aylin, Gordon, Brian A, Hari-Raj, Amrita, Keefe, Sarah J, Flores, Shaney, McKay, Nicole S, Paulick, Angela M, Lewis, Kristine E Shady, Feldman, Rebecca L, Hornbeck, Russ C, Allegri, Ricardo, Ances, Beau M, Berman, Sarah B, Brickman, Adam M, Brooks, William S, Cash, David M, Chhatwal, Jasmeer P, Farlow, Martin R, la Fougère, Christian, Fox, Nick C, Fulham, Michael J, Jack, Clifford R, Joseph-Mathurin, Nelly, Karch, Celeste M, Lee, Athene, Levin, Johannes, Masters, Colin L, McDade, Eric M, Oh, Hwamee, Perrin, Richard J, Raji, Cyrus, Salloway, Stephen P, Schofield, Peter R, Su, Yi, Villemagne, Victor L, Wang, Qing, Weiner, Michael W, Xiong, Chengjie, Yakushev, Igor, Morris, John C, Bateman, Randall J, Benzinger, Tammie LS, and DIAN, for the Dominantly Inherited Alzheimer Network
Subjects: Neurodegenerative, Dementia, Clinical Research, Brain Disorders, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurosciences, Aging, Acquired Cognitive Impairment, Alzheimer's Disease, 2.1 Biological and endogenous factors, Aetiology, Neurological, Alzheimer Disease, Amyloid beta-Peptides, Atrophy, Hippocampus, Humans, Magnetic Resonance Imaging, Positron-Emission Tomography, Alzheimer disease, Autosomal dominant Alzheimer disease, Preclinical, Cortical signature, Amyloid, Cortical thickness, Dominantly Inherited Alzheimer Network DIAN
Abstract: Defining a signature of cortical regions of interest preferentially affected by Alzheimer disease (AD) pathology may offer improved sensitivity to early AD compared to hippocampal volume or mesial temporal lobe alone. Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals. We defined and analyzed the LOAD cortical maps of cortical thickness in 588 participants from the Knight Alzheimer Disease Research Center (Knight ADRC) and the ADAD cortical maps in 269 participants from the Dominantly Inherited Alzheimer Network (DIAN) observational study. Both cohorts were divided into three groups: cognitively normal controls (nADRC = 381; nDIAN = 145), preclinical (nADRC = 153; nDIAN = 76), and cognitively impaired (nADRC = 54; nDIAN = 48). Both cohorts underwent clinical assessments, 3T MRI, and amyloid PET imaging with either 11C-Pittsburgh compound B or 18F-florbetapir. To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume. We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.
Published: 2020

39. Women can bear a bigger burden: ante- and post-mortem evidence for reserve in the face of tau

Author: Digma, Leonardino A, Madsen, John R, Rissman, Robert A, Jacobs, Diane M, Brewer, James B, Banks, Sarah J, 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, Vanderswag, 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: Biological Psychology, Psychology, Brain Disorders, Neurodegenerative, Alzheimer's Disease, Neurosciences, Acquired Cognitive Impairment, Women's Health, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Clinical Research, tau, verbal memory, sex differences, Alzheimer's disease, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s disease, Clinical sciences, Biological psychology
Abstract: In this study, we aimed to assess whether women are able to withstand more tau before exhibiting verbal memory impairment. Using data from 121 amyloid-β-positive Alzheimer's Disease Neuroimaging Initiative participants, we fit a linear model with Rey Auditory Verbal Learning Test score as the response variable and tau-PET standard uptake value ratio as the predictor and took the residuals as an estimate of verbal memory reserve for each subject. Women demonstrated higher reserve (i.e. residuals), whether the Learning (t = 2.78, P = 0.006) or Delay (t = 2.14, P = 0.03) score from the Rey Auditory Verbal Learning Test was used as a measure of verbal memory ability. To validate these findings, we examined 662 National Alzheimer's Coordinating Center participants with a C2/C3 score (Consortium to Establish a Registry for Alzheimer's Disease) at autopsy. We stratified our National Alzheimer's Coordinating Center sample into Braak 1/2, Braak 3/4 and Braak 5/6 subgroups. Within each subgroup, we compared Logical Memory scores between men and women. Men had worse verbal memory scores within the Braak 1/2 (Logical Memory Immediate: β = -5.960 ± 1.517, P
Published: 2020

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

Author: Su, Yi, Flores, Shaney, Wang, Guoqiao, Hornbeck, Russ C, Speidel, Benjamin, Joseph‐Mathurin, Nelly, Vlassenko, Andrei G, Gordon, Brian A, Koeppe, Robert A, Klunk, William E, Jack, Clifford R, Farlow, Martin R, Salloway, Stephen, Snider, Barbara J, Berman, Sarah B, Roberson, Erik D, Brosch, Jared, Jimenez‐Velazques, Ivonne, Dyck, Christopher H, Galasko, Douglas, Yuan, Shauna H, Jayadev, Suman, Honig, Lawrence S, Gauthier, Serge, Hsiung, Ging‐Yuek R, Masellis, Mario, Brooks, William S, Fulham, Michael, Clarnette, Roger, Masters, Colin L, Wallon, David, Hannequin, Didier, Dubois, Bruno, Pariente, Jeremie, Sanchez‐Valle, Raquel, Mummery, Catherine, Ringman, John M, Bottlaender, Michel, Klein, Gregory, Milosavljevic‐Ristic, Smiljana, McDade, Eric, Xiong, Chengjie, Morris, John C, Bateman, Randall J, and Benzinger, Tammie LS
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Bioengineering, Aging, Alzheimer's Disease, Clinical Research, Brain Disorders, Neurodegenerative, Dementia, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Biomedical Imaging, Neurosciences, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, PiB, Florbetapir, Amyloid imaging, Centiloid, Positron emission tomography, Genetics, Biological psychology
Abstract: IntroductionQuantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B-based and florbetapir-based amyloid imaging in the same participants from two independent cohorts using a crossover design.MethodsPittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter-individual variability of the two tracers were compared using multivariate linear models both cross-sectionally and longitudinally.ResultsGlobal amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers.DiscussionAlthough the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers.
Published: 2019

41. Tracking white matter degeneration in asymptomatic and symptomatic MAPT mutation carriers

Author: Chen, Qin, Boeve, Bradley F, Schwarz, Christopher G, Reid, Robert, Tosakulwong, Nirubol, Lesnick, Timothy G, Bove, Jessica, Brannelly, Patrick, Brushaber, Danielle, Coppola, Giovanni, Dheel, Christina, Dickerson, Bradford C, Dickinson, Susan, Faber, Kelley, Fields, Julie, Fong, Jamie, Foroud, Tatiana, Forsberg, Leah, Gavrilova, Ralitza H, Gearhart, Debra, Ghoshal, Nupur, Goldman, Jill, Graff-Radford, Jonathan, Graff-Radford, Neill R, Grossman, Murray, Haley, Dana, Heuer, Hilary W, Hsiung, Ging-Yuek R, Huey, Edward, Irwin, David J, Jack, Clifford R, Jones, David T, Jones, Lynne, Karydas, Anna M, Knopman, David S, Kornak, John, Kramer, Joel, Kremers, Walter, Kukull, Walter A, Lapid, Maria, Lucente, Diane, Lungu, Codrin, Mackenzie, Ian RA, Manoochehri, Masood, McGinnis, Scott, Miller, Bruce L, Pearlman, Rodney, Petrucelli, Leonard, Potter, Madeline, Rademakers, Rosa, Ramos, Eliana M, Rankin, Katherine P, Rascovsky, Katya, Sengdy, Pheth, Shaw, Leslie, Syrjanen, Jeremy, Tatton, Nadine, Taylor, Joanne, Toga, Arthur W, Trojanowski, John, Weintraub, Sandra, Wong, Bonnie, Boxer, Adam L, Rosen, Howie, Wszolek, Zbigniew, Kantarci, Kejal, and Consortium, LEFFTDS
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Dementia, Neurodegenerative, Aging, Acquired Cognitive Impairment, Biomedical Imaging, Neurosciences, Clinical Trials and Supportive Activities, Clinical Research, Brain Disorders, 2.1 Biological and endogenous factors, Adult, Aged, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Disease Progression, Female, Frontotemporal Dementia, Gray Matter, Heterozygote, Humans, Male, Middle Aged, Mutation, Neurodegenerative Diseases, Neuropsychological Tests, White Matter, tau Proteins, Diffusion tensor image, MAPT, Asymptomatic, Frontotemporal dementia, Longitudinal, LEFFTDS Consortium, Neurology & Neurosurgery, Biological psychology
Abstract: Our aim was to investigate the patterns and trajectories of white matter (WM) diffusion abnormalities in microtubule-associated protein tau (MAPT) mutations carriers. We studied 22 MAPT mutation carriers (12 asymptomatic, 10 symptomatic) and 20 noncarriers from 8 families, who underwent diffusion tensor imaging (DTI) and a subset (10 asymptomatic, 6 symptomatic MAPT mutation carriers, and 10 noncarriers) were followed annually (median = 4 years). Cross-sectional and longitudinal changes in mean diffusivity (MD) and fractional anisotropy were analyzed. Asymptomatic MAPT mutation carriers had higher MD in entorhinal WM, which propagated to the limbic tracts and frontotemporal projections in the symptomatic stage compared with noncarriers. Reduced fractional anisotropy and increased MD in the entorhinal WM were associated with the proximity to estimated and actual age of symptom onset. The annualized change of entorhinal MD on serial DTI was accelerated in MAPT mutation carriers compared with noncarriers. Entorhinal WM diffusion abnormalities precede the symptom onset and track with disease progression in MAPT mutation carriers. Our cross-sectional and longitudinal data showed a potential clinical utility for DTI to track neurodegenerative disease progression for MAPT mutation carriers in clinical trials.
Published: 2019

42. Longitudinal clinical decline and baseline predictors in progressive supranuclear palsy

Author: Pavone, Costanza, Weigand, Stephen W., Ali, Farwa, Clark, Heather M., Botha, Hugo, Machulda, Mary M., Savica, Rodolfo, Pham, Nha Trang Thu, Grijalva, Rosalie M., Schwarz, Christopher G., Senjem, Matthew L., Agosta, Federica, Filippi, Massimo, Jack, Clifford R., Lowe, Val J., Josephs, Keith A., and Whitwell, Jennifer L.
Published: 2023
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43. Spatial patterns of elevated magnetic susceptibility in progressive apraxia of speech

Author: Satoh, Ryota, Arani, Arvin, Senjem, Matthew L., Duffy, Joseph R., Clark, Heather M., Utianski, Rene L., Botha, Hugo, Machulda, Mary M., Jack, Clifford R., Jr, Whitwell, Jennifer L., and Josephs, Keith A.
Published: 2023
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44. Advancements in dementia research, diagnostics, and care in Latin America: Highlights from the 2023 Alzheimer's Association International conference satellite symposium in Mexico City

Author: Sosa, Ana Luisa, primary, Brucki, Sonia MD, additional, Crivelli, Lucia, additional, Lopera, Francisco Javier, additional, Acosta, Daisy M, additional, Acosta‐Uribe, Juliana, additional, Aguilar, Diego, additional, Aguilar‐Navarro, Sara G, additional, Allegri, Ricardo F, additional, Bertolucci, Paulo HF, additional, Calandri, Ismael L, additional, Carrillo, Maria C, additional, Mendez, Patricio Alexis Chrem, additional, Cornejo‐Olivas, Mario, additional, Custodio, Nilton, additional, Damian, Andrés, additional, de Souza, Leonardo Cruz, additional, Duran‐Aniotz, Claudia, additional, García, Adolfo M, additional, García‐Peña, Carmen, additional, Gonzales, Mitzi M, additional, Grinberg, Lea T, additional, Ibanez, Agustin M, additional, Illanes‐Manrique, Maryenela Zaida, additional, Jack, Clifford R, additional, Leon‐Salas, Jorge Mario, additional, Llibre‐Guerra, Jorge J, additional, Luna‐Muñoz, José, additional, Matallana, Diana, additional, Miller, Bruce L, additional, Naci, Lorina, additional, Parra, Mario A, additional, Pericak‐Vance, Margaret, additional, Piña‐Escudero, Stefanie D, additional, França Resende, Elisa de Paula, additional, Ringman, John M, additional, Sevlever, Gustavo, additional, Slachevsky, Andrea, additional, Suemoto, Claudia Kimie, additional, Valcour, Victor, additional, Villegas‐Lanau, Andres, additional, Yassuda, Mônica S, additional, Mahinrad, Simin, additional, and Sexton, Claire, additional
Published: 2024
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45. Patterns of Early Neocortical Amyloid-β Accumulation: A PET Population-Based Study

Author: Lecy, Emily E., primary, Min, Hoon-Ki, additional, Apgar, Christopher J., additional, Maltais, Daniela D., additional, Lundt, Emily S., additional, Albertson, Sabrina M., additional, Senjem, Matthew L., additional, Schwarz, Christopher G., additional, Botha, Hugo, additional, Graff-Radford, Jonathan, additional, Jones, David T., additional, Vemuri, Prashanthi, additional, Kantarci, Kejal, additional, Knopman, David S., additional, Petersen, Ronald C., additional, Jack, Clifford R., additional, Lee, Jeyeon, additional, and Lowe, Val J., additional
Published: 2024
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46. TDP-43 Is Associated with Subiculum and Cornu Ammonis 1 Hippocampal Subfield Atrophy in Primary Age-Related Tauopathy

Author: Youssef, Hossam, primary, Gatto, Rodolfo G., additional, Pham, Nha Trang Thu, additional, Petersen, Ronald C., additional, Machulda, Mary M., additional, Reichard, R. Ross, additional, Dickson, Dennis W., additional, Jack, Clifford R., additional, Whitwell, Jennifer L., additional, and Josephs, Keith A., additional
Published: 2024
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47. The clinical importance of suspected non-Alzheimer disease pathophysiology

Author: Vos, Stephanie J. B., primary, Delvenne, Aurore, additional, Jack, Clifford R., additional, Thal, Dietmar R., additional, and Visser, Pieter Jelle, additional
Published: 2024
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48. Clinicopathologic Heterogeneity and Glial Activation Patterns in Alzheimer Disease

Author: Kouri, Naomi, primary, Frankenhauser, Isabelle, additional, Peng, Zhongwei, additional, Labuzan, Sydney A., additional, Boon, Baayla D. C., additional, Moloney, Christina M., additional, Pottier, Cyril, additional, Wickland, Daniel P., additional, Caetano-Anolles, Kelsey, additional, Corriveau-Lecavalier, Nick, additional, Tranovich, Jessica F., additional, Wood, Ashley C., additional, Hinkle, Kelly M., additional, Lincoln, Sarah J., additional, Spychalla, A. J., additional, Senjem, Matthew L., additional, Przybelski, Scott A., additional, Engelberg-Cook, Erica, additional, Schwarz, Christopher G., additional, Kwan, Rain S., additional, Lesser, Elizabeth R., additional, Crook, Julia E., additional, Carter, Rickey E., additional, Ross, Owen A., additional, Lachner, Christian, additional, Ertekin-Taner, Nilüfer, additional, Ferman, Tanis J., additional, Fields, Julie A., additional, Machulda, Mary M., additional, Ramanan, Vijay K., additional, Nguyen, Aivi T., additional, Reichard, R. Ross, additional, Jones, David T., additional, Graff-Radford, Jonathan, additional, Boeve, Bradley F., additional, Knopman, David S., additional, Petersen, Ronald C., additional, Jack, Clifford R., additional, Kantarci, Kejal, additional, Day, Gregory S., additional, Duara, Ranjan, additional, Graff-Radford, Neill R., additional, Dickson, Dennis W., additional, Lowe, Val J., additional, Vemuri, Prashanthi, additional, and Murray, Melissa E., additional
Published: 2024
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49. Influences of amyloid-β and tau on white matter neurite alterations in dementia with Lewy bodies

Author: Mak, Elijah, primary, Reid, Robert I., additional, Przybelski, Scott A., additional, Lesnick, Timothy G., additional, Schwarz, Christopher G., additional, Senjem, Matthew L., additional, Raghavan, Sheelakumari, additional, Vemuri, Prashanthi, additional, Jack, Clifford R., additional, Min, Hoon Ki, additional, Jain, Manoj K., additional, Miyagawa, Toji, additional, Forsberg, Leah K., additional, Fields, Julie A., additional, Savica, Rodolfo, additional, Graff-Radford, Jonathan, additional, Jones, David T., additional, Botha, Hugo, additional, St. Louis, Erik K., additional, Knopman, David S., additional, Ramanan, Vijay K., additional, Dickson, Dennis W., additional, Graff-Radford, Neill R., additional, Ferman, Tanis J., additional, Petersen, Ronald C., additional, Lowe, Val J., additional, Boeve, Bradley F., additional, O’Brien, John T., additional, and Kantarci, Kejal, additional
Published: 2024
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50. Atypical brain aging and its association with working memory performance in major depressive disorder

Author: Ho, Natalie C.W., primary, Bethlehem, Richard AI., additional, Seidlitz, Jakob, additional, Nogovitsyn, Nikita, additional, Metzak, Paul, additional, Ballester, Pedro L., additional, Hassel, Stefanie, additional, Rotzinger, Susan, additional, Poppenk, Jordan, additional, Lam, Raymond W., additional, Taylor, Valerie H., additional, Milev, Roumen, additional, Adamson, Chris, additional, Adler, Sophie, additional, Alexander-Bloch, Aaron F., additional, Anagnostou, Evdokia, additional, Anderson, Kevin M., additional, Areces-Gonzalez, Ariosky, additional, Astle, Duncan E., additional, Auyeung, Bonnie, additional, Ayub, Muhammad, additional, Bae, Jong Bin, additional, Ball, Gareth, additional, Baron-Cohen, Simon, additional, Beare, Richard, additional, Bedford, Saashi A., additional, Benegal, Vivek, additional, Bethlehem, Richard A.I., additional, Beyer, Frauke, additional, Blangero, John, additional, Blesa Cábez, Manuel, additional, Boardman, James P., additional, Borzage, Matthew, additional, Bosch-Bayard, Jorge F., additional, Bourke, Niall, additional, Bullmore, Edward T., additional, Calhoun, Vince D., additional, Chakravarty, Mallar M., additional, Chen, Christina, additional, Chertavian, Casey, additional, Chetelat, Gaël, additional, Chong, Yap S., additional, Corvin, Aiden, additional, Costantino, Manuela, additional, Courchesne, Eric, additional, Crivello, Fabrice, additional, Cropley, Vanessa L., additional, Crosbie, Jennifer, additional, Crossley, Nicolas, additional, Delarue, Marion, additional, Delorme, Richard, additional, Desrivieres, Sylvane, additional, Devenyi, Gabriel, additional, Di Biase, Maria A., additional, Dolan, Ray, additional, Donald, Kirsten A., additional, Donohoe, Gary, additional, Dorfschmidt, Lena, additional, Dunlop, Katharine, additional, Edwards, Anthony D., additional, Elison, Jed T., additional, Ellis, Cameron T., additional, Elman, Jeremy A., additional, Eyler, Lisa, additional, Fair, Damien A., additional, Fletcher, Paul C., additional, Fonagy, Peter, additional, Franz, Carol E., additional, Galan-Garcia, Lidice, additional, Gholipour, Ali, additional, Giedd, Jay, additional, Gilmore, John H., additional, Glahn, David C., additional, Goodyer, Ian M., additional, Grant, P.E., additional, Groenewold, Nynke A., additional, Gudapati, Shreya, additional, Gunning, Faith M., additional, Gur, Raquel E., additional, Gur, Ruben C., additional, Hammill, Christopher F., additional, Hansson, Oskar, additional, Hedden, Trey, additional, Heinz, Andreas, additional, Henson, Richard N., additional, Heuer, Katja, additional, Hoare, Jacqueline, additional, Holla, Bharath, additional, Holmes, Avram J., additional, Huang, Hao, additional, Ipser, Jonathan, additional, Jack, Clifford R., additional, Jackowski, Andrea P., additional, Jia, Tianye, additional, Jones, David T., additional, Jones, Peter B., additional, Kahn, Rene S., additional, Karlsson, Hasse, additional, Karlsson, Linnea, additional, Kawashima, Ryuta, additional, Kelley, Elizabeth A., additional, Kern, Silke, additional, Kim, Ki-Woong, additional, Kitzbichler, Manfred G., additional, Kremen, William S., additional, Lalonde, François, additional, Landeau, Brigitte, additional, Lerch, Jason, additional, Lewis, John D., additional, Li, Jiao, additional, Liao, Wei, additional, Liston, Conor, additional, Lombardo, Michael V., additional, Lv, Jinglei, additional, Mallard, Travis T., additional, Marcelis, Machteld, additional, Mathias, Samuel R., additional, Mazoyer, Bernard, additional, McGuire, Philip, additional, Meaney, Michael J., additional, Mechelli, Andrea, additional, Misic, Bratislav, additional, Morgan, Sarah E., additional, Mothersill, David, additional, Ortinau, Cynthia, additional, Ossenkoppele, Rik, additional, Ouyang, Minhui, additional, Palaniyappan, Lena, additional, Paly, Leo, additional, Pan, Pedro M., additional, Pantelis, Christos, additional, Park, Min Tae M., additional, Paus, Tomas, additional, Pausova, Zdenka, additional, Paz-Linares, Deirel, additional, Pichet Binette, Alexa, additional, Pierce, Karen, additional, Qian, Xing, additional, Qiu, Anqi, additional, Raznahan, Armin, additional, Rittman, Timothy, additional, Rodrigue, Amanda, additional, Rollins, Caitlin K., additional, Romero-Garcia, Rafael, additional, Ronan, Lisa, additional, Rosenberg, Monica D., additional, Rowitch, David H., additional, Salum, Giovanni A., additional, Satterthwaite, Theodore D., additional, Schaare, H. Lina, additional, Schabdach, Jenna, additional, Schachar, Russell J., additional, Schöll, Michael, additional, Schultz, Aaron P., additional, Sharp, David, additional, Shinohara, Russell T., additional, Skoog, Ingmar, additional, Smyser, Christopher D., additional, Sperling, Reisa A., additional, Stein, Dan J., additional, Stolicyn, Aleks, additional, Suckling, John, additional, Sullivan, Gemma, additional, Thyreau, Benjamin, additional, Toro, Roberto, additional, Traut, Nicolas, additional, Tsvetanov, Kamen A., additional, Turk-Browne, Nicholas B., additional, Tuulari, Jetro J., additional, Tzourio, Christophe, additional, Vachon-Presseau, Étienne, additional, Valdes-Sosa, Mitchell J., additional, Valdes-Sosa, Pedro A., additional, Valk, Sofie L., additional, van Amelsvoort, Therese, additional, Vandekar, Simon N., additional, Vasung, Lana, additional, Vértes, Petra E., additional, Victoria, Lindsay W., additional, Villeneuve, Sylvia, additional, Villringer, Arno, additional, Vogel, Jacob W., additional, Wagstyl, Konrad, additional, Wang, Yin-Shan S., additional, Warfield, Simon K., additional, Warrier, Varun, additional, Westman, Eric, additional, Westwater, Margaret L., additional, Whalley, Heather C., additional, White, Simon R., additional, Witte, A. Veronica, additional, Yang, Ning, additional, Yeo, B.T. Thomas, additional, Yun, Hyuk Jin, additional, Zalesky, Andrew, additional, Zar, Heather J., additional, Zettergren, Anna, additional, Zhou, Juan H., additional, Ziauddeen, Hisham, additional, Zimmerman, Dabriel, additional, Zugman, Andre, additional, Zuo, Xi-Nian N., additional, Frey, Benicio N., additional, Harkness, Kate L., additional, Addington, Jean, additional, and Kennedy, Sidney H., additional
Published: 2024
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