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1. Clinical recognition of frontotemporal dementia with right anterior temporal predominance: A multicenter retrospective cohort study

Author: Ulugut, Hulya, Bertoux, Maxime, Younes, Kyan, Montembeault, Maxime, Fumagalli, Giorgio G, Samanci, Bedia, Illán‐Gala, Ignacio, Kuchcinski, Gregory, Leroy, Melanie, Thompson, Jennifer C, Kobylecki, Christopher, Santillo, Alexander F, Englund, Elisabet, Waldö, Maria Landqvist, Riedl, Lina, Van den Stock, Jan, Vandenbulcke, Mathieu, Vandenberghe, Rik, Laforce, Robert, Ducharme, Simon, Pressman, Peter S, Caramelli, Paulo, de Souza, Leonardo Cruz, Takada, Leonel T, Gurvit, Hakan, Hansson, Oskar, Diehl‐Schmid, Janine, Galimberti, Daniela, Pasquier, Florence, Miller, Bruce L, Scheltens, Philip, Ossenkoppele, Rik, van der Flier, Wiesje M, Barkhof, Frederik, Fox, Nick C, Sturm, Virginia E, Miyagawa, Toji, Whitwell, Jennifer L, Boeve, Bradley, Rohrer, Jonathan D, Gorno‐Tempini, Maria Luisa, Josephs, Keith A, Snowden, Julie, Warren, Jason D, Rankin, Katherine P, Pijnenburg, Yolande AL, and Group, International rtvFTD Working
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Psychology, Acquired Cognitive Impairment, Dementia, Neurosciences, Alzheimer's Disease Related Dementias (ADRD), Aging, Neurodegenerative, Clinical Research, Mental Health, Basic Behavioral and Social Science, Frontotemporal Dementia (FTD), Rare Diseases, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Behavioral and Social Science, Mental health, Neurological, Good Health and Well Being, Humans, Male, Frontotemporal Dementia, Retrospective Studies, Female, Temporal Lobe, Aged, Middle Aged, Neuropsychological Tests, Atrophy, emotion recognition, frontotemporal dementia, frontotemporal lobar degeneration, right anterior temporal lobe, semantic dementia, social cognition, International rtvFTD Working Group, Clinical Sciences, Geriatrics, Clinical sciences, Biological psychology
Abstract: IntroductionAlthough frontotemporal dementia (FTD) with right anterior temporal lobe (RATL) predominance has been recognized, a uniform description of the syndrome is still missing. This multicenter study aims to establish a cohesive clinical phenotype.MethodsRetrospective clinical data from 18 centers across 12 countries yielded 360 FTD patients with predominant RATL atrophy through initial neuroimaging assessments.ResultsCommon symptoms included mental rigidity/preoccupations (78%), disinhibition/socially inappropriate behavior (74%), naming/word-finding difficulties (70%), memory deficits (67%), apathy (65%), loss of empathy (65%), and face-recognition deficits (60%). Real-life examples unveiled impairments regarding landmarks, smells, sounds, tastes, and bodily sensations (74%). Cognitive test scores indicated deficits in emotion, people, social interactions, and visual semantics however, lacked objective assessments for mental rigidity and preoccupations.DiscussionThis study cumulates the largest RATL cohort unveiling unique RATL symptoms subdued in prior diagnostic guidelines. Our novel approach, combining real-life examples with cognitive tests, offers clinicians a comprehensive toolkit for managing these patients.HighlightsThis project is the first international collaboration and largest reported cohort. Further efforts are warranted for precise nomenclature reflecting neural mechanisms. Our results will serve as a clinical guideline for early and accurate diagnoses.
Published: 2024

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

Author: Iaccarino, Leonardo, Llibre-Guerra, Jorge J, McDade, Eric, Edwards, Lauren, Gordon, Brian, Benzinger, Tammie, Hassenstab, Jason, Kramer, Joel H, Li, Yan, Miller, Bruce L, Miller, Zachary, Morris, John C, Mundada, Nidhi, Perrin, Richard J, Rosen, Howard J, Soleimani-Meigooni, David, Strom, Amelia, Tsoy, Elena, Wang, Guoqiao, Xiong, Chengjie, Allegri, Ricardo, Chrem, Patricio, Vazquez, Silvia, Berman, Sarah B, Chhatwal, Jasmeer, Masters, Colin L, Farlow, Martin R, Jucker, Mathias, Levin, Johannes, Salloway, Stephen, Fox, Nick C, Day, Gregory S, Gorno-Tempini, Maria Luisa, Boxer, Adam L, La Joie, Renaud, Bateman, Randall, and Rabinovici, Gil D
Subjects: Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Acquired Cognitive Impairment, Brain Disorders, Dementia, Biomedical Imaging, Clinical Research, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aging, Alzheimer's Disease, 2.1 Biological and endogenous factors, Aetiology, Neurological, Good Health and Well Being, Alzheimer's disease, amyloid-PET, brain glucose metabolism, FDG-PET, Alzheimer’s disease, Clinical sciences, Biological psychology
Abstract: Approximately 5% of Alzheimer's disease patients develop symptoms before age 65 (early-onset Alzheimer's disease), with either sporadic (sporadic early-onset Alzheimer's disease) or dominantly inherited (dominantly inherited Alzheimer's disease) presentations. Both sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease are characterized by brain amyloid-β accumulation, tau tangles, hypometabolism and neurodegeneration, but differences in topography and magnitude of these pathological changes are not fully elucidated. In this study, we directly compared patterns of amyloid-β plaque deposition and glucose hypometabolism in sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease individuals. Our analysis included 134 symptomatic sporadic early-onset Alzheimer's disease amyloid-Positron Emission Tomography (PET)-positive cases from the University of California, San Francisco, Alzheimer's Disease Research Center (mean ± SD age 59.7 ± 5.6 years), 89 symptomatic dominantly inherited Alzheimer's disease cases (age 45.8 ± 9.3 years) and 102 cognitively unimpaired non-mutation carriers from the Dominantly Inherited Alzheimer Network study (age 44.9 ± 9.2). Each group underwent clinical and cognitive examinations, 11C-labelled Pittsburgh Compound B-PET and structural MRI. 18F-Fluorodeoxyglucose-PET was also available for most participants. Positron Emission Tomography scans from both studies were uniformly processed to obtain a standardized uptake value ratio (PIB50-70 cerebellar grey reference and FDG30-60 pons reference) images. Statistical analyses included pairwise global and voxelwise group comparisons and group-independent component analyses. Analyses were performed also adjusting for covariates including age, sex, Mini-Mental State Examination, apolipoprotein ε4 status and average composite cortical of standardized uptake value ratio. Compared with dominantly inherited Alzheimer's disease, sporadic early-onset Alzheimer's disease participants were older at age of onset (mean ± SD, 54.8 ± 8.2 versus 41.9 ± 8.2, Cohen's d = 1.91), with more years of education (16.4 ± 2.8 versus 13.5 ± 3.2, d = 1) and more likely to be apolipoprotein ε4 carriers (54.6% ε4 versus 28.1%, Cramer's V = 0.26), but similar Mini-Mental State Examination (20.6 ± 6.1 versus 21.2 ± 7.4, d = 0.08). Sporadic early-onset Alzheimer's disease had higher global cortical Pittsburgh Compound B-PET binding (mean ± SD standardized uptake value ratio, 1.92 ± 0.29 versus 1.58 ± 0.44, d = 0.96) and greater global cortical 18F-fluorodeoxyglucose-PET hypometabolism (mean ± SD standardized uptake value ratio, 1.32 ± 0.1 versus 1.39 ± 0.19, d = 0.48) compared with dominantly inherited Alzheimer's disease. Fully adjusted comparisons demonstrated relatively higher Pittsburgh Compound B-PET standardized uptake value ratio in the medial occipital, thalami, basal ganglia and medial/dorsal frontal regions in dominantly inherited Alzheimer's disease versus sporadic early-onset Alzheimer's disease. Sporadic early-onset Alzheimer's disease showed relatively greater 18F-fluorodeoxyglucose-PET hypometabolism in Alzheimer's disease signature temporoparietal regions and caudate nuclei, whereas dominantly inherited Alzheimer's disease showed relatively greater hypometabolism in frontal white matter and pericentral regions. Independent component analyses largely replicated these findings by highlighting common and unique Pittsburgh Compound B-PET and 18F-fluorodeoxyglucose-PET binding patterns. In summary, our findings suggest both common and distinct patterns of amyloid and glucose hypometabolism in sporadic and dominantly inherited early-onset Alzheimer's disease.
Published: 2024

3. 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

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

Author: Llibre-Guerra, Jorge J, Iaccarino, Leonardo, Coble, Dean, Edwards, Lauren, Li, Yan, McDade, Eric, Strom, Amelia, Gordon, Brian, Mundada, Nidhi, Schindler, Suzanne E, Tsoy, Elena, Ma, Yinjiao, Lu, Ruijin, Fagan, Anne M, Benzinger, Tammie LS, Soleimani-Meigooni, David, Aschenbrenner, Andrew J, Miller, Zachary, Wang, Guoqiao, Kramer, Joel H, Hassenstab, Jason, Rosen, Howard J, Morris, John C, Miller, Bruce L, Xiong, Chengjie, Perrin, Richard J, Allegri, Ricardo, Chrem, Patricio, Surace, Ezequiel, Berman, Sarah B, Chhatwal, Jasmeer, Masters, Colin L, Farlow, Martin R, Jucker, Mathias, Levin, Johannes, Fox, Nick C, Day, Gregory, Gorno-Tempini, Maria Luisa, Boxer, Adam L, La Joie, Renaud, Rabinovici, Gil D, and Bateman, Randall
Subjects: Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Acquired Cognitive Impairment, Clinical Research, Clinical Trials and Supportive Activities, Alzheimer's Disease, Neurodegenerative, Genetics, Aging, Dementia, Brain Disorders, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), 2.1 Biological and endogenous factors, Aetiology, Neurological, early-onset Alzheimer's disease, sporadic, dominantly inherited, early-onset Alzheimer’s disease, Clinical sciences, Biological psychology
Abstract: Approximately 5% of Alzheimer's disease cases have an early age at onset (
Published: 2023

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. Updating the study protocol: Insight 46 – a longitudinal neuroscience sub-study of the MRC National Survey of Health and Development – phases 2 and 3

Author: Murray-Smith, Heidi, Barker, Suzie, Barkhof, Frederik, Barnes, Josephine, Brown, Thomas M., Captur, Gabriella, R.E.Cartlidge, Molly, Cash, David M., Coath, William, Davis, Daniel, Dickson, John C., Groves, James, Hughes, Alun D., James, Sarah-Naomi, Keshavan, Ashvini, Keuss, Sarah E., King-Robson, Josh, Lu, Kirsty, Malone, Ian B., Nicholas, Jennifer M., Rapala, Alicja, Scott, Catherine J., Street, Rebecca, Sudre, Carole H., Thomas, David L., Wong, Andrew, Wray, Selina, Zetterberg, Henrik, Chaturvedi, Nishi, Fox, Nick C., Crutch, Sebastian J., Richards, Marcus, and Schott, Jonathan M.
Published: 2024
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7. Data-driven modelling of neurodegenerative disease progression: thinking outside the black box

Author: Young, Alexandra L., Oxtoby, Neil P., Garbarino, Sara, Fox, Nick C., Barkhof, Frederik, Schott, Jonathan M., and Alexander, Daniel C.
Published: 2024
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8. Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s disease

Author: Holstege, Henne, Hulsman, Marc, Charbonnier, Camille, Grenier-Boley, Benjamin, Quenez, Olivier, Grozeva, Detelina, van Rooij, Jeroen GJ, Sims, Rebecca, Ahmad, Shahzad, Amin, Najaf, Norsworthy, Penny J, Dols-Icardo, Oriol, Hummerich, Holger, Kawalia, Amit, Amouyel, Philippe, Beecham, Gary W, Berr, Claudine, Bis, Joshua C, Boland, Anne, Bossù, Paola, Bouwman, Femke, Bras, Jose, Campion, Dominique, Cochran, J Nicholas, Daniele, Antonio, Dartigues, Jean-François, Debette, Stéphanie, Deleuze, Jean-François, Denning, Nicola, DeStefano, Anita L, Farrer, Lindsay A, Fernández, Maria Victoria, Fox, Nick C, Galimberti, Daniela, Genin, Emmanuelle, Gille, Johan JP, Le Guen, Yann, Guerreiro, Rita, Haines, Jonathan L, Holmes, Clive, Ikram, M Arfan, Ikram, M Kamran, Jansen, Iris E, Kraaij, Robert, Lathrop, Marc, Lemstra, Afina W, Lleó, Alberto, Luckcuck, Lauren, Mannens, Marcel MAM, Marshall, Rachel, Martin, Eden R, Masullo, Carlo, Mayeux, Richard, Mecocci, Patrizia, Meggy, Alun, Mol, Merel O, Morgan, Kevin, Myers, Richard M, Nacmias, Benedetta, Naj, Adam C, Napolioni, Valerio, Pasquier, Florence, Pastor, Pau, Pericak-Vance, Margaret A, Raybould, Rachel, Redon, Richard, Reinders, Marcel JT, Richard, Anne-Claire, Riedel-Heller, Steffi G, Rivadeneira, Fernando, Rousseau, Stéphane, Ryan, Natalie S, Saad, Salha, Sanchez-Juan, Pascual, Schellenberg, Gerard D, Scheltens, Philip, Schott, Jonathan M, Seripa, Davide, Seshadri, Sudha, Sie, Daoud, Sistermans, Erik A, Sorbi, Sandro, van Spaendonk, Resie, Spalletta, Gianfranco, Tesi, Niccolo’, Tijms, Betty, Uitterlinden, André G, van der Lee, Sven J, Visser, Pieter Jelle, Wagner, Michael, Wallon, David, Wang, Li-San, Zarea, Aline, Clarimon, Jordi, van Swieten, John C, Greicius, Michael D, Yokoyama, Jennifer S, Cruchaga, Carlos, Hardy, John, and Ramirez, Alfredo
Subjects: Biological Sciences, Genetics, Dementia, Alzheimer's Disease, Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Human Genome, Prevention, Acquired Cognitive Impairment, Neurodegenerative, Aging, Clinical Research, Biotechnology, Brain Disorders, 2.1 Biological and endogenous factors, Aetiology, Neurological, Humans, Adenosine Triphosphatases, Alzheimer Disease, ATP Binding Cassette Transporter 1, Genome-Wide Association Study, Risk Factors, Exosomes, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract: Alzheimer's disease (AD), the leading cause of dementia, has an estimated heritability of approximately 70%1. The genetic component of AD has been mainly assessed using genome-wide association studies, which do not capture the risk contributed by rare variants2. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals-16,036 AD cases and 16,522 controls. Next to variants in TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Additionally, the rare-variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential drivers of respective AD-genome-wide association study loci. Variants associated with the strongest effect on AD risk, in particular loss-of-function variants, are enriched in early-onset AD cases. Our results provide additional evidence for a major role for amyloid-β precursor protein processing, amyloid-β aggregation, lipid metabolism and microglial function in AD.
Published: 2022

9. 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

10. 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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11. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission

Author: Livingston, Gill, Huntley, Jonathan, Liu, Kathy Y, Costafreda, Sergi G, Selbæk, Geir, Alladi, Suvarna, Ames, David, Banerjee, Sube, Burns, Alistair, Brayne, Carol, Fox, Nick C, Ferri, Cleusa P, Gitlin, Laura N, Howard, Robert, Kales, Helen C, Kivimäki, Mika, Larson, Eric B, Nakasujja, Noeline, Rockwood, Kenneth, Samus, Quincy, Shirai, Kokoro, Singh-Manoux, Archana, Schneider, Lon S, Walsh, Sebastian, Yao, Yao, Sommerlad, Andrew, and Mukadam, Naaheed
Published: 2024
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12. 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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13. Comparison of tau spread in people with Down syndrome versus autosomal-dominant Alzheimer's disease: a cross-sectional study

Author: Aizenstein, Howard J., Andrews, Howard F., Bell, Karen, Birn, Rasmus M., Bulova, Peter, Cheema, Amrita, Chen, Kewei, Clare, Isabel, Clark, Lorraine, Cohen, Ann D., Constantino, John N., Doran, Eric W., Feingold, Eleanor, Foroud, Tatiana M., Hartley, Sigan L., Hom, Christy, Honig, Lawrence, Ikonomovic, Milos D., Johnson, Sterling C., Jordan, Courtney, Kamboh, M. Ilyas, Keator, David, Klunk, William E., Kofler, Julia K., Kreisl, William C., Krinsky-McHale, Sharon J., Lao, Patrick, Laymon, Charles, Lott, Ira T., Lupson, Victoria, Mathis, Chester A., Minhas, Davneet S., Nadkarni, Neelesh, Pang, Deborah, Petersen, Melissa, Price, Julie C., Pulsifer, Margaret, Reiman, Eric, Rizvi, Batool, Sabbagh, Marwan N., Schupf, Nicole, Tudorascu, Dana L., Tumuluru, Rameshwari, Tycko, Benjamin, Varadarajan, Badri, White, Desiree A., Yassa, Michael A., Zaman, Shahid, Zhang, Fan, Bateman, Randall, Daniels, Alisha J., Courtney, Laura, McDade, Eric, Llibre-Guerra, Jorge J., Supnet-Bell, Charlene, Xiong, Chengie, Xu, Xiong, Lu, Ruijin, Wang, Guoqiao, Li, Yan, Gremminger, Emily, Perrin, Richard J., Franklin, Erin, Ibanez, Laura, Jerome, Gina, Herries, Elizabeth, Stauber, Jennifer, Baker, Bryce, Minton, Matthew, Cruchaga, Carlos, Goate, Alison M., Renton, Alan E., Picarello, Danielle M., Benzinger, Tammie, Gordon, Brian A., Hornbeck, Russall, Hassenstab, Jason, Smith, Jennifer, Stout, Sarah, Aschenbrenner, Andrew J., Karch, Celeste M., Marsh, Jacob, Morris, John C., Holtzman, David M., Barthelemy, Nicolas, Xu, Jinbin, Noble, James M., Berman, Sarah B., Ikonomovic, Snezana, Nadkarni, Neelesh K., Day, Gregory, Graff-Radford, Neill R., Farlow, Martin, Chhatwal, Jasmeer P., Ikeuchi, Takeshi, Kasuga, Kensaku, Niimi, Yoshiki, Huey, Edward D., Salloway, Stephen, Schofield, Peter R., Brooks, William S., Bechara, Jacob A., Martins, Ralph, Fox, Nick C., Cash, David M., Ryan, Natalie S., Jucker, Mathias, Laske, Christoph, Hofmann, Anna, Kuder-Buletta, Elke, Graber-Sultan, Susanne, Obermueller, Ulrike, Levin, Johannes, Roedenbeck, Yvonne, Vöglein, Jonathan, Lee, Jae-Hong, Roh, Jee Hoon, Sanchez-Valle, Raquel, Rosa-Neto, Pedro, Allegri, Ricardo F., Chrem Mendez, Patricio, Surace, Ezequiel, Vazquez, Silvia, Lopera, Francisco, Leon, Yudy Milena, Ramirez, Laura, Aguillon, David, Levey, Allan I., Johnson, Erik C.B, Seyfried, Nicholas T., Ringman, John, Mori, Hiroshi, Wisch, Julie K, McKay, Nicole S, Boerwinkle, Anna H, Kennedy, James, Flores, Shaney, Handen, Benjamin L, Christian, Bradley T, Head, Elizabeth, Mapstone, Mark, Rafii, Michael S, O’Bryant, Sid E, Price, Julie C, Laymon, Charles M, Krinsky-McHale, Sharon J, Lai, Florence, Rosas, H Diana, Hartley, Sigan L, Lott, Ira T, Tudorascu, Dana, Zammit, Matthew, Brickman, Adam M, Lee, Joseph H, Bird, Thomas D, Cohen, Annie, Chrem, Patricio, Daniels, Alisha, Chhatwal, Jasmeer P, Karch, Celeste M, Day, Gregory S, Llibre-Guerra, Jorge, Ringman, John M, van Dyck, Christopher H, Xiong, Chengjie, Morris, John C, Bateman, Randall J, Benzinger, Tammie L S, Gordon, Brian A, and Ances, Beau M
Published: 2024
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14. Deconstructing pathological tau by biological process in early stages of Alzheimer disease: a method for quantifying tau spatial spread in neuroimaging

Author: Noble, James M., Day, Gregory S., Graff-Radford, Neill R., Voglein, Jonathan, Levin, Johannes, Allegri, Ricardo F., Mendez, Patricio Chrem, Surace, Ezequiel, Berman, Sarah B., Ikonomovic, Snezana, Nadkarni, Neelesh K., Lopera, Francisco, Ramirez, Laura, Aguillon, David, Leon, Yudy, Ramos, Claudia, Alzate, Diana, Baena, Ana, Londono, Natalia, Moreno, Sonia, Jucker, Mathias, Laske, Christoph, Kuder-Buletta, Elke, Graber-Sultan, Susanne, Preische, Oliver, Hofmann, Anna, Ikeuchi, Takeshi, Kasuga, Kensaku, Niimi, Yoshiki, Ishii, Kenji, Senda, Michio, Sanchez-Valle, Raquel, Rosa-Neto, Pedro, Fox, Nick C., Cash, Dave, Lee, Jae-Hong, Roh, Jee Hoon, Salloway, Stephen, Riddle, Meghan C., Menard, William, Bodge, Courtney, Surti, Mustafa, Takada, Leonel Tadao, Farlow, Martin, Chhatwal, Jasmeer P., Sanchez-Gonzalez, V.J., Orozco-Barajas, Maribel, Goate, Alison M., Renton, Alan E., Esposito, Bianca T., Karch, Celeste M., Marsh, Jacob, Cruchaga, Carlos, Fernanadez, Victoria, Gordon, Brian A., Fagan, Anne M., Jerome, Gina, Herries, Elizabeth, Llibre-Guerra, Jorge, Levey, Allan I., Johnson, Erik C.B., Seyfried, Nicholas T., Schofield, Peter R., Brooks, William S., Bechara, Jacob A., Bateman, Randall, McDade, Eric, Hassenstab, Jason, Perrin, Richard J., Franklin, Erin E., Benzinger, Tammie, Chen, Allison, Chen, Charles, Flores, Shaney, Friedrichsen, Nelly, Gordon, Brian, Hantler, Nancy, Hornbeck, Russ, Jarman, Steve, Keefe, Sarah, Koudelis, Deborah, Massoumzadeh, Parinaz, McCullough, Austin, McKay, Nicole, Nicklaus, Joyce, Pulizos, Christine, Wang, Qing, Mishall, Sheetal, Sabaredzovic, Edita, Deng, Emily, Candela, Madison, Smith, Hunter, Hobbs, Diana, Scott, Jalen, Xiong, Chengjie, Wang, Peter, Xu, Xiong, Li, Yan, Gremminger, Emily, Ma, Yinjiao, Bui, Ryan, Lu, Ruijin, Martins, Ralph, Sosa Ortiz, Ana Luisa, Daniels, Alisha, Courtney, Laura, Mori, Hiroshi, Supnet-Bell, Charlene, Xu, Jinbin, Ringman, John, Barthelemy, Nicolas, Morris, John, Smith, Jennifer, Doering, Stephanie, Chen, Charles D., Jarman, Stephen, Jackson, Kelley, Hornbeck, Russ C., Ances, Beau M., Aschenbrenner, Andrew J., Bateman, Randall J., Morris, John C., and Benzinger, Tammie L.S.
Published: 2024
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15. Tau accumulation in autosomal dominant Alzheimer’s disease: a longitudinal [18F]flortaucipir study

Author: O’Connor, Antoinette, Cash, David M., Poole, Teresa, Markiewicz, Pawel J., Fraser, Maggie R., Malone, Ian B., Jiao, Jieqing, Weston, Philip S. J., Flores, Shaney, Hornbeck, Russ, McDade, Eric, Schöll, Michael, Gordon, Brian A., Bateman, Randall J., Benzinger, Tammie L. S., and Fox, Nick C.
Published: 2023
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16. Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease

Author: Green, Rebecca E., Lord, Jodie, Scelsi, Marzia A., Xu, Jin, Wong, Andrew, Naomi-James, Sarah, Handy, Alex, Gilchrist, Lachlan, Williams, Dylan M., Parker, Thomas D., Lane, Christopher A., Malone, Ian B., Cash, David M., Sudre, Carole H., Coath, William, Thomas, David L., Keuss, Sarah, Dobson, Richard, Legido-Quigley, Cristina, Fox, Nick C., Schott, Jonathan M., Richards, Marcus, and Proitsi, Petroula
Published: 2023
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17. The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

Author: Marinescu, Razvan V., Oxtoby, Neil P., Young, Alexandra L., Bron, Esther E., Toga, Arthur W., Weiner, Michael W., Barkhof, Frederik, Fox, Nick C., Eshaghi, Arman, Toni, Tina, Salaterski, Marcin, Lunina, Veronika, Ansart, Manon, Durrleman, Stanley, Lu, Pascal, Iddi, Samuel, Li, Dan, Thompson, Wesley K., Donohue, Michael C., Nahon, Aviv, Levy, Yarden, Halbersberg, Dan, Cohen, Mariya, Liao, Huiling, Li, Tengfei, Yu, Kaixian, Zhu, Hongtu, Tamez-Pena, Jose G., Ismail, Aya, Wood, Timothy, Bravo, Hector Corrada, Nguyen, Minh, Sun, Nanbo, Feng, Jiashi, Yeo, B. T. Thomas, Chen, Gang, Qi, Ke, Chen, Shiyang, Qiu, Deqiang, Buciuman, Ionut, Kelner, Alex, Pop, Raluca, Rimocea, Denisa, Ghazi, Mostafa M., Nielsen, Mads, Ourselin, Sebastien, Sorensen, Lauge, Venkatraghavan, Vikram, Liu, Keli, Rabe, Christina, Manser, Paul, Hill, Steven M., Howlett, James, Huang, Zhiyue, Kiddle, Steven, Mukherjee, Sach, Rouanet, Anais, Taschler, Bernd, Tom, Brian D. M., White, Simon R., Faux, Noel, Sedai, Suman, Oriol, Javier de Velasco, Clemente, Edgar E. V., Estrada, Karol, Aksman, Leon, Altmann, Andre, Stonnington, Cynthia M., Wang, Yalin, Wu, Jianfeng, Devadas, Vivek, Fourrier, Clementine, Raket, Lars Lau, Sotiras, Aristeidis, Erus, Guray, Doshi, Jimit, Davatzikos, Christos, Vogel, Jacob, Doyle, Andrew, Tam, Angela, Diaz-Papkovich, Alex, Jammeh, Emmanuel, Koval, Igor, Moore, Paul, Lyons, Terry J., Gallacher, John, Tohka, Jussi, Ciszek, Robert, Jedynak, Bruno, Pandya, Kruti, Bilgel, Murat, Engels, William, Cole, Joseph, Golland, Polina, Klein, Stefan, and Alexander, Daniel C.
Subjects: Quantitative Biology - Populations and Evolution, Statistics - Applications
Abstract: We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of biomarkers. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials., Comment: Presents final results of the TADPOLE competition. 60 pages, 7 tables, 14 figures
Published: 2020

18. TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data

Author: Marinescu, Razvan V., Oxtoby, Neil P., Young, Alexandra L., Bron, Esther E., Toga, Arthur W., Weiner, Michael W., Barkhof, Frederik, Fox, Nick C., Golland, Polina, Klein, Stefan, and Alexander, Daniel C.
Subjects: Quantitative Biology - Populations and Evolution, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Applications
Abstract: The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Participants are then required to make forecasts of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, ADAS-Cog 13, and total volume of the ventricles -- which are then compared with future measurements. Strong points of the challenge are that the test data did not exist at the time of forecasting (it was acquired afterwards), and that it focuses on the challenging problem of cohort selection for clinical trials by identifying fast progressors. The submission phase of TADPOLE was open until 15 November 2017; since then data has been acquired until April 2019 from 219 subjects with 223 clinical visits and 150 Magnetic Resonance Imaging (MRI) scans, which was used for the evaluation of the participants' predictions. Thirty-three teams participated with a total of 92 submissions. No single submission was best at predicting all three outcomes. For diagnosis prediction, the best forecast (team Frog), which was based on gradient boosting, obtained a multiclass area under the receiver-operating curve (MAUC) of 0.931, while for ventricle prediction the best forecast (team EMC1), which was based on disease progression modelling and spline regression, obtained mean absolute error of 0.41% of total intracranial volume (ICV). For ADAS-Cog 13, no forecast was considerably better than the benchmark mixed effects model (BenchmarkME), provided to participants before the submission deadline. Further analysis can help understand which input features and algorithms are most suitable for Alzheimer's disease prediction and for aiding patient stratification in clinical trials., Comment: 10 pages, 1 figure, 4 tables. arXiv admin note: substantial text overlap with arXiv:1805.03909
Published: 2020
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19. 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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20. Prioritization of Drug Targets for Neurodegenerative Diseases by Integrating Genetic and Proteomic Data From Brain and Blood

Author: Ferrari, Raffaele, Hernandez, Dena G., Nalls, Michael A., Rohrer, Jonathan D., Ramasamy, Adaikalavan, Kwok, John B.J., Dobson-Stone, Carol, Brooks, William S., Schofield, Peter R., Halliday, Glenda M., Hodges, John R., Piguet, Olivier, Bartley, Lauren, Thompson, Elizabeth, Haan, Eric, Hernández, Isabel, Ruiz, Agustín, Boada, Mercè, Borroni, Barbara, Padovani, Alessandro, Cruchaga, Carlos, Cairns, Nigel J., Benussi, Luisa, Binetti, Giuliano, Ghidoni, Roberta, Forloni, Gianluigi, Galimberti, Daniela, Fenoglio, Chiara, Serpente, Maria, Scarpini, Elio, Clarimón, Jordi, Lleó, Alberto, Blesa, Rafael, Waldö, Maria Landqvist, Nilsson, Karin, Nilsson, Christer, Mackenzie, Ian R.A., Hsiung, Ging-Yuek R., Mann, David M.A., Grafman, Jordan, Morris, Christopher M., Attems, Johannes, Griffiths, Timothy D., McKeith, Ian G., Thomas, Alan J., Pietrini, P., Huey, Edward D., Wassermann, Eric M., Baborie, Atik, Jaros, Evelyn, Tierney, Michael C., Pastor, Pau, Razquin, Cristina, Ortega-Cubero, Sara, Alonso, Elena, Perneczky, Robert, Diehl-Schmid, Janine, Alexopoulos, Panagiotis, Kurz, Alexander, Rainero, Innocenzo, Rubino, Elisa, Pinessi, Lorenzo, Rogaeva, Ekaterina, St. George-Hyslop, Peter, Rossi, Giacomina, Tagliavini, Fabrizio, Giaccone, Giorgio, Rowe, James B., Schlachetzki, Johannes C.M., Uphill, James, Collinge, John, Mead, Simon, Danek, Adrian, Van Deerlin, Vivianna M., Grossman, Murray, Trojanowski, John Q., van der Zee, Julie, Deschamps, William, Van Langenhove, Tim, Cruts, Marc, Van Broeckhoven, Christine, Cappa, Stefano F., Le Ber, Isabelle, Hannequin, Didier, Golfier, Véronique, Vercelletto, Martine, Brice, Alexis, Nacmias, Benedetta, Sorbi, Sandro, Bagnoli, Silvia, Piaceri, Irene, Nielsen, Jørgen E., Hjermind, Lena E., Riemenschneider, Matthias, Mayhaus, Manuel, Ibach, Bernd, Gasparoni, Gilles, Pichler, Sabrina, Gu, Wei, Rossor, Martin N., Fox, Nick C., Warren, Jason D., Spillantini, Maria Grazia, Morris, Huw R., Rizzu, Patrizia, Heutink, Peter, Snowden, Julie S., Rollinson, Sara, Richardson, Anna, Gerhard, Alexander, Bruni, Amalia C., Maletta, Raffaele, Frangipane, Francesca, Cupidi, Chiara, Bernardi, Livia, Anfossi, Maria, Gallo, Maura, Conidi, Maria Elena, Smirne, Nicoletta, Rademakers, Rosa, Baker, Matt, Dickson, Dennis W., Graff-Radford, Neill R., Petersen, Ronald C., Knopman, David, Josephs, Keith A., Boeve, Bradley F., Parisi, Joseph E., Seeley, William W., Miller, Bruce L., Karydas, Anna M., Rosen, Howard, van Swieten, John C., Dopper, Elise G.P., Seelaar, Harro, Pijnenburg, Yolande A.L., Scheltens, Philip, Logroscino, Giancarlo, Capozzo, Rosa, Novelli, Valeria, Puca, Annibale A., Franceschi, Massimo, Postiglione, Alfredo, Milan, Graziella, Sorrentino, Paolo, Kristiansen, Mark, Chiang, Huei-Hsin, Graff, Caroline, Pasquier, Florence, Rollin, Adeline, Deramecourt, Vincent, Lebert, Florence, Kapogiannis, Dimitrios, Ferrucci, Luigi, Pickering-Brown, Stuart, Singleton, Andrew B., Hardy, John, Momeni, Parastoo, Ge, Yi-Jun, Ou, Ya-Nan, Deng, Yue-Ting, Wu, Bang-Sheng, Yang, Liu, Zhang, Ya-Ru, Chen, Shi-Dong, Huang, Yu-Yuan, Dong, Qiang, Tan, Lan, and Yu, Jin-Tai
Published: 2023
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21. Age at symptom onset and death and disease duration in genetic frontotemporal dementia: an international retrospective cohort study.

Author: Moore, Katrina M, Nicholas, Jennifer, Grossman, Murray, McMillan, Corey T, Irwin, David J, Massimo, Lauren, Van Deerlin, Vivianna M, Warren, Jason D, Fox, Nick C, Rossor, Martin N, Mead, Simon, Bocchetta, Martina, Boeve, Bradley F, Knopman, David S, Graff-Radford, Neill R, Forsberg, Leah K, Rademakers, Rosa, Wszolek, Zbigniew K, van Swieten, John C, Jiskoot, Lize C, Meeter, Lieke H, Dopper, Elise Gp, Papma, Janne M, Snowden, Julie S, Saxon, Jennifer, Jones, Matthew, Pickering-Brown, Stuart, Le Ber, Isabelle, Camuzat, Agnès, Brice, Alexis, Caroppo, Paola, Ghidoni, Roberta, Pievani, Michela, Benussi, Luisa, Binetti, Giuliano, Dickerson, Bradford C, Lucente, Diane, Krivensky, Samantha, Graff, Caroline, Öijerstedt, Linn, Fallström, Marie, Thonberg, Håkan, Ghoshal, Nupur, Morris, John C, Borroni, Barbara, Benussi, Alberto, Padovani, Alessandro, Galimberti, Daniela, Scarpini, Elio, Fumagalli, Giorgio G, Mackenzie, Ian R, Hsiung, Ging-Yuek R, Sengdy, Pheth, Boxer, Adam L, Rosen, Howie, Taylor, Joanne B, Synofzik, Matthis, Wilke, Carlo, Sulzer, Patricia, Hodges, John R, Halliday, Glenda, Kwok, John, Sanchez-Valle, Raquel, Lladó, Albert, Borrego-Ecija, Sergi, Santana, Isabel, Almeida, Maria Rosário, Tábuas-Pereira, Miguel, Moreno, Fermin, Barandiaran, Myriam, Indakoetxea, Begoña, Levin, Johannes, Danek, Adrian, Rowe, James B, Cope, Thomas E, Otto, Markus, Anderl-Straub, Sarah, de Mendonça, Alexandre, Maruta, Carolina, Masellis, Mario, Black, Sandra E, Couratier, Philippe, Lautrette, Geraldine, Huey, Edward D, Sorbi, Sandro, Nacmias, Benedetta, Laforce, Robert, Tremblay, Marie-Pier L, Vandenberghe, Rik, Damme, Philip Van, Rogalski, Emily J, Weintraub, Sandra, Gerhard, Alexander, Onyike, Chiadi U, Ducharme, Simon, Papageorgiou, Sokratis G, Ng, Adeline Su Lyn, Brodtmann, Amy, Finger, Elizabeth, and Guerreiro, Rita
Subjects: FTD Prevention Initiative, Humans, Disease Progression, tau Proteins, Retrospective Studies, Cohort Studies, Family, Age of Onset, Phenotype, Mutation, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Frontotemporal Dementia, C9orf72 Protein, Progranulins, Clinical Research, Rare Diseases, Dementia, Aging, Brain Disorders, Genetic Testing, Neurodegenerative, Neurosciences, Alzheimer's Disease Related Dementias (ADRD), Prevention, Genetics, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), 2.1 Biological and endogenous factors, Neurological, Neurology & Neurosurgery, Clinical Sciences
Abstract: BackgroundFrontotemporal dementia is a heterogenous neurodegenerative disorder, with about a third of cases being genetic. Most of this genetic component is accounted for by mutations in GRN, MAPT, and C9orf72. In this study, we aimed to complement previous phenotypic studies by doing an international study of age at symptom onset, age at death, and disease duration in individuals with mutations in GRN, MAPT, and C9orf72.MethodsIn this international, retrospective cohort study, we collected data on age at symptom onset, age at death, and disease duration for patients with pathogenic mutations in the GRN and MAPT genes and pathological expansions in the C9orf72 gene through the Frontotemporal Dementia Prevention Initiative and from published papers. We used mixed effects models to explore differences in age at onset, age at death, and disease duration between genetic groups and individual mutations. We also assessed correlations between the age at onset and at death of each individual and the age at onset and at death of their parents and the mean age at onset and at death of their family members. Lastly, we used mixed effects models to investigate the extent to which variability in age at onset and at death could be accounted for by family membership and the specific mutation carried.FindingsData were available from 3403 individuals from 1492 families: 1433 with C9orf72 expansions (755 families), 1179 with GRN mutations (483 families, 130 different mutations), and 791 with MAPT mutations (254 families, 67 different mutations). Mean age at symptom onset and at death was 49·5 years (SD 10·0; onset) and 58·5 years (11·3; death) in the MAPT group, 58·2 years (9·8; onset) and 65·3 years (10·9; death) in the C9orf72 group, and 61·3 years (8·8; onset) and 68·8 years (9·7; death) in the GRN group. Mean disease duration was 6·4 years (SD 4·9) in the C9orf72 group, 7·1 years (3·9) in the GRN group, and 9·3 years (6·4) in the MAPT group. Individual age at onset and at death was significantly correlated with both parental age at onset and at death and with mean family age at onset and at death in all three groups, with a stronger correlation observed in the MAPT group (r=0·45 between individual and parental age at onset, r=0·63 between individual and mean family age at onset, r=0·58 between individual and parental age at death, and r=0·69 between individual and mean family age at death) than in either the C9orf72 group (r=0·32 individual and parental age at onset, r=0·36 individual and mean family age at onset, r=0·38 individual and parental age at death, and r=0·40 individual and mean family age at death) or the GRN group (r=0·22 individual and parental age at onset, r=0·18 individual and mean family age at onset, r=0·22 individual and parental age at death, and r=0·32 individual and mean family age at death). Modelling showed that the variability in age at onset and at death in the MAPT group was explained partly by the specific mutation (48%, 95% CI 35-62, for age at onset; 61%, 47-73, for age at death), and even more by family membership (66%, 56-75, for age at onset; 74%, 65-82, for age at death). In the GRN group, only 2% (0-10) of the variability of age at onset and 9% (3-21) of that of age of death was explained by the specific mutation, whereas 14% (9-22) of the variability of age at onset and 20% (12-30) of that of age at death was explained by family membership. In the C9orf72 group, family membership explained 17% (11-26) of the variability of age at onset and 19% (12-29) of that of age at death.InterpretationOur study showed that age at symptom onset and at death of people with genetic frontotemporal dementia is influenced by genetic group and, particularly for MAPT mutations, by the specific mutation carried and by family membership. Although estimation of age at onset will be an important factor in future pre-symptomatic therapeutic trials for all three genetic groups, our study suggests that data from other members of the family will be particularly helpful only for individuals with MAPT mutations. Further work in identifying both genetic and environmental factors that modify phenotype in all groups will be important to improve such estimates.FundingUK Medical Research Council, National Institute for Health Research, and Alzheimer's Society.
Published: 2020

22. 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

23. Adulthood cognitive trajectories over 26 years and brain health at 70 years of age: findings from the 1946 British Birth Cohort

Author: James, Sarah-Naomi, Nicholas, Jennifer M., Lu, Kirsty, Keshavan, Ashvini, Lane, Christopher A., Parker, Thomas, Buchanan, Sarah M., Keuss, Sarah E., Murray-Smith, Heidi, Wong, Andrew, Cash, David M., Malone, Ian B., Barnes, Josephine, Sudre, Carole H., Coath, William, Modat, Marc, Ourselin, Sebastien, Crutch, Sebastian J., Kuh, Diana, Fox, Nick C., Schott, Jonathan M., and Richards, Marcus
Published: 2023
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24. Climate change and disorders of the nervous system

Author: Sisodiya, Sanjay M, primary, Gulcebi, Medine I, additional, Fortunato, Francesco, additional, Mills, James D, additional, Haynes, Ethan, additional, Bramon, Elvira, additional, Chadwick, Paul, additional, Ciccarelli, Olga, additional, David, Anthony S, additional, De Meyer, Kris, additional, Fox, Nick C, additional, Davan Wetton, Joanna, additional, Koltzenburg, Martin, additional, Kullmann, Dimitri M, additional, Kurian, Manju A, additional, Manji, Hadi, additional, Maslin, Mark A, additional, Matharu, Manjit, additional, Montgomery, Hugh, additional, Romanello, Marina, additional, Werring, David J, additional, Zhang, Lisa, additional, Friston, Karl J, additional, and Hanna, Michael G, additional
Published: 2024
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25. Deconstructing pathological tau by biological process in early stages of Alzheimer disease: a method for quantifying tau spatial spread in neuroimaging

Author: Doering, Stephanie, primary, McCullough, Austin, additional, Gordon, Brian A., additional, Chen, Charles D., additional, McKay, Nicole, additional, Hobbs, Diana, additional, Keefe, Sarah, additional, Flores, Shaney, additional, Scott, Jalen, additional, Smith, Hunter, additional, Jarman, Stephen, additional, Jackson, Kelley, additional, Hornbeck, Russ C., additional, Ances, Beau M., additional, Xiong, Chengjie, additional, Aschenbrenner, Andrew J., additional, Hassenstab, Jason, additional, Cruchaga, Carlos, additional, Daniels, Alisha, additional, Bateman, Randall J., additional, Noble, James M., additional, Day, Gregory S., additional, Graff-Radford, Neill R., additional, Voglein, Jonathan, additional, Levin, Johannes, additional, Allegri, Ricardo F., additional, Mendez, Patricio Chrem, additional, Surace, Ezequiel, additional, Berman, Sarah B., additional, Ikonomovic, Snezana, additional, Nadkarni, Neelesh K., additional, Lopera, Francisco, additional, Ramirez, Laura, additional, Aguillon, David, additional, Leon, Yudy, additional, Ramos, Claudia, additional, Alzate, Diana, additional, Baena, Ana, additional, Londono, Natalia, additional, Moreno, Sonia, additional, Jucker, Mathias, additional, Laske, Christoph, additional, Kuder-Buletta, Elke, additional, Graber-Sultan, Susanne, additional, Preische, Oliver, additional, Hofmann, Anna, additional, Ikeuchi, Takeshi, additional, Kasuga, Kensaku, additional, Niimi, Yoshiki, additional, Ishii, Kenji, additional, Senda, Michio, additional, Sanchez-Valle, Raquel, additional, Rosa-Neto, Pedro, additional, Fox, Nick C., additional, Cash, Dave, additional, Lee, Jae-Hong, additional, Roh, Jee Hoon, additional, Salloway, Stephen, additional, Riddle, Meghan C., additional, Menard, William, additional, Bodge, Courtney, additional, Surti, Mustafa, additional, Takada, Leonel Tadao, additional, Farlow, Martin, additional, Chhatwal, Jasmeer P., additional, Sanchez-Gonzalez, V.J., additional, Orozco-Barajas, Maribel, additional, Goate, Alison M., additional, Renton, Alan E., additional, Esposito, Bianca T., additional, Karch, Celeste M., additional, Marsh, Jacob, additional, Fernanadez, Victoria, additional, Fagan, Anne M., additional, Jerome, Gina, additional, Herries, Elizabeth, additional, Llibre-Guerra, Jorge, additional, Levey, Allan I., additional, Johnson, Erik C.B., additional, Seyfried, Nicholas T., additional, Schofield, Peter R., additional, Brooks, William S., additional, Bechara, Jacob A., additional, Bateman, Randall, additional, McDade, Eric, additional, Perrin, Richard J., additional, Franklin, Erin E., additional, Benzinger, Tammie, additional, Chen, Allison, additional, Chen, Charles, additional, Friedrichsen, Nelly, additional, Gordon, Brian, additional, Hantler, Nancy, additional, Hornbeck, Russ, additional, Jarman, Steve, additional, Koudelis, Deborah, additional, Massoumzadeh, Parinaz, additional, Nicklaus, Joyce, additional, Pulizos, Christine, additional, Wang, Qing, additional, Mishall, Sheetal, additional, Sabaredzovic, Edita, additional, Deng, Emily, additional, Candela, Madison, additional, Wang, Peter, additional, Xu, Xiong, additional, Li, Yan, additional, Gremminger, Emily, additional, Ma, Yinjiao, additional, Bui, Ryan, additional, Lu, Ruijin, additional, Martins, Ralph, additional, Sosa Ortiz, Ana Luisa, additional, Courtney, Laura, additional, Mori, Hiroshi, additional, Supnet-Bell, Charlene, additional, Xu, Jinbin, additional, Ringman, John, additional, Barthelemy, Nicolas, additional, Morris, John, additional, Smith, Jennifer, additional, Morris, John C., additional, and Benzinger, Tammie L.S., additional
Published: 2024
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26. Aβ profiles generated by Alzheimer’s disease causing PSEN1 variants determine the pathogenicity of the mutation and predict age at disease onset

Author: Petit, Dieter, Fernández, Sara Gutiérrez, Zoltowska, Katarzyna Marta, Enzlein, Thomas, Ryan, Natalie S., O’Connor, Antoinette, Szaruga, Maria, Hill, Elizabeth, Vandenberghe, Rik, Fox, Nick C., and Chávez-Gutiérrez, Lucía
Published: 2022
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27. TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease

Author: Marinescu, Razvan V., Oxtoby, Neil P., Young, Alexandra L., Bron, Esther E., Toga, Arthur W., Weiner, Michael W., Barkhof, Frederik, Fox, Nick C., Klein, Stefan, Alexander, Daniel C., and Consortium, the EuroPOND
Subjects: Quantitative Biology - Populations and Evolution, Statistics - Applications
Abstract: The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge compares the performance of algorithms at predicting future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study or any other datasets to which they have access. Participants are then required to make monthly forecasts over a period of 5 years from January 2018, of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. These individual forecasts are later compared with the corresponding future measurements in ADNI-3 (obtained after the TADPOLE submission deadline). The first submission phase of TADPOLE was open for prize-eligible submissions between 15 June and 15 November 2017. The submission system remains open via the website: https://tadpole.grand-challenge.org, although since 15 November 2017 submissions are not eligible for the first round of prizes. This paper describes the design of the TADPOLE Challenge., Comment: For more details on TADPOLE Challenge, see https://tadpole.grand-challenge.org/ This paper outlines the design of the TADPOLE Challenge. Paper contains 8 pages, 2 figures, 5 tables
Published: 2018

28. Author Correction: Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing

Author: Kunkle, Brian W, Grenier-Boley, Benjamin, Sims, Rebecca, Bis, Joshua C, Damotte, Vincent, Naj, Adam C, Boland, Anne, Vronskaya, Maria, van der Lee, Sven J, Amlie-Wolf, Alexandre, Bellenguez, Céline, Frizatti, Aura, Chouraki, Vincent, Martin, Eden R, Sleegers, Kristel, Badarinarayan, Nandini, Jakobsdottir, Johanna, Hamilton-Nelson, Kara L, Moreno-Grau, Sonia, Olaso, Robert, Raybould, Rachel, Chen, Yuning, Kuzma, Amanda B, Hiltunen, Mikko, Morgan, Taniesha, Ahmad, Shahzad, Vardarajan, Badri N, Epelbaum, Jacques, Hoffmann, Per, Boada, Merce, Beecham, Gary W, Garnier, Jean-Guillaume, Harold, Denise, Fitzpatrick, Annette L, Valladares, Otto, Moutet, Marie-Laure, Gerrish, Amy, Smith, Albert V, Qu, Liming, Bacq, Delphine, Denning, Nicola, Jian, Xueqiu, Zhao, Yi, Del Zompo, Maria, Fox, Nick C, Choi, Seung-Hoan, Mateo, Ignacio, Hughes, Joseph T, Adams, Hieab H, Malamon, John, Sanchez-Garcia, Florentino, Patel, Yogen, Brody, Jennifer A, Dombroski, Beth A, Naranjo, Maria Candida Deniz, Daniilidou, Makrina, Eiriksdottir, Gudny, Mukherjee, Shubhabrata, Wallon, David, Uphill, James, Aspelund, Thor, Cantwell, Laura B, Garzia, Fabienne, Galimberti, Daniela, Hofer, Edith, Butkiewicz, Mariusz, Fin, Bertrand, Scarpini, Elio, Sarnowski, Chloe, Bush, Will S, Meslage, Stéphane, Kornhuber, Johannes, White, Charles C, Song, Yuenjoo, Barber, Robert C, Engelborghs, Sebastiaan, Sordon, Sabrina, Voijnovic, Dina, Adams, Perrie M, Vandenberghe, Rik, Mayhaus, Manuel, Cupples, L Adrienne, Albert, Marilyn S, De Deyn, Peter P, Gu, Wei, Himali, Jayanadra J, Beekly, Duane, Squassina, Alessio, Hartmann, Annette M, Orellana, Adelina, Blacker, Deborah, Rodriguez-Rodriguez, Eloy, Lovestone, Simon, Garcia, Melissa E, Doody, Rachelle S, Munoz-Fernadez, Carmen, Sussams, Rebecca, Lin, Honghuang, Fairchild, Thomas J, and Benito, Yolanda A
Subjects: Biochemistry and Cell Biology, Biological Sciences, Alzheimer Disease Genetics Consortium, European Alzheimer’s Disease Initiative, Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium, Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology, Genetics
Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Published: 2019

29. Longitudinal neuroanatomical and cognitive progression of posterior cortical atrophy.

Author: Firth, Nicholas C, Primativo, Silvia, Marinescu, Razvan-Valentin, Shakespeare, Timothy J, Suarez-Gonzalez, Aida, Lehmann, Manja, Carton, Amelia, Ocal, Dilek, Pavisic, Ivanna, Paterson, Ross W, Slattery, Catherine F, Foulkes, Alexander JM, Ridha, Basil H, Gil-Néciga, Eulogio, Oxtoby, Neil P, Young, Alexandra L, Modat, Marc, Cardoso, M Jorge, Ourselin, Sebastien, Ryan, Natalie S, Miller, Bruce L, Rabinovici, Gil D, Warrington, Elizabeth K, Rossor, Martin N, Fox, Nick C, Warren, Jason D, Alexander, Daniel C, Schott, Jonathan M, Yong, Keir XX, and Crutch, Sebastian J
Subjects: Cerebral Cortex, Humans, Alzheimer Disease, Disease Progression, Magnetic Resonance Imaging, Case-Control Studies, Longitudinal Studies, Neuropsychological Tests, Models, Neurological, Middle Aged, Female, Male, Cognitive Dysfunction, Alzheimer’s disease, brain atrophy, dementia, memory, structural MRI, Alzheimer's disease, Models, Neurological, Neurology & Neurosurgery, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract: Posterior cortical atrophy is a clinico-radiological syndrome characterized by progressive decline in visual processing and atrophy of posterior brain regions. With the majority of cases attributable to Alzheimer's disease and recent evidence for genetic risk factors specifically related to posterior cortical atrophy, the syndrome can provide important insights into selective vulnerability and phenotypic diversity. The present study describes the first major longitudinal investigation of posterior cortical atrophy disease progression. Three hundred and sixty-one individuals (117 posterior cortical atrophy, 106 typical Alzheimer's disease, 138 controls) fulfilling consensus criteria for posterior cortical atrophy-pure and typical Alzheimer's disease were recruited from three centres in the UK, Spain and USA. Participants underwent up to six annual assessments involving MRI scans and neuropsychological testing. We constructed longitudinal trajectories of regional brain volumes within posterior cortical atrophy and typical Alzheimer's disease using differential equation models. We compared and contrasted the order in which regional brain volumes become abnormal within posterior cortical atrophy and typical Alzheimer's disease using event-based models. We also examined trajectories of cognitive decline and the order in which different cognitive tests show abnormality using the same models. Temporally aligned trajectories for eight regions of interest revealed distinct (P < 0.002) patterns of progression in posterior cortical atrophy and typical Alzheimer's disease. Patients with posterior cortical atrophy showed early occipital and parietal atrophy, with subsequent higher rates of temporal atrophy and ventricular expansion leading to tissue loss of comparable extent later. Hippocampal, entorhinal and frontal regions underwent a lower rate of change and never approached the extent of posterior cortical involvement. Patients with typical Alzheimer's disease showed early hippocampal atrophy, with subsequent higher rates of temporal atrophy and ventricular expansion. Cognitive models showed tests sensitive to visuospatial dysfunction declined earlier in posterior cortical atrophy than typical Alzheimer's disease whilst tests sensitive to working memory impairment declined earlier in typical Alzheimer's disease than posterior cortical atrophy. These findings indicate that posterior cortical atrophy and typical Alzheimer's disease have distinct sites of onset and different profiles of spatial and temporal progression. The ordering of disease events both motivates investigation of biological factors underpinning phenotypic heterogeneity, and informs the selection of measures for clinical trials in posterior cortical atrophy.
Published: 2019

30. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing

Author: Kunkle, Brian W, Grenier-Boley, Benjamin, Sims, Rebecca, Bis, Joshua C, Damotte, Vincent, Naj, Adam C, Boland, Anne, Vronskaya, Maria, van der Lee, Sven J, Amlie-Wolf, Alexandre, Bellenguez, Céline, Frizatti, Aura, Chouraki, Vincent, Martin, Eden R, Sleegers, Kristel, Badarinarayan, Nandini, Jakobsdottir, Johanna, Hamilton-Nelson, Kara L, Moreno-Grau, Sonia, Olaso, Robert, Raybould, Rachel, Chen, Yuning, Kuzma, Amanda B, Hiltunen, Mikko, Morgan, Taniesha, Ahmad, Shahzad, Vardarajan, Badri N, Epelbaum, Jacques, Hoffmann, Per, Boada, Merce, Beecham, Gary W, Garnier, Jean-Guillaume, Harold, Denise, Fitzpatrick, Annette L, Valladares, Otto, Moutet, Marie-Laure, Gerrish, Amy, Smith, Albert V, Qu, Liming, Bacq, Delphine, Denning, Nicola, Jian, Xueqiu, Zhao, Yi, Del Zompo, Maria, Fox, Nick C, Choi, Seung-Hoan, Mateo, Ignacio, Hughes, Joseph T, Adams, Hieab H, Malamon, John, Sanchez-Garcia, Florentino, Patel, Yogen, Brody, Jennifer A, Dombroski, Beth A, Naranjo, Maria Candida Deniz, Daniilidou, Makrina, Eiriksdottir, Gudny, Mukherjee, Shubhabrata, Wallon, David, Uphill, James, Aspelund, Thor, Cantwell, Laura B, Garzia, Fabienne, Galimberti, Daniela, Hofer, Edith, Butkiewicz, Mariusz, Fin, Bertrand, Scarpini, Elio, Sarnowski, Chloe, Bush, Will S, Meslage, Stéphane, Kornhuber, Johannes, White, Charles C, Song, Yuenjoo, Barber, Robert C, Engelborghs, Sebastiaan, Sordon, Sabrina, Voijnovic, Dina, Adams, Perrie M, Vandenberghe, Rik, Mayhaus, Manuel, Cupples, L Adrienne, Albert, Marilyn S, De Deyn, Peter P, Gu, Wei, Himali, Jayanadra J, Beekly, Duane, Squassina, Alessio, Hartmann, Annette M, Orellana, Adelina, Blacker, Deborah, Rodriguez-Rodriguez, Eloy, Lovestone, Simon, Garcia, Melissa E, Doody, Rachelle S, Munoz-Fernadez, Carmen, Sussams, Rebecca, Lin, Honghuang, Fairchild, Thomas J, and Benito, Yolanda A
Subjects: Biochemistry and Cell Biology, Genetics, Biological Sciences, Neurodegenerative, Dementia, Alzheimer's Disease, Aging, Prevention, Brain Disorders, Neurosciences, Clinical Research, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Human Genome, Acquired Cognitive Impairment, 2.1 Biological and endogenous factors, Aetiology, Inflammatory and immune system, Neurological, Aged, Alzheimer Disease, Amyloid beta-Peptides, Case-Control Studies, Female, Genetic Loci, Genetic Predisposition to Disease, Genetic Testing, Genome-Wide Association Study, Haplotypes, Humans, Immunity, Lipid Metabolism, Lipids, Male, tau Proteins, Alzheimer Disease Genetics Consortium (ADGC), European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease Consortium (GERAD/PERADES), Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract: Risk for late-onset Alzheimer's disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer's or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and Aβ processing are associated not only with early-onset autosomal dominant Alzheimer's disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10-7), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education.
Published: 2019

31. Prevalence of amyloid‐β pathology in distinct variants of primary progressive aphasia

Author: Bergeron, David, Gorno‐Tempini, Maria L, Rabinovici, Gil D, Santos‐Santos, Miguel A, Seeley, William, Miller, Bruce L, Pijnenburg, Yolande, Keulen, M Antoinette, Groot, Colin, van Berckel, Bart NM, van der Flier, Wiesje M, Scheltens, Philip, Rohrer, Jonathan D, Warren, Jason D, Schott, Jonathan M, Fox, Nick C, Sanchez‐Valle, Raquel, Grau‐Rivera, Oriol, Gelpi, Ellen, Seelaar, Harro, Papma, Janne M, van Swieten, John C, Hodges, John R, Leyton, Cristian E, Piguet, Olivier, Rogalski, Emily J, Mesulam, Marsel M, Koric, Lejla, Kristensen, Nora, Pariente, Jeéreémie, Dickerson, Bradford, Mackenzie, Ian R, Hsiung, Ging‐Yuek R, Belliard, Serge, Irwin, David J, Wolk, David A, Grossman, Murray, Jones, Matthew, Harris, Jennifer, Mann, David, Snowden, Julie S, Chrem‐Mendez, Patricio, Calandri, Ismael L, Amengual, Alejandra A, Miguet‐Alfonsi, Carole, Magnin, Eloi, Magnani, Giuseppe, Santangelo, Roberto, Deramecourt, Vincent, Pasquier, Florence, Mattsson, Niklas, Nilsson, Christer, Hansson, Oskar, Keith, Julia, Masellis, Mario, Black, Sandra E, Matías‐Guiu, Jordi A, Cabrera‐Martin, María‐Nieves, Paquet, Claire, Dumurgier, Julien, Teichmann, Marc, Sarazin, Marie, Bottlaender, Michel, Dubois, Bruno, Rowe, Christopher C, Villemagne, Victor L, Vandenberghe, Rik, Granadillo, Elias, Teng, Edmond, Mendez, Mario, Meyer, Philipp T, Frings, Lars, Lleó, Alberto, Blesa, Rafael, Fortea, Juan, Seo, Sang Won, Diehl‐Schmid, Janine, Grimmer, Timo, Frederiksen, Kristian Steen, Sánchez‐Juan, Pascual, Chételat, Gaël, Jansen, Willemijn, Bouchard, Rémi W, Laforce, Robert Jr, Visser, Pieter Jelle, and Ossenkoppele, Rik
Subjects: Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Dementia, Rare Diseases, Aging, Neurodegenerative, Brain Disorders, Frontotemporal Dementia (FTD), Clinical Research, Aphasia, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Acquired Cognitive Impairment, Alzheimer's Disease Related Dementias (ADRD), Genetics, Alzheimer's Disease, 4.2 Evaluation of markers and technologies, Aetiology, Detection, screening and diagnosis, 2.1 Biological and endogenous factors, Neurological, Age Factors, Aged, Aged, 80 and over, Amyloid beta-Peptides, Aphasia, Primary Progressive, Apolipoproteins E, Brain, Female, Genotype, Humans, Male, Middle Aged, Prevalence, Neurology & Neurosurgery, Clinical sciences
Abstract: ObjectiveTo estimate the prevalence of amyloid positivity, defined by positron emission tomography (PET)/cerebrospinal fluid (CSF) biomarkers and/or neuropathological examination, in primary progressive aphasia (PPA) variants.MethodsWe conducted a meta-analysis with individual participant data from 1,251 patients diagnosed with PPA (including logopenic [lvPPA, n = 443], nonfluent [nfvPPA, n = 333], semantic [svPPA, n = 401], and mixed/unclassifiable [n = 74] variants of PPA) from 36 centers, with a measure of amyloid-β pathology (CSF [n = 600], PET [n = 366], and/or autopsy [n = 378]) available. The estimated prevalence of amyloid positivity according to PPA variant, age, and apolipoprotein E (ApoE) ε4 status was determined using generalized estimating equation models.ResultsAmyloid-β positivity was more prevalent in lvPPA (86%) than in nfvPPA (20%) or svPPA (16%; p
Published: 2018

32. Association between carotid atherosclerosis and brain activation patterns during the Stroop task in older adults: An fNIRS investigation

Author: Mason, Sarah A., Al Saikhan, Lamia, Jones, Siana, James, Sarah-Naomi, Murray-Smith, Heidi, Rapala, Alicja, Williams, Suzanne, Sudre, Carole, Wong, Brian, Richards, Marcus, Fox, Nick C., Hardy, Rebecca, Schott, Jonathan M., Chaturvedi, Nish, and Hughes, Alun D.
Published: 2022
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33. New insights into the genetic etiology of Alzheimer’s disease and related dementias

Author: Bellenguez, Céline, Küçükali, Fahri, Jansen, Iris E., Kleineidam, Luca, Moreno-Grau, Sonia, Amin, Najaf, Naj, Adam C., Campos-Martin, Rafael, Grenier-Boley, Benjamin, Andrade, Victor, Holmans, Peter A., Boland, Anne, Damotte, Vincent, van der Lee, Sven J., Costa, Marcos R., Kuulasmaa, Teemu, Yang, Qiong, de Rojas, Itziar, Bis, Joshua C., Yaqub, Amber, Prokic, Ivana, Chapuis, Julien, Ahmad, Shahzad, Giedraitis, Vilmantas, Aarsland, Dag, Garcia-Gonzalez, Pablo, Abdelnour, Carla, Alarcón-Martín, Emilio, Alcolea, Daniel, Alegret, Montserrat, Alvarez, Ignacio, Álvarez, Victoria, Armstrong, Nicola J., Tsolaki, Anthoula, Antúnez, Carmen, Appollonio, Ildebrando, Arcaro, Marina, Archetti, Silvana, Pastor, Alfonso Arias, Arosio, Beatrice, Athanasiu, Lavinia, Bailly, Henri, Banaj, Nerisa, Baquero, Miquel, Barral, Sandra, Beiser, Alexa, Pastor, Ana Belén, Below, Jennifer E., Benchek, Penelope, Benussi, Luisa, Berr, Claudine, Besse, Céline, Bessi, Valentina, Binetti, Giuliano, Bizarro, Alessandra, Blesa, Rafael, Boada, Mercè, Boerwinkle, Eric, Borroni, Barbara, Boschi, Silvia, Bossù, Paola, Bråthen, Geir, Bressler, Jan, Bresner, Catherine, Brodaty, Henry, Brookes, Keeley J., Brusco, Luis Ignacio, Buiza-Rueda, Dolores, Bûrger, Katharina, Burholt, Vanessa, Bush, William S., Calero, Miguel, Cantwell, Laura B., Chene, Geneviève, Chung, Jaeyoon, Cuccaro, Michael L., Carracedo, Ángel, Cecchetti, Roberta, Cervera-Carles, Laura, Charbonnier, Camille, Chen, Hung-Hsin, Chillotti, Caterina, Ciccone, Simona, Claassen, Jurgen A. H. R., Clark, Christopher, Conti, Elisa, Corma-Gómez, Anaïs, Costantini, Emanuele, Custodero, Carlo, Daian, Delphine, Dalmasso, Maria Carolina, Daniele, Antonio, Dardiotis, Efthimios, Dartigues, Jean-François, de Deyn, Peter Paul, de Paiva Lopes, Katia, de Witte, Lot D., Debette, Stéphanie, Deckert, Jürgen, del Ser, Teodoro, Denning, Nicola, DeStefano, Anita, Dichgans, Martin, Diehl-Schmid, Janine, Diez-Fairen, Mónica, Rossi, Paolo Dionigi, Djurovic, Srdjan, Duron, Emmanuelle, Düzel, Emrah, Dufouil, Carole, Eiriksdottir, Gudny, Engelborghs, Sebastiaan, Escott-Price, Valentina, Espinosa, Ana, Ewers, Michael, Faber, Kelley M., Fabrizio, Tagliavini, Nielsen, Sune Fallgaard, Fardo, David W., Farotti, Lucia, Fenoglio, Chiara, Fernández-Fuertes, Marta, Ferrari, Raffaele, Ferreira, Catarina B., Ferri, Evelyn, Fin, Bertrand, Fischer, Peter, Fladby, Tormod, Fließbach, Klaus, Fongang, Bernard, Fornage, Myriam, Fortea, Juan, Foroud, Tatiana M., Fostinelli, Silvia, Fox, Nick C., Franco-Macías, Emlio, Bullido, María J., Frank-García, Ana, Froelich, Lutz, Fulton-Howard, Brian, Galimberti, Daniela, García-Alberca, Jose Maria, García-González, Pablo, Garcia-Madrona, Sebastian, Garcia-Ribas, Guillermo, Ghidoni, Roberta, Giegling, Ina, Giorgio, Giaccone, Goate, Alison M., Goldhardt, Oliver, Gomez-Fonseca, Duber, González-Pérez, Antonio, Graff, Caroline, Grande, Giulia, Green, Emma, Grimmer, Timo, Grünblatt, Edna, Grunin, Michelle, Gudnason, Vilmundur, Guetta-Baranes, Tamar, Haapasalo, Annakaisa, Hadjigeorgiou, Georgios, Haines, Jonathan L., Hamilton-Nelson, Kara L., Hampel, Harald, Hanon, Olivier, Hardy, John, Hartmann, Annette M., Hausner, Lucrezia, Harwood, Janet, Heilmann-Heimbach, Stefanie, Helisalmi, Seppo, Heneka, Michael T., Hernández, Isabel, Herrmann, Martin J., Hoffmann, Per, Holmes, Clive, Holstege, Henne, Vilas, Raquel Huerto, Hulsman, Marc, Humphrey, Jack, Biessels, Geert Jan, Jian, Xueqiu, Johansson, Charlotte, Jun, Gyungah R., Kastumata, Yuriko, Kauwe, John, Kehoe, Patrick G., Kilander, Lena, Ståhlbom, Anne Kinhult, Kivipelto, Miia, Koivisto, Anne, Kornhuber, Johannes, Kosmidis, Mary H., Kukull, Walter A., Kuksa, Pavel P., Kunkle, Brian W., Kuzma, Amanda B., Lage, Carmen, Laukka, Erika J., Launer, Lenore, Lauria, Alessandra, Lee, Chien-Yueh, Lehtisalo, Jenni, Lerch, Ondrej, Lleó, Alberto, Longstreth, Jr, William, Lopez, Oscar, de Munain, Adolfo Lopez, Love, Seth, Löwemark, Malin, Luckcuck, Lauren, Lunetta, Kathryn L., Ma, Yiyi, Macías, Juan, MacLeod, Catherine A., Maier, Wolfgang, Mangialasche, Francesca, Spallazzi, Marco, Marquié, Marta, Marshall, Rachel, Martin, Eden R., Montes, Angel Martín, Rodríguez, Carmen Martínez, Masullo, Carlo, Mayeux, Richard, Mead, Simon, Mecocci, Patrizia, Medina, Miguel, Meggy, Alun, Mehrabian, Shima, Mendoza, Silvia, Menéndez-González, Manuel, Mir, Pablo, Moebus, Susanne, Mol, Merel, Molina-Porcel, Laura, Montrreal, Laura, Morelli, Laura, Moreno, Fermin, Morgan, Kevin, Mosley, Thomas, Nöthen, Markus M., Muchnik, Carolina, Mukherjee, Shubhabrata, Nacmias, Benedetta, Ngandu, Tiia, Nicolas, Gael, Nordestgaard, Børge G., Olaso, Robert, Orellana, Adelina, Orsini, Michela, Ortega, Gemma, Padovani, Alessandro, Paolo, Caffarra, Papenberg, Goran, Parnetti, Lucilla, Pasquier, Florence, Pastor, Pau, Peloso, Gina, Pérez-Cordón, Alba, Pérez-Tur, Jordi, Pericard, Pierre, Peters, Oliver, Pijnenburg, Yolande A. L., Pineda, Juan A., Piñol-Ripoll, Gerard, Pisanu, Claudia, Polak, Thomas, Popp, Julius, Posthuma, Danielle, Priller, Josef, Puerta, Raquel, Quenez, Olivier, Quintela, Inés, Thomassen, Jesper Qvist, Rábano, Alberto, Rainero, Innocenzo, Rajabli, Farid, Ramakers, Inez, Real, Luis M., Reinders, Marcel J. T., Reitz, Christiane, Reyes-Dumeyer, Dolly, Ridge, Perry, Riedel-Heller, Steffi, Riederer, Peter, Roberto, Natalia, Rodriguez-Rodriguez, Eloy, Rongve, Arvid, Allende, Irene Rosas, Rosende-Roca, Maitée, Royo, Jose Luis, Rubino, Elisa, Rujescu, Dan, Sáez, María Eugenia, Sakka, Paraskevi, Saltvedt, Ingvild, Sanabria, Ángela, Sánchez-Arjona, María Bernal, Sanchez-Garcia, Florentino, Juan, Pascual Sánchez, Sánchez-Valle, Raquel, Sando, Sigrid B., Sarnowski, Chloé, Satizabal, Claudia L., Scamosci, Michela, Scarmeas, Nikolaos, Scarpini, Elio, Scheltens, Philip, Scherbaum, Norbert, Scherer, Martin, Schmid, Matthias, Schneider, Anja, Schott, Jonathan M., Selbæk, Geir, Seripa, Davide, Serrano, Manuel, Sha, Jin, Shadrin, Alexey A., Skrobot, Olivia, Slifer, Susan, Snijders, Gijsje J. L., Soininen, Hilkka, Solfrizzi, Vincenzo, Solomon, Alina, Song, Yeunjoo, Sorbi, Sandro, Sotolongo-Grau, Oscar, Spalletta, Gianfranco, Spottke, Annika, Squassina, Alessio, Stordal, Eystein, Tartan, Juan Pablo, Tárraga, Lluís, Tesí, Niccolo, Thalamuthu, Anbupalam, Thomas, Tegos, Tosto, Giuseppe, Traykov, Latchezar, Tremolizzo, Lucio, Tybjærg-Hansen, Anne, Uitterlinden, Andre, Ullgren, Abbe, Ulstein, Ingun, Valero, Sergi, Valladares, Otto, Broeckhoven, Christine Van, Vance, Jeffery, Vardarajan, Badri N., van der Lugt, Aad, Dongen, Jasper Van, van Rooij, Jeroen, van Swieten, John, Vandenberghe, Rik, Verhey, Frans, Vidal, Jean-Sébastien, Vogelgsang, Jonathan, Vyhnalek, Martin, Wagner, Michael, Wallon, David, Wang, Li-San, Wang, Ruiqi, Weinhold, Leonie, Wiltfang, Jens, Windle, Gill, Woods, Bob, Yannakoulia, Mary, Zare, Habil, Zhao, Yi, Zhang, Xiaoling, Zhu, Congcong, Zulaica, Miren, Farrer, Lindsay A., Psaty, Bruce M., Ghanbari, Mohsen, Raj, Towfique, Sachdev, Perminder, Mather, Karen, Jessen, Frank, Ikram, M. Arfan, de Mendonça, Alexandre, Hort, Jakub, Tsolaki, Magda, Pericak-Vance, Margaret A., Amouyel, Philippe, Williams, Julie, Frikke-Schmidt, Ruth, Clarimon, Jordi, Deleuze, Jean-François, Rossi, Giacomina, Seshadri, Sudha, Andreassen, Ole A., Ingelsson, Martin, Hiltunen, Mikko, Sleegers, Kristel, Schellenberg, Gerard D., van Duijn, Cornelia M., Sims, Rebecca, van der Flier, Wiesje M., Ruiz, Agustín, Ramirez, Alfredo, and Lambert, Jean-Charles
Published: 2022
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34. Cerebrospinal fluid metallomics in cerebral amyloid angiopathy: an exploratory analysis

Author: Banerjee, Gargi, Forsgard, Niklas, Ambler, Gareth, Keshavan, Ashvini, Paterson, Ross W., Foiani, Martha S., Toombs, Jamie, Heslegrave, Amanda, Thompson, Edward J., Lunn, Michael P., Fox, Nick C., Zetterberg, Henrik, Schott, Jonathan M., and Werring, David J.
Published: 2022
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35. Imaging tau pathology in Alzheimer’s disease with positron emission tomography: lessons learned from imaging-neuropathology validation studies

Author: Moscoso, Alexis, Wren, Melissa C., Lashley, Tammaryn, Arstad, Erik, Murray, Melissa E., Fox, Nick C., Sander, Kerstin, and Schöll, Michael
Published: 2022
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36. Sex-related differences in whole brain volumes at age 70 in association with hyperglycemia during adult life

Author: Fatih, Nasrtullah, Chaturvedi, Nish, Lane, Christopher A, Parker, Thomas D., Lu, Kirsty, Cash, David M., Malone, Ian B., Silverwood, Richard, Wong, Andrew, Barnes, Josephine, Sudre, Carole H., Richards, Marcus, Fox, Nick C., Schott, Jonathan M., Hughes, Alun, and James, Sarah-Naomi
Published: 2022
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37. Variant-dependent heterogeneity in amyloid β burden in autosomal dominant Alzheimer's disease: cross-sectional and longitudinal analyses of an observational study

Author: Chhatwal, Jasmeer P, Schultz, Stephanie A, McDade, Eric, Schultz, Aaron P, Liu, Lei, Hanseeuw, Bernard J, Joseph-Mathurin, Nelly, Feldman, Rebecca, Fitzpatrick, Colleen D, Sparks, Kathryn P, Levin, Johannes, Berman, Sarah B, Renton, Alan E, Esposito, Bianca T, Fernandez, Maria Vitoria, Sung, Yun Ju, Lee, Jae Hong, Klunk, William E, Hofmann, Anna, Noble, James M, Graff-Radford, Neill, Mori, Hiroshi, Salloway, Steven M, Masters, Colin L, Martins, Ralph, Karch, Celeste M, Xiong, Chengjie, Cruchaga, Carlos, Perrin, Richard J, Gordon, Brian A, Benzinger, Tammie L S, Fox, Nick C, Schofield, Peter R, Fagan, Anne M, Goate, Alison M, Morris, John C, Bateman, Randall J, Johnson, Keith A, and Sperling, Reisa A
Published: 2022
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38. Overview of ADNI MRI.

Author: Jack, Clifford R., Arani, Arvin, Borowski, Bret J., Cash, Dave M., Crawford, Karen, Das, Sandhitsu R., DeCarli, Charles, Fletcher, Evan, Fox, Nick C., Gunter, Jeffrey L., Ittyerah, Ranjit, Harvey, Danielle J., Jahanshad, Neda, Maillard, Pauline, Malone, Ian B., Nir, Talia M., Reid, Robert I., Reyes, Denise A., Schwarz, Christopher G., and Senjem, Matthew L.
Abstract: The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi‐platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1‐weighted and fluid‐attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. Highlights: The MRI Core provides multi‐platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups.The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current.As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF‐fMRI; and 2861 HighResHippo (see Table 1 for abbreviations).As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM‐SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF‐fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations).ADNI MRI is an underutilized resource that could be more useful to the research community. [ABSTRACT FROM AUTHOR]
Published: 2024
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39. Peripheral hearing loss at age 70 predicts brain atrophy and associated cognitive change.

Author: Parker, Thomas D., Hardy, Chris, Keuss, Sarah, Coath, William, Cash, David M., Lu, Kirsty, Nicholas, Jennifer M., James, Sarah-Naomi, Sudre, Carole, Crutch, Sebastian, Bamiou, Doris-Eva, Warren, Jason D., Fox, Nick C., Richards, Marcus, and Schott, Jonathan M.
Subjects: ALZHEIMER'S disease, AUDIOMETRY, SCHOLARSHIPS, CEREBRAL atrophy, RESEARCH institutes, HEARING levels, HEARING
Published: 2024
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40. The Open-Access European Prevention of Alzheimer’s Dementia (EPAD) MRI dataset and processing workflow

Author: Lorenzini, Luigi, Ingala, Silvia, Wink, Alle Meije, Kuijer, Joost P.A., Wottschel, Viktor, Dijsselhof, Mathijs, Sudre, Carole H., Haller, Sven, Molinuevo, José Luis, Gispert, Juan Domingo, Cash, David M., Thomas, David L., Vos, Sjoerd B., Prados, Ferran, Petr, Jan, Wolz, Robin, Palombit, Alessandro, Schwarz, Adam J., Chételat, Gaël, Payoux, Pierre, Di Perri, Carol, Wardlaw, Joanna M., Frisoni, Giovanni B., Foley, Christopher, Fox, Nick C., Ritchie, Craig, Pernet, Cyril, Waldman, Adam, Barkhof, Frederik, and Mutsaerts, Henk J.M.M.
Published: 2022
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41. Dissociable effects of APOE ε4 and β-amyloid pathology on visual working memory

Author: Lu, Kirsty, Nicholas, Jennifer M., Pertzov, Yoni, Grogan, John, Husain, Masud, Pavisic, Ivanna M., James, Sarah-Naomi, Parker, Thomas D., Lane, Christopher A., Keshavan, Ashvini, Keuss, Sarah E., Buchanan, Sarah M., Murray-Smith, Heidi, Cash, David M., Malone, Ian B., Sudre, Carole H., Coath, William, Wong, Andrew, Henley, Susie M. D., Fox, Nick C., Richards, Marcus, Schott, Jonathan M., and Crutch, Sebastian J.
Published: 2021
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42. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference.

Author: Young, Alexandra L, Marinescu, Razvan V, Oxtoby, Neil P, Bocchetta, Martina, Yong, Keir, Firth, Nicholas C, Cash, David M, Thomas, David L, Dick, Katrina M, Cardoso, Jorge, van Swieten, John, Borroni, Barbara, Galimberti, Daniela, Masellis, Mario, Tartaglia, Maria Carmela, Rowe, James B, Graff, Caroline, Tagliavini, Fabrizio, Frisoni, Giovanni B, Laforce, Robert, Finger, Elizabeth, de Mendonça, Alexandre, Sorbi, Sandro, Warren, Jason D, Crutch, Sebastian, Fox, Nick C, Ourselin, Sebastien, Schott, Jonathan M, Rohrer, Jonathan D, Alexander, Daniel C, Genetic FTD Initiative (GENFI), and Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Subjects: Genetic FTD Initiative, Alzheimer’s Disease Neuroimaging Initiative, Humans, Alzheimer Disease, Neurodegenerative Diseases, Reproducibility of Results, Genotype, Phenotype, Models, Neurological, Time Factors, Frontotemporal Dementia, Models, Neurological, Alzheimer's Disease, Brain Disorders, Genetics, Aging, Neurodegenerative, Acquired Cognitive Impairment, Dementia, Alzheimer's Disease including Alzheimer's Disease Related Dementias, Neurosciences, Rare Diseases, 2.1 Biological and endogenous factors, MD Multidisciplinary
Abstract: The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique-Subtype and Stage Inference (SuStaIn)-able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer's disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10-4) or temporal stage (p = 3.96 × 10-5). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.
Published: 2018

43. White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease

Author: Caballero, Miguel Ángel Araque, Suárez-Calvet, Marc, Duering, Marco, Franzmeier, Nicolai, Benzinger, Tammie, Fagan, Anne M, Bateman, Randall J, Jack, Clifford R, Levin, Johannes, Dichgans, Martin, Jucker, Mathias, Karch, Celeste, Masters, Colin L, Morris, John C, Weiner, Michael, Rossor, Martin, Fox, Nick C, Lee, Jae-Hong, Salloway, Stephen, Danek, Adrian, Goate, Alison, Yakushev, Igor, Hassenstab, Jason, Schofield, Peter R, Haass, Christian, and Ewers, Michael
Subjects: Health Services and Systems, Health Sciences, Neurosciences, Dementia, Biomedical Imaging, Clinical Research, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Acquired Cognitive Impairment, Brain Disorders, Alzheimer's Disease, Neurodegenerative, Aging, 2.1 Biological and endogenous factors, Aetiology, Neurological, Adult, Alzheimer Disease, Brain, Diffusion Tensor Imaging, Female, Humans, Male, Middle Aged, White Matter, Alzheimer's disease, autosomal dominant, white matter, diffusion tensor imaging, TREM2, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences, Psychology
Abstract: White matter alterations are present in the majority of patients with Alzheimer's disease type dementia. However, the spatiotemporal pattern of white matter changes preceding dementia symptoms in Alzheimer's disease remains unclear, largely due to the inherent diagnostic uncertainty in the preclinical phase and increased risk of confounding age-related vascular disease and stroke in late-onset Alzheimer's disease. In early-onset autosomal-dominantly inherited Alzheimer's disease, participants are destined to develop dementia, which provides the opportunity to assess brain changes years before the onset of symptoms, and in the absence of ageing-related vascular disease. Here, we assessed mean diffusivity alterations in the white matter in 64 mutation carriers compared to 45 non-carrier family non-carriers. Using tract-based spatial statistics, we mapped the interaction of mutation status by estimated years from symptom onset on mean diffusivity. For major atlas-derived fibre tracts, we determined the earliest time point at which abnormal mean diffusivity changes in the mutation carriers were detectable. Lastly, we assessed the association between mean diffusivity and cerebrospinal fluid biomarkers of amyloid, tau, phosphorylated-tau, and soluble TREM2, i.e. a marker of microglia activity. Results showed a significant interaction of mutations status by estimated years from symptom onset, i.e. a stronger increase of mean diffusivity, within the posterior parietal and medial frontal white matter in mutation carriers compared with non-carriers. The earliest increase of mean diffusivity was observed in the forceps major, forceps minor and long projecting fibres-many connecting default mode network regions-between 5 to 10 years before estimated symptom onset. Higher mean diffusivity in fibre tracts was associated with lower grey matter volume in the tracts' projection zones. Global mean diffusivity was correlated with lower cerebrospinal fluid levels of amyloid-β1-42 but higher levels of tau, phosphorylated-tau and soluble TREM2. Together, these results suggest that regionally selective white matter degeneration occurs years before the estimated symptom onset. Such white matter alterations are associated with primary Alzheimer's disease pathology and microglia activity in the brain.
Published: 2018

44. Analysis of shared heritability in common disorders of the brain

Author: Consortium, The Brainstorm, Anttila, Verneri, Bulik-Sullivan, Brendan, Finucane, Hilary K, Walters, Raymond K, Bras, Jose, Duncan, Laramie, Escott-Price, Valentina, Falcone, Guido J, Gormley, Padhraig, Malik, Rainer, Patsopoulos, Nikolaos A, Ripke, Stephan, Wei, Zhi, Yu, Dongmei, Lee, Phil H, Turley, Patrick, Grenier-Boley, Benjamin, Chouraki, Vincent, Kamatani, Yoichiro, Berr, Claudine, Letenneur, Luc, Hannequin, Didier, Amouyel, Philippe, Boland, Anne, Deleuze, Jean-François, Duron, Emmanuelle, Vardarajan, Badri N, Reitz, Christiane, Goate, Alison M, Huentelman, Matthew J, Kamboh, M Ilyas, Larson, Eric B, Rogaeva, Ekaterina, St George-Hyslop, Peter, Hakonarson, Hakon, Kukull, Walter A, Farrer, Lindsay A, Barnes, Lisa L, Beach, Thomas G, Demirci, F Yesim, Head, Elizabeth, Hulette, Christine M, Jicha, Gregory A, Kauwe, John SK, Kaye, Jeffrey A, Leverenz, James B, Levey, Allan I, Lieberman, Andrew P, Pankratz, Vernon S, Poon, Wayne W, Quinn, Joseph F, Saykin, Andrew J, Schneider, Lon S, Smith, Amanda G, Sonnen, Joshua A, Stern, Robert A, Van Deerlin, Vivianna M, Van Eldik, Linda J, Harold, Denise, Russo, Giancarlo, Rubinsztein, David C, Bayer, Anthony, Tsolaki, Magda, Proitsi, Petra, Fox, Nick C, Hampel, Harald, Owen, Michael J, Mead, Simon, Passmore, Peter, Morgan, Kevin, Nöthen, Markus M, Schott, Jonathan M, Rossor, Martin, Lupton, Michelle K, Hoffmann, Per, Kornhuber, Johannes, Lawlor, Brian, McQuillin, Andrew, Al-Chalabi, Ammar, Bis, Joshua C, Ruiz, Agustin, Boada, Mercè, Seshadri, Sudha, Beiser, Alexa, Rice, Kenneth, van der Lee, Sven J, De Jager, Philip L, Geschwind, Daniel H, Riemenschneider, Matthias, Riedel-Heller, Steffi, Rotter, Jerome I, Ransmayr, Gerhard, Hyman, Bradley T, Cruchaga, Carlos, Alegret, Montserrat, Winsvold, Bendik, Palta, Priit, Farh, Kai-How, and Cuenca-Leon, Ester
Subjects: Biological Sciences, Genetics, Biological Psychology, Health Sciences, Psychology, Brain Disorders, Human Genome, Clinical Research, Neurosciences, Mental Illness, Mental Health, 2.1 Biological and endogenous factors, Neurological, Mental health, Brain Diseases, Genetic Variation, Genome-Wide Association Study, Humans, Mental Disorders, Phenotype, Quantitative Trait, Heritable, Risk Factors, Brainstorm Consortium, General Science & Technology
Abstract: Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.
Published: 2018

45. Analysis of shared heritability in common disorders of the brain.

Author: Brainstorm Consortium, Anttila, Verneri, Bulik-Sullivan, Brendan, Finucane, Hilary K, Walters, Raymond K, Bras, Jose, Duncan, Laramie, Escott-Price, Valentina, Falcone, Guido J, Gormley, Padhraig, Malik, Rainer, Patsopoulos, Nikolaos A, Ripke, Stephan, Wei, Zhi, Yu, Dongmei, Lee, Phil H, Turley, Patrick, Grenier-Boley, Benjamin, Chouraki, Vincent, Kamatani, Yoichiro, Berr, Claudine, Letenneur, Luc, Hannequin, Didier, Amouyel, Philippe, Boland, Anne, Deleuze, Jean-François, Duron, Emmanuelle, Vardarajan, Badri N, Reitz, Christiane, Goate, Alison M, Huentelman, Matthew J, Kamboh, M Ilyas, Larson, Eric B, Rogaeva, Ekaterina, St George-Hyslop, Peter, Hakonarson, Hakon, Kukull, Walter A, Farrer, Lindsay A, Barnes, Lisa L, Beach, Thomas G, Demirci, F Yesim, Head, Elizabeth, Hulette, Christine M, Jicha, Gregory A, Kauwe, John SK, Kaye, Jeffrey A, Leverenz, James B, Levey, Allan I, Lieberman, Andrew P, Pankratz, Vernon S, Poon, Wayne W, Quinn, Joseph F, Saykin, Andrew J, Schneider, Lon S, Smith, Amanda G, Sonnen, Joshua A, Stern, Robert A, Van Deerlin, Vivianna M, Van Eldik, Linda J, Harold, Denise, Russo, Giancarlo, Rubinsztein, David C, Bayer, Anthony, Tsolaki, Magda, Proitsi, Petra, Fox, Nick C, Hampel, Harald, Owen, Michael J, Mead, Simon, Passmore, Peter, Morgan, Kevin, Nöthen, Markus M, Rossor, Martin, Lupton, Michelle K, Hoffmann, Per, Kornhuber, Johannes, Lawlor, Brian, McQuillin, Andrew, Al-Chalabi, Ammar, Bis, Joshua C, Ruiz, Agustin, Boada, Mercè, Seshadri, Sudha, Beiser, Alexa, Rice, Kenneth, van der Lee, Sven J, De Jager, Philip L, Geschwind, Daniel H, Riemenschneider, Matthias, Riedel-Heller, Steffi, Rotter, Jerome I, Ransmayr, Gerhard, Hyman, Bradley T, Cruchaga, Carlos, Alegret, Montserrat, Winsvold, Bendik, Palta, Priit, Farh, Kai-How, Cuenca-Leon, Ester, and Furlotte, Nicholas
Subjects: Brainstorm Consortium, Humans, Brain Diseases, Risk Factors, Mental Disorders, Quantitative Trait, Heritable, Phenotype, Genetic Variation, Genome-Wide Association Study, Clinical Research, Neurosciences, Rare Diseases, Human Genome, Brain Disorders, Genetics, Mental Health, 2.1 Biological and endogenous factors, Mental health, Neurological, General Science & Technology
Abstract: Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.
Published: 2018

46. Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study

Author: Gordon, Brian A, Blazey, Tyler M, Su, Yi, Hari-Raj, Amrita, Dincer, Aylin, Flores, Shaney, Christensen, Jon, McDade, Eric, Wang, Guoqiao, Xiong, Chengjie, Cairns, Nigel J, Hassenstab, Jason, Marcus, Daniel S, Fagan, Anne M, Jack, Clifford R, Hornbeck, Russ C, Paumier, Katrina L, Ances, Beau M, Berman, Sarah B, Brickman, Adam M, Cash, David M, Chhatwal, Jasmeer P, Correia, Stephen, Förster, Stefan, Fox, Nick C, Graff-Radford, Neill R, la Fougère, Christian, Levin, Johannes, Masters, Colin L, Rossor, Martin N, Salloway, Stephen, Saykin, Andrew J, Schofield, Peter R, Thompson, Paul M, Weiner, Michael M, Holtzman, David M, Raichle, Marcus E, Morris, John C, Bateman, Randall J, and Benzinger, Tammie LS
Subjects: Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurosciences, Acquired Cognitive Impairment, Neurodegenerative, Clinical Research, Dementia, Alzheimer's Disease, Brain Disorders, Biomedical Imaging, Aging, Rare Diseases, Aetiology, Detection, screening and diagnosis, 2.1 Biological and endogenous factors, 4.1 Discovery and preclinical testing of markers and technologies, Neurological, Adult, Alzheimer Disease, Amyloid beta-Protein Precursor, Aniline Compounds, Brain, Brain Mapping, Family Health, Female, Fluorodeoxyglucose F18, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Positron-Emission Tomography, Presenilin-1, Presenilin-2, Statistics, Nonparametric, Thiazoles, Clinical Sciences, Neurology & Neurosurgery
Abstract: BackgroundModels of Alzheimer's disease propose a sequence of amyloid β (Aβ) accumulation, hypometabolism, and structural decline that precedes the onset of clinical dementia. These pathological features evolve both temporally and spatially in the brain. In this study, we aimed to characterise where in the brain and when in the course of the disease neuroimaging biomarkers become abnormal.MethodsBetween Jan 1, 2009, and Dec 31, 2015, we analysed data from mutation non-carriers, asymptomatic carriers, and symptomatic carriers from families carrying gene mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) enrolled in the Dominantly Inherited Alzheimer's Network. We analysed 11C-Pittsburgh Compound B (11C-PiB) PET, 18F-Fluorodeoxyglucose (18F-FDG) PET, and structural MRI data using regions of interest to assess change throughout the brain. We estimated rates of biomarker change as a function of estimated years to symptom onset at baseline using linear mixed-effects models and determined the earliest point at which biomarker trajectories differed between mutation carriers and non-carriers. This study is registered at ClinicalTrials.gov (number NCT00869817) FINDINGS: 11C-PiB PET was available for 346 individuals (162 with longitudinal imaging), 18F-FDG PET was available for 352 individuals (175 with longitudinal imaging), and MRI data were available for 377 individuals (201 with longitudinal imaging). We found a sequence to pathological changes, with rates of Aβ deposition in mutation carriers being significantly different from those in non-carriers first (across regions that showed a significant difference, at a mean of 18·9 years [SD 3·3] before expected onset), followed by hypometabolism (14·1 years [5·1] before expected onset), and lastly structural decline (4·7 years [4·2] before expected onset). This biomarker ordering was preserved in most, but not all, regions. The temporal emergence within a biomarker varied across the brain, with the precuneus being the first cortical region for each method to show divergence between groups (22·2 years before expected onset for Aβ accumulation, 18·8 years before expected onset for hypometabolism, and 13·0 years before expected onset for cortical thinning).InterpretationMutation carriers had elevations in Aβ deposition, reduced glucose metabolism, and cortical thinning compared with non-carriers which preceded the expected onset of dementia. Accrual of these pathologies varied throughout the brain, suggesting differential regional and temporal vulnerabilities to Aβ, metabolic decline, and structural atrophy, which should be taken into account when using biomarkers in a clinical setting as well as designing and evaluating clinical trials.FundingUS National Institutes of Health, the German Center for Neurodegenerative Diseases, and the Medical Research Council Dementias Platform UK.
Published: 2018

47. Presymptomatic atrophy in autosomal dominant Alzheimer's disease: A serial magnetic resonance imaging study

Author: Kinnunen, Kirsi M, Cash, David M, Poole, Teresa, Frost, Chris, Benzinger, Tammie LS, Ahsan, R Laila, Leung, Kelvin K, Cardoso, M Jorge, Modat, Marc, Malone, Ian B, Morris, John C, Bateman, Randall J, Marcus, Daniel S, Goate, Alison, Salloway, Stephen P, Correia, Stephen, Sperling, Reisa A, Chhatwal, Jasmeer P, Mayeux, Richard P, Brickman, Adam M, Martins, Ralph N, Farlow, Martin R, Ghetti, Bernardino, Saykin, Andrew J, Jack, Clifford R, Schofield, Peter R, McDade, Eric, Weiner, Michael W, Ringman, John M, Thompson, Paul M, Masters, Colin L, Rowe, Christopher C, Rossor, Martin N, Ourselin, Sebastien, Fox, Nick C, and Network, Dominantly Inherited Alzheimer
Subjects: Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Clinical Research, Acquired Cognitive Impairment, Alzheimer's Disease, Bioengineering, Biomedical Imaging, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Neurodegenerative, Brain Disorders, Neurological, Adult, Alzheimer Disease, Apolipoproteins E, Atrophy, Brain, Female, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Psychiatric Status Rating Scales, Statistics, Nonparametric, Time Factors, Longitudinal, Alzheimer's disease, Autosomal dominant, Neuroimaging, MRI, Boundary Shift Integral, Nonlinear modeling, Change-point, Dominantly Inherited Alzheimer Network, Geriatrics, Clinical sciences, Biological psychology
Abstract: IntroductionIdentifying at what point atrophy rates first change in Alzheimer's disease is important for informing design of presymptomatic trials.MethodsSerial T1-weighted magnetic resonance imaging scans of 94 participants (28 noncarriers, 66 carriers) from the Dominantly Inherited Alzheimer Network were used to measure brain, ventricular, and hippocampal atrophy rates. For each structure, nonlinear mixed-effects models estimated the change-points when atrophy rates deviate from normal and the rates of change before and after this point.ResultsAtrophy increased after the change-point, which occurred 1-1.5 years (assuming a single step change in atrophy rate) or 3-8 years (assuming gradual acceleration of atrophy) before expected symptom onset. At expected symptom onset, estimated atrophy rates were at least 3.6 times than those before the change-point.DiscussionAtrophy rates are pathologically increased up to seven years before "expected onset". During this period, atrophy rates may be useful for inclusion and tracking of disease progression.
Published: 2018

48. White matter hyperintensities and the mediating role of cerebral amyloid angiopathy in dominantly-inherited Alzheimer’s disease

Author: Lee, Seonjoo, Zimmerman, Molly E, Narkhede, Atul, Nasrabady, Sara E, Tosto, Giuseppe, Meier, Irene B, Benzinger, Tammie LS, Marcus, Daniel S, Fagan, Anne M, Fox, Nick C, Cairns, Nigel J, Holtzman, David M, Buckles, Virginia, Ghetti, Bernardino, McDade, Eric, Martins, Ralph N, Saykin, Andrew J, Masters, Colin L, Ringman, John M, Fӧrster, Stefan, Schofield, Peter R, Sperling, Reisa A, Johnson, Keith A, Chhatwal, Jasmeer P, Salloway, Stephen, Correia, Stephen, Jack, Clifford R, Weiner, Michael, Bateman, Randall J, Morris, John C, Mayeux, Richard, and Brickman, Adam M
Subjects: Health Services and Systems, Health Sciences, Genetics, Biological Sciences, Neurodegenerative, Vascular Cognitive Impairment/Dementia, Aging, Alzheimer's Disease, Dementia, Clinical Research, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Acquired Cognitive Impairment, Neurosciences, Brain Disorders, 2.1 Biological and endogenous factors, Aetiology, Neurological, Adult, Alzheimer Disease, Cerebral Amyloid Angiopathy, Female, Hemorrhage, Humans, Magnetic Resonance Imaging, Male, Mutation, Organ Size, White Matter, Dominantly Inherited Alzheimer Network, General Science & Technology
Abstract: IntroductionWhite matter hyperintensity (WMH) volume on MRI is increased among presymptomatic individuals with autosomal dominant mutations for Alzheimer's disease (AD). One potential explanation is that WMH, conventionally considered a marker of cerebrovascular disease, are a reflection of cerebral amyloid angiopathy (CAA) and that increased WMH in this population is a manifestation of this vascular form of primary AD pathology. We examined whether the presence of cerebral microbleeds, a marker of CAA, mediates the relationship between WMH and estimated symptom onset in individuals with and without autosomal dominant mutations for AD.Participants and methodsParticipants (n = 175, mean age = 41.1 years) included 112 with an AD mutation and 63 first-degree non-carrier controls. We calculated the estimated years from expected symptom onset (EYO) and analyzed baseline MRI data for WMH volume and presence of cerebral microbleeds. Mixed effects regression and tests of mediation were used to examine microbleed and WMH differences between carriers and non-carriers and to test the whether the association between WMH and mutation status is dependent on the presence of microbleeds.ResultsMutation carriers were more likely to have microbleeds than non-carriers (p
Published: 2018

49. Neuroimaging and plasma evidence of early white matter loss in Parkinson’s disease with poor outcomes

Author: Zarkali, Angeliki, primary, Hannaway, Naomi, additional, McColgan, Peter, additional, Heslegrave, Amanda J, additional, Veleva, Elena, additional, Laban, Rhiannon, additional, Zetterberg, Henrik, additional, Lees, Andrew J, additional, Fox, Nick C, additional, and Weil, Rimona S, additional
Published: 2024
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50. Visual short-term memory impairments in presymptomatic familial Alzheimer's disease: A longitudinal observational study

Author: Pavisic, Ivanna M., Nicholas, Jennifer M., Pertzov, Yoni, O'Connor, Antoinette, Liang, Yuying, Collins, Jessica D., Lu, Kirsty, Weston, Philip S.J., Ryan, Natalie S., Husain, Masud, Fox, Nick C., and Crutch, Sebastian J.
Published: 2021
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