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1. The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

Author: Weiner, MW, Aisen, P, Petersen, R, Jack, CR, Jagust, W, Trojanowki, JQ, Toga, AW, Beckett, L, Green, RC, Saykin, AJ, Morris, JC, Perrin, RJ, Shaw, LM, Khachaturian, Z, Carrillo, M, Potter, W, Barnes, L, Bernard, M, González, H, Ho, C, Hsiao, JK, Jackson, J, Masliah, E, Masterman, D, Okonkwo, O, Perrin, R, Ryan, L, Silverberg, N, Fleisher, A, Sacrey, DT, Fockler, J, Conti, C, Veitch, D, Neuhaus, J, Jin, C, Nosheny, R, Ashford, M, Flenniken, D, Kormos, A, Monine, T, Rafii, M, Raman, R, Jimenez, G, Donohue, M, Gessert, D, Salazar, J, Zimmerman, C, Cabrera, Y, Walter, S, Miller, G, Coker, G, Clanton, T, Hergesheimer, L, Smith, S, Adegoke, O, Mahboubi, P, Moore, S, Pizzola, J, Shaffer, E, Sloan, B, Harvey, D, Forghanian-Arani, A, Borowski, B, Ward, C, Schwarz, C, Jones, D, Gunter, J, Kantarci, K, Senjem, M, Vemuri, P, Reid, R, Fox, NC, Malone, I, Thompson, P, Thomopoulos, SI, Nir, TM, Jahanshad, N, DeCarli, C, Knaack, A, Fletcher, E, Tosun-Turgut, D, Chen, SR, Choe, M, Crawford, K, Yushkevich, PA, Das, S, Koeppe, RA, Reiman, EM, Chen, K, Mathis, C, Landau, S, Cairns, NJ, Householder, E, Franklin, E, Bernhardt, H, Taylor-Reinwald, L, Tojanowki, JQ, Korecka, M, and Figurski, M
Subjects: Biological Sciences, Genetics, Prevention, Human Genome, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Knowledge Bases, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Risk Factors, Alzheimer’s Disease Neuroimaging Initiative, Biological sciences, Biomedical and clinical sciences
Abstract: The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.
Published: 2022

2. A novel Alzheimer disease locus located near the gene encoding tau protein.

Author: Jun, G, Ibrahim-Verbaas, CA, Vronskaya, M, Lambert, J-C, Chung, J, Naj, AC, Kunkle, BW, Wang, L-S, Bis, JC, Bellenguez, C, Harold, D, Lunetta, KL, Destefano, AL, Grenier-Boley, B, Sims, R, Beecham, GW, Smith, AV, Chouraki, V, Hamilton-Nelson, KL, Ikram, MA, Fievet, N, Denning, N, Martin, ER, Schmidt, H, Kamatani, Y, Dunstan, ML, Valladares, O, Laza, AR, Zelenika, D, Ramirez, A, Foroud, TM, Choi, S-H, Boland, A, Becker, T, Kukull, WA, van der Lee, SJ, Pasquier, F, Cruchaga, C, Beekly, D, Fitzpatrick, AL, Hanon, O, Gill, M, Barber, R, Gudnason, V, Campion, D, Love, S, Bennett, DA, Amin, N, Berr, C, Tsolaki, Magda, Buxbaum, JD, Lopez, OL, Deramecourt, V, Fox, NC, Cantwell, LB, Tárraga, L, Dufouil, C, Hardy, J, Crane, PK, Eiriksdottir, G, Hannequin, D, Clarke, R, Evans, D, Mosley, TH, Letenneur, L, Brayne, C, Maier, W, De Jager, P, Emilsson, V, Dartigues, J-F, Hampel, H, Kamboh, MI, de Bruijn, RFAG, Tzourio, C, Pastor, P, Larson, EB, Rotter, JI, O'Donovan, MC, Montine, TJ, Nalls, MA, Mead, S, Reiman, EM, Jonsson, PV, Holmes, C, St George-Hyslop, PH, Boada, M, Passmore, P, Wendland, JR, Schmidt, R, Morgan, K, Winslow, AR, Powell, JF, Carasquillo, M, Younkin, SG, Jakobsdóttir, J, Kauwe, JSK, Wilhelmsen, KC, Rujescu, D, Nöthen, MM, and Hofman, A
Subjects: IGAP Consortium, Chromosomes, Human, Pair 17, Humans, Alzheimer Disease, tau Proteins, Polymorphism, Single Nucleotide, Apolipoprotein E4, Genome-Wide Association Study, Alzheimer's Disease, Prevention, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Human Genome, Neurodegenerative, Neurosciences, Acquired Cognitive Impairment, Genetics, Brain Disorders, Aging, 2.1 Biological and endogenous factors, Neurological, Psychiatry, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract: APOE ɛ4, the most significant genetic risk factor for Alzheimer disease (AD), may mask effects of other loci. We re-analyzed genome-wide association study (GWAS) data from the International Genomics of Alzheimer's Project (IGAP) Consortium in APOE ɛ4+ (10 352 cases and 9207 controls) and APOE ɛ4- (7184 cases and 26 968 controls) subgroups as well as in the total sample testing for interaction between a single-nucleotide polymorphism (SNP) and APOE ɛ4 status. Suggestive associations (P
Published: 2016

3. Cerebrospinal fluid markers including trefoil factor 3 are associated with neurodegeneration in amyloid-positive individuals.

Author: Paterson, RW, Bartlett, JW, Blennow, K, Fox, NC, Alzheimer's Disease Neuroimaging Initiative, Shaw, LM, Trojanowski, JQ, Zetterberg, H, and Schott, JM
Subjects: Alzheimer's Disease Neuroimaging Initiative, Brain, Cerebral Ventricles, Hippocampus, Humans, Alzheimer Disease, Amyloidosis, Atrophy, Peptides, tau Proteins, Mental Status Schedule, Aged, Aged, 80 and over, Female, Male, Apolipoprotein E4, Statistics as Topic, Cystatin C, Biomarkers, Trefoil Factor-3, Cognitive Dysfunction, and over, Psychology, Clinical Sciences, Public Health and Health Services
Abstract: We aimed to identify cerebrospinal fluid (CSF) biomarkers associated with neurodegeneration in individuals with and without CSF evidence of Alzheimer pathology. We investigated 287 Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects (age=74.9±6.9; 22/48/30% with Alzheimer's disease/mild cognitive impairment/controls) with CSF multiplex analyte data and serial volumetric MRI. We calculated brain and hippocampal atrophy rates, ventricular expansion and Mini Mental State Examination decline. We used false discovery rate corrected regression analyses to assess associations between CSF variables and atrophy rates in individuals with and without amyloid pathology, adjusting in stages for tau, baseline volume, p-tau, age, sex, ApoE4 status and diagnosis. Analytes showing statistically significant independent relationships were entered into reverse stepwise analyses. Adjusting for tau, baseline volume, p-tau, age, sex and ApoE4, 4/83 analytes were significantly independently associated with brain atrophy rate, 1/83 with ventricular expansion and 2/83 with hippocampal atrophy. The strongest CSF predictor for the three atrophy measures was low trefoil factor 3 (TFF3). High cystatin C (CysC) was associated with higher whole brain atrophy and hippocampal atrophy rates. Lower levels of vascular endothelial growth factor and chromogranin A (CrA) were associated with higher whole brain atrophy. In exploratory reverse stepwise analyses, lower TFF3 was associated with higher rates of whole brain, hippocampal atrophy and ventricular expansion. Lower levels of CrA were associated with higher whole brain atrophy rate. The relationship between low TFF3 and increased hippocampal atrophy rate remained after adjustment for diagnosis. We identified a series of CSF markers that are independently associated with rate of neurodegeneration in amyloid-positive individuals. TFF3, a substrate for NOTCH processing may be an important biomarker of neurodegeneration across the Alzheimer spectrum.
Published: 2014

4. EFNS task force: the use of neuroimaging in the diagnosis of dementia

Author: Filippi, M, Agosta, F, Barkhof, F, Dubois, B, Fox, NC, Frisoni, GB, Jack, CR, Johannsen, P, Miller, BL, Nestor, PJ, Scheltens, P, Sorbi, S, Teipel, S, Thompson, PM, and Wahlund, L‐O
Subjects: Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Biomedical Imaging, Dementia, Vascular Cognitive Impairment/Dementia, Neurodegenerative, Acquired Cognitive Impairment, Alzheimer's Disease, Aging, Brain Disorders, Clinical Research, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Neurological, Advisory Committees, Alzheimer Disease, Humans, Neuroimaging, Alzheimer's disease, amyloid imaging, dementia, diagnosis, guidelines, magnetic resonance imaging, positron emission tomography, single photon emission computed tomography, European Federation of the Neurologic Societies, Neurology & Neurosurgery, Clinical sciences
Abstract: Background and purposeThe European Federation of the Neurological Societies (EFNS) guidelines on the use of neuroimaging in the diagnosis and management of dementia are designed to revise and expand previous EFNS recommendations for the diagnosis and management of patients with Alzheimer's disease (AD) and to provide an overview of the evidence for the use of neuroimaging techniques in non-AD dementias, as well as general recommendations that apply to all types of dementia in clinical practice.MethodsThe task force working group reviewed evidence from original research articles, meta-analyses and systematic reviews, published before April 2012. The evidence was classified, and consensus recommendations were given and graded according to the EFNS guidance regulations.ResultsStructural imaging, which should be performed at least once in the diagnostic work-up of patients with cognitive impairment, serves to exclude other potentially treatable diseases, to recognize vascular lesions and to identify specific findings to help distinguish different forms of neurodegenerative types of dementia. Although typical cases of dementia may not benefit from routine functional imaging, these tools are recommended in those cases where diagnosis remains in doubt after clinical and structural imaging work-up and in particular clinical settings. Amyloid imaging is likely to find clinical utility in several fields, including the stratification of patients with mild cognitive impairment into those with and without underlying AD and the evaluation of atypical AD presentations.ConclusionsA number of recommendations and good practice points are made to improve the diagnosis of AD and other dementias.
Published: 2012

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

Author: McKay, NS, Gordon, BA, Hornbeck, RC, Dincer, A, Flores, S, Keefe, SJ, Joseph-Mathurin, N, Jack, CR, Koeppe, R, Millar, PR, Ances, BM, Chen, CD, Daniels, A, Hobbs, DA, Jackson, K, Koudelis, D, Massoumzadeh, P, McCullough, A, Nickels, ML, Rahmani, F, Swisher, L, Wang, Q, Allegri, RF, Berman, SB, Brickman, AM, Brooks, WS, Cash, DM, Chhatwal, JP, Day, GS, Farlow, MR, la Fougère, C, Fox, NC, Fulham, M, Ghetti, B, Graff-Radford, N, Ikeuchi, T, Klunk, W, Lee, J-H, Levin, J, Martins, R, Masters, CL, McConathy, J, Mori, H, Noble, JM, Reischl, G, Rowe, C, Salloway, S, Sanchez-Valle, R, Schofield, PR, Shimada, H, Shoji, M, Su, Y, Suzuki, K, Vöglein, J, Yakushev, I, Cruchaga, C, Hassenstab, J, Karch, C, McDade, E, Perrin, RJ, Xiong, C, Morris, JC, Bateman, RJ, Benzinger, TLS, Dominantly Inherited Alzheimer Network, McKay, NS, Gordon, BA, Hornbeck, RC, Dincer, A, Flores, S, Keefe, SJ, Joseph-Mathurin, N, Jack, CR, Koeppe, R, Millar, PR, Ances, BM, Chen, CD, Daniels, A, Hobbs, DA, Jackson, K, Koudelis, D, Massoumzadeh, P, McCullough, A, Nickels, ML, Rahmani, F, Swisher, L, Wang, Q, Allegri, RF, Berman, SB, Brickman, AM, Brooks, WS, Cash, DM, Chhatwal, JP, Day, GS, Farlow, MR, la Fougère, C, Fox, NC, Fulham, M, Ghetti, B, Graff-Radford, N, Ikeuchi, T, Klunk, W, Lee, J-H, Levin, J, Martins, R, Masters, CL, McConathy, J, Mori, H, Noble, JM, Reischl, G, Rowe, C, Salloway, S, Sanchez-Valle, R, Schofield, PR, Shimada, H, Shoji, M, Su, Y, Suzuki, K, Vöglein, J, Yakushev, I, Cruchaga, C, Hassenstab, J, Karch, C, McDade, E, Perrin, RJ, Xiong, C, Morris, JC, Bateman, RJ, Benzinger, TLS, and Dominantly Inherited Alzheimer Network
Abstract: The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case-control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual's point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of 'sporadic' AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers.
Published: 2023

6. Patterns and implications of neurological examination findings in autosomal dominant Alzheimer disease

Author: Voeglein, J, Franzmeier, N, Morris, JC, Dieterich, M, McDade, E, Simons, M, Preische, O, Hofmann, A, Hassenstab, J, Benzinger, TL, Fagan, A, Noble, JM, Berman, SB, Graff-Radford, NR, Ghetti, B, Farlow, MR, Chhatwal, JP, Salloway, S, Xiong, C, Karch, CM, Cairns, N, Perrin, RJ, Day, G, Martins, R, Sanchez-Valle, R, Mori, H, Shimada, H, Ikeuchi, T, Suzuki, K, Schofield, PR, Masters, CL, Goate, A, Buckles, V, Fox, NC, Chrem, P, Allegri, R, Ringman, JM, Yakushev, I, Laske, C, Jucker, M, Hoglinger, G, Bateman, RJ, Danek, A, Levin, J, Voeglein, J, Franzmeier, N, Morris, JC, Dieterich, M, McDade, E, Simons, M, Preische, O, Hofmann, A, Hassenstab, J, Benzinger, TL, Fagan, A, Noble, JM, Berman, SB, Graff-Radford, NR, Ghetti, B, Farlow, MR, Chhatwal, JP, Salloway, S, Xiong, C, Karch, CM, Cairns, N, Perrin, RJ, Day, G, Martins, R, Sanchez-Valle, R, Mori, H, Shimada, H, Ikeuchi, T, Suzuki, K, Schofield, PR, Masters, CL, Goate, A, Buckles, V, Fox, NC, Chrem, P, Allegri, R, Ringman, JM, Yakushev, I, Laske, C, Jucker, M, Hoglinger, G, Bateman, RJ, Danek, A, and Levin, J
Abstract: INTRODUCTION: As knowledge about neurological examination findings in autosomal dominant Alzheimer disease (ADAD) is incomplete, we aimed to determine the frequency and significance of neurological examination findings in ADAD. METHODS: Frequencies of neurological examination findings were compared between symptomatic mutation carriers and non mutation carriers from the Dominantly Inherited Alzheimer Network (DIAN) to define AD neurological examination findings. AD neurological examination findings were analyzed regarding frequency, association with and predictive value regarding cognitive decline, and association with brain atrophy in symptomatic mutation carriers. RESULTS: AD neurological examination findings included abnormal deep tendon reflexes, gait disturbance, pathological cranial nerve examination findings, tremor, abnormal finger to nose and heel to shin testing, and compromised motor strength. The frequency of AD neurological examination findings was 65.1%. Cross-sectionally, mutation carriers with AD neurological examination findings showed a more than two-fold faster cognitive decline and had greater parieto-temporal atrophy, including hippocampal atrophy. Longitudinally, AD neurological examination findings predicted a significantly greater decline over time. DISCUSSION: ADAD features a distinct pattern of neurological examination findings that is useful to estimate prognosis and may inform clinical care and therapeutic trial designs.
Published: 2023

7. Biomarker clustering in autosomal dominant Alzheimer's disease

Author: Luckett, PH, Chen, C, Gordon, BA, Wisch, J, Berman, SB, Chhatwal, JP, Cruchaga, C, Fagan, AM, Farlow, MR, Fox, NC, Jucker, M, Levin, J, Masters, CL, Mori, H, Noble, JM, Salloway, S, Schofield, PR, Brickman, AM, Brooks, WS, Cash, DM, Fulham, MJ, Ghetti, B, Jack, CR, Voeglein, J, Klunk, WE, Koeppe, R, Su, Y, Weiner, M, Wang, Q, Marcus, D, Koudelis, D, Joseph-Mathurin, N, Cash, L, Hornbeck, R, Xiong, C, Perrin, RJ, Karch, CM, Hassenstab, J, McDade, E, Morris, JC, Benzinger, TLS, Bateman, RJ, Ances, BM, Luckett, PH, Chen, C, Gordon, BA, Wisch, J, Berman, SB, Chhatwal, JP, Cruchaga, C, Fagan, AM, Farlow, MR, Fox, NC, Jucker, M, Levin, J, Masters, CL, Mori, H, Noble, JM, Salloway, S, Schofield, PR, Brickman, AM, Brooks, WS, Cash, DM, Fulham, MJ, Ghetti, B, Jack, CR, Voeglein, J, Klunk, WE, Koeppe, R, Su, Y, Weiner, M, Wang, Q, Marcus, D, Koudelis, D, Joseph-Mathurin, N, Cash, L, Hornbeck, R, Xiong, C, Perrin, RJ, Karch, CM, Hassenstab, J, McDade, E, Morris, JC, Benzinger, TLS, Bateman, RJ, and Ances, BM
Abstract: INTRODUCTION: As the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others. METHODS: We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation. RESULTS: Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors. DISCUSSION: These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.
Published: 2023

8. Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer's disease

Author: Green, RE, Lord, J, Scelsi, MA, Xu, J, Wong, A, Naomi-James, S, Handy, A, Gilchrist, L, Williams, DM, Parker, TD, Lane, CA, Malone, IB, Cash, DM, Sudre, CH, Coath, W, Thomas, DL, Keuss, S, Dobson, R, Legido-Quigley, C, Fox, NC, Schott, JM, Richards, M, Proitsi, P, and Radiology and nuclear medicine
Subjects: Neurology, Cognitive Neuroscience, Neurology (clinical)
Abstract: Background Identifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46—the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer’s disease (AD). Methods Following quality control, levels of 1019 metabolites—detected with liquid chromatography-mass spectrometry—were available for 1740 participants at age 60–64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69–71). Regression analyses tested relationships between metabolite measures—modules and hub metabolites—and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant (pFDR APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated (pFDR N = 1638). Results In the fully adjusted model, three lipid modules were associated with a brain volume measure (pFDR ß = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß = − 0.072, 95%CI = [− 0.12, − 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß = − 0.066, 95% CI = [− 0.11, − 0.020]). Twenty-two hub metabolites were associated (pFDR Conclusions Our findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality.
Published: 2023

9. Avoid or Embrace? Practice Effects in Alzheimer's Disease Prevention Trials

Author: Aschenbrenner, AJ, Hassenstab, J, Wang, G, Li, Y, Xiong, C, McDade, E, Clifford, DB, Salloway, S, Farlow, M, Yaari, R, Cheng, EYJ, Holdridge, KC, Mummery, CJ, Masters, CL, Hsiung, G-Y, Surti, G, Day, GS, Weintraub, S, Honig, LS, Galvin, JE, Ringman, JM, Brooks, WS, Fox, NC, Snyder, PJ, Suzuki, K, Shimada, H, Graeber, S, Bateman, RJ, Aschenbrenner, AJ, Hassenstab, J, Wang, G, Li, Y, Xiong, C, McDade, E, Clifford, DB, Salloway, S, Farlow, M, Yaari, R, Cheng, EYJ, Holdridge, KC, Mummery, CJ, Masters, CL, Hsiung, G-Y, Surti, G, Day, GS, Weintraub, S, Honig, LS, Galvin, JE, Ringman, JM, Brooks, WS, Fox, NC, Snyder, PJ, Suzuki, K, Shimada, H, Graeber, S, and Bateman, RJ
Abstract: Demonstrating a slowing in the rate of cognitive decline is a common outcome measure in clinical trials in Alzheimer's disease (AD). Selection of cognitive endpoints typically includes modeling candidate outcome measures in the many, richly phenotyped observational cohort studies available. An important part of choosing cognitive endpoints is a consideration of improvements in performance due to repeated cognitive testing (termed "practice effects"). As primary and secondary AD prevention trials are comprised predominantly of cognitively unimpaired participants, practice effects may be substantial and may have considerable impact on detecting cognitive change. The extent to which practice effects in AD prevention trials are similar to those from observational studies and how these potential differences impact trials is unknown. In the current study, we analyzed data from the recently completed DIAN-TU-001 clinical trial (TU) and the associated DIAN-Observational (OBS) study. Results indicated that asymptomatic mutation carriers in the TU exhibited persistent practice effects on several key outcomes spanning the entire trial duration. Critically, these practice related improvements were larger on certain tests in the TU relative to matched participants from the OBS study. Our results suggest that the magnitude of practice effects may not be captured by modeling potential endpoints in observational studies where assessments are typically less frequent and drug expectancy effects are absent. Using alternate instrument forms (represented in our study by computerized tasks) may partly mitigate practice effects in clinical trials but incorporating practice effects as outcomes may also be viable. Thus, investigators must carefully consider practice effects (either by minimizing them or modeling them directly) when designing cognitive endpoint AD prevention trials by utilizing trial data with similar assessment frequencies.
Published: 2022

10. Different rates of cognitive decline in autosomal dominant and late-onset Alzheimer disease

Author: Buckles, VD, Xiong, C, Bateman, RJ, Hassenstab, J, Allegri, R, Berman, SB, Chhatwal, JP, Danek, A, Fagan, AM, Ghetti, B, Goate, A, Graff-Radford, N, Jucker, M, Levin, J, Marcus, DS, Masters, CL, McCue, L, McDade, E, Mori, H, Moulder, KL, Noble, JM, Paumier, K, Preische, O, Ringman, JM, Fox, NC, Salloway, S, Schofield, PR, Martins, R, Voglein, J, Morris, JC, Buckles, VD, Xiong, C, Bateman, RJ, Hassenstab, J, Allegri, R, Berman, SB, Chhatwal, JP, Danek, A, Fagan, AM, Ghetti, B, Goate, A, Graff-Radford, N, Jucker, M, Levin, J, Marcus, DS, Masters, CL, McCue, L, McDade, E, Mori, H, Moulder, KL, Noble, JM, Paumier, K, Preische, O, Ringman, JM, Fox, NC, Salloway, S, Schofield, PR, Martins, R, Voglein, J, and Morris, JC
Abstract: As prevention trials advance with autosomal dominant Alzheimer disease (ADAD) participants, understanding the similarities and differences between ADAD and “sporadic” late-onset AD (LOAD) is critical to determine generalizability of findings between these cohorts. Cognitive trajectories of ADAD mutation carriers (MCs) and autopsy-confirmed LOAD individuals were compared to address this question. Longitudinal rates of change on cognitive measures were compared in ADAD MCs (n = 310) and autopsy-confirmed LOAD participants (n = 163) before and after symptom onset (estimated/observed). LOAD participants declined more rapidly in the presymptomatic (preclinical) period and performed more poorly at symptom onset than ADAD participants on a cognitive composite. After symptom onset, however, the younger ADAD MCs declined more rapidly. The similar but not identical cognitive trajectories (declining but at different rates) for ADAD and LOAD suggest common AD pathologies but with some differences.
Published: 2022

11. A C6orf10/LOC101929163 locus is associated with age of onset in C9orf72 carriers

Author: Zhang M1, 2 3, Ferrari R4, Tartaglia MC3, 5 6, Keith J7, Surace EI8, Wolf U9, Sato C3, Grinberg M3, Liang Y3, Xi Z3, Dupont K3, McGoldrick P3, Weichert A3, McKeever PM3, Schneider R3, 6 7, McCorkindale MD4, Manzoni C10, Rademakers R11, Graff-Radford NR12, Dickson DW11, Parisi JE13, Boeve BF14, Petersen RC14, Miller BL15, Seeley WW16, van Swieten JC17, van Rooij J17, Pijnenburg Y18, van der Zee J19, Van Broeckhoven C19, Le Ber I21, Van Deerlin V23, Suh E23, Rohrer JD24, Mead S25, Graff C26, Öijerstedt L26, Pickering-Brown S28, Rollinson S28, Rossi G29, Tagliavini F30, Brooks WS31, Dobson-Stone C32, Halliday GM32, Hodges JR32, Piguet O34, Binetti G36, Benussi L37, Ghidoni R37, Nacmias B38, Sorbi S38, Bruni AC40, Galimberti D41, Scarpini E41, Rainero I42, Rubino E42, Clarimon J43, Lleó A43, Ruiz A45, Hernández I45, Pastor P46, Diez-Fairen M46, Borroni B48, Pasquier F49, Deramecourt V49, Lebouvier T49, Perneczky R50, 51 52, Diehl-Schmid J50, Grafman J53, Huey ED55, Mayeux R55, Nalls MA57, Hernandez D57, Singleton A57, Momeni P58, Zeng Z59, Hardy J4, Robertson J3, Zinman L6, 7, Rogaeva E3, 6, International FTD-Genomics Consortium (IFGC), Ferrari R, Hernandez DG, Nalls MA, Rohrer JD, Ramasamy A, Kwok JBJ, Dobson-Stone C, Brooks WS, Schofield PR, Halliday GM, Hodges JR, Piguet O, Bartley L, Thompson E, Hernández I, Ruiz A, Boada M, Borroni B, Padovani A, Cruchaga C, Cairns NJ, Benussi L, Binetti G, Ghidoni R, Forloni G, Albani D, Galimberti D, Fenoglio C, Serpente M, Scarpini E, Clarimón J, Lleó A, Blesa R, Wald Ouml ML, Nilsson K, Nilsson C, Mackenzie IRA, Hsiung GR, Mann DMA, Grafman J, Morris CM, Attems J, Griffiths TD, McKeith IG, Thomas AJ, Pietrini P, Huey ED, Wassermann EM, Baborie A, Jaros E, Tierney MC, Pastor P, Razquin C, Ortega-Cubero S, Alonso E, Perneczky R, Diehl-Schmid J, Alexopoulos P, Kurz A, Rainero I, Rubino E, Pinessi L, Rogaeva E, St George-Hyslop P, Rossi G, Tagliavini F, Giaccone G, Rowe JB, Schlachetzki JCM, Uphill J, Collinge J, Mead S, Danek A, Van Deerlin VM, Grossman M, Trojanowski JQ, van der Zee J, Van Broeckhoven C, Cappa SF, Leber I, Hannequin D, Golfier V, Vercelletto M, Brice A, Nacmias B, Sorbi S, Bagnoli S, Piaceri I, Nielsen JE, Hjermind LE, Riemenschneider M, Mayhaus M, Ibach B, Gasparoni G, Pichler S, Gu W, Rossor MN, Fox NC, Warren JD, Grazia Spillantini M, Morris HR, Rizzu P, Heutink P, Snowden JS, Rollinson S, Richardson A, Gerhard A, Bruni AC, Maletta R, Frangipane F, Cupidi C, Bernardi L, Anfossi M, Gallo M, Elena Conidi M, Smirne N, Rademakers R, Baker M, Dickson DW, Graff-Radford NR, Petersen RC, Knopman D, Josephs KA, Boeve BF, Parisi JE, Seeley WW, Miller BL, Karydas AM, Rosen H, van Swieten JC, Dopper EGP, Seelaar H, Pijnenburg YAL, Scheltens P, Logroscino G, Capozzo R, Novelli V, Puca AA, Franceschi M, Postiglione A, Milan G, Sorrentino P, Kristiansen M, Chiang HH, Graff C, Pasquier F, Rollin A, Deramecourt V, Lebouvier T, Kapogiannis D, Ferrucci L, Pickering-Brown S, Singleton AB, Hardy J, Momeni P, Human genetics, Amsterdam Neuroscience - Neurodegeneration, Neurology, Divisions, Zhang, M1, 2, 3, Ferrari, R4, Tartaglia, Mc3, 5, 6, Keith, J7, Surace, Ei8, Wolf, U9, Sato, C3, Grinberg, M3, Liang, Y3, Xi, Z3, Dupont, K3, Mcgoldrick, P3, Weichert, A3, Mckeever, Pm3, Schneider, R3, 6, 7, Mccorkindale, Md4, Manzoni, C10, Rademakers, R11, Graff-Radford, Nr12, Dickson, Dw11, Parisi, Je13, Boeve, Bf14, Petersen, Rc14, Miller, Bl15, Seeley, Ww16, van Swieten, Jc17, van Rooij, J17, Pijnenburg, Y18, van der Zee, J19, Van Broeckhoven, C19, Le Ber, I21, Van Deerlin, V23, Suh, E23, Rohrer, Jd24, Mead, S25, Graff, C26, Öijerstedt, L26, Pickering-Brown, S28, Rollinson, S28, Rossi, G29, Tagliavini, F30, Brooks, Ws31, Dobson-Stone, C32, Halliday, Gm32, Hodges, Jr32, Piguet, O34, Binetti, G36, Benussi, L37, Ghidoni, R37, Nacmias, B38, Sorbi, S38, Bruni, Ac40, Galimberti, D41, Scarpini, E41, Rainero, I42, Rubino, E42, Clarimon, J43, Lleó, A43, Ruiz, A45, Hernández, I45, Pastor, P46, Diez-Fairen, M46, Borroni, B48, Pasquier, F49, Deramecourt, V49, Lebouvier, T49, Perneczky, R50, 51, 52, Diehl-Schmid, J50, Grafman, J53, Huey, Ed55, Mayeux, R55, Nalls, Ma57, Hernandez, D57, Singleton, A57, Momeni, P58, Zeng, Z59, Hardy, J4, Robertson, J3, Zinman, L6, Rogaeva, E3, International FTD-Genomics Consortium, (IFGC), Ferrari, R, Hernandez, Dg, Nalls, Ma, Rohrer, Jd, Ramasamy, A, Kwok, Jbj, Dobson-Stone, C, Brooks, W, Schofield, Pr, Halliday, Gm, Hodges, Jr, Piguet, O, Bartley, L, Thompson, E, Hernández, I, Ruiz, A, Boada, M, Borroni, B, Padovani, A, Cruchaga, C, Cairns, Nj, Benussi, L, Binetti, G, Ghidoni, R, Forloni, G, Albani, D, Galimberti, D, Fenoglio, C, Serpente, M, Scarpini, E, Clarimón, J, Lleó, A, Blesa, R, Wald Ouml, Ml, Nilsson, K, Nilsson, C, Mackenzie, Ira, Hsiung, Gr, Mann, Dma, Grafman, J, Morris, Cm, Attems, J, Griffiths, Td, Mckeith, Ig, Thomas, Aj, Pietrini, P, Huey, Ed, Wassermann, Em, Baborie, A, Jaros, E, Tierney, Mc, Pastor, P, Razquin, C, Ortega-Cubero, S, Alonso, E, Perneczky, R, Diehl-Schmid, J, Alexopoulos, P, Kurz, A, Rainero, I, Rubino, E, Pinessi, L, Rogaeva, E, St George-Hyslop, P, Rossi, G, Tagliavini, F, Giaccone, G, Rowe, Jb, Schlachetzki, Jcm, Uphill, J, Collinge, J, Mead, S, Danek, A, Van Deerlin, Vm, Grossman, M, Trojanowski, Jq, van der Zee, J, Van Broeckhoven, C, Cappa, Sf, Leber, I, Hannequin, D, Golfier, V, Vercelletto, M, Brice, A, Nacmias, B, Sorbi, S, Bagnoli, S, Piaceri, I, Nielsen, Je, Hjermind, Le, Riemenschneider, M, Mayhaus, M, Ibach, B, Gasparoni, G, Pichler, S, Gu, W, Rossor, Mn, Fox, Nc, Warren, Jd, Grazia Spillantini, M, Morris, Hr, Rizzu, P, Heutink, P, Snowden, J, Rollinson, S, Richardson, A, Gerhard, A, Bruni, Ac, Maletta, R, Frangipane, F, Cupidi, C, Bernardi, L, Anfossi, M, Gallo, M, Elena Conidi, M, Smirne, N, Rademakers, R, Baker, M, Dickson, Dw, Graff-Radford, Nr, Petersen, Rc, Knopman, D, Josephs, Ka, Boeve, Bf, Parisi, Je, Seeley, Ww, Miller, Bl, Karydas, Am, Rosen, H, van Swieten, Jc, Dopper, Egp, Seelaar, H, Pijnenburg, Yal, Scheltens, P, Logroscino, G, Capozzo, R, Novelli, V, Puca, Aa, Franceschi, M, Postiglione, A, Milan, G, Sorrentino, P, Kristiansen, M, Chiang, Hh, Graff, C, Pasquier, F, Rollin, A, Deramecourt, V, Lebouvier, T, Kapogiannis, D, Ferrucci, L, Pickering-Brown, S, Singleton, Ab, Hardy, J, Momeni, P, and Int FTD-Genomics Consortium IFGC
Subjects: Male, Heterozygote, amyotrophic lateral sclerosis, Genotype, genetic association, Age of onset, Polymorphism, Single Nucleotide, frontotemporal dementia, age of onset, C9orf72, Humans, amyotrophic lateral sclerosi, Aged, C9orf72 Protein, Original Articles, DNA Methylation, Middle Aged, Amyotrophic lateral sclerosis, Gene Expression Regulation, Genetic association, CpG Islands, Female, Human medicine, Neurology (clinical), Frontotemporal dementia
Abstract: Discovery of disease age-of-onset modifiers is important for clinical trials and drug design. Zhang et al. perform a genome-wide analysis of epigenetic functional polymorphisms and identify an association between the C6orf10/LOC101929163 locus and age of FTD/ALS onset. The risk allele may be associated with a pro-inflammatory state in the brain., The G4C2-repeat expansion in C9orf72 is the most common known cause of amyotrophic lateral sclerosis and frontotemporal dementia. The high phenotypic heterogeneity of C9orf72 patients includes a wide range in age of onset, modifiers of which are largely unknown. Age of onset could be influenced by environmental and genetic factors both of which may trigger DNA methylation changes at CpG sites. We tested the hypothesis that age of onset in C9orf72 patients is associated with some common single nucleotide polymorphisms causing a gain or loss of CpG sites and thus resulting in DNA methylation alterations. Combined analyses of epigenetic and genetic data have the advantage of detecting functional variants with reduced likelihood of false negative results due to excessive correction for multiple testing in genome-wide association studies. First, we estimated the association between age of onset in C9orf72 patients (n = 46) and the DNA methylation levels at all 7603 CpG sites available on the 450 k BeadChip that are mapped to common single nucleotide polymorphisms. This was followed by a genetic association study of the discovery (n = 144) and replication (n = 187) C9orf72 cohorts. We found that age of onset was reproducibly associated with polymorphisms within a 124.7 kb linkage disequilibrium block tagged by top-significant variation, rs9357140, and containing two overlapping genes (LOC101929163 and C6orf10). A meta-analysis of all 331 C9orf72 carriers revealed that every A-allele of rs9357140 reduced hazard by 30% (P = 0.0002); and the median age of onset in AA-carriers was 6 years later than GG-carriers. In addition, we investigated a cohort of C9orf72 negative patients (n = 2634) affected by frontotemporal dementia and/or amyotrophic lateral sclerosis; and also found that the AA-genotype of rs9357140 was associated with a later age of onset (adjusted P = 0.007 for recessive model). Phenotype analyses detected significant association only in the largest subgroup of patients with frontotemporal dementia (n = 2142, adjusted P = 0.01 for recessive model). Gene expression studies of frontal cortex tissues from 25 autopsy cases affected by amyotrophic lateral sclerosis revealed that the G-allele of rs9357140 is associated with increased brain expression of LOC101929163 (a non-coding RNA) and HLA-DRB1 (involved in initiating immune responses), while the A-allele is associated with their reduced expression. Our findings suggest that carriers of the rs9357140 GG-genotype (linked to an earlier age of onset) might be more prone to be in a pro-inflammatory state (e.g. by microglia) than AA-carriers. Further, investigating the functional links within the C6orf10/LOC101929163/HLA-DRB1 pathway will be critical to better define age-dependent pathogenesis of frontotemporal dementia and amyotrophic lateral sclerosis.
Published: 2018

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

Author: Dincer, A, Gordon, BA, Hari-Raj, A, Keefe, SJ, Flores, S, McKay, NS, Paulick, AM, Lewis, KES, Feldman, RL, Hornbeck, RC, Allegri, R, Ances, BM, Berman, SB, Brickman, AM, Brooks, WS, Cash, DM, Chhatwal, JP, Farlow, MR, la Fougere, C, Fox, NC, Fulham, MJ, Jack, CR, Joseph-Mathurin, N, Karch, CM, Lee, A, Levin, J, Masters, CL, McDade, EM, Oh, H, Perrin, RJ, Raji, C, Salloway, SP, Schofield, PR, Su, Y, Villemagne, VL, Wang, Q, Weiner, MW, Xiong, C, Yakushev, I, Morris, JC, Bateman, RJ, Benzinger, TLS, Dincer, A, Gordon, BA, Hari-Raj, A, Keefe, SJ, Flores, S, McKay, NS, Paulick, AM, Lewis, KES, Feldman, RL, Hornbeck, RC, Allegri, R, Ances, BM, Berman, SB, Brickman, AM, Brooks, WS, Cash, DM, Chhatwal, JP, Farlow, MR, la Fougere, C, Fox, NC, Fulham, MJ, Jack, CR, Joseph-Mathurin, N, Karch, CM, Lee, A, Levin, J, Masters, CL, McDade, EM, Oh, H, Perrin, RJ, Raji, C, Salloway, SP, Schofield, PR, Su, Y, Villemagne, VL, Wang, Q, Weiner, MW, Xiong, C, Yakushev, I, Morris, JC, Bateman, RJ, and Benzinger, TLS
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
Published: 2020

13. Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease

Author: Vermunt, L, Dicks, E, Wang, G, Dincer, A, Flores, S, Keefe, SJ, Berman, SB, Cash, DM, Chhatwal, JP, Cruchaga, C, Fox, NC, Ghetti, B, Graff-Radford, NR, Hassenstab, J, Karch, CM, Laske, C, Levin, J, Masters, CL, McDade, E, Mori, H, Morris, JC, Noble, JM, Perrin, RJ, Schofield, PR, Xiong, C, Scheltens, P, Visser, PJ, Bateman, RJ, Benzinger, TLS, Tijms, BM, Gordon, BA, Vermunt, L, Dicks, E, Wang, G, Dincer, A, Flores, S, Keefe, SJ, Berman, SB, Cash, DM, Chhatwal, JP, Cruchaga, C, Fox, NC, Ghetti, B, Graff-Radford, NR, Hassenstab, J, Karch, CM, Laske, C, Levin, J, Masters, CL, McDade, E, Mori, H, Morris, JC, Noble, JM, Perrin, RJ, Schofield, PR, Xiong, C, Scheltens, P, Visser, PJ, Bateman, RJ, Benzinger, TLS, Tijms, BM, and Gordon, BA
Abstract: Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer's disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer's disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset -9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We
Published: 2020

14. Age at symptom onset and death and disease duration in genetic frontotemporal dementia: an international retrospective cohort study

Author: Moore, KM, Nicholas, J, Grossman, M, McMillan, CT, Irwin, DJ, Massimo, L, Van Deerlin, VM, Warren, JD, Fox, NC, Rossor, MN, Mead, S, Bocchetta, M, Boeve, BF, Knopman, DS, Graff-Radford, NR, Forsberg, LK, Rademakers, R, Wszolek, ZK, van Swieten, JC, Jiskoot, LC, Meeter, LH, Dopper, EGP, Papma, JM, Snowden, JS, Saxon, J, Jones, M, Pickering-Brown, S, Le Ber, I, Camuzat, A, Brice, A, Caroppo, P, Ghidoni, R, Pievani, M, Benussi, L, Binetti, G, Dickerson, BC, Lucente, D, Krivensky, S, Graff, C, Oijerstedt, L, Fallstrom, M, Thonberg, H, Ghoshal, N, Morris, JC, Borroni, B, Benussi, A, Padovani, A, Galimberti, D, Scarpini, E, Fumagalli, GG, Mackenzie, IR, Hsiung, G-YR, Sengdy, P, Boxer, AL, Rosen, H, Taylor, JB, Synofzik, M, Wilke, C, Sulzer, P, Hodges, JR, Halliday, G, Kwok, J, Sanchez-Valle, R, Llado, A, Borrego-Ecija, S, Santana, I, Almeida, MR, Tabuas-Pereira, M, Moreno, F, Barandiaran, M, Indakoetxea, B, Levin, J, Danek, A, Rowe, JB, Cope, TE, Otto, M, Anderl-Straub, S, de Mendonca, A, Maruta, C, Masellis, M, Black, SE, Couratier, P, Lautrette, G, Huey, ED, Sorbi, S, Nacmias, B, Laforce, R, Tremblay, M-PL, Vandenberghe, R, Van Damme, P, Rogalski, EJ, Weintraub, S, Gerhard, A, Onyike, CU, Ducharme, S, Papageorgiou, SG, Ng, ASL, Brodtmann, A, Finger, E, Guerreiro, R, Bras, J, Rohrer, JD, Heller, C, Convery, R, Woollacott, IOC, Shafei, R, Graff-Radford, J, Jones, DT, Dheel, CM, Savica, R, Lapid, MI, Baker, M, Fields, JA, Gavrilova, R, Domoto-Reilly, K, Poos, JM, van der Ende, EL, Panman, JL, Kaat, LD, Seelaar, H, Richardson, A, Frisoni, G, Mega, A, Fostinelli, S, Chiang, H-H, Alberici, A, Arighi, A, Fenoglio, C, Heuer, H, Miller, B, Karydas, A, Fong, J, Leitao, MJ, Santiago, B, Duro, D, Ferreira, C, Gabilondo, A, de Arriba, M, Tainta, M, Zulaica, M, Ferreira, CB, Semler, E, Ludolph, A, Landwehrmeyer, B, Volk, AE, Miltenberger, G, Verdelho, A, Afonso, S, Tartaglia, MC, Freedman, M, Rogaeva, E, Ferrari, C, Piaceri, I, Bessi, V, Lombardi, G, St-Onge, F, Dore, M-C, Bruffaerts, R, Vandenbulcke, M, Van den Stock, J, Mesulam, MM, Bigio, E, Koros, C, Papatriantafyllou, J, Kroupis, C, Stefanis, L, Shoesmith, C, Roberson, E, Coppola, G, Ramos, EMDS, Geschwind, D, Moore, KM, Nicholas, J, Grossman, M, McMillan, CT, Irwin, DJ, Massimo, L, Van Deerlin, VM, Warren, JD, Fox, NC, Rossor, MN, Mead, S, Bocchetta, M, Boeve, BF, Knopman, DS, Graff-Radford, NR, Forsberg, LK, Rademakers, R, Wszolek, ZK, van Swieten, JC, Jiskoot, LC, Meeter, LH, Dopper, EGP, Papma, JM, Snowden, JS, Saxon, J, Jones, M, Pickering-Brown, S, Le Ber, I, Camuzat, A, Brice, A, Caroppo, P, Ghidoni, R, Pievani, M, Benussi, L, Binetti, G, Dickerson, BC, Lucente, D, Krivensky, S, Graff, C, Oijerstedt, L, Fallstrom, M, Thonberg, H, Ghoshal, N, Morris, JC, Borroni, B, Benussi, A, Padovani, A, Galimberti, D, Scarpini, E, Fumagalli, GG, Mackenzie, IR, Hsiung, G-YR, Sengdy, P, Boxer, AL, Rosen, H, Taylor, JB, Synofzik, M, Wilke, C, Sulzer, P, Hodges, JR, Halliday, G, Kwok, J, Sanchez-Valle, R, Llado, A, Borrego-Ecija, S, Santana, I, Almeida, MR, Tabuas-Pereira, M, Moreno, F, Barandiaran, M, Indakoetxea, B, Levin, J, Danek, A, Rowe, JB, Cope, TE, Otto, M, Anderl-Straub, S, de Mendonca, A, Maruta, C, Masellis, M, Black, SE, Couratier, P, Lautrette, G, Huey, ED, Sorbi, S, Nacmias, B, Laforce, R, Tremblay, M-PL, Vandenberghe, R, Van Damme, P, Rogalski, EJ, Weintraub, S, Gerhard, A, Onyike, CU, Ducharme, S, Papageorgiou, SG, Ng, ASL, Brodtmann, A, Finger, E, Guerreiro, R, Bras, J, Rohrer, JD, Heller, C, Convery, R, Woollacott, IOC, Shafei, R, Graff-Radford, J, Jones, DT, Dheel, CM, Savica, R, Lapid, MI, Baker, M, Fields, JA, Gavrilova, R, Domoto-Reilly, K, Poos, JM, van der Ende, EL, Panman, JL, Kaat, LD, Seelaar, H, Richardson, A, Frisoni, G, Mega, A, Fostinelli, S, Chiang, H-H, Alberici, A, Arighi, A, Fenoglio, C, Heuer, H, Miller, B, Karydas, A, Fong, J, Leitao, MJ, Santiago, B, Duro, D, Ferreira, C, Gabilondo, A, de Arriba, M, Tainta, M, Zulaica, M, Ferreira, CB, Semler, E, Ludolph, A, Landwehrmeyer, B, Volk, AE, Miltenberger, G, Verdelho, A, Afonso, S, Tartaglia, MC, Freedman, M, Rogaeva, E, Ferrari, C, Piaceri, I, Bessi, V, Lombardi, G, St-Onge, F, Dore, M-C, Bruffaerts, R, Vandenbulcke, M, Van den Stock, J, Mesulam, MM, Bigio, E, Koros, C, Papatriantafyllou, J, Kroupis, C, Stefanis, L, Shoesmith, C, Roberson, E, Coppola, G, Ramos, EMDS, and Geschwind, D
Abstract: BACKGROUND: Frontotemporal 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. METHODS: In 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. FINDINGS: Data 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
Published: 2020

15. Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning

Author: Franzmeier, N, Koutsouleris, N, Benzinger, T, Goate, A, Karch, CM, Fagan, AM, McDade, E, Duering, M, Dichgans, M, Levin, J, Gordon, BA, Lim, YY, Masters, CL, Rossor, M, Fox, NC, O'Connor, A, Chhatwal, J, Salloway, S, Danek, A, Hassenstab, J, Schofield, PR, Morris, JC, Bateman, RJ, Ewers, M, Franzmeier, N, Koutsouleris, N, Benzinger, T, Goate, A, Karch, CM, Fagan, AM, McDade, E, Duering, M, Dichgans, M, Levin, J, Gordon, BA, Lim, YY, Masters, CL, Rossor, M, Fox, NC, O'Connor, A, Chhatwal, J, Salloway, S, Danek, A, Hassenstab, J, Schofield, PR, Morris, JC, Bateman, RJ, and Ewers, M
Abstract: INTRODUCTION: Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge. METHODS: We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid-PET and fluorodeoxyglucose positron-emission tomography (FDG-PET) to predict rates of cognitive decline. Prediction models were trained in autosomal-dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross-validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model-based risk enrichment was estimated. RESULTS: A model combining all biomarker modalities and established in ADAD predicted the 4-year rate of decline in global cognition (R2 = 24%) and memory (R2 = 25%) in sporadic AD. Model-based risk-enrichment reduced the sample size required for detecting simulated intervention effects by 50%-75%. DISCUSSION: Our independently validated machine-learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD.
Published: 2020

16. Social cognition impairment in genetic frontotemporal dementia within the GENFI cohort

Author: Russell, LL, Greaves, CV, Bocchetta, M, Nicholas, J, Convery, RS, Moore, K, Cash, DM, van Swieten, J.C., Jiskoot, Lize, Moreno, F, Sanchez-Valle, R, Borroni, B, Laforce, R, Jr, Masellis, M, Tartaglia, MC, Graff, C, Rotondo, E, Galimberti, D, Rowe, JB, Finger, E, Synofzik, M, Vandenberghe, R, Mendonça, A, Tagliavini, F, Santana, I, Ducharme, S, Butler, C, Gerhard, A, Levin, J, Danek, A, Otto, M, Warren, JD, Rohrer, JD, Rossor, MN, Fox, NC, Woollacott, IOC, Shafei, R, Heller, C, Guerreiro, R, Bras, J, Thomas, DL, Mead, S, Meeter, Lieke, Panman, J, Papma, J, Poos, J, van Minkelen, Rick, Al Pijnenburg, Y, Barandiaran, M, Indakoetxea, B, Gabilondo, A, Tainta, M, de Arriba, M, Gorostidi, A, Zulaica, M, Villanua, J, Diaz, Z, Borrego-Ecija, S, Olives, J, Lladó, A, Balasa, M, Antonell, A, Bargallo, N, Premi, E, Cosseddu Mpsych, M, Gazzina, S, Padovani, A, Gasparotti, R, Archetti, S, Black, S, Mitchell, S, Rogaeva, E, Freedman, M, Keren, R, Tang-Wai, D, Öijerstedt, L, Andersson, C, Jelic, V, Thonberg, H, Arighi, A, Fenoglio, C, Scarpini, E, Fumagalli, G, Cope, T, Timberlake, C, Rittman, T, Shoesmith, C, Bartha, R, Rademakers, R, Wilke, C, Karnarth, HO, Bender, B, Bruffaerts, R, Vandamme, P, Vandenbulcke, M, Ferreira, CB, Miltenberger, G, Maruta Mpsych, C, Verdelho, A, Afonso, S, Taipa, R, Caroppo, P, Di Fede, G, Giaccone, G, Muscio, C, Prioni, S, Redaelli, V, Rossi, G, Tiraboschi, P, Duro Npsych, D, Almeida, M R, Castelo-Branco, M, Leitão, MJ, Tabuas-Pereira, M, Santiago, B, Gauthier, S, Rosa-Neto, P, Veldsman, M, Thompson, P, Langheinrich, T, Prix, C, Hoegen, T, Wlasich, E, Loosli, S, Schonecker, S, Semler, E, Anderl-Straub, S, Russell, LL, Greaves, CV, Bocchetta, M, Nicholas, J, Convery, RS, Moore, K, Cash, DM, van Swieten, J.C., Jiskoot, Lize, Moreno, F, Sanchez-Valle, R, Borroni, B, Laforce, R, Jr, Masellis, M, Tartaglia, MC, Graff, C, Rotondo, E, Galimberti, D, Rowe, JB, Finger, E, Synofzik, M, Vandenberghe, R, Mendonça, A, Tagliavini, F, Santana, I, Ducharme, S, Butler, C, Gerhard, A, Levin, J, Danek, A, Otto, M, Warren, JD, Rohrer, JD, Rossor, MN, Fox, NC, Woollacott, IOC, Shafei, R, Heller, C, Guerreiro, R, Bras, J, Thomas, DL, Mead, S, Meeter, Lieke, Panman, J, Papma, J, Poos, J, van Minkelen, Rick, Al Pijnenburg, Y, Barandiaran, M, Indakoetxea, B, Gabilondo, A, Tainta, M, de Arriba, M, Gorostidi, A, Zulaica, M, Villanua, J, Diaz, Z, Borrego-Ecija, S, Olives, J, Lladó, A, Balasa, M, Antonell, A, Bargallo, N, Premi, E, Cosseddu Mpsych, M, Gazzina, S, Padovani, A, Gasparotti, R, Archetti, S, Black, S, Mitchell, S, Rogaeva, E, Freedman, M, Keren, R, Tang-Wai, D, Öijerstedt, L, Andersson, C, Jelic, V, Thonberg, H, Arighi, A, Fenoglio, C, Scarpini, E, Fumagalli, G, Cope, T, Timberlake, C, Rittman, T, Shoesmith, C, Bartha, R, Rademakers, R, Wilke, C, Karnarth, HO, Bender, B, Bruffaerts, R, Vandamme, P, Vandenbulcke, M, Ferreira, CB, Miltenberger, G, Maruta Mpsych, C, Verdelho, A, Afonso, S, Taipa, R, Caroppo, P, Di Fede, G, Giaccone, G, Muscio, C, Prioni, S, Redaelli, V, Rossi, G, Tiraboschi, P, Duro Npsych, D, Almeida, M R, Castelo-Branco, M, Leitão, MJ, Tabuas-Pereira, M, Santiago, B, Gauthier, S, Rosa-Neto, P, Veldsman, M, Thompson, P, Langheinrich, T, Prix, C, Hoegen, T, Wlasich, E, Loosli, S, Schonecker, S, Semler, E, and Anderl-Straub, S
Published: 2020

17. A novel Alzheimer disease locus located near the gene encoding tau protein

Author: Jun, G, Ibrahim-Verbaas, CA, Vronskaya, M, Lambert, J-C, Chung, J, Naj, AC, Kunkle, BW, Wang, L-S, Bis, JC, Bellenguez, C, Harold, D, Lunetta, KL, Destefano, AL, Grenier-Boley, B, Sims, R, Beecham, GW, Smith, AV, Chouraki, V, Hamilton-Nelson, KL, Ikram, MA, Fievet, N, Denning, N, Martin, ER, Schmidt, H, Kamatani, Y, Dunstan, ML, Valladares, O, Laza, AR, Zelenika, D, Ramirez, A, Foroud, TM, Choi, S-H, Boland, A, Becker, T, Kukull, WA, Van Der Lee, SJ, Pasquier, F, Cruchaga, C, Beekly, D, Fitzpatrick, AL, Hanon, O, Gill, M, Barber, R, Gudnason, V, Campion, D, Love, S, Bennett, DA, Amin, N, Berr, C, Tsolaki, Magda, Buxbaum, JD, Lopez, OL, Deramecourt, V, Fox, NC, Cantwell, LB, Tárraga, L, Dufouil, C, Hardy, J, Crane, PK, Eiriksdottir, G, Hannequin, D, Clarke, R, Evans, D, Mosley, TH, Letenneur, L, Brayne, C, Maier, W, De Jager, P, Emilsson, V, Dartigues, J-F, Hampel, H, Kamboh, MI, De Bruijn, RFAG, Tzourio, C, Pastor, P, Larson, EB, Rotter, JI, O'Donovan, MC, Montine, TJ, Nalls, MA, Mead, S, Reiman, EM, Jonsson, PV, Holmes, C, St George-Hyslop, PH, Boada, M, Passmore, P, Wendland, Schmidt, R, Morgan, K, Winslow, AR, Powell, JF, Carasquillo, M, Younkin, SG, Jakobsdóttir, J, Kauwe, JSK, Wilhelmsen, KC, Rujescu, D, Nöthen, MM, Hofman, A, Jones, L, IGAP Consortium, Haines, JL, Psaty, BM, Van Broeckhoven, C, Holmans, P, Launer, LJ, Mayeux, R, Lathrop, M, Goate, AM, Escott-Price, V, Seshadri, S, Pericak-Vance, MA, Amouyel, P, Williams, J, Van Duijn, CM, Schellenberg, GD, Farrer, LA, Chouraki, V [0000-0002-4698-1794], Amouyel, P [0000-0001-9088-234X], and Apollo - University of Cambridge Repository
Subjects: Alzheimer Disease, Apolipoprotein E4, Humans, tau Proteins, Polymorphism, Single Nucleotide, Chromosomes, Human, Pair 17, Genome-Wide Association Study
Abstract: APOE ɛ4, the most significant genetic risk factor for Alzheimer disease (AD), may mask effects of other loci. We re-analyzed genome-wide association study (GWAS) data from the International Genomics of Alzheimer's Project (IGAP) Consortium in APOE ɛ4+ (10 352 cases and 9207 controls) and APOE ɛ4- (7184 cases and 26 968 controls) subgroups as well as in the total sample testing for interaction between a single-nucleotide polymorphism (SNP) and APOE ɛ4 status. Suggestive associations (P
Published: 2019
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18. Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates A beta, tau, immunity and lipid processing (vol 51, pg 414, 2019)

Author: Kunkle, BW, Grenier-Boley, B, Sims, R, Bis, JC, Damotte, V, Naj, AC, Boland, A, Vronskaya, M, van der Lee, SJ, Amlie-Wolf, A, Bellenguez, C, Frizatti, A, Chouraki, V, Martin, ER, Sleegers, K, Badarinarayan, N, Jakobsdottir, J, Hamilton-Nelson, KL, Moreno-Grau, S, Olaso, R, Raybould, R, Chen, YN, Kuzma, AB, Hiltunen, M, Morgan, T, Ahmad, S, Vardarajan, BN, Epelbaum, J, Hoffmann, P, Boada, M, Beecham, GW, Garnier, JG, Harold, D, Fitzpatrick, AL, Valladares, O, Moutet, ML, Gerrish, A, Smith, AV, Qu, LM, Bacq, D, Denning, N, Jian, XQ, Zhao, Y, Del Zompo, M, Fox, NC, Choi, SH, Mateo, I, Hughes, JT, Adams, HH, Malamon, J, Sanchez-Garcia, F, Patel, Y, Brody, JA, Dombroski, BA, Naranjo, MCD, Daniilidou, M, Eiriksdottir, G, Mukherjee, S, Wallon, D, Uphill, J, Aspelund, T, Cantwell, LB, Garzia, F, Galimberti, D, Hofer, E, Butkiewicz, M, Fin, B, Scarpini, E, Sarnowski, C, Bush, WS, Meslage, S, Kornhuber, J, White, CC, Song, Y, Barber, RC, Engelborghs, S, Sordon, S, Voijnovic, D, Adams, PM, Vandenberghe, R, Mayhaus, M, Cupples, LA, Albert, MS, De Deyn, PP, Gu, W, Himali, JJ, Beekly, D, Squassina, A, Hartmann, AM, Orellana, A, Blacker, D, Rodriguez-Rodriguez, E, Lovestone, S, Garcia, ME, Doody, RS, Munoz-Fernadez, C, Sussams, R, Lin, HH, Fairchild, TJ, Benito, YA, Holmes, C, Karamujic-Comic, H, Frosch, MP, Thonberg, H, Maier, W, Roshchupkin, G, Ghetti, B, Giedraitis, V, Kawalia, A, Li, S, Huebinger, RM, Kilander, L, Moebus, S, Hernandez, I, Kamboh, MI, Brundin, R, Turton, J, Yang, Q, Katz, MJ, Concari, L, Lord, J, Beiser, AS, Keene, CD, Helisalmi, S, Kloszewska, I, Kukull, WA, Koivisto, AM, Lynch, A, Tarraga, L, Larson, EB, Haapasalo, A, Lawlor, B, Mosley, TH, Lipton, RB, Solfrizzi, V, Gill, M, Longstreth, WT, Montine, TJ, Frisardi, V, Diez-Fairen, M, Rivadeneira, F, Petersen, RC, Deramecourt, V, Alvarez, I, Salani, F, Ciaramella, A, Boerwinkle, E, Reiman, EM, Fievet, N, Rotter, JI, Reisch, JS, Hanon, O, Cupidi, C, Uitterlinden, AGA, Royall, DR, Dufouil, C, Maletta, RG, de Rojas, I, Sano, M, Brice, A, Cecchetti, R, St George-Hyslop, P, Ritchie, K, Tsolaki, M, Tsuang, DW, Dubois, B, Craig, D, Wu, CK, Soininen, H, Avramidou, D, Albin, RL, Fratiglioni, L, Germanou, A, Apostolova, LG, Keller, L, Koutroumani, M, Arnold, SE, Panza, F, Gkatzima, O, Asthana, S, Hannequin, D, Whitehead, P, Atwood, CS, Caffarra, P, Hampel, H, Quintela, I, Carracedo, A, Lannfelt, L, Rubinsztein, DC, Barnes, LL, Pasquier, F, Frolich, L, Barral, S, McGuinness, B, Beach, TG, Johnston, JA, Becker, JT, Passmore, P, Bigio, EH, Schott, JM, Bird, TD, Warren, JD, Boeve, BF, Lupton, MK, Bowen, JD, Proitsi, P, Boxer, A, Powell, JF, Burke, JR, Kauwe, JSK, Burns, JM, Mancuso, M, Buxbaum, JD, Bonuccelli, U, Cairns, NJ, McQuillin, A, Cao, CH, Livingston, G, Carlson, CS, Bass, NJ, Carlsson, CM, Hardy, J, Carney, RM, Bras, J, Carrasquillo, MM, Guerreiro, R, Allen, M, Chui, HC, Fisher, E, Masullo, C, Crocco, EA, DeCarli, C, Bisceglio, G, Dick, M, Ma, L, Duara, R, Graff-Radford, NR, Evans, DA, Hodges, A, Faber, KM, Scherer, M, Fallon, KB, Riemenschneider, M, Fardo, DW, Heun, R, Farlow, MR, Kolsch, H, Ferris, S, Leber, M, Foroud, TM, Heuser, I, Galasko, DR, Giegling, I, Gearing, M, Hull, M, Geschwind, DH, Gilbert, JR, Morris, J, Green, RC, Mayo, K, Growdon, JH, Feulner, T, Hamilton, RL, Harrell, LE, Drichel, D, Honig, LS, Cushion, TD, Huentelman, MJ, Hollingworth, P, Hulette, CM, Hyman, BT, Marshall, R, Jarvik, GP, Meggy, A, Abner, E, Menzies, GE, Jin, LW, Leonenko, G, Real, LM, Jun, GR, Baldwin, CT, Grozeva, D, Karydas, A, Russo, G, Kaye, JA, Kim, R, Jessen, F, Kowall, NW, Vellas, B, Kramer, JH, Vardy, E, LaFerla, FM, Jockel, KH, Lah, JJ, Dichgans, M, Leverenz, JB, Mann, D, Levey, AI, Pickering-Brown, S, Lieberman, AP, Klopp, N, Lunetta, KL, Wichmann, HE, Lyketsos, CG, Morgan, K, Marson, DC, Brown, K, Martiniuk, F, Medway, C, Mash, DC, Nothen, MM, Masliah, E, Hooper, NM, McCormick, WC, Daniele, A, McCurry, SM, Bayer, A, McDavid, AN, Gallacher, J, Mckee, AC, van den Bussche, H, Mesulam, M, Brayne, C, Miller, BL, Riedel-Heller, S, Miller, CA, Miller, JW, Al-Chalabi, A, Morris, JC, Shaw, CE, Myers, AJ, Wiltfang, J, O'Bryant, S, Olichney, JM, Alvarez, V, Parisi, JE, Singleton, AB, Paulson, HL, Collinge, J, Perry, WR, Mead, S, Peskind, E, Cribbs, DH, Rossor, M, Pierce, A, Ryan, NS, Poon, WW, Nacmias, B, Potter, H, Sorbi, S, Quinn, JF, Sacchinelli, E, Raj, A, Spalletta, G, Raskind, M, Caltagirone, C, Bossu, P, Orfei, MD, Reisberg, B, Clarke, R, Reitz, C, Smith, AD, Ringman, JM, Warden, D, Roberson, ED, Wilcock, G, Rogaeva, E, Bruni, AC, Rosen, HJ, Gallo, M, Rosenberg, RN, Ben-Shlomo, Y, Sager, MA, Mecocci, P, Saykin, AJ, Pastor, P, Cuccaro, ML, Vance, JM, Schneider, JA, Schneider, LS, Slifer, S, Seeley, WW, Smith, AG, Sonnen, JA, Spina, S, Stern, RA, Swerdlow, RH, Tang, M, Tanzi, RE, Trojanowski, JQ, Troncoso, JC, Van Deerlin, VM, Van Eldik, LJ, Vinters, HV, Vonsattel, JP, Weintraub, S, Welsh-Bohmer, KA, Wilhelmsen, KC, Williamson, J, Wingo, TS, Woltjer, RL, Wright, CB, Yu, CE, Yu, L, Saba, Y, Pilotto, A, Bullido, MJ, Peters, O, Crane, PK, Bennett, D, Bosco, P, Coto, E, Boccardi, V, De Jager, PL, Lleo, A, Warner, N, Lopez, OL, Ingelsson, M, Deloukas, P, Cruchaga, C, Graff, C, Gwilliam, R, Fornage, M, Goate, AM, Sanchez-Juan, P, Kehoe, PG, Amin, N, Ertekin-Taner, N, Berr, C, Debette, S, Love, S, Launer, LJ, Younkin, SG, Dartigues, JF, Corcoran, C, Ikram, MA, Dickson, DW, Nicolas, G, Campion, D, Tschanz, J, Schmidt, H, Hakonarson, H, Clarimon, J, Munger, R, Schmidt, R, Farrer, LA, Van Broeckhoven, C, O'Donovan, MC, DeStefano, AL, Jones, L, Haines, JL, Deleuze, JF, Owen, MJ, Gudnason, V, Mayeux, R, Escott-Price, V, Psaty, BM, Ramirez, A, Wang, LS, Ruiz, A, van Duijn, CM, Holmans, PA, Seshadri, S, Williams, J, Amouyel, P, Schellenberg, GD, Lambert, JC, Pericak-Vance, MA, ADGC, EADI, Cohorts Heart Aging Res Genomic, and Genetic Environm Risk AD Defining
Published: 2019

19. Polygenic risk and hazard scores for Alzheimer's disease prediction

Author: Leonenko, G, Sims, R, Shoai, M, Frizzati, A, Bossù, P, Spalletta, G, Fox, Nc, Williams, J, for the GERAD consortium: Hardy, J, Escott-Price, V, Tsolaki, M, Craig, D, Avramidou, D, Germanou, A, Koutroumani, M, Gkatzima, O, Hampel, H, Kehoe, Pg, Love, S, Rubinsztein, Dc, Frölich, L, Mcguinness, B, Johnston, Ja, Passmore, P, Drichel, D, Rossor, M, Schott, Jm, Warren, Jd, Bras, J, Guerreiro, R, Kawalia, A, Hughes, Jt, Patel, Y, Lupton, Mk, Proitsi, P, Powell, J, Kauwe, Jsk, Mancuso, M, Bonuccelli, U, Uphill, J, Fisher, E, Masullo, C, Soininen, H, Bisceglio, G, Ma, L, Dickson, Dw, Graff‐radford, Nr, Carrasquillo, Mm, Younkin, Sg, Sandro Sorbi, S, Daniilidou, M, Hodges, A, Galimberti, D, Scarpini, E, Scherer, M, Peters, O, Ramirez, A, Leber, M, Pichler, S, Mayhaus, M, Gu, W, Riemenschneider, M, Wiltfang, J, Heun, R, Kölsch, H, Kornhuber, J, Heuser, I, Rujescu, D, Hartmann, Am, Giegling, I, Hüll, M, Lovestone, S, Cruchaga, C, Morris, J, Mayo, K, Feulner, T, Sussams, R, Holmes, C, Mann, D, Pickering‐brown, S, Hooper, Nm, Mcquillin, A, Livingston, G, Bass, Nj, Vronskaya, M, Morgan, T, Denning, N, Cushion, Td, Jones, L, Marshall, R, Meggy, A, Menzies, G, Grozeva, D, O'Donovan, Mc, Owen, Mj, Holmans, Pa, Salani, F, Russo, G, Maier, W, Jessen, F, Wichmann, H-E, Morgan, K, Goate, Am, Vellas, B, Vardy, E, Moebus, S, Jöckel, K-H, Dichgans, M, Klopp, N, Turton, J, Lord, J, Brown, K, Medway, C, Nöthen, Mm, Hoffmann, P, Daniele, A, Bayer, A, Gallacher, J, van den Bussche, H, Brayne, C, Riedel‐heller, S, Powell, Jf, Al‐chalabi, A, Shaw, Ce, Kloszewska, I, Pastor, P, Diez‐fairen, M, Lynch, A, Lawlor, B, Gill, M, Coto, E, Alvarez, V, Singleton, Ab, Collinge, J, Mead, S, Ryan, N, Nacmias, B, Ortega‐cubero, S, Rodriguez‐rodriguez, E, Sanchez‐juan, P, Shofany, J, Banaj, N, Ciullo, V, Sacchinelli, E, Robert, Clarke, A David Smith, Donald, Warden, Yoav, Ben‐shlomo, Chiara, Cupidi, Raffaele Giovanni Maletta, Runi, Gallo, M, Harold, D, Cecchetti, R, Mecocci, P, Boccardi, V, Warner, N, Wilcock, G, Deloukas, P, Gwilliam, R, Corcoran, C, Tschanz, J, Munger, R., and Consortium, Gerad
Subjects: Male, Multifactorial Inheritance, Polygenic risk, Alzheimer's disease, hazard score, Polymorphism, Single Nucleotide, Settore MED/26 - NEUROLOGIA, Apolipoproteins E, Alzheimer Disease, Risk Factors, Case-Control Studies, Humans, Female, Genetic Predisposition to Disease, Research Articles, Research Article, Genome-Wide Association Study, Proportional Hazards Models
Abstract: Objective: Genome‐wide association studies (GWAS) have identified over 30 susceptibility loci associated with Alzheimer's disease (AD). Using AD GWAS data from the International Genomics of Alzheimer's Project (IGAP), Polygenic Risk Score (PRS) was successfully applied to predict life time risk of AD development. A recently introduced Polygenic Hazard Score (PHS) is able to quantify individuals with age‐specific genetic risk for AD. The aim of this study was to quantify the age‐specific genetic risk for AD with PRS and compare the results generated by PRS with those from PHS. Methods: Quantification of individual differences in age‐specific genetic risk for AD identified by the PRS, was performed with Cox Regression on 9903 (2626 cases and 7277 controls) individuals from the Genetic and Environmental Risk in Alzheimer's Disease consortium (GERAD). Polygenic Hazard Scores were generated for the same individuals. The age‐specific genetic risk for AD identified by the PRS was compared with that generated by the PHS. This was repeated using varying SNPs P‐value thresholds for disease association. Results: Polygenic Risk Score significantly predicted the risk associated with age at AD onset when SNPs were preselected for association to AD at P ≤ 0.001. The strongest effect (B = 0.28, SE = 0.04, P = 2.5 × 10−12) was observed for PRS based upon genome‐wide significant SNPs (P ≤ 5 × 10−8). The strength of association was weaker with less stringent SNP selection thresholds. Interpretation: Both PRS and PHS can be used to predict an age‐specific risk for developing AD. The PHS approach uses SNP effect sizes derived with the Cox Proportional Hazard Regression model. When SNPs were selected based upon AD GWAS case/control P ≤ 10−3, we found no advantage of using SNP effects sizes calculated with the Cox Proportional Hazard Regression model in our study. When SNPs are selected for association with AD risk at P > 10−3, the age‐specific risk prediction results are not significant for either PRS or PHS. However PHS could be more advantageous than PRS of age specific AD risk predictions when SNPs are prioritized for association with AD age at onset (i.e., powerful Cox Regression GWAS study).
Published: 2019

20. MSH3 modifies somatic instability and disease severity in Huntington's and myotonic dystrophy type 1

Author: Flower, M, Lomeikaite, V, Ciosi, M, Cumming, S, Morales, F, Lo, K, Hensman Moss, D, Jones, L, Holmans, P, Monckton, DG, Tabrizi, SJ, Kraus, P, Hoffman, R, Tobin, A, Borowsky, B, Keenan, S, Whitlock, KB, Queller, S, Campbell, C, Wang, C, Langbehn, D, Axelson, E, Johnson, H, Acharya, T, Cash, DM, Frost, C, Jones, R, Jurgens, C, Hart, EPT, Van Der Grond, J, Witjes- Ane, MNN, Roos, RAC, Dumas, EM, Van Den Bogaard, SJA, Stopford, C, Craufurd, D, Callaghan, J, Arran, N, Rosas, DD, Lee, S, Monaco, W, O'Regan, A, Milchman, C, Frajman, E, Labuschagne, I, Stout, J, Campbell, M, Andrews, SC, Bechtel, N, Reilmann, R, Bohlen, S, Kennard, C, Berna, C, Hicks, S, Durr, A, Pourchot, C, Bardinet, E, Nigaud, K, Valabrègue, R, Lehericy, S, Marelli, C, Jauffret, C, Justo, D, Leavitt, B, Decolongon, J, Sturrock, A, Coleman, A, Dar Santos, R, Patel, A, Gibbard, C, Whitehead, D, Wild, E, Owen, G, Crawford, H, Malone, I, Lahiri, N, Fox, NC, Hobbs, NZ, Scahill, RI, Ordidge, R, Pepple, T, Read, J, Say, MJ, Landwehrmeyer, B, Daidj, F, Bassez, G, Lignier, B, Couppey, F, Delmas, S, Deux, JF, Hankiewicz, K, Dogan, C, Minier, L, Chevalier, P, Hamadouche, A, Catt, M, Van Hees, V, Catt, S, Schwalber, A, Dittrich, J, Flower, M, Lomeikaite, V, Ciosi, M, Cumming, S, Morales, F, Lo, K, Hensman Moss, D, Jones, L, Holmans, P, Monckton, DG, Tabrizi, SJ, Kraus, P, Hoffman, R, Tobin, A, Borowsky, B, Keenan, S, Whitlock, KB, Queller, S, Campbell, C, Wang, C, Langbehn, D, Axelson, E, Johnson, H, Acharya, T, Cash, DM, Frost, C, Jones, R, Jurgens, C, Hart, EPT, Van Der Grond, J, Witjes- Ane, MNN, Roos, RAC, Dumas, EM, Van Den Bogaard, SJA, Stopford, C, Craufurd, D, Callaghan, J, Arran, N, Rosas, DD, Lee, S, Monaco, W, O'Regan, A, Milchman, C, Frajman, E, Labuschagne, I, Stout, J, Campbell, M, Andrews, SC, Bechtel, N, Reilmann, R, Bohlen, S, Kennard, C, Berna, C, Hicks, S, Durr, A, Pourchot, C, Bardinet, E, Nigaud, K, Valabrègue, R, Lehericy, S, Marelli, C, Jauffret, C, Justo, D, Leavitt, B, Decolongon, J, Sturrock, A, Coleman, A, Dar Santos, R, Patel, A, Gibbard, C, Whitehead, D, Wild, E, Owen, G, Crawford, H, Malone, I, Lahiri, N, Fox, NC, Hobbs, NZ, Scahill, RI, Ordidge, R, Pepple, T, Read, J, Say, MJ, Landwehrmeyer, B, Daidj, F, Bassez, G, Lignier, B, Couppey, F, Delmas, S, Deux, JF, Hankiewicz, K, Dogan, C, Minier, L, Chevalier, P, Hamadouche, A, Catt, M, Van Hees, V, Catt, S, Schwalber, A, and Dittrich, J
Abstract: The mismatch repair gene MSH3 has been implicated as a genetic modifier of the CAG·CTG repeat expansion disorders Huntington's disease and myotonic dystrophy type 1. A recent Huntington's disease genome-wide association study found rs557874766, an imputed single nucleotide polymorphism located within a polymorphic 9 bp tandem repeat in MSH3/DHFR, as the variant most significantly associated with progression in Huntington's disease. Using Illumina sequencing in Huntington's disease and myotonic dystrophy type 1 subjects, we show that rs557874766 is an alignment artefact, the minor allele for which corresponds to a three-repeat allele in MSH3 exon 1 that is associated with a reduced rate of somatic CAG·CTG expansion (P = 0.004) and delayed disease onset (P = 0.003) in both Huntington's disease and myotonic dystrophy type 1, and slower progression (P = 3.86 × 10-7) in Huntington's disease. RNA-Seq of whole blood in the Huntington's disease subjects found that repeat variants are associated with MSH3 and DHFR expression. A transcriptome-wide association study in the Huntington's disease cohort found increased MSH3 and DHFR expression are associated with disease progression. These results suggest that variation in the MSH3 exon 1 repeat region influences somatic expansion and disease phenotype in Huntington's disease and myotonic dystrophy type 1, and suggests a common DNA repair mechanism operates in both repeat expansion diseases.
Published: 2019

21. Meta-analysis of genetic association with diagnosed Alzheimer's disease identifies novel risk loci and implicates Abeta, Tau, immunity and lipid processing

Author: Kunkle, BW, Grenier-Boley, B, Sims, R, Bis, JC, Naj, AC, Boland, A, Vronskaya, M, van der Lee, SJ, Amlie-Wolf, A, Bellenguez, C, Frizatti, A, Chouraki, V, Martin, ER, Sleegers, K, Badarinarayan, N, Jakobsdottir, J, Hamilton-Nelson, KL, Aloso, R, Raybould, R, Chen, Y, Kuzma, AB, Hiltunen, M, Morgan, T, Ahmad, S, Vardarajan, BN, Epelbaum, J, Hoffmann, P, Boada, M, Beecham, GW, Garnier, JG, Harold, D, Fitzpatrick, AL, Valladares, O, Moutet, ML, Gerrish, A, Smith, AV, Qu, L, Bacq, D, Denning, N, Jian, X, Zhao, Y, Zompo, MD, Fox, NC, Grove, ML, Choi, SH, Mateo, I, Hughes, JT, Adams, HH, Malamon, J, Garcia, FS, Patel, Y, Brody, JA, Dombroski, B, Naranjo, MCD, Daniilidou, M, Eiriksdottir, G, Mukherjee, S, Wallon, D, Uphill, J, Aspelund, T, Cantwell, LB, Garzia, F, Galimberti, D, Hofer, E, Butkiewics, M, Fin, B, Scarpini, E, Sarnowski, C, Bush, W, Meslage, S, Kornhuber, J, White, CC, Song, Y, Barber, RC, Engelborghs, S, Pichler, S, Voijnovic, D, Adams, PM, Vandenberghe, R, Mayhaus, M, Cupples, LA, Albert, MS, De Deyn, PP, Gu, W, Himali, JJ, Beekly, D, Squassina, A, Hartmann, AM, Orellana, A, Blacker, D, Rodriguez-Rodriguez, E, Lovestone, S, Garcia, ME, Doody, RS, Fernadez, CM, Sussams, R, Lin, H, Fairchild, TJ, Benito, YA, Holmes, C, Comic, H, Frosch, MP, Thonberg, H, Maier, W, Roschupkin, G, Ghetti, B, Giedraitis, V, Kawalia, A, Li, S, Huebinger, RM, Kilander, L, Moebus, S, Hernández, I, Kamboh, MI, Brundin, R, Turton, J, Yang, Q, Katz, MJ, Concari, L, Lord, J, Beiser, AS, Keene, CD, Helisalmi, S, Kloszewska, I, Kukull, WA, Koivisto, AM, Lynch, A, Tarraga, L, Larson, EB, Haapasalo, A, Lawlor, B, Mosley, TH, Lipton, RB, Solfrizzi, V, Gill, M, Longstreth Jr, WT, Montine, TJ, Frisardi, V, Ortega-Cubero, S, Rivadeneira, F, Petersen, RC, Deramecourt, V, Ciaramella, A, Boerwinkle, E, Reiman, EM, Fievet, N, Caltagirone, C, Rotter, JI, Reisch, JS, Hanon, O, Cupidi, C, Uitterlinden, AG, Royall, DR, Dufouil, C, Maletta, RG, Moreno-Grau, S, Sano, M, Brice, A, Cecchetti, R, St George-Hyslop, P, Ritchie, K, Tsolaki, M, Tsuang, DW, Dubois, B, Craig, D, Wu, CK, Soininen, H, Avramidou, D, Albin, RL, Fratiglioni, L, Germanou, A, Apostolova, LG, Keller, L, Koutroumani, M, Arnold, SE, Panza, F, Gkatzima, O, Asthana, S, Hannequin, D, Whitehead, P, Atwood, CS, Caffarra, P, Hampel, H, Baldwin, CT, Lannfelt, L, Rubinsztein, DC, Barnes, LL, Pasquier, F, Frölich, L, Barral, S, McGuinness, B, Beach, TG, Johnston, JI, Becker, JT, Passmore, P, Bigio, EH, Schott, JM, Bird, TD, Warren, JD, Boeve, BF, Lupton, MK, Bowen, JD, Proitsi, P, Boxer, A, Powell, JF, Burke, JR, Kauwe, JK, Burns, JM, Mancuso, M, Buxbaum, JD, Bonuccelli, U, Cairns, NJ, McQuillin, A, Cao, C, Livingston, G, Carlson, CS, Bass, NJ, Carlsson, CM, Hardy, J, Carney, RM, Bras, J, Carrasquillo, MM, Guerreiro, R, Allen, M, Chui, HC, Fisher, E, Cribbs, DH, Masullo, C, Crocco, EA, DeCarli, C, Bisceglio, G, Dick, M, Ma, L, Duara, R, Graff-Radford, NR, Evans, DA, Hodges, A, Faber, KM, Scherer, M, Fallon, KB, Riemenschneider, M, Fardo, DW, Heun, R, Farlow, MR, Ferris, S, Leber, M, Foroud, TM, Heuser, I, Galasko, DR, Giegling, I, Gearing, M, Hüll, M, Geschwind, DH, Gilbert, JR, Morris, J, Green, RC, Mayo, K, Growdon, JH, Feulner, T, Hamilton, RL, Harrell, LE, Drichel, D, Honig, LS, Cushion, TD, Huentelman, MJ, Hollingworth, P, Hulette, CM, Hyman, BT, Marshall, R, Jarvik, GP, Meggy, A, Abner, E, Menzies, G, Jin, LW, Leonenko, G, Jun, G, Grozeva, D, Karydas, A, Russo, G, Kaye, JA, Kim, R, Jessen, F, Kowall, NW, Vellas, B, Kramer, JH, Vardy, E, LaFerla, FM, Jöckel, KH, Lah, JJ, Dichgans, M, Leverenz, JB, Mann, D, Levey, AI, Pickering-Brown, S, Lieberman, AP, Klopp, N, Lunetta, KL, Wichmann, HE, Lyketsos, CG, Morgan, K, Marson, DC, Brown, K, Martiniuk, F, Medway, C, Mash, DC, Nöthen, MM, Masliah, E, Hooper, NM, McCormick, WC, Daniele, A, McCurry, SM, Bayer, A, McDavid, AN, Gallacher, J, McKee, AC, van den Bussche, H, Mesulam, M, Brayne, C, Miller, BL, Riedel-Heller, S, Miller, CA, Miller, JW, Al-Chalabi, A, Morris, JC, Shaw, CE, Myers, AJ, Wiltfang, J, O’Bryant, S, Coto, E, Olichney, JM, Alvarez, V, Parisi, JE, Singleton, AB, Paulson, HL, Collinge, J, Perry, W, Mead, S, Peskind, E, Rosser, M, Pierce, A, Ryan, N, Poon, WW, Nacmias, B, Potter, H, Sorbi, S, Quinn, JF, Sacchinelli, E, Raj, A, Spalletta, G, Raskind, M, Bossù, P, Reisberg, B, Clarke, R, Reitz, C, Smith, AD, Ringman, JM, Warden, D, Roberson, ED, Wilcock, G, Rogaeva, E, Bruni, AC, Rosen, HJ, Gallo, M, Rosenberg, RN, Ben-Shlomo, Y, Sager, MA, Mecocci, P, Saykin, AJ, Pastor, P, Cuccaro, ML, Vance, JM, Schneider, JA, Schneider, LS, Seeley, WW, Smith, AG, Sonnen, JA, Spina, S, Stern, RA, Swerdlow, RH, Tanzi, RE, Trojanowski, JQ, Troncoso, JC, Van Deerlin, VM, Van Eldik, LJ, Vinters, HV, Vonsattel, JP, Weintraub, S, Welsh-Bohmer, KA, Wilhelmsen, KC, Williamson, J, Wingo, TS, Woltjer, RL, Wright, CB, Yu, CE, Yu, L, Crane, PK, Bennett, DA, Boccardi, V, De Jager, PL, Warner, N, Lopez, OL, McDonough, S, Ingelsson, M, Deloukas, P, Cruchaga, C, Graff, C, Gwilliam, R, Fornage, M, Goate, AM, Sanchez-Juan, P, Kehoe, PG, Amin, N, Ertekin-Taner, N, Berr, C, Debette, S, Love, S, Launer, LJ, Younkin, SG, Dartigues, JF, Corcoran, C, Ikram, MA, Dickson, DW, Campion, D, Tschanz, J, Schmidt, H, Hakonarson, H, Munger, R, Schmidt, R, Farrer, LA, Van Broeckhoven, C, O’Donovan, MC, DeStefano, AL, Jones, L, Haines, JL, Deleuze, JF, Owen, MJ, Gudnason, V, Mayeux, R, Escott-Price, V, Psaty, BM, Ruiz, A, Ramirez, A, Wang, LS, van Duijn, CM, Holmans, PA, Seshadri, S, Williams, J, Amouyel, P, Schellenberg, GD, Lambert, JC, Pericak-Vance, MA, Bis, JC [0000-0002-3409-1110], Garnier, JG [0000-0003-4991-763X], Smith, AV [0000-0001-9088-234X], Denning, N [0000-0001-8467-7382], Vandenberghe, R [0000-0001-6237-2502], Himali, JJ [0000-0003-1391-9481], Rodriguez-Rodriguez, E [0000-0001-7742-677X], Frisardi, V [0000-0003-0764-7387], Ortega-Cubero, S [0000-0003-0520-9439], Hanon, O [0000-0002-4697-122X], Brice, A [0000-0002-0941-3990], Albin, RL [0000-0002-0629-608X], Buxbaum, JD [0000-0001-8898-8313], Bass, NJ [0000-0002-4481-778X], Fisher, E [0000-0003-2850-9936], Bayer, A [0000-0002-7514-248X], Gallacher, J [0000-0002-2394-5299], Brayne, C [0000-0001-5307-663X], Riedel-Heller, S [0000-0003-4321-6090], Al-Chalabi, A [0000-0002-4924-7712], and Apollo - University of Cambridge Repository
Subjects: Aging, 4202 Epidemiology, Genome-wide association study, Disease, Neurodegenerative, Biology, 3101 Biochemistry and Cell Biology, Alzheimer's Disease, 3105 Genetics, 03 medical and health sciences, 0302 clinical medicine, Clinical Research, Acquired Cognitive Impairment, Genetics, medicine, 2.1 Biological and endogenous factors, Dementia, Gene, 030304 developmental biology, Genetic association, 2 Aetiology, 0303 health sciences, Prevention, Human Genome, 42 Health Sciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Lipid metabolism, medicine.disease, Brain Disorders, 3. Good health, Meta-analysis, Neurological, Alzheimer's disease, 030217 neurology & neurosurgery, 31 Biological Sciences
Abstract: IntroductionLate-onset Alzheimer’s disease (LOAD, onset age > 60 years) is the most prevalent dementia in the elderly1, and risk is partially driven by genetics2. Many of the loci responsible for this genetic risk were identified by genome-wide association studies (GWAS)3–8. To identify additional LOAD risk loci, the we performed the largest GWAS to date (89,769 individuals), analyzing both common and rare variants. We confirm 20 previous LOAD risk loci and identify four new genome-wide loci (IQCK, ACE, ADAM10, and ADAMTS1). Pathway analysis of these data implicates the immune system and lipid metabolism, and for the first time tau binding proteins and APP metabolism. These findings show that genetic variants affecting APP and Aβ processing are not only associated with early-onset autosomal dominant AD but also with LOAD. Analysis of AD risk genes and pathways show enrichment for rare variants (P = 1.32 × 10−7) indicating that additional rare variants remain to be identified.
Published: 2018
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22. Protein network analysis reveals selectively vulnerable regions and biological processes in FTD

Author: Bonham, Lw1, Steele, Nzr1, Karch, Cm1, Manzoni, C1, Geier, Eg1, Wen, N1, Ofori-Kuragu, A1, Momeni, P1, Hardy, J1, Miller, Za1, Hess, Cp1, Lewis, P1, Miller, Bl1, Seeley, Ww1, Baranzini, Se1, Desikan, Rs1, Ferrari, R1, Yokoyama, Js1, ( Ferrari R, International FTD-Genomics Consortium, Hernandez, Dg, Nalls, Ma, Rohrer, Jd, Ramasamy, A, Kwok, Jbj, Dobson-Stone, C, Schofield, Pr, Halliday, Gm, Hodges, Jr, Piguet, O, Bartley, L, Thompson, E, Haan, E, Hernández, I, Ruiz, A, Boada, M, Borroni, B, Padovani, A, Cruchaga, C, Cairns, Nj, Benussi, L, Binetti, G, Ghidoni, R, Forloni, G, Albani, D, Galimberti, D, Fenoglio, C, Serpente, M, Scarpini, E, Clarimón, J, Lleó, A, Blesa, R, Landqvist Waldö, M, Nilsson, C, Mackenzie, Ira, Hsiung, Gyr, Mann, Dma, Grafman, J, Morris, Cm, Attems, J, Griffiths, Td, Mckeith, Ig, Thomas, Aj, Pietrini, P, Huey, Ed, Wassermann, Em, Baborie, A, Jaros, E, Tierney, Mc, Pastor, P, Razquin, C, Ortega-Cubero, S, Alonso, E, Perneczky, R, Diehl-Schmid, J, Alexopoulos, P, Kurz, A, Rainero, I, Rubino, E, Pinessi, L, Rogaeva, E, St George-Hyslop, P, Rossi, G, Tagliavini, F, Giaccone, G, Rowe, Jb, Schlachetzki, Jcm, Uphill, J, Collinge, J, Mead, S, Danek, A, Van Deerlin VM, Grossman, M, Trojanowski, Jq, van der Zee, J, Cruts, M, Van Broeckhoven, C, Cappa, Sf, Leber, I, Hannequin, D, Golfier, V, Vercelletto, M, Brice, A, Nacmias, B, Sorbi, S, Bagnoli, S, Piaceri, I, Nielsen, Je, Hjermind, Le, Riemenschneider, M, Mayhaus, M, Ibach, B, Gasparoni, G, Pichler, S, Gu, W, Rossor, Mn, Fox, Nc, Warren, Jd, Spillantini, Mg, Morris, Hr, Rizzu, P, Heutink, P, Snowden, Js, Rollinson, S, Richardson, A, Gerhard, A, Bruni, Ac, Maletta, R, Frangipane, F, Cupidi, C, Bernardi, L, Anfossi, M, Gallo, M, Conidi, Me, Smirne, N, Rademakers, R, Baker, M, Dickson, Dw, Graff-Radford, Nr, Petersen, Rc, Knopman, D, Josephs, Ka, Boeve, Bf, Parisi, Je, Seeley, Ww, Miller, Bl, Karydas, Am, Rosen, H, van Swieten JC, Dopper, Eg, Seelaar, H, Pijnenburg, Yal, Scheltens, P, Logroscino, G, Capozzo, R, Novelli, V, Puca, Aa, Franceschi, M, Postiglione, A, Milan, G, Sorrentino, P, Kristiansen, M, Chiang, Hh, Graff, C, Pasquier, F, Rollin, A, Deramecourt, V, Lebouvier, T, Kapogiannis, D, Ferrucci, L, Pickering-Brown, S, Singleton, Ab, Hardy, J, and Momeni, P.
Subjects: 0301 basic medicine, Cell type, Disease, Frontotemporal lobar degeneration, Biology, medicine.disease, Article, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Interaction network, Genetic variation, medicine, Neurology (clinical), Gene, Neuroscience, 030217 neurology & neurosurgery, Genetics (clinical), Frontotemporal dementia, Genetic association
Abstract: ObjectiveThe neuroanatomical profile of behavioral variant frontotemporal dementia (bvFTD) suggests a common biological etiology of disease despite disparate pathologic causes; we investigated the genetic underpinnings of this selective regional vulnerability to identify new risk factors for bvFTD.MethodsWe used recently developed analytical techniques designed to address the limitations of genome-wide association studies to generate a protein interaction network of 63 bvFTD risk genes. We characterized this network using gene expression data from healthy and diseased human brain tissue, evaluating regional network expression patterns across the lifespan as well as the cell types and biological processes most affected in bvFTD.ResultsWe found that bvFTD network genes show enriched expression across the human lifespan in vulnerable neuronal populations, are implicated in cell signaling, cell cycle, immune function, and development, and are differentially expressed in pathologically confirmed frontotemporal lobar degeneration cases. Five of the genes highlighted by our differential expression analyses, BAIAP2, ERBB3, POU2F2, SMARCA2, and CDC37, appear to be novel bvFTD risk loci.ConclusionsOur findings suggest that the cumulative burden of common genetic variation in an interacting protein network expressed in specific brain regions across the lifespan may influence susceptibility to bvFTD.
Published: 2018

23. P68 Divergent associations between life course cognitive trajectories and brain pathologies: findings from the 1946 british birth cohort

Author: James, SN, primary, Lane, CA, additional, Parker, TD, additional, Lu, K, additional, Keshavan, A, additional, Buchanan, SM, additional, Keuss, SE, additional, Cash, DM, additional, Malone, IB, additional, Nicholas, JM, additional, Murray-Smith, H, additional, Wong, A, additional, Fox, NC, additional, Schott, JM, additional, and Richards, M, additional
Published: 2019
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24. Genetic architecture of sporadic frontotemporal dementia and overlap with Alzheimer’s and Parkinson’s diseases

Author: Ferrari R, Wang Y, Vandrovcova J, Guelfi S, Witeolar A, Karch CM, Schork AJ, Fan CC, Brewer JB, International FTD-Genomics Consortium (IFGC), International Parkinson's Disease Genomics Consortium (IPDGC), International Genomics of Alzheimer's Project (IGAP), Momeni P, Schellenberg GD, Dillon WP, Sugrue LP, Hess CP, Yokoyama JS, Bonham LW, Rabinovici GD, Miller BL, Andreassen OA, Dale AM, Hardy J, Desikan RS, Collaborators: Ferrari R, Hernandez DG, Nalls MA, Rohrer JD, Ramasamy A, Kwok JBJ, Dobson-Stone C, Schofield PR, Halliday GM, Hodges JR, Piguet O, Bartley L, Thompson E, Haan E, Hernández I, Ruiz A, Boada M, Borroni B, Padovani A, Cruchaga C, Cairns NJ, Benussi L, Binetti G, Ghidoni R, Forloni G, Albani D, Galimberti D, Fenoglio C, Serpente M, Scarpini E, Clarimón J, Lleó A, Blesa R, Landqvist Waldö M, Nilsson C, Mackenzie IRA, Hsiung GYR, Mann DMA, Grafman J, Morris CM, Attems J, Griffiths TD, McKeith IG, Thomas AJ, Pietrini P, Huey ED, Wassermann EM, Baborie A, Jaros E, Tierney MC, Pastor P, Razquin C, Ortega-Cubero S, Alonso E, Perneczky R, Diehl-Schmid J, Alexopoulos P, Kurz A, Rainero I, Rubino E, Pinessi L, Rogaeva E, St George-Hyslop P, Rossi G, Tagliavini F, Giaccone G, Rowe JB, Schlachetzki JCM, Uphill J, Collinge J, Mead S, Danek A, Van Deerlin VM, Grossman M, Trojanowski JQ, van der Zee J, Cruts M, Van Broeckhoven C, Cappa SF, Leber I, Hannequin D, Golfier V, Vercelletto M, Brice A, Nacmias B, Sorbi S, Bagnoli S, Piaceri I, Nielsen JE, Hjermind LE, Riemenschneider M, Mayhaus M, Ibach B, Gasparoni G, Pichler S, Gu W, Rossor MN, Fox NC, Warren JD, Spillantini MG, Morris HR, Rizzu P, Heutink P, Snowden JS, Rollinson S, Richardson A, Gerhard A, Bruni AC, Maletta R, Frangipane F, Cupidi C, Bernardi L, Anfossi M, Gallo M, Conidi ME, Smirne N, Rademakers R, Baker M, Dickson DW, Graff-Radford NR, Petersen RC, Knopman D, Josephs KA, Boeve BF, Parisi JE, Seeley WW, Karydas AM, Rosen H, van Swieten JC, Dopper EG, Seelaar H, Pijnenburg YAL, Scheltens P, Logroscino G, Capozzo R, Novelli V, Puca AA, Franceschi M, Postiglione A, Milan G, Sorrentino P, Kristiansen M, Chiang HH, Graff C, Pasquier F, Rollin A, Deramecourt V, Lebouvier T, Kapogiannis D, Ferrucci L, Pickering-Brown S, Singleton AB, Momeni P., Neurology, VU University medical center, Human genetics, Amsterdam Neuroscience - Neurodegeneration, CCA - Imaging and biomarkers, Divisions, Van Broeckhoven, Christine, Rademakers, Rosa, International FTD-Genomics Consortium (IFGC), International Parkinson's Disease Genomics Consortium (IPDGC), International Genomics of Alzheimer's Project (IGAP), Ferrari, R, Wang, Y, Vandrovcova, J, Guelfi, S, Witeolar, A, Karch, Cm, Schork, Aj, Fan, Cc, Brewer, Jb, International FTD-Genomics Consortium, (IFGC), International Parkinson's Disease Genomics Consortium, (IPDGC), International Genomics of Alzheimer's Project, (IGAP), Momeni, P, Schellenberg, Gd, Dillon, Wp, Sugrue, Lp, Hess, Cp, Yokoyama, J, Bonham, Lw, Rabinovici, Gd, Miller, Bl, Andreassen, Oa, Dale, Am, Hardy, J, Desikan, R, Collaborators: Ferrari, R, Hernandez, Dg, Nalls, Ma, Rohrer, Jd, Ramasamy, A, Kwok, Jbj, Dobson-Stone, C, Schofield, Pr, Halliday, Gm, Hodges, Jr, Piguet, O, Bartley, L, Thompson, E, Haan, E, Hernández, I, Ruiz, A, Boada, M, Borroni, B, Padovani, A, Cruchaga, C, Cairns, Nj, Benussi, L, Binetti, G, Ghidoni, R, Forloni, G, Albani, D, Galimberti, D, Fenoglio, C, Serpente, M, Scarpini, E, Clarimón, J, Lleó, A, Blesa, R, Landqvist Waldö, M, Nilsson, C, Mackenzie, Ira, Hsiung, Gyr, Mann, Dma, Grafman, J, Morris, Cm, Attems, J, Griffiths, Td, Mckeith, Ig, Thomas, Aj, Pietrini, P, Huey, Ed, Wassermann, Em, Baborie, A, Jaros, E, Tierney, Mc, Pastor, P, Razquin, C, Ortega-Cubero, S, Alonso, E, Perneczky, R, Diehl-Schmid, J, Alexopoulos, P, Kurz, A, Rainero, I, Rubino, E, Pinessi, L, Rogaeva, E, St George-Hyslop, P, Rossi, G, Tagliavini, F, Giaccone, G, Rowe, Jb, Schlachetzki, Jcm, Uphill, J, Collinge, J, Mead, S, Danek, A, Van Deerlin, Vm, Grossman, M, Trojanowski, Jq, van der Zee, J, Cruts, M, Van Broeckhoven, C, Cappa, Sf, Leber, I, Hannequin, D, Golfier, V, Vercelletto, M, Brice, A, Nacmias, B, Sorbi, S, Bagnoli, S, Piaceri, I, Nielsen, Je, Hjermind, Le, Riemenschneider, M, Mayhaus, M, Ibach, B, Gasparoni, G, Pichler, S, Gu, W, Rossor, Mn, Fox, Nc, Warren, Jd, Spillantini, Mg, Morris, Hr, Rizzu, P, Heutink, P, Snowden, J, Rollinson, S, Richardson, A, Gerhard, A, Bruni, Ac, Maletta, R, Frangipane, F, Cupidi, C, Bernardi, L, Anfossi, M, Gallo, M, Conidi, Me, Smirne, N, Rademakers, R, Baker, M, Dickson, Dw, Graff-Radford, Nr, Petersen, Rc, Knopman, D, Josephs, Ka, Boeve, Bf, Parisi, Je, Seeley, Ww, Karydas, Am, Rosen, H, van Swieten, Jc, Dopper, Eg, Seelaar, H, Pijnenburg, Yal, Scheltens, P, Logroscino, G, Capozzo, R, Novelli, V, Puca, Aa, Franceschi, M, Postiglione, A, Milan, G, Sorrentino, P, Kristiansen, M, Chiang, Hh, Graff, C, Pasquier, F, Rollin, A, Deramecourt, V, Lebouvier, T, Kapogiannis, D, Ferrucci, L, Pickering-Brown, S, Singleton, Ab, and Momeni, P.
Subjects: 0301 basic medicine, Genotype, Single-nucleotide polymorphism, Genome-wide association study, Polymorphism, Single Nucleotide, Article, 03 medical and health sciences, 0302 clinical medicine, Alzheimer Disease, mental disorders, Genetic Pleiotropy, Genetic predisposition, Medicine, Humans, Genetic Predisposition to Disease, Allele, Polymorphism, Biology, Alleles, Genetic association, Genetics, business.industry, Frontotemporal Dementia, Genome-Wide Association Study, Parkinson Disease, Surgery, Neurology (clinical), Psychiatry and Mental Health, Single Nucleotide, medicine.disease, Genetic architecture, nervous system diseases, 030104 developmental biology, Human medicine, business, Neuroscience, 030217 neurology & neurosurgery, Frontotemporal dementia
Abstract: Background Clinical, pathological and genetic overlap between sporadic frontotemporal dementia (FTD), Alzheimer9s disease (AD) and Parkinson9s disease (PD) has been suggested; however, the relationship between these disorders is still not well understood. Here we evaluated genetic overlap between FTD, AD and PD to assess shared pathobiology and identify novel genetic variants associated with increased risk for FTD. Methods Summary statistics were obtained from the International FTD Genomics Consortium, International PD Genetics Consortium and International Genomics of AD Project (n>75 000 cases and controls). We used conjunction false discovery rate (FDR) to evaluate genetic pleiotropy and conditional FDR to identify novel FTD-associated SNPs. Relevant variants were further evaluated for expression quantitative loci. Results We observed SNPs within the HLA , MAPT and APOE regions jointly contributing to increased risk for FTD and AD or PD. By conditioning on polymorphisms associated with PD and AD, we found 11 loci associated with increased risk for FTD. Meta-analysis across two independent FTD cohorts revealed a genome-wide signal within the APOE region (rs6857, 3′-UTR= PVRL2 , p=2.21×10 –12 ), and a suggestive signal for rs1358071 within the MAPT region (intronic= CRHR1 , p=4.91×10 −7 ) with the effect allele tagging the H1 haplotype. Pleiotropic SNPs at the HLA and MAPT loci associated with expression changes in cis -genes supporting involvement of intracellular vesicular trafficking, immune response and endo/lysosomal processes. Conclusions Our findings demonstrate genetic pleiotropy in these neurodegenerative diseases and indicate that sporadic FTD is a polygenic disorder where multiple pleiotropic loci with small effects contribute to increased disease risk.
Published: 2016

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

Author: Gordon, BA, Blazey, TM, Su, Y, Hari-Raj, A, Dincer, A, Flores, S, Christensen, J, McDade, E, Wang, G, Xiong, C, Cairns, NJ, Hassenstab, J, Marcus, DS, Fagan, AM, Jack, CR, Hornbeck, RC, Paumier, KL, Ances, BM, Berman, SB, Brickman, AM, Cash, DM, Chhatwal, JP, Correia, S, Förster, S, Fox, NC, Graff-Radford, NR, la Fougère, C, Levin, J, Masters, CL, Rossor, MN, Salloway, S, Saykin, AJ, Schofield, PR, Thompson, PM, Weiner, MM, Holtzman, DM, Raichle, ME, Morris, JC, Bateman, RJ, Benzinger, TLS, Gordon, BA, Blazey, TM, Su, Y, Hari-Raj, A, Dincer, A, Flores, S, Christensen, J, McDade, E, Wang, G, Xiong, C, Cairns, NJ, Hassenstab, J, Marcus, DS, Fagan, AM, Jack, CR, Hornbeck, RC, Paumier, KL, Ances, BM, Berman, SB, Brickman, AM, Cash, DM, Chhatwal, JP, Correia, S, Förster, S, Fox, NC, Graff-Radford, NR, la Fougère, C, Levin, J, Masters, CL, Rossor, MN, Salloway, S, Saykin, AJ, Schofield, PR, Thompson, PM, Weiner, MM, Holtzman, DM, Raichle, ME, Morris, JC, Bateman, RJ, and Benzinger, TLS
Abstract: Background: Models 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. Methods: Between 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 th
Published: 2018

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

Author: Lee, S, Zimmerman, ME, Narkhede, A, Nasrabady, SE, Tosto, G, Meier, IB, Benzinger, TLS, Marcus, DS, Fagan, AM, Fox, NC, Cairns, NJ, Holtzman, DM, Buckles, V, Ghetti, B, McDade, E, Martins, RN, Saykin, AJ, Masters, CL, Ringman, JM, Fӧrster, S, Schofield, PR, Sperling, RA, Johnson, KA, Chhatwal, JP, Salloway, S, Correia, S, Jack, CR, Weiner, M, Bateman, RJ, Morris, JC, Mayeux, R, Brickman, AM, Lee, S, Zimmerman, ME, Narkhede, A, Nasrabady, SE, Tosto, G, Meier, IB, Benzinger, TLS, Marcus, DS, Fagan, AM, Fox, NC, Cairns, NJ, Holtzman, DM, Buckles, V, Ghetti, B, McDade, E, Martins, RN, Saykin, AJ, Masters, CL, Ringman, JM, Fӧrster, S, Schofield, PR, Sperling, RA, Johnson, KA, Chhatwal, JP, Salloway, S, Correia, S, Jack, CR, Weiner, M, Bateman, RJ, Morris, JC, Mayeux, R, and Brickman, AM
Abstract: Introduction White 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 methods Participants (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. Results Mutation carriers were more likely to have microbleeds than non-carriers (p<0.05) and individuals with microbleeds had higher WMH volume than those without (p<0.05). Total WMH volume was increased in mutation carriers compared with non-carriers, up to 20 years prior to EYO, after controlling for microbleed status, as we demonstrated previously. Formal testing of mediation demonstrated that 21% of the association between mutation status and WMH was mediated by presence of microbleeds (p = 0.03) but a significant direct effect of WMH remained (p = 0.02) after controlling for presence of microbleeds. Discussion Although there is some co-dependency between WMH and microbleeds, the observed increases in WMH among mutation carriers does not appear to be fully mediated by this marker of CAA. Th
Published: 2018

27. White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer's disease

Author: Caballero, MÁA, Suárez-Calvet, M, Duering, M, Franzmeier, N, Benzinger, T, Fagan, AM, Bateman, RJ, Jack, CR, Levin, J, Dichgans, M, Jucker, M, Karch, C, Masters, CL, Morris, JC, Weiner, M, Rossor, M, Fox, NC, Lee, JH, Salloway, S, Danek, A, Goate, A, Yakushev, I, Hassenstab, J, Schofield, PR, Haass, C, Ewers, M, Caballero, MÁA, Suárez-Calvet, M, Duering, M, Franzmeier, N, Benzinger, T, Fagan, AM, Bateman, RJ, Jack, CR, Levin, J, Dichgans, M, Jucker, M, Karch, C, Masters, CL, Morris, JC, Weiner, M, Rossor, M, Fox, NC, Lee, JH, Salloway, S, Danek, A, Goate, A, Yakushev, I, Hassenstab, J, Schofield, PR, Haass, C, and Ewers, M
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-β 1-42 but
Published: 2018

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

Author: Kinnunen, KM, Cash, DM, Poole, T, Frost, C, Benzinger, TLS, Ahsan, RL, Leung, KK, Cardoso, MJ, Modat, M, Malone, IB, Morris, JC, Bateman, RJ, Marcus, DS, Goate, A, Salloway, SP, Correia, S, Sperling, RA, Chhatwal, JP, Mayeux, RP, Brickman, AM, Martins, RN, Farlow, MR, Ghetti, B, Saykin, AJ, Jack, CR, Schofield, PR, McDade, E, Weiner, MW, Ringman, JM, Thompson, PM, Masters, CL, Rowe, CC, Rossor, MN, Ourselin, S, Fox, NC, Kinnunen, KM, Cash, DM, Poole, T, Frost, C, Benzinger, TLS, Ahsan, RL, Leung, KK, Cardoso, MJ, Modat, M, Malone, IB, Morris, JC, Bateman, RJ, Marcus, DS, Goate, A, Salloway, SP, Correia, S, Sperling, RA, Chhatwal, JP, Mayeux, RP, Brickman, AM, Martins, RN, Farlow, MR, Ghetti, B, Saykin, AJ, Jack, CR, Schofield, PR, McDade, E, Weiner, MW, Ringman, JM, Thompson, PM, Masters, CL, Rowe, CC, Rossor, MN, Ourselin, S, and Fox, NC
Abstract: Introduction Identifying at what point atrophy rates first change in Alzheimer's disease is important for informing design of presymptomatic trials. Methods Serial 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. Results Atrophy 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. Discussion Atrophy 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

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

Author: Ginsberg, SD, Lee, S, Zimmerman, ME, Narkhede, A, Nasrabady, SE, Tosto, G, Meier, IB, Benzinger, TLS, Marcus, DS, Fagan, AM, Fox, NC, Cairns, NJ, Holtzman, DM, Buckles, V, Ghetti, B, McDade, E, Martins, RN, Saykin, AJ, Masters, CL, Ringman, JM, Foerster, S, Schofield, PR, Sperling, RA, Johnson, KA, Chhatwal, JP, Salloway, S, Correia, S, Jack, CR, Weiner, M, Bateman, RJ, Morris, JC, Mayeux, R, Brickman, AM, Ginsberg, SD, Lee, S, Zimmerman, ME, Narkhede, A, Nasrabady, SE, Tosto, G, Meier, IB, Benzinger, TLS, Marcus, DS, Fagan, AM, Fox, NC, Cairns, NJ, Holtzman, DM, Buckles, V, Ghetti, B, McDade, E, Martins, RN, Saykin, AJ, Masters, CL, Ringman, JM, Foerster, S, Schofield, PR, Sperling, RA, Johnson, KA, Chhatwal, JP, Salloway, S, Correia, S, Jack, CR, Weiner, M, Bateman, RJ, Morris, JC, Mayeux, R, and Brickman, AM
Abstract: INTRODUCTION: White 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 METHODS: Participants (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. RESULTS: Mutation carriers were more likely to have microbleeds than non-carriers (p<0.05) and individuals with microbleeds had higher WMH volume than those without (p<0.05). Total WMH volume was increased in mutation carriers compared with non-carriers, up to 20 years prior to EYO, after controlling for microbleed status, as we demonstrated previously. Formal testing of mediation demonstrated that 21% of the association between mutation status and WMH was mediated by presence of microbleeds (p = 0.03) but a significant direct effect of WMH remained (p = 0.02) after controlling for presence of microbleeds. DISCUSSION: Although there is some co-dependency between WMH and microbleeds, the observed increases in WMH among mutation carriers does not appear to be fully mediated by this marker of CAA
Published: 2018

30. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

Author: Young, AL, Marinescu, RV, Oxtoby, NP, Bocchetta, M, Yong, K, Firth, NC, Cash, DM, Thomas, DL, Dick, K M, Cardoso, J, van Swieten, J.C., Borroni, B, Galimberti, D, Masellis, M, Tartaglia, MC, Rowe, JB, Graff, C, Tagliavini, F, Frisoni, GB, Laforce, R, Finger, E, De Mendonca, A, Sorbi, S, Warren, JD, Crutch, S, Fox, NC, Ourselin, S, Schott, JM, Rohrer, JD, Alexander, DC, Young, AL, Marinescu, RV, Oxtoby, NP, Bocchetta, M, Yong, K, Firth, NC, Cash, DM, Thomas, DL, Dick, K M, Cardoso, J, van Swieten, J.C., Borroni, B, Galimberti, D, Masellis, M, Tartaglia, MC, Rowe, JB, Graff, C, Tagliavini, F, Frisoni, GB, Laforce, R, Finger, E, De Mendonca, A, Sorbi, S, Warren, JD, Crutch, S, Fox, NC, Ourselin, S, Schott, JM, Rohrer, JD, and Alexander, DC
Published: 2018

31. Immune-related genetic enrichment in frontotemporal dementia: An analysis of genome-wide association studies

Author: Broce, Iris, Karch, Celeste M., Wen, Natalie, Fan, Chun C., Wang, Yunpeng, Hong Tan, Chin, Kouri, Naomi, Ross, Owen A., Höglinger, Günter U., Muller, Ulrich, Hardy, John, Momeni, Parastoo, Hess, Christopher P., Dillon, William P., Miller, Zachary A., Bonham, Luke W., Rabinovici, Gil D., Rosen, Howard J., Schellenberg, Gerard D., Franke, Andre, Karlsen, Tom H., Veldink, Jan H., Ferrari, Raffaele, Yokoyama, Jennifer S., Miller, Bruce L., Andreassen, Ole A., Dale, Anders M., Desikan, Rahul S., Sugrue, Leo P., Ferrari R, Hernandez DG, Nalls MA, Rohrer JD, Ramasamy A, Kwok JBJ, Dobson-Stone C, Brooks WS, Schofield PR, Halliday GM, Hodges JR, Piguet O, Bartley L, Thompson E, Haan E, Hernández I, Ruiz A, Boada M, Borroni B, Padovani A, Cruchaga C, Cairns NJ, Benussi L, Binetti G, Ghidoni R, Forloni G, Galimberti D, Fenoglio C, Serpente M, Scarpini E, Clarimón J, Lleó A, Blesa R, Waldö ML, Nilsson K, Nilsson C, Mackenzie IRA, Hsuing GYR, Mann DMA, Grafman J, Morris CM, Attems J, Griffiths TD, McKeith IG, Thomas AJ, Pietrini P, Huey ED, Wasserman EM, Baborie A, Jaros E, Tierney MC, Pastor P, Razquin C, Ortega-Cubero S, Alonso E, Perneczky E, Diehl-Schmid J, Alexopoulos P, Kurz A, Rainero I, Rubino E, Pinessi L, Rogaeva E, St George-Hyslop P, Rossi G, Tagliavini F, Giaccone G, Rowe JB, Schlachetzki JCM, Uphill J, Collinge J, Mead S, Danek A, Van Deerlin VM, Grossmann M, Trojanowski JQ, van der Zee J, Deschamps W, Van Langenhove T, Cruts M, Van Broeckhoven C, Cappa SF, Le Ber I, Hannequin D, Golfier V, Vercelletto M, Brice A, Nacmias B, Sorbi S, Bagnoli S, Piaceri I, Nielsen JE, Hjermind LE, Riemenschneider M, Mayhaus M, Ibach B, Gasparoni G, Pichler S, Gu W, Rossor MN, Fox NC, Warren JD, Spillantini MG, Morris HR, Rizzu P, Heutnik P, Snowden J, Rollinson S, Richardson A, Gerhard A, Bruni AC, Maletta R, Frangipane F, Cupidi C, Bernardi L, Anfossi M, Gallo M, Conidi ME, Smirne N, Rademakers R, Baker M, Dickson DW, Graff-Radford NR, Peterson RC, Knopman D, Josephs KA, Boeve BF, Parisi JE, Seeley WW, Miller BL, Karydas AM, Rosen H, van Swieten JC, Dopper EGP, Seelaar H, Pijnenburg YAL, Scheltens P, Logroscino G, Capozzo R, Novelli V, Puca AA, Franceschi M, Postiglione A, Milan G, Sorrentino P, Kristiansen M, Chiang HH, Graff C, Pasquier F, Rollin A, Deramecourt V, Lebert F, Kapogiannis D, Ferucci L, Pickering-Brown S, Singleton AB, Hardy J, Momeni P., Broce, Iris [0000-0003-4932-1430], Karch, Celeste M [0000-0002-6854-5547], Wang, Yunpeng [0000-0001-9831-1090], Tan, Chin Hong [0000-0002-0980-9936], Kouri, Naomi [0000-0002-6841-9882], Hess, Christopher P [0000-0002-5132-5302], Miller, Zachary A [0000-0002-5991-3053], Bonham, Luke W [0000-0002-2533-1266], Veldink, Jan H [0000-0001-5572-9657], Dale, Anders M [0000-0002-6126-2966], Desikan, Rahul S [0000-0002-4151-6017], Sugrue, Leo P [0000-0001-7315-4519], Apollo - University of Cambridge Repository, Neurology, Human genetics, Amsterdam Neuroscience - Neurodegeneration, Divisions, Rademakers, Rosa, Int FTD-Genomics Consortium, Broce, Iri, Karch, Celeste M., Wen, Natalie, Fan, Chun C., Wang, Yunpeng, Hong Tan, Chin, Kouri, Naomi, Ross, Owen A., Höglinger, Günter U., Muller, Ulrich, Hardy, John, Momeni, Parastoo, Hess, Christopher P., Dillon, William P., Miller, Zachary A., Bonham, Luke W., Rabinovici, Gil D., Rosen, Howard J., Schellenberg, Gerard D., Franke, Andre, Karlsen, Tom H., Veldink, Jan H., Ferrari, Raffaele, Yokoyama, Jennifer S., Miller, Bruce L., Andreassen, Ole A., Dale, Anders M., Desikan, Rahul S., Sugrue, Leo P., Ferrari, R, Hernandez, Dg, Nalls, Ma, Rohrer, Jd, Ramasamy, A, Kwok, Jbj, Dobson-Stone, C, Brooks, W, Schofield, Pr, Halliday, Gm, Hodges, Jr, Piguet, O, Bartley, L, Thompson, E, Haan, E, Hernández, I, Ruiz, A, Boada, M, Borroni, B, Padovani, A, Cruchaga, C, Cairns, Nj, Benussi, L, Binetti, G, Ghidoni, R, Forloni, G, Galimberti, D, Fenoglio, C, Serpente, M, Scarpini, E, Clarimón, J, Lleó, A, Blesa, R, Waldö, Ml, Nilsson, K, Nilsson, C, Mackenzie, Ira, Hsuing, Gyr, Mann, Dma, Grafman, J, Morris, Cm, Attems, J, Griffiths, Td, Mckeith, Ig, Thomas, Aj, Pietrini, P, Huey, Ed, Wasserman, Em, Baborie, A, Jaros, E, Tierney, Mc, Pastor, P, Razquin, C, Ortega-Cubero, S, Alonso, E, Perneczky, E, Diehl-Schmid, J, Alexopoulos, P, Kurz, A, Rainero, I, Rubino, E, Pinessi, L, Rogaeva, E, St George-Hyslop, P, Rossi, G, Tagliavini, F, Giaccone, G, Rowe, Jb, Schlachetzki, Jcm, Uphill, J, Collinge, J, Mead, S, Danek, A, Van Deerlin, Vm, Grossmann, M, Trojanowski, Jq, van der Zee, J, Deschamps, W, Van Langenhove, T, Cruts, M, Van Broeckhoven, C, Cappa, Sf, Le Ber, I, Hannequin, D, Golfier, V, Vercelletto, M, Brice, A, Nacmias, B, Sorbi, S, Bagnoli, S, Piaceri, I, Nielsen, Je, Hjermind, Le, Riemenschneider, M, Mayhaus, M, Ibach, B, Gasparoni, G, Pichler, S, Gu, W, Rossor, Mn, Fox, Nc, Warren, Jd, Spillantini, Mg, Morris, Hr, Rizzu, P, Heutnik, P, Snowden, J, Rollinson, S, Richardson, A, Gerhard, A, Bruni, Ac, Maletta, R, Frangipane, F, Cupidi, C, Bernardi, L, Anfossi, M, Gallo, M, Conidi, Me, Smirne, N, Rademakers, R, Baker, M, Dickson, Dw, Graff-Radford, Nr, Peterson, Rc, Knopman, D, Josephs, Ka, Boeve, Bf, Parisi, Je, Seeley, Ww, Miller, Bl, Karydas, Am, Rosen, H, van Swieten, Jc, Dopper, Egp, Seelaar, H, Pijnenburg, Yal, Scheltens, P, Logroscino, G, Capozzo, R, Novelli, V, Puca, Aa, Franceschi, M, Postiglione, A, Milan, G, Sorrentino, P, Kristiansen, M, Chiang, Hh, Graff, C, Pasquier, F, Rollin, A, Deramecourt, V, Lebert, F, Kapogiannis, D, Ferucci, L, Pickering-Brown, S, Singleton, Ab, Hardy, J, and Momeni, P.
Subjects: 0301 basic medicine, Linkage disequilibrium, Gene Expression, Genome-wide association study, Neurodegenerative, Medical and Health Sciences, Motor Neuron Diseases, 0302 clinical medicine, Medicine and Health Sciences, 2.1 Biological and endogenous factors, Corticobasal degeneration, genetics [Genetic Predisposition to Disease], genetics [Frontotemporal Dementia], Genetics, Medicine (all), Neurodegenerative Diseases, Single Nucleotide, Genomics, General Medicine, Middle Aged, Colitis, LRRK2, 3. Good health, Neurology, Manchester Institute for Collaborative Research on Ageing, Frontotemporal Dementia, Neurological, Medicine, Research Article, Frontotemporal dementia, ResearchInstitutes_Networks_Beacons/MICRA, Immunology, Rheumatoid Arthritis, Single-nucleotide polymorphism, Gastroenterology and Hepatology, Human leukocyte antigen, Biology, Autoimmune Disease, Polymorphism, Single Nucleotide, Autoimmune Diseases, 03 medical and health sciences, Rare Diseases, Rheumatology, Clinical Research, General & Internal Medicine, FTD GWA, Mental Health and Psychiatry, mental disorders, Acquired Cognitive Impairment, Genome-Wide Association Studies, medicine, Ulcerative Colitis, Humans, Inflammatory and Immune System, Genetic Predisposition to Disease, ddc:610, Polymorphism, Aged, Genetic association, Genome-Wide Association Study, International FTD-Genomics Consortium, Prevention, Arthritis, Human Genome, Inflammatory Bowel Disease, Amyotrophic Lateral Sclerosis, Neurosciences, Correction, Biology and Life Sciences, Computational Biology, nutritional and metabolic diseases, Human Genetics, Genome Analysis, medicine.disease, Brain Disorders, nervous system diseases, 030104 developmental biology, Genetic Loci, Genetics of Disease, Dementia, Clinical Immunology, Human medicine, Clinical Medicine, Digestive Diseases, 030217 neurology & neurosurgery
Abstract: Background Converging evidence suggests that immune-mediated dysfunction plays an important role in the pathogenesis of frontotemporal dementia (FTD). Although genetic studies have shown that immune-associated loci are associated with increased FTD risk, a systematic investigation of genetic overlap between immune-mediated diseases and the spectrum of FTD-related disorders has not been performed. Methods and findings Using large genome-wide association studies (GWASs) (total n = 192,886 cases and controls) and recently developed tools to quantify genetic overlap/pleiotropy, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with FTD-related disorders—namely, FTD, corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), and amyotrophic lateral sclerosis (ALS)—and 1 or more immune-mediated diseases including Crohn disease, ulcerative colitis (UC), rheumatoid arthritis (RA), type 1 diabetes (T1D), celiac disease (CeD), and psoriasis. We found up to 270-fold genetic enrichment between FTD and RA, up to 160-fold genetic enrichment between FTD and UC, up to 180-fold genetic enrichment between FTD and T1D, and up to 175-fold genetic enrichment between FTD and CeD. In contrast, for CBD and PSP, only 1 of the 6 immune-mediated diseases produced genetic enrichment comparable to that seen for FTD, with up to 150-fold genetic enrichment between CBD and CeD and up to 180-fold enrichment between PSP and RA. Further, we found minimal enrichment between ALS and the immune-mediated diseases tested, with the highest levels of enrichment between ALS and RA (up to 20-fold). For FTD, at a conjunction false discovery rate < 0.05 and after excluding SNPs in linkage disequilibrium, we found that 8 of the 15 identified loci mapped to the human leukocyte antigen (HLA) region on Chromosome (Chr) 6. We also found novel candidate FTD susceptibility loci within LRRK2 (leucine rich repeat kinase 2), TBKBP1 (TBK1 binding protein 1), and PGBD5 (piggyBac transposable element derived 5). Functionally, we found that the expression of FTD–immune pleiotropic genes (particularly within the HLA region) is altered in postmortem brain tissue from patients with FTD and is enriched in microglia/macrophages compared to other central nervous system cell types. The main study limitation is that the results represent only clinically diagnosed individuals. Also, given the complex interconnectedness of the HLA region, we were not able to define the specific gene or genes on Chr 6 responsible for our pleiotropic signal. Conclusions We show immune-mediated genetic enrichment specifically in FTD, particularly within the HLA region. Our genetic results suggest that for a subset of patients, immune dysfunction may contribute to FTD risk. These findings have potential implications for clinical trials targeting immune dysfunction in patients with FTD., Rahul Desikan and colleagues use summary data from genome-wide association studies to investigate genetic overlap between frontotemporal dementia and a several immune-mediated diseases, and identify microglia and inflammation-associated genes that may play a role in FTD pathogenesis., Author summary Why was this study done? Frontotemporal dementia (FTD) is the leading cause of dementia in individuals less than 65 years old. Currently, there is no approved treatment of FTD and no diagnostic tests for predicting disease onset or measuring progression. Increasing evidence suggests that inflammation and immune system dysfunction play an important role in the pathogenesis of FTD. What did the researchers do and find? We used summary data from genome-wide association studies to investigate genetic overlap, or “pleiotropy,” between FTD and a variety of immune-mediated diseases. Through this approach, we found extensive FTD–immune genetic overlap within the HLA region on Chromosome 6, an area rich in genes related to microglial function, as well as in 3 genes not previously identified as contributing to the pathophysiology of FTD. Pointing to the functional relevance of these genetic results, we found that these candidate FTD–immune genes are differentially expressed in postmortem brains from patients with FTD compared to controls, and in microglia/macrophages compared with other central nervous system cells. Using bioinformatics tools, we explored protein and genetic interactions among our candidate FTD–immune genes. These results suggest that rather than a few individual loci, large portions of the HLA region may be associated with increased FTD risk. What do these findings mean? Immune dysfunction may play a role in the pathophysiology of a subset of FTD cases. For a subset of patients in whom immune dysfunction in general—and microglial activation in particular—is central to disease pathophysiology, anti-inflammatory treatment is an important area for further investigation.
Published: 2018

32. Patterns of atrophy in pathologically confirmed dementias: A voxelwise analysis

Author: Harper, L, Bouwman, F, Burton, EJ, Barkhof, F, Scheltens, P, O'Brien, JT, Fox, NC, Ridgway, G, Schott, JM, Neurology, Amsterdam Neuroscience - Neurodegeneration, Radiology and nuclear medicine, O'Brien, John [0000-0002-0837-5080], and Apollo - University of Cambridge Repository
Subjects: neuropathology, Alzheimer’s disease, brain atrophy, MRI, dementia
Abstract: Objective Imaging is recommended to support the clinical diagnoses of dementias, yet imaging research studies rarely have pathological confirmation of disease. This study aims to characterise patterns of brain volume loss in six primary pathologies compared with controls and to each other. Methods One hundred and eighty-six patients with a clinical diagnosis of dementia and histopathological confirmation of underlying pathology, and 73 healthy controls were included in this study. Voxel-based morphometry, based on ante-mortem T1-weighted MRI, was used to identify cross-sectional group differences in brain volume. Results Early-onset and late-onset Alzheimer’s disease exhibited different patterns of grey matter volume loss, with more extensive temporoparietal involvement in the early-onset group, and more focal medial temporal lobe loss in the late-onset group. The Presenilin-1 group had similar parietal involvement to the early-onset group with localised volume loss in the thalamus, medial temporal lobe and temporal neocortex. Lewy body pathology was associated with less extensive volume loss than the other pathologies, although precentral/postcentral gyri volume was reduced in comparison with other pathological groups. Tau and TDP43A pathologies demonstrated similar patterns of frontotemporal volume loss, although less extensive on the right in the 4-repeat-tau group, with greater parietal involvement in the TDP43A group. The TDP43C group demonstrated greater left anterior-temporal involvement. Conclusions Pathologically distinct dementias exhibit characteristic patterns of regional volume loss compared with controls and other dementias. Voxelwise differences identified in these cohorts highlight imaging signatures that may aid in the differentiation of dementia subtypes during life. The results of this study are available for further examination via NeuroVault (http://neurovault.org/collections/ADHMHOPN/).
Published: 2017

33. Susceptible genes and disease mechanisms identified in frontotemporal dementia and frontotemporal dementia with Amyotrophic Lateral Sclerosis by DNA-methylation and GWAS

Author: Taskesen, E, Mishra, A, van der Sluis, S, Ferrari, R, Veldink, Jh, van Es MA4, Smit, Ab5, Posthuma, D1, 2, Hernandez DG, Pijnenburg Y., Nalls, Ma, Rohrer, Jd, Ramasamy, A, Kwok, Jbj, Dobson-Stone, C, Schofield, Pr, Halliday, Gm, Hodges, Jr, Piguet, O, Bartley, L, Thompson, E, Haan, E, Hernández, I, Ruiz, A, Boada, M, Borroni, B, Padovani, A, Cruchaga, C, Cairns, Nj, Benussi, L, Binetti, G, Ghidoni, Roberta, Forloni, G, Albani, D, Galimberti, D, Fenoglio, C, Serpente, M, Scarpini, E, Clarimón, J, Lleó, A, Blesa, R, Waldö, Ml, Nilsson, K, Nilsson, C, Mackenzie, Ira, Hsiung, Gr, Mann, Dma, Grafman, J, Morris, Cm, Attems, J, Griffiths, Td, Mckeith, Ig, Thomas, Aj, Pietrini, P, Huey, Ed, Wassermann, Em, Baborie, A, Jaros, E, Tierney, Mc, Pastor, P, Razquin, C, Ortega-Cubero, S, Alonso, E, Perneczky, R, Diehl-Schmid, J, Alexopoulos, P, Kurz, A, Rainero, I, Rubino, E, Pinessi, L, Rogaeva, E, George-Hyslop, Ps, Rossi, G, Tagliavini, F, Giaccone, G, Rowe, Jb, Schlachetzki, Jcm, Uphill, J, Collinge, J, Mead, S, Danek, A, Van Deerlin VM, Grossman, M, Trojanowski, Jq, van der Zee, J, Van Broeckhoven, C, Cappa, Sf, Leber, I, Hannequin, D, Golfier, V, Vercelletto, M, Brice, A, Nacmias, B, Sorbi, S, Bagnoli, S, Piaceri, I, Nielsen, Je, Hjermind, Le, Riemenschneider, GUNNAR MARKUS, Mayhaus, M, Ibach, B, Gasparoni, G, Pichler, S, Gu, W, Rossor, Mn, Fox, Nc, Warren, Jd, Spillantini, Mg, Morris, Hr, Rizzu, P, Heutink, P, Snowden, Js, Rollinson, S, Richardson, A, Gerhard, A, Bruni, Ac, Maletta, R, Frangipane, F, Cupidi, C, Bernardi, Lara, Anfossi, M, Gallo, M, Conidi, Me, Smirne, N, Rademakers, R, Baker, M, Dickson, Dw, Graff-Radford, Nr, Petersen, Rc, Knopman, D, Josephs, Ka, Boeve, Bf, Parisi, Je, Seeley, Ww, Miller, Bl, Karydas, Am, Rosen, H, van Swieten JC, Dopper, Egp, Seelaar, H, Scheltens, P, Logroscino, G, Capozzo, R, Novelli, V, Puca, Aa, Franceschi, M, Postiglione, Antonio, Milan, Gian Luca, Sorrentino, Paolo Luigi, Kristiansen, M, Chiang, Hh, Graff, C, Pasquier, F, Rollin, A, Deramecourt, V, Lebouvier, T, Kapogiannis, D, Ferrucci, L, Pickering-Brown, S, Singleton, Ab, Hardy, J, Momeni, P., Rademakers, Rosa, International FTD-Genomics Consortium, Complex Trait Genetics, Amsterdam Neuroscience - Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience - Neurodegeneration, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Human genetics, APH - Quality of Care, Midwifery Science, Divisions, Neurology, Amsterdam Reproduction & Development (AR&D), Mishra, A [0000-0002-8141-1543], van der Sluis, S [0000-0001-9958-7216], van Es, MA [0000-0002-7709-5883], Posthuma, D [0000-0001-7582-2365], and Apollo - University of Cambridge Repository
Subjects: 0301 basic medicine, Candidate gene, Science, Genome-wide association study, Biology, Neurodegenerative, Article, 03 medical and health sciences, 0302 clinical medicine, Rare Diseases, SDG 3 - Good Health and Well-being, mental disorders, medicine, Journal Article, Acquired Cognitive Impairment, Genetics, 2.1 Biological and endogenous factors, Amyotrophic lateral sclerosis, Aetiology, General, Alzheimer's Disease Related Dementias (ADRD), Genetic association, Multidisciplinary, Genetic heterogeneity, International FTD-Genomics Consortium, Neurodegeneration, Human Genome, Neurosciences, nutritional and metabolic diseases, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), medicine.disease, nervous system diseases, Brain Disorders, Frontotemporal Dementia (FTD), 030104 developmental biology, DNA methylation, Neurological, Medicine, Dementia, Human medicine, ALS, 030217 neurology & neurosurgery, Frontotemporal dementia
Abstract: Frontotemporal dementia (FTD) is a neurodegenerative disorder predominantly affecting the frontal and temporal lobes. Genome-wide association studies (GWAS) on FTD identified only a few risk loci. One of the possible explanations is that FTD is clinically, pathologically, and genetically heterogeneous. An important open question is to what extent epigenetic factors contribute to FTD and whether these factors vary between FTD clinical subgroup. We compared the DNA-methylation levels of FTD cases (n = 128), and of FTD cases with Amyotrophic Lateral Sclerosis (FTD-ALS; n = 7) to those of unaffected controls (n = 193), which resulted in 14 and 224 candidate genes, respectively. Cluster analysis revealed significant class separation of FTD-ALS from controls. We could further specify genes with increased susceptibility for abnormal gene-transcript behavior by jointly analyzing DNA-methylation levels with the presence of mutations in a GWAS FTD-cohort. For FTD-ALS, this resulted in 9 potential candidate genes, whereas for FTD we detected 1 candidate gene (ELP2). Independent validation-sets confirmed the genes DLG1, METTL7A, KIAA1147, IGHMBP2, PCNX, UBTD2, WDR35, and ELP2/SLC39A6 among others. We could furthermore demonstrate that genes harboring mutations and/or displaying differential DNA-methylation, are involved in common pathways, and may therefore be critical for neurodegeneration in both FTD and FTD-ALS.
Published: 2017

34. Shared genetic risk between corticobasal degeneration, progressive supranuclear palsy, and frontotemporal dementia

Author: Yokoyama, Jennifer S., Karch, Celeste M., Fan, Chun C., Bonham, Luke W., Naomi, Kouri, Ross, Owen A., Rosa, Rademakers, Jungsu, Kim, Yunpeng, Wang, Höglinger, Günter U., Ulrich, Muller, Raffaele, Ferrari, John, Hardy, International FTD-Genomics Consortium (IFGC Ferrari, R, Hernandez, Dg, Nalls, Ma, Rohrer, Jd, Ramasamy, A, Kwok, Jbj, Dobson-Stone, C, Schofield, Pr, Halliday, Gm, Hodges, Jr, Piguet, O, Bartley, L, Thompson, E, Haan, E, Hernández, I, Ruiz, A, Boada, M, Borroni, B, Padovani, A, Cruchaga, C, Cairns, Nj, Benussi, L, Binetti, G, Ghidoni, R, Forloni, G, Albani, D, Galimberti, D, Fenoglio, C, Serpente, M, Scarpini, E, Clarimón, J, Lleó, A, Blesa, R, Landqvist Waldö, M, Nilsson, C, Mackenzie, Ira, Hsiung, Gyr, Mann, Dma, Grafman, J, Morris, Cm, Attems, J, Griffiths, Td, Mckeith, Ig, Thomas, Aj, Pietrini, P, Huey, Ed, Wassermann, Em, Baborie, A, Jaros, E, Tierney, Mc, Pastor, P, Razquin, C, Ortega-Cubero, S, Alonso, E, Perneczky, R, Diehl-Schmid, J, Alexopoulos, P, Kurz, A, Rainero, I, Rubino, E, Pinessi, L, Rogaeva, E, St George-Hyslop, P, Rossi, G, Tagliavini, F, Giaccone, G, Rowe, Jb, Schlachetzki, Jcm, Uphill, J, Collinge, J, Mead, S, Danek, A, Van Deerlin VM, Grossman, M, Trojanowski, Jq, van der Zee, J, Cruts, M, Van Broeckhoven, C, Cappa, Sf, Leber, I, Hannequin, D, Golfier, V, Vercelletto, M, Brice, A, Nacmias, B, Sorbi, S, Bagnoli, S, Piaceri, I, Nielsen, Je, Hjermind, Le, Riemenschneider, M, Mayhaus, M, Ibach, B, Gasparoni, G, Pichler, S, Gu, W, Rossor, Mn, Fox, Nc, Warren, Jd, Spillantini, Mg, Morris, Hr, Rizzu, P, Heutink, P, Snowden, Js, Rollinson, S, Richardson, A, Gerhard, A, Bruni, Ac, Maletta, R, Frangipane, F, Cupidi, C, Bernardi, L, Anfossi, M, Gallo, M, Conidi, Me, Smirne, N, Rademakers, R, Baker, M, Dickson, Dw, Graff-Radford, Nr, Petersen, Rc, Knopman, D, Josephs, Ka, Boeve, Bf, Parisi, Je, Seeley, Ww, Miller, Bl, Karydas, Am, Rosen, H, van Swieten JC, Dopper, Eg, Seelaar, H, Pijnenburg, Yal, Scheltens, P, Logroscino, G, Capozzo, R, Novelli, V, Puca, Aa, Franceschi, M, Postiglione, A, Milan, G, Sorrentino, P, Kristiansen, M, Chiang, Hh, Graff, C, Pasquier, F, Rollin, A, Deramecourt, V, Lebouvier, T, Kapogiannis, D, Ferrucci, L, Pickering-Brown, S, Singleton, Ab, Hardy, J, Momeni, P. )., Parastoo, Momeni, Sugrue, Leo P., Hess, Christopher P., James Barkovich, A., Boxer, Adam L., Seele, William W., Rabinovici, Gil D., Rosen, Howard J., Miller, Bruce L., Schmansky, Nicholas J., Bruce, Fischl, Hyman, Bradley T., Dickson, Dennis W., Schellenberg, Gerard D., Andreassen, Ole A., Dale, Anders M., Desikan, and Rahul S., and Int FTD-Genomics Consortium
Subjects: pathology [Tauopathies], 0301 basic medicine, Pathology, Aging, genetics [Basal Ganglia Diseases], Genome-wide association study, Neurodegenerative, diagnosis [Supranuclear Palsy, Progressive], diagnosis [Frontotemporal Dementia], pathology [Inclusion Bodies], 0302 clinical medicine, Neurology (clinical), Cellular and Molecular Neuroscience, Risk Factors, pathology [Neurons], Corticobasal degeneration, Supranuclear Palsy, 2.1 Biological and endogenous factors, Aetiology, genetics [Frontotemporal Dementia], Alzheimer's Disease Related Dementias (ADRD), Genetics, Inclusion Bodies, Neurons, genetics [Supranuclear Palsy, Progressive], Frontotemporal Dementia (FTD), Tauopathies, Frontotemporal Dementia, Neurological, Supranuclear Palsy, Progressive, Frontotemporal dementia, medicine.medical_specialty, pathology [Supranuclear Palsy, Progressive], Clinical Sciences, MAPT protein, human, Locus (genetics), Single-nucleotide polymorphism, tau Proteins, Biology, Article, Pathology and Forensic Medicine, Progressive supranuclear palsy, 03 medical and health sciences, Rare Diseases, Progressive, Basal Ganglia Diseases, mental disorders, medicine, Acquired Cognitive Impairment, Humans, ddc:610, Genetic association, Neurology & Neurosurgery, International FTD-Genomics Consortium, Prevention, Haplotype, Human Genome, Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), medicine.disease, metabolism [tau Proteins], digestive system diseases, Brain Disorders, 030104 developmental biology, pathology [Frontotemporal Dementia], Dementia, Human medicine, pathology [Basal Ganglia Diseases], 030217 neurology & neurosurgery
Abstract: Corticobasal degeneration (CBD), progressive supranuclear palsy (PSP) and a subset of frontotemporal dementia (FTD) are neurodegenerative disorders characterized by tau inclusions in neurons and glia (tauopathies). Although clinical, pathological and genetic evidence suggests overlapping pathobiology between CBD, PSP, and FTD, the relationship between these disorders is still not well understood. Using summary statistics (odds ratios and p values) from large genome-wide association studies (total n=14,286 cases and controls) and recently established genetic methods, we investigated the genetic overlap between CBD and PSP and CBD and FTD. We found up to 800-fold enrichment of genetic risk in CBD across different levels of significance for PSP or FTD. In addition to NSF (tagging the MAPT H1 haplotype), we observed that SNPs in or near MOBP, CXCR4, EGFR, and GLDC showed significant genetic overlap between CBD and PSP, whereas only SNPs tagging the MAPT haplotype overlapped between CBD and FTD. The risk alleles of the shared SNPs were associated with expression changes in cis-genes. Evaluating transcriptome levels across adult human brains, we found a unique neuroanatomic gene expression signature for each of the five overlapping gene loci (omnibus ANOVA p&nbsp
Published: 2017

35. Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2

Author: Jack, CR, Barnes, J, Bernstein, MA, Borowski, BJ, Brewer, J, Clegg, S, Dale, AM, Carmichael, O, Ching, C, DeCarli, C, Desikan, RS, Fennema-Notestine, C, Fjell, AM, Fletcher, E, Fox, NC, Gunter, J, Gutman, BA, Holland, D, Hua, X, Insel, P, Kantarci, K, Killiany, RJ, Krueger, G, Leung, KK, Mackin, S, Maillard, P, Malone, IB, Mattsson, N, McEvoy, L, Modat, M, Mueller, S, Nosheny, R, Ourselin, S, Schuff, N, Senjem, ML, Simonson, A, Thompson, PM, Rettmann, D, Vemuri, P, Walhovd, K, Zhao, Y, Zuk, S, and Weiner, M
Subjects: mental disorders
Abstract: © 2015 The Authors. Published by Elsevier Inc. Introduction Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders. Methods We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3. Results Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials. Discussion Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.
Published: 2015

36. Longitudinal diffusion tensor imaging in frontotemporal dementia

Author: Mahoney, CJ, Simpson, IJA, Nicholas, JM, Fletcher, PD, Downey, LE, Golden, HL, Clark, CN, Schmitz, N, Rohrer, JD, Schott, JM, Zhang, H, Ourselin, S, Warren, JD, and Fox, NC
Subjects: Male, Brain, Middle Aged, Neuropsychological Tests, Sensitivity and Specificity, White Matter, Diffusion Tensor Imaging, Case-Control Studies, Frontotemporal Dementia, Disease Progression, Anisotropy, Humans, Female, Longitudinal Studies, Cognition Disorders, Research Articles, Aged
Abstract: Objective\ud Novel biomarkers for monitoring progression in neurodegenerative conditions are needed. Measurement of microstructural changes in white matter (WM) using diffusion tensor imaging (DTI) may be a useful outcome measure. Here we report trajectories of WM change using serial DTI in a cohort with behavioral variant frontotemporal dementia (bvFTD).\ud \ud Methods\ud Twenty‐three patients with bvFTD (12 having genetic mutations), and 18 age‐matched control participants were assessed using DTI and neuropsychological batteries at baseline and ∼1.3 years later. Baseline and follow‐up DTI scans were registered using a groupwise approach. Annualized rates of change for DTI metrics, neuropsychological measures, and whole brain volume were calculated. DTI metric performances were compared, and sample sizes for potential clinical trials were calculated.\ud \ud Results\ud In the bvFTD group as a whole, rates of change in fractional anisotropy (FA) and mean diffusivity (MD) within the right paracallosal cingulum were greatest (FA: −6.8%/yr, p
Published: 2015

37. Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis

Author: Rohrer, Jd, Nicholas, Jm, Cash, Dm, van Swieten, J, Dopper, E, Jiskoot, L, van Minkelen, R, Rombouts, Sa, Cardoso, Mj, Clegg, S, Espak, M, Mead, S, Thomas, Dl, De Vita, E, Masellis, M, Black, Se, Freedman, M, Keren, R, Macintosh, Bj, Rogaeva, E, Tang Wai, D, Tartaglia, Mc, Laforce, R, Tagliavini, F, Tiraboschi, P, Redaelli, V, Prioni, S, Grisoli, M, Borroni, Barbara, Padovani, Alessandro, Galimberti, D, Scarpini, E, Arighi, A, Fumagalli, G, Rowe, Jb, Coyle Gilchrist, I, Graff, C, Fallström, M, Jelic, V, Ståhlbom, Ak, Andersson, C, Thonberg, H, Lilius, L, Frisoni, Gb, Pievani, M, Bocchetta, M, Benussi, L, Ghidoni, R, Finger, E, Sorbi, S, Nacmias, B, Lombardi, G, Polito, C, Warren, Jd, Ourselin, S, and Fox, Nc
Published: 2015

38. Neurological manifestations of autosomal dominant familial Alzheimer's disease: a comparison of the published literature with the Dominantly Inherited Alzheimer Network observational study (DIAN-OBS)

Author: Tang, M, Ryman, DC, McDade, E, Jasielec, MS, Buckles, VD, Cairns, NJ, Fagan, AM, Goate, A, Marcus, DS, Xiong, C, Allegri, RF, Chhatwal, JP, Danek, A, Farlow, MR, Fox, NC, Ghetti, B, Graff-Radford, NR, Laske, C, Martins, RN, Masters, CL, Mayeux, RP, Ringman, JM, Rossor, MN, Salloway, SP, Schofield, PR, Morris, JC, Bateman, RJ, Tang, M, Ryman, DC, McDade, E, Jasielec, MS, Buckles, VD, Cairns, NJ, Fagan, AM, Goate, A, Marcus, DS, Xiong, C, Allegri, RF, Chhatwal, JP, Danek, A, Farlow, MR, Fox, NC, Ghetti, B, Graff-Radford, NR, Laske, C, Martins, RN, Masters, CL, Mayeux, RP, Ringman, JM, Rossor, MN, Salloway, SP, Schofield, PR, Morris, JC, and Bateman, RJ
Abstract: Background Autosomal dominant familial Alzheimer's disease (ADAD) is a rare disorder with non-amnestic neurological symptoms in some clinical presentations. We aimed to compile and compare data from symptomatic participants in the Dominantly Inherited Alzheimer Network observational study (DIAN-OBS) with those reported in the literature to estimate the prevalences of non-amnestic neurological symptoms in participants with ADAD. Methods We prospectively collected data from the DIAN-OBS database, which recruited participants from study centres in the USA, Europe, and Australia, between Feb 29, 2008, and July 1, 2014. We also did a systematic review of publications to extract individual-level clinical data for symptomatic participants with ADAD. We used data for age of onset (from first report of cognitive decline), disease course from onset to death, and the presence of 13 neurological findings that have been reported in association with ADAD. Using multivariable linear regression, we investigated the prevalences of various non-amnestic neurological symptoms and the contributions of age of onset and specific mutation type on symptoms. Findings The DIAN-OBS dataset included 107 individuals with detailed clinical data (forming the DIAN-OBS cohort). Our systematic review yielded 188 publications reporting on 1228 symptomatic individuals, with detailed neurological examination descriptions available for 753 individuals (forming the published data cohort). The most prevalent non-amnestic cognitive manifestations in participants in the DIAN-OBS cohort were those typical of mild to moderate Alzheimer's disease, including visual agnosia (55·1%, 95% CI 45·7–64·6), aphasia (57·9%, 48·6–67·3), and behavioural changes (61·7%, 51·5–70·0). Non-amnestic cognitive manifestations were less prevalent in the published data cohort (eg, visual agnosia [5·6%, 3·9–7·2], aphasia [23·0%, 20·0–26·0], and behavioural changes [31·7%, 28·4–35·1]). Prevalence of non-cognitive neurological manifest
Published: 2016

39. White matter hyperintensities are a core feature of Alzheimer's disease: Evidence from the dominantly inherited Alzheimer network

Author: Lee, S, Viqar, F, Zimmerman, ME, Narkhede, A, Tosto, G, Benzinger, TLS, Marcus, DS, Fagan, AM, Goate, A, Fox, NC, Cairns, NJ, Holtzman, DM, Buckles, V, Ghetti, B, McDade, E, Martins, RN, Saykin, AJ, Masters, CL, Ringman, JM, Ryan, NS, Förster, S, Laske, C, Schofield, PR, Sperling, RA, Salloway, S, Correia, S, Jack, C, Weiner, M, Bateman, RJ, Morris, JC, Mayeux, R, Brickman, AM, Lee, S, Viqar, F, Zimmerman, ME, Narkhede, A, Tosto, G, Benzinger, TLS, Marcus, DS, Fagan, AM, Goate, A, Fox, NC, Cairns, NJ, Holtzman, DM, Buckles, V, Ghetti, B, McDade, E, Martins, RN, Saykin, AJ, Masters, CL, Ringman, JM, Ryan, NS, Förster, S, Laske, C, Schofield, PR, Sperling, RA, Salloway, S, Correia, S, Jack, C, Weiner, M, Bateman, RJ, Morris, JC, Mayeux, R, and Brickman, AM
Abstract: Objective White matter hyperintensities (WMHs) are areas of increased signal on T2-weighted magnetic resonance imaging (MRI) scans that most commonly reflect small vessel cerebrovascular disease. Increased WMH volume is associated with risk and progression of Alzheimer's disease (AD). These observations are typically interpreted as evidence that vascular abnormalities play an additive, independent role contributing to symptom presentation, but not core features of AD. We examined the severity and distribution of WMH in presymptomatic PSEN1, PSEN2, and APP mutation carriers to determine the extent to which WMH manifest in individuals genetically determined to develop AD. Methods The study comprised participants (n = 299; age = 39.03 ± 10.13) from the Dominantly Inherited Alzheimer Network, including 184 (61.5%) with a mutation that results in AD and 115 (38.5%) first-degree relatives who were noncarrier controls. We calculated the estimated years from expected symptom onset (EYO) by subtracting the affected parent's symptom onset age from the participant's age. Baseline MRI data were analyzed for total and regional WMH. Mixed-effects piece-wise linear regression was used to examine WMH differences between carriers and noncarriers with respect to EYO. Results Mutation carriers had greater total WMH volumes, which appeared to increase approximately 6 years before expected symptom onset. Effects were most prominent for the parietal and occipital lobe, which showed divergent effects as early as 22 years before estimated onset. Interpretation Autosomal-dominant AD is associated with increased WMH well before expected symptom onset. The findings suggest the possibility that WMHs are a core feature of AD, a potential therapeutic target, and a factor that should be integrated into pathogenic models of the disease.
Published: 2016

40. Quantitative Amyloid imaging in autosomal Dominant Alzheimer's disease: Results from the DIAN study group

Author: Su, Y, Blazey, TM, Owen, CJ, Christensen, JJ, Friedrichsen, K, Joseph-Mathurin, N, Wang, Q, Hornbeck, RC, Ances, BM, Snyder, AZ, Cash, LA, Koeppe, RA, Klunk, WE, Galasko, D, Brickman, AM, McDade, E, Ringman, JM, Thompson, PM, Saykin, AJ, Ghetti, B, Sperling, RA, Johnson, KA, Salloway, SP, Schofield, PR, Masters, CL, Villemagne, VL, Fox, NC, Förster, S, Chen, K, Reiman, EM, Xiong, C, Marcus, DS, Weiner, MW, Morris, JC, Bateman, RJ, Benzinger, TLS, Su, Y, Blazey, TM, Owen, CJ, Christensen, JJ, Friedrichsen, K, Joseph-Mathurin, N, Wang, Q, Hornbeck, RC, Ances, BM, Snyder, AZ, Cash, LA, Koeppe, RA, Klunk, WE, Galasko, D, Brickman, AM, McDade, E, Ringman, JM, Thompson, PM, Saykin, AJ, Ghetti, B, Sperling, RA, Johnson, KA, Salloway, SP, Schofield, PR, Masters, CL, Villemagne, VL, Fox, NC, Förster, S, Chen, K, Reiman, EM, Xiong, C, Marcus, DS, Weiner, MW, Morris, JC, Bateman, RJ, and Benzinger, TLS
Abstract: Amyloid imaging plays an important role in the research and diagnosis of dementing disorders. Substantial variation in quantitative methods to measure brain amyloid burden exists in the field. The aim of this work is to investigate the impact of methodological variations to the quantification of amyloid burden using data from the Dominantly Inherited Alzheimer's Network (DIAN), an autosomal dominant Alzheimer's disease population. Cross-sectional and longitudinal [11C]-Pittsburgh Compound B (PiB) PET imaging data from the DIAN study were analyzed. Four candidate reference regions were investigated for estimation of brain amyloid burden. A regional spread function based technique was also investigated for the correction of partial volume effects. Cerebellar cortex, brain-stem, and white matter regions all had stable tracer retention during the course of disease. Partial volume correction consistently improves sensitivity to group differences and longitudinal changes over time. White matter referencing improved statistical power in the detecting longitudinal changes in relative tracer retention; however, the reason for this improvement is unclear and requires further investigation. Full dynamic acquisition and kinetic modeling improved statistical power although it may add cost and time. Several technical variations to amyloid burden quantification were examined in this study. Partial volume correction emerged as the strategy that most consistently improved statistical power for the detection of both longitudinal changes and across-group differences. For the autosomal dominant Alzheimer's disease population with PiB imaging, utilizing brainstem as a reference region with partial volume correction may be optimal for current interventional trials. Further investigation of technical issues in quantitative amyloid imaging in different study populations using different amyloid imaging tracers is warranted.
Published: 2016

41. Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer's Disease: Results from the DIAN Study Group

Author: Herholz, K, Su, Y, Blazey, TM, Owen, CJ, Christensen, JJ, Friedrichsen, K, Joseph-Mathurin, N, Wang, Q, Hornbeck, RC, Ances, BM, Snyder, AZ, Cash, LA, Koeppe, RA, Klunk, WE, Galasko, D, Brickman, AM, McDade, E, Ringman, JM, Thompson, PM, Saykin, AJ, Ghetti, B, Sperling, RA, Johnson, KA, Salloway, SP, Schofield, PR, Masters, CL, Villemagne, VL, Fox, NC, Foerster, S, Chen, K, Reiman, EM, Xiong, C, Marcus, DS, Weiner, MW, Morris, JC, Bateman, RJ, Benzinger, TLS, Herholz, K, Su, Y, Blazey, TM, Owen, CJ, Christensen, JJ, Friedrichsen, K, Joseph-Mathurin, N, Wang, Q, Hornbeck, RC, Ances, BM, Snyder, AZ, Cash, LA, Koeppe, RA, Klunk, WE, Galasko, D, Brickman, AM, McDade, E, Ringman, JM, Thompson, PM, Saykin, AJ, Ghetti, B, Sperling, RA, Johnson, KA, Salloway, SP, Schofield, PR, Masters, CL, Villemagne, VL, Fox, NC, Foerster, S, Chen, K, Reiman, EM, Xiong, C, Marcus, DS, Weiner, MW, Morris, JC, Bateman, RJ, and Benzinger, TLS
Abstract: Amyloid imaging plays an important role in the research and diagnosis of dementing disorders. Substantial variation in quantitative methods to measure brain amyloid burden exists in the field. The aim of this work is to investigate the impact of methodological variations to the quantification of amyloid burden using data from the Dominantly Inherited Alzheimer's Network (DIAN), an autosomal dominant Alzheimer's disease population. Cross-sectional and longitudinal [11C]-Pittsburgh Compound B (PiB) PET imaging data from the DIAN study were analyzed. Four candidate reference regions were investigated for estimation of brain amyloid burden. A regional spread function based technique was also investigated for the correction of partial volume effects. Cerebellar cortex, brain-stem, and white matter regions all had stable tracer retention during the course of disease. Partial volume correction consistently improves sensitivity to group differences and longitudinal changes over time. White matter referencing improved statistical power in the detecting longitudinal changes in relative tracer retention; however, the reason for this improvement is unclear and requires further investigation. Full dynamic acquisition and kinetic modeling improved statistical power although it may add cost and time. Several technical variations to amyloid burden quantification were examined in this study. Partial volume correction emerged as the strategy that most consistently improved statistical power for the detection of both longitudinal changes and across-group differences. For the autosomal dominant Alzheimer's disease population with PiB imaging, utilizing brainstem as a reference region with partial volume correction may be optimal for current interventional trials. Further investigation of technical issues in quantitative amyloid imaging in different study populations using different amyloid imaging tracers is warranted.
Published: 2016

42. Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer's Disease: Results from the DIAN Study Group (vol 11, e0152082, 2016)

Author: Su, Y, Blazey, TM, Owen, CJ, Christensen, JJ, Friedrichsen, K, Joseph-Mathurin, N, Wang, Q, Hornbeck, RC, Ances, BM, Snyder, AZ, Cash, LA, Koeppe, RA, Klunk, WE, Galasko, D, Brickman, AM, McDade, E, Ringman, JM, Thompson, PM, Saykin, AJ, Ghetti, B, Sperling, RA, Johnson, KA, Salloway, SP, Schofield, PR, Masters, CL, Villemagne, VL, Fox, NC, Forster, S, Chen, K, Reiman, EM, Xiong, C, Marcus, DS, Weiner, MW, Morris, JC, Bateman, RJ, Benzinger, TLS, Su, Y, Blazey, TM, Owen, CJ, Christensen, JJ, Friedrichsen, K, Joseph-Mathurin, N, Wang, Q, Hornbeck, RC, Ances, BM, Snyder, AZ, Cash, LA, Koeppe, RA, Klunk, WE, Galasko, D, Brickman, AM, McDade, E, Ringman, JM, Thompson, PM, Saykin, AJ, Ghetti, B, Sperling, RA, Johnson, KA, Salloway, SP, Schofield, PR, Masters, CL, Villemagne, VL, Fox, NC, Forster, S, Chen, K, Reiman, EM, Xiong, C, Marcus, DS, Weiner, MW, Morris, JC, Bateman, RJ, and Benzinger, TLS
Abstract: [This corrects the article DOI: 10.1371/journal.pone.0152082.].
Published: 2016

43. A decade of prediagnostic assessment in a case of Familial Alzheimer's disease: tracking progression from asymptomatic to MCI and dementia

Author: GODBOLT AK, ANDERSON VM, ARCHER HA, JANSSEN JC, PRICE S, FOX NC, ROSSOR MN, CIPOLOTTI, Lisa, GODBOLT AK, CIPOLOTTI L, ANDERSON VM, ARCHER HA, JANSSEN JC, PRICE S, FOX NC, and ROSSOR MN
Published: 2005

44. Knight's move thinking? Mild cognitive impairment in a chess player

Author: ARCHER HA, SCHOTT JM, BARNES J, FOX NC, REVEREZ T, HOLTON JL, ROSSOR MN, CIPOLOTTI, Lisa, ARCHER HA, SCHOTT JM, BARNES J, FOX NC, REVEREZ T, HOLTON JL, CIPOLOTTI L, and ROSSOR MN
Published: 2005

45. Tracking progression from pre-MCI to dementia: a decade of pre-diagnostic assessment in a case of Familial Alzheimer's disease

Author: GODBOLT AK, ANDERSON VM, ARCHER HA, JANSSEN JC, PRICE S, FOX NC, ROSSOR MN, CIPOLOTTI, Lisa, GODBOLT AK, CIPOLOTTI L, ANDERSON VM, ARCHER HA, JANSSEN JC, PRICE S, FOX NC, and ROSSOR MN
Published: 2005

46. The natural history of Alzheimer disease: a longitudinal presymptomatic and symptomatic study of a familial cohort

Author: GODBOLT AK, CIPOLOTTI, Lisa, WATT H, FOX NC, JANSSEN JC, ROSSOR MN, GODBOLT AK, CIPOLOTTI L, WATT H, FOX NC, JANSSEN JC, and ROSSOR MN
Subjects: RISK, Settore M-PSI/02 - Psicobiologia E Psicologia Fisiologica, COGNITIVE DECLINE, PRESENILIN-1 MUTATION, DIAGNOSIS
Abstract: BACKGROUND: Knowledge of the evolution of cognitive deficits in Alzheimer disease is important for our understanding of disease progression. Previous reports, however, have either lacked detail or have not covered the presymptomatic stages. OBJECTIVE: To delineate the onset and progression of clinical and neuropsychological abnormalities in familial Alzheimer disease. METHODS: Nineteen subjects with familial Alzheimer disease underwent serial clinical and neuropsychological assessments. Eight of these had undergone presymptomatic assessments. The follow-up period was 1 to 10 years (mean, 5 years). The relative timing of the occurrence of 3 markers of disease onset and progression (onset of symptoms, Mini-Mental State Examination score < or = 24, and impaired scores on a range of neuropsychological tests) were compared using the binomial exact test. RESULTS: Neurological abnormalities were not prominent, although myoclonus appeared early in some. Mini-Mental State Examination score was not sensitive to early disease. Memory and general intelligence deficits appeared at an earlier stage, in some patients when presymptomatic. Perceptual, naming, and especially spelling skills were preserved to a late stage. CONCLUSION: Familial Alzheimer disease may have a long prodromal phase of several years with subtle deficits initially of general intelligence and memory, while spelling, naming, and perception are relatively preserved until a late stage.
Published: 2004

47. TREM2 variants in Alzheimer's disease

Author: Guerreiro, R, Wojtas, A, Bras, J, Carrasquillo, M, Rogaeva, E, Majounie, E, Cruchaga, C, Sassi, C, Kauwe, Js, Lupton, Mk, Ryten, M, Brown, K, Lowe, J, Ridge, Pg, Hammer, Mb, Wakutani, Y, Hazrati, L, Proitsi, P, Newhouse, S, Lohmann, E, Erginel Unaltuna, N, Medway, C, Hanagasi, H, Troakes, C, Gurvit, H, Bilgic, B, Al Sarraj, S, Benitez, B, Cooper, B, Carrell, D, Emre, M, Zou, F, Ma, L, Murray, M, Dickson, D, Younkin, S, Petersen, Rc, Corcoran, Cd, Cai, Y, Oliveira, C, Ribeiro, Mh, Santana, I, Tschanz, Jt, Gibbs, J, Norton, Mc, Kloszewska, I, Mecocci, Patrizia, Soininen, H, Tsolaki, M, Vellas, B, Munger, Rg, Mann, Dm, Pickering Brown, S, Lovestone, S, Beck, J, Mead, S, Collinge, J, Parsons, L, Pocock, J, Morris, Jc, Revesz, T, Lashley, T, Fox, Nc, Rossor, Mn, Grenier Boley, B, Bellenguez, C, Moskvina, V, Sims, R, Harold, D, Williams, J, Lambert, Jc, Amouyel, P, Graff Radford, N, Goate, A, Rademakers, R, Morgan, K, Powell, J, St George Hyslop, P, Singleton, A, Hardy, J, Gerrish, A, Chapman, J, Abraham, R, Hollingworth, P, Hamshere, M, Pahwa, Js, Dowzell, K, Williams, A, Jones, N, Thomas, C, Stretton, A, Morgan, A, Williams, K, Thomas, S, Brayne, C, Rubinsztein, Dc, Gill, M, Lawlor, B, Lynch, A, Passmore, P, Craig, D, Mcguinness, B, Johnston, Ja, Todd, S, Holmes, C, Smith, A, Love, S, Kehoe, Pg, Maier, W, Jessen, F, Heun, R, Kölsch, H, Schürmann, B, Ramirez, A, van den Bussche, H, Heuser, I, Kornhuber, J, Wiltfang, J, Dichgans, M, Frölich, L, Hampel, H, Hüll, M, Rujescu, D, Nowotny, P, Mayo, K, Livingston, G, Bass, Nj, Gurling, H, Mcquillin, A, Gwilliam, R, Deloukas, P, Nöthen, Mm, Holmans, P, O'Donovan, M, Owen, Mj, Zelenika, D, Epelbaum, J, Dartigues, Jf, Tzourio, C, Berr, C, Boland, A, Campion, D, Alpérovitch, A, Lathrop, M, Smith, C, Trabzuni, D, Walker, R, Weale, M., Wiltfang, J. (Beitragende*r), EADI Consortium, GERAD Consortium, UKBE Consortium, and Alzheimer Genetic Anal Grp
Subjects: Genetics, TREM2, SORL1, Medizin, Genome-wide association study, General Medicine, Biology, medicine.disease, PSEN2, medicine, Dementia, Human medicine, Alzheimer's disease, Exome, Common disease-common variant
Abstract: BACKGROUND Homozygous loss-of-function mutations in TREM2, encoding the triggering receptor expressed on myeloid cells 2 protein, have previously been associated with an autosomal recessive form of early-onset dementia. METHODS We used genome, exome, and Sanger sequencing to analyze the genetic variability in TREM2 in a series of 1092 patients with Alzheimer's disease and 1107 controls (the discovery set). We then performed a meta-analysis on imputed data for the TREM2 variant rs75932628 (predicted to cause a R47H substitution) from three genomewide association studies of Alzheimer's disease and tested for the association of the variant with disease. We genotyped the R47H variant in an additional 1887 cases and 4061 controls. We then assayed the expression of TREM2 across different regions of the human brain and identified genes that are differentially expressed in a mouse model of Alzheimer's disease and in control mice. RESULTS We found significantly more variants in exon 2 of TREM2 in patients with Alzheimer's disease than in controls in the discovery set (P = 0.02). There were 22 variant alleles in 1092 patients with Alzheimer's disease and 5 variant alleles in 1107 controls (P
Published: 2013

48. USING FLORBETAPIR PET TO INCREASE DIAGNOSTIC CERTAINTY IN ATYPICAL DEMENTIAS

Author: Weston, PSJ, primary, Paterson, RW, additional, Lehmann, M, additional, Modat, M, additional, Bomanji, JB, additional, Kayani, I, additional, Dickson, J, additional, Barnes, A, additional, Cash, DM, additional, Ourselin, S, additional, Zetterberg, H, additional, Toombs, J, additional, Warren, JD, additional, Rossor, MN, additional, Fox, NC, additional, and Schott, JM, additional
Published: 2015
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49. Functional connectivity in autosomal dominant and late-onset Alzheimer disease

Author: Thomas, JB, Brier, MR, Bateman, RJ, Snyder, AZ, Benzinger, TL, Xiong, C, Raichle, M, Holtzman, DM, Sperling, RA, Mayeux, R, Ghetti, B, Ringman, JM, Salloway, S, McDade, E, Rossor, MN, Ourselin, S, Schofield, PR, Masters, CL, Martins, RN, Weiner, MW, Thompson, PM, Fox, NC, Koeppe, RA, Jack, CR, Mathis, CA, Oliver, A, Blazey, TM, Moulder, K, Buckles, V, Hornbeck, R, Chhatwal, J, Schultz, AP, Goate, AM, Fagan, AM, Cairns, NJ, Marcus, DS, Morris, JC, Ances, BM, Thomas, JB, Brier, MR, Bateman, RJ, Snyder, AZ, Benzinger, TL, Xiong, C, Raichle, M, Holtzman, DM, Sperling, RA, Mayeux, R, Ghetti, B, Ringman, JM, Salloway, S, McDade, E, Rossor, MN, Ourselin, S, Schofield, PR, Masters, CL, Martins, RN, Weiner, MW, Thompson, PM, Fox, NC, Koeppe, RA, Jack, CR, Mathis, CA, Oliver, A, Blazey, TM, Moulder, K, Buckles, V, Hornbeck, R, Chhatwal, J, Schultz, AP, Goate, AM, Fagan, AM, Cairns, NJ, Marcus, DS, Morris, JC, and Ances, BM
Abstract: IMPORTANCE: Autosomal dominant Alzheimer disease (ADAD) is caused by rare genetic mutations in 3 specific genes in contrast to late-onset Alzheimer disease (LOAD), which has a more polygenetic risk profile. OBJECTIVE: To assess the similarities and differences in functional connectivity changes owing to ADAD and LOAD. DESIGN, SETTING, AND PARTICIPANTS: We analyzed functional connectivity in multiple brain resting state networks (RSNs) in a cross-sectional cohort of participants with ADAD (n = 79) and LOAD (n = 444), using resting-state functional connectivitymagnetic resonance imaging at multiple international academic sites. MAIN OUTCOMES AND MEASURES: For both types of AD, we quantified and compared functional connectivity changes in RSNs as a function of dementia severity measured by the Clinical Dementia Rating Scale. In ADAD, we qualitatively investigated functional connectivity changes with respect to estimated years from onset of symptoms within 5 RSNs. RESULTS: A decrease in functional connectivity with increasing Clinical Dementia Rating scores were similar for both LOAD and ADAD in multiple RSNs. Ordinal logistic regression models constructed in one type of Alzheimer disease accurately predicted clinical dementia rating scores in the other, further demonstrating the similarity of functional connectivity loss in each disease type. Among participants with ADAD, functional connectivity in multiple RSNs appeared qualitatively lower in asymptomatic mutation carriers near their anticipated age of symptom onset compared with asymptomatic mutation noncarriers. CONCLUSIONS AND RELEVANCE: Resting-state functional connectivity magnetic resonance imaging changes with progressing AD severity are similar between ADAD and LOAD. Resting-state functional connectivitymagnetic resonance imagingmay be a useful end point for LOAD and ADAD therapy trials. Moreover, the disease process of ADAD may be an effective model for the LOAD disease process.
Published: 2014

50. Gene-wide analysis detects two new susceptibility genes for Alzheimer's disease

Author: Escott-Price, V, Bellenguez, C, Wang, LS, Choi, SH, Harold, D, Jones, L, Holmans, P, Gerrish, A, Vedernikov, A, Richards, A, DeStefano, AL, Lambert, JC, Ibrahim-Verbaas, CA, Naj, AC, Sims, R, Jun, G, Bis, JC, Beecham, GW, Grenier-Boley, B, Russo, G, Thornton-Wells, TA, Denning, N, Smith, AV, Chouraki, V, Thomas, C, Arfan Ikram, M, Zelenika, D, Vardarajan, BN, Kamatani, Y, Lin, CF, Schmidt, H, Kunkle, B, Dunstan, ML, Vronskaya, M, Johnson, AD, Ruiz, A, Bihoreau, MT, Reitz, C, Pasquier, F, Hollingworth, P, Hanon, O, Fitzpatrick, AL, Buxbaum, JD, Campion, D, Crane, PK, Becker, CBT, Gudnason, V, Cruchaga, C, Craig, D, Amin, N, Berr, C, Lopez, OL, De Jager, PL, Deramecourt, V, Johnston, JA, Evans, D, Lovestone, S, Letenneur, L, Hernández, I, Rubinsztein, DC, Eiriksdottir, G, Sleegers, K, Goate, AM, Fiévet, N, Huentelman, MJ, Gill, M, Brown, K, Kamboh, MI, Keller, L, Barberger-Gateau, P, McGuinness, B, Larson, EB, Myers, AJ, Dufouil, C, Todd, S, Wallon, D, Love, S, Rogaeva, E, Gallacher, J, St George-Hyslop, P, Clarimon, J, Lleo, A, Bayer, A, Tsuang, DW, Yu, L, Tsolaki, M, Bossù, P, Spalletta, G, Proitsi, P, Collinge, J, Sorbi, S, Sanchez Garcia, F, Fox, NC, Hardy, J, Naranjo, MCD, Bosco, P, Clarke, R, Brayne, C, Galimberti, D, Scarpini, E, Escott-Price, V, Bellenguez, C, Wang, LS, Choi, SH, Harold, D, Jones, L, Holmans, P, Gerrish, A, Vedernikov, A, Richards, A, DeStefano, AL, Lambert, JC, Ibrahim-Verbaas, CA, Naj, AC, Sims, R, Jun, G, Bis, JC, Beecham, GW, Grenier-Boley, B, Russo, G, Thornton-Wells, TA, Denning, N, Smith, AV, Chouraki, V, Thomas, C, Arfan Ikram, M, Zelenika, D, Vardarajan, BN, Kamatani, Y, Lin, CF, Schmidt, H, Kunkle, B, Dunstan, ML, Vronskaya, M, Johnson, AD, Ruiz, A, Bihoreau, MT, Reitz, C, Pasquier, F, Hollingworth, P, Hanon, O, Fitzpatrick, AL, Buxbaum, JD, Campion, D, Crane, PK, Becker, CBT, Gudnason, V, Cruchaga, C, Craig, D, Amin, N, Berr, C, Lopez, OL, De Jager, PL, Deramecourt, V, Johnston, JA, Evans, D, Lovestone, S, Letenneur, L, Hernández, I, Rubinsztein, DC, Eiriksdottir, G, Sleegers, K, Goate, AM, Fiévet, N, Huentelman, MJ, Gill, M, Brown, K, Kamboh, MI, Keller, L, Barberger-Gateau, P, McGuinness, B, Larson, EB, Myers, AJ, Dufouil, C, Todd, S, Wallon, D, Love, S, Rogaeva, E, Gallacher, J, St George-Hyslop, P, Clarimon, J, Lleo, A, Bayer, A, Tsuang, DW, Yu, L, Tsolaki, M, Bossù, P, Spalletta, G, Proitsi, P, Collinge, J, Sorbi, S, Sanchez Garcia, F, Fox, NC, Hardy, J, Naranjo, MCD, Bosco, P, Clarke, R, Brayne, C, Galimberti, D, and Scarpini, E
Abstract: Background: Alzheimer's disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer's Project Consortium, comprising over 7 m genotypes from 25,580 Alzheimer's cases and 48,466 controls. Principal Findings: In addition to earlier reported genes, we detected genome-wide significant loci on chromosomes 8 (TP53INP1, p = 1.4×10-6) and 14 (IGHV1-67 p = 7.9×10-8) which indexed novel susceptibility loci. Significance: The additional genes identified in this study, have an array of functions previously implicated in Alzheimer's disease, including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in Alzheimer's disease.
Published: 2014

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