Childhood adversity in parents of patients with pediatric multiple sclerosis
(2025) Multiple Sclerosis and Related Disorders, 98, art. no. 106424, .
O’Neill, K.A.a , Charvet, L.a , George, A.a , Waltz, M.b , Casper, T.C.b , Benson, L.c , Gorman, M.c , Mar, S.d , Ness, J.e , Schreiner, T.f , Waubant, E.g , Weinstock-Guttman, B.h , Wheeler, Y.e , Ortiz, R.a , Krupp, L.B.a
a NYU Grossman School of Medicine, New York, NY, United States
b University of Utah School of Medicine, Salt Lake City, UT, United States
c Boston Children’s Hospital, Boston, MA, United States
d Washington University in St. Louis School of Medicine, St. Louis, MO, United States
e University of Alabama at Birmingham School of Medicine, Birmingham, AL, United States
f University of Colorado School of Medicine, Aurora, CO, United States
g University of California San Francisco School of Medicine, San Francisco, CA, United States
h University of Buffalo Jacobs School of Medicine, Buffalo, NY, United States
Abstract
Background: Childhood environmental factors back to the prenatal environment can contribute to MS risk. Childhood adversity, which causes biological, behavioral, and epigenetic changes that can be passed down through families, has been understudied in MS. Here, we emphasize the need to understand the role that intergenerational adversity may play among families affected by MS. Objective: To evaluate the frequency and types of adverse childhood experiences among parents of children with MS. Methods: Individuals with pediatric MS (n = 68) were enrolled in a longitudinal study of cognition. At enrollment, the patient and one caregiver or parent completed questionnaires. As the pediatric participants were under age 18 at time of enrollment, one parent completed the Adverse Childhood Experiences (ACEs, a 10-item self-report measure) about the parents’ own childhood. Results from the ACE questionnaire among parents of pediatric healthy controls (n = 96) and adults in a national cohort are also reported for comparison. Results: Over half of pediatric MS parents reported at least one ACE exposure. Of parents that did have ACE exposures, the exposures were broad in terms of abuse, neglect, and household dysfunction. Over 10 % of parents reported total ACE scores of 7 or above. Conclusion: Over half of pediatric MS parents experienced some degree of childhood adversity. The impact of intergenerational adversity on the development of pediatric onset MS warrants further study. © 2025 Elsevier B.V.
Author Keywords
Adverse childhood experiences; Early onset multiple sclerosis; Multiple sclerosis; Pediatric multiple sclerosis; Social determinants of health
Funding details
National Multiple Sclerosis SocietyNMSSRG-1507-05285, HC-1509-06233
National Multiple Sclerosis SocietyNMSS
Document Type: Article
Publication Stage: Final
Source: Scopus
Disuse-driven plasticity in the human thalamus and putamen
(2025) Cell Reports, 44 (4), art. no. 115570, .
Chauvin, R.J.a , Newbold, D.J.b , Nielsen, A.N.a , Miller, R.L.c , Krimmel, S.R.a , Metoki, A.a , Wang, A.a d , Van, A.N.a e , Montez, D.F.a f , Marek, S.g , Suljic, V.a , Baden, N.J.a , Ramirez-Perez, N.a , Scheidter, K.M.a , Monk, J.S.a , Whiting, F.I.a , Adeyemo, B.a , Roland, J.L.h , Snyder, A.Z.a g , Kay, B.P.a , Raichle, M.E.a g i j , Laumann, T.O.f , Gordon, E.M.g , Dosenbach, N.U.F.a d g i k
a Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
b Department of Neurology, New York University Grossman School of Medicine, New York, NY 10016, United States
c Basque Center on Cognition, Brain and Language, Gipuzkoa, Donostia, Spain
d Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States
e Division of Computation and Data Science, Washington University School of Medicine, St. Louis, MO 63110, United States
f Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States
g Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States
h Taylor Family Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110, United States
i Department of Psychological and Brain Sciences, Washington University in St. Louis, St Louis, MO 63110, United States
j Department of Neuroscience, Washington University School of Medicine, St Louis, MO 63110, United States
k Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
Abstract
Subcortical plasticity has mainly been studied using invasive electrophysiology in animals. Here, we leverage precision functional mapping (PFM) to study motor plasticity in the human subcortex during 2 weeks of upper-extremity immobilization with daily resting-state and motor task fMRI. We found previously that, in the cortex, limb disuse drastically impacts disused primary motor cortex functional connectivity (FC) and is associated with spontaneous fMRI pulses. It remains unknown whether disuse-driven plasticity pulses and FC changes are cortex specific or whether they could also affect movement-critical nodes in the thalamus and striatum. Tailored analysis methods now show spontaneous disuse pulses and FC changes in the dorsal posterior putamen and central thalamus (centromedian [CM], ventral-intermediate [VIM], and ventroposterior-lateral nuclei), representing a motor circuit-wide plasticity phenomenon. The posterior putamen effects suggest plasticity in stimulus-driven habit circuitry. Importantly, thalamic plasticity effects are focal to nuclei used as deep brain stimulation targets for essential tremor/Parkinson’s disease (VIM) and epilepsy/coma (CM). © 2025 The Authors
Author Keywords
CP: Neuroscience; disused; fMRI; motor plasticity; putamen; resting state; thalamus
Funding details
Intellectual and Developmental Disabilities Research CenterIDDRC
Hope Center for Neurological Disorders, Washington University in St. Louis
Pacific Northwest Kiwanis Foundation
McDonnell Center for Systems Neuroscience
Mallinckrodt Institute of Radiology, School of Medicine, Washington University in St. LouisMIR1S10OD018091-01, S10OD025200, 1S10RR022984-01A1
National Institutes of HealthNIHMH124567, NS133486, NS123345, NS129521, NS088590, MH096773, MH129616, MH121276, NS140256, MH122066, T32DA007261, NS098482
Document Type: Article
Publication Stage: Final
Source: Scopus
Whole-genome sequencing reveals the impact of lipid pathway and APOE genotype on brain amyloidosis
(2025) Human Molecular Genetics, 34 (8), pp. 739-748.
Patel, M.a , Pottier, C.b , Fan, K.-H.c , Cetin, A.a , Johnson, M.a , Ali, M.a , Liu, M.a , Gorijala, P.a , Budde, J.a , Shi, R.c , Cohen, A.D.d , Becker, J.T.e , Snitz, B.E.e , Aizenstein, H.c , Lopez, O.L.e , Morris, J.C.f , Kamboh, M.I.g , Cruchaga, C.h
a Department of Psychiatry, Neurogenomics and Informatics, Washington University School of Medicine, 4444 Forest Park Ave, St. Louis, MO 63108, United States
b Department of Psychiatry, Neurogenomics and Informatics, Department of Neurology, Washington University, School of Medicine, 4444 Forest Park Ave, St. Louis, MO 63108, United States
c Department of Human Genetics, University of Pittsburgh, 130 De Soto St, Pittsburgh, PA 15261, United States
d Department of Psychiatry, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213, United States
e Department of Neurology, University of Pittsburgh, 3471 Fifth Avenue, Pittsburgh, PA 15213, United States
f Department of Neurology, Hope Center for Neurologic Diseases, Section on Aging & Dementia, Institute of Clinical and Translational Sciences, Knight Alzheimer Disease Research Center, Washington University, School of Medicine, 4901 Forest Park Ave 4th floor, St. Louis, MO 63108, United States
g Department of Human Genetics, Department of Psychiatry, University of Pittsburgh, 130 De Soto St, Pittsburgh, PA 15261, United States
h Department of Psychiatry, Neurogenomics and Informatics, Department of Neurology, Hope Center for Neurologic Diseases, Knight Alzheimer Disease Research Center, Washington University, School of Medicine, 4444 Forest Park Ave, St. Louis, MO 63108, United States
Abstract
Amyloid-PET imaging tracks the accumulation of amyloid beta (Aβ) deposits in the brain. Amyloid plaques accumulation may begin 10 to 20 years before the individual experiences clinical symptoms associated with Alzheimer’s diseases (ad). Recent large-scale genome-wide association studies reported common risk factors associated with brain amyloidosis, suggesting that this endophenotype is driven by genetic variants. However, these loci pinpoint to large genomic regions and the functional variants remain to be identified. To identify new risk factors associated with brain amyloid deposition, we performed whole-genome sequencing on a large cohort of European descent individuals with amyloid PET imaging data (n = 1,888). Gene-based analysis for coding variants was performed using SKAT-O for amyloid PET as a quantitative endophenotype that identified genome-wide significant association for APOE (P = 2.45 × 10-10), and 26 new candidate genes with suggestive significance association (P < 5. 0 × 10-03) including SCN7A (P = 7.31 × 10-05), SH3GL1 (P = 7.56 × 10-04), and MFSD12 (P = 8.51 × 10-04). Enrichment analysis highlighted the lipid binding pathways as associated with Aβ deposition in brain driven by PITPNM3 (P = 4.27 × 10-03), APOE (P = 2.45 × 10-10), AP2A2 (P = 1.06 × 10-03), and SH3GL1 (P = 7.56 × 10-04). Overall, our data strongly support a connection between lipid metabolism and the deposition of Aβ in the brain. Our study illuminates promising avenues for therapeutic interventions targeting lipid metabolism to address brain amyloidosis. © The Author(s) 2025.
Author Keywords
Amyloid-PET; APOE; Brain amyloidosis; Lipid; Whole Genome Sequencing
Funding details
Northern California Institute for Research and EducationNCIRE
Hope Center for Neurological Disorders, Washington University in St. Louis
National Institute of Biomedical Imaging and BioengineeringNIBIB
Alzheimer’s Disease Neuroimaging InitiativeADNI
National Institutes of HealthNIHP30 AG066444, P01 AG026276, U01 AG024904
University of Southern CaliforniaUSCR01AG064877
National Institute on AgingNIAR01AG064877
U.S. Department of DefenseDODW81XWH-12-2-0012
P30 AG066468, RF1AG052525, R01AG052446, R01AG052521, U01AT000162, P01AG025204, UF1AG051197, AG025516
Document Type: Article
Publication Stage: Final
Source: Scopus
Somatic and Stem Cell Bank to study the contribution of African ancestry to dementia: African iPSC Initiative
(2025) Alzheimer’s and Dementia, 21 (4), art. no. e70145, .
Maina, M.B.a b , Isah, M.B.a c , Marsh, J.A.d , Muhammad, Z.a b , Babazau, L.a , Idris, A.A.a , Aladyeva, E.e , Miller, N.d , Starr, E.d , Miller, K.J.d , Lee, S.d , Minaya, M.d , Wray, S.f , Harari, O.e , Goni, B.W.a , Serpell, L.C.b , Karch, C.M.d
a Biomedical Science Research and Training Centre, Yobe State University, Damaturu, Nigeria
b Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, United Kingdom
c Department of Biochemistry, Umaru Musa Yar’adua University Katsina, Katsina, Nigeria
d Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
e Division of Neurogenetics, Department of Neurology, The Neuroscience Research Institute, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, United States
f Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, United Kingdom
Abstract
INTRODUCTION: Africa, home to 1.4 billion people and the highest genetic diversity globally, harbors unique genetic variants crucial for understanding complex diseases like neurodegenerative disorders. However, African populations remain underrepresented in induced pluripotent stem cell (iPSC) collections, limiting the exploration of population-specific disease mechanisms and therapeutic discoveries. METHODS: To address this gap, we established an open-access African Somatic and Stem Cell Bank. RESULTS: In this initial phase, we generated 10 rigorously characterized iPSC lines from fibroblasts representing five Nigerian ethnic groups and both sexes. These lines underwent extensive profiling for pluripotency, genetic stability, differentiation potential, and Alzheimer’s disease and Parkinson’s disease risk variants. Clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 9 technology was used to introduce frontotemporal dementia-associated MAPT mutations (P301L and R406W). DISCUSSION: This collection offers a renewable, genetically diverse resource to investigate disease pathogenicity in African populations, facilitating breakthroughs in neurodegenerative research, drug discovery, and regenerative medicine. Highlights: We established an open-access African Somatic and Stem Cell Bank. 10 induced pluripotent stem cell lines from five Nigerian ethnic groups were rigorously characterized. Clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 9 technology was used to introduce frontotemporal dementia-causing MAPT mutations. The African Somatic and Stem Cell Bank is a renewable, genetically diverse resource for neurodegenerative research. © 2025 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.
Author Keywords
African ancestry; Alzheimer’s disease; cell bank; clustered regularly interspaced palindromic repeats/CRISPR-associated protein 9; fibroblasts; frontotemporal dementia; induced pluripotent stem cells; Parkinson’s disease; polygenic risk scores
Funding details
Rainwater Charitable FoundationRCF
Hope Center for Neurological Disorders, Washington University in St. Louis
Wellcome TrustWT
National Institutes of HealthNIHUL1 TR002345, K01 AG083215, P30 AG066444, U19 AG069701, RF1 NS110890
National Institutes of HealthNIH
Document Type: Article
Publication Stage: Final
Source: Scopus
Characterizing and validating 12-month reliable cognitive change in Early-Onset Alzheimer’s Disease for use in clinical trials
(2025) The Journal of Prevention of Alzheimer’s Disease, 12 (4), p. 100075.
Hammers, D.B.a , Musema, J.a , Dage, J.L.a , Kirby, K.a , Clark, D.a , Eloyan, A.b , Thangarajah, M.b , Taurone, A.b , La Joie, R.c , Kramer, J.c , Rabinovici, G.D.c , Touroutoglou, A.d , Dickerson, B.C.d , Vemuri, P.e , Jones, D.T.e , Aisen, P.f , Nudelman, K.N.g , Atri, A.h , Day, G.S.i , Graff-Radford, N.R.i , Duara, R.j , Grant, I.k , Honig, L.S.l , Johnson, E.C.B.m , Masdeu, J.C.n , Mendez, M.F.o , Womack, K.p , Musiek, E.p , Onyike, C.U.q , Riddle, M.r , Salloway, S.r , Rogalski, E.s , Sha, S.J.t , Turner, R.S.u , Wingo, T.S.v , Wolk, D.A.w , Carrillo, M.C.x , Apostolova, L.G.y , LEADS Consortiumz
a Department of Neurology, Indiana University School of Medicine, Indianapolis, IN 46202, United States
b Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI 02912, United States
c Department of Neurology, University of California – San Francisco, San Francisco, CA 94143, United States
d Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States
e Department of Neurology, Mayo Clinic, Rochester, MN 55905, United States
f Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA 92121, United States
g Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, United States
h Banner Sun Health Research Institute, Sun City, AZ 85351, United States
i Department of Neurology, Mayo Clinic, Jacksonville, FL 32224, United States
j Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL 33140, United States
k Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States
l Taub Institute and Department of Neurology, Columbia University Irving Medical CenterNY 10032, United States
m Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, 30307, United States
n Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, TX 77030, United States
o Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States
p Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, United States
q Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
r Department of Neurology, Alpert Medical School, Brown University, Providence, RI 02912, United States
s Healthy Aging & Alzheimer’s Research Care Center, Department of Neurology, University of Chicago, Chicago, IL 60637, United States
t Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA 94304, United States
u Department of Neurology, Georgetown University, United States
v Department of Neurology, UC Davis Alzheimer’s Disease Research Center, University of California – Davis, Davis, CA 95816, United States
w Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
x Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, IL 60603, United States
y Department of Neurology, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA; Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, 46202, USA
Abstract
BACKGROUND: As literature suggests that Early-Onset Alzheimer’s Disease (EOAD) and late-onset AD may differ in important ways, need exists for randomized clinical trials for treatments tailored to EOAD. Accurately measuring reliable cognitive change in individual patients with EOAD will have great value for these trials. OBJECTIVES: The current study sought to characterize and validate 12-month reliable change from the Longitudinal Early-Onset Alzheimer’s Disease Study (LEADS) neuropsychological battery. DESIGN: Standardized regression-based (SRB) prediction equations were developed from age-matched cognitively intact participants within LEADS, and applied to clinically impaired participants from LEADS. SETTING: Participants were recruited from outpatient academic medical centers. PARTICIPANTS: Participants were enrolled in LEADS and diagnosed with amyloid-positive EOAD (n = 189) and amyloid-negative early-onset cognitive impairment not related to AD (EOnonAD; n = 43). MEASUREMENT: 12-month reliable change (Z-scores) was compared between groups across cognitive domain composites, and distributions of individual participant trajectories were examined. Prediction of Z-scores by common AD biomarkers was also considered. RESULTS: Both EOAD and EOnonAD displayed significantly lower 12-month follow-up scores than were predicted based on SRB equations, with declines more pronounced for EOAD across several domains. AD biomarkers of cerebral β-amyloid, tau, and EOAD-specific atrophy were predictive of 12-month change scores. CONCLUSIONS: The current results support including EOAD patients in longitudinal clinical trials, and generate evidence of validation for using 12-month reliable cognitive change as a clinical outcome metric in clinical trials in EOAD cohorts like LEADS. Doing so will enhance the success of EOAD trials and permit a better understanding of individual responses to treatment. Copyright © 2025. Published by Elsevier Masson SAS.
Author Keywords
Cognition; Early-onset Alzheimer’s disease, memory; Longitudinal; Non-Amnestic
Document Type: Article
Publication Stage: Final
Source: Scopus
Early increase of the synaptic blood marker β-synuclein in asymptomatic autosomal dominant Alzheimer’s disease
(2025) Alzheimer’s and Dementia, 21 (4), art. no. e70146, .
Oeckl, P.a b , Mayer, B.c , Bateman, R.J.d , Day, G.S.e , Fox, N.C.f , Huey, E.D.g , Ibanez, L.h , Ikeuchi, T.i , Jucker, M.j k , Lee, J.-H.l , Levin, J.m n o , Llibre-Guerra, J.J.d , Lopera, F.p , McDade, E.d , Morris, J.C.q , Niimi, Y.r , Roh, J.H.s , Sánchez-Valle, R.t , Schofield, P.R.u v , Otto, M.w , the Dominantly Inherited Alzheimer Networkx
a Department of Neurology, Ulm University Hospital, Ulm, Germany
b German Center for Neurodegenerative Diseases (DZNE) Ulm, Ulm, Germany
c Institute for Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
d Department of Neurology, Washington University School of Medicine, Saint Louis, MO, United States
e Department of Neurology, Mayo Clinic in Florida, Jacksonville, FL, United States
f The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, United Kingdom
g Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, United States
h Department of Psychiatry, Department of Neurology, and NeuroGenomics and Informatics Center, Washington University, Saint Louis, MO, United States
i Brain Research Institute, Niigata University, Niigata, Japan
j German Center for Neurodegenerative Diseases (DZNE) Tübingen, Tübingen, Germany
k Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
l Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
m Department of Neurology, LMU University Hospital, LMU Munich, Munich, Germany
n German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
o Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
p Neuroscience Group of Antioquia (GNA), Medicine Faculty, Antioquia University, Medellín, Colombia
q Department of Neurology and the Knight Alzheimer Disease Research Center, Washington University, Saint Louis, MO, United States
r Unit for Early and Exploratory Clinical Development, The University of Tokyo, Tokyo, Japan
s Departments of Neurology and Physiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, South Korea
t Alzheimer’s Disease and Other Cognitive Disorders Unit. Hospital Clínic de Barcelona, FRCB-IDIBAPS, University of Barcelona, Barcelona, Spain
u Neuroscience Research Australia, Sydney, NSW, Australia
v School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
w Department of Neurology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
Abstract
INTRODUCTION: β-synuclein is a promising blood marker to track synaptic degeneration in Alzheimer’s disease (AD) but changes in preclinical AD are unclear. METHODS: We investigated serum β-synuclein in 69 cognitively unimpaired mutation non-carriers, 78 cognitively unimpaired AD mutation carriers (asymptomatic AD), and 31 symptomatic mutation carriers from the Dominantly Inherited Alzheimer Network. RESULTS: β-synuclein levels were already higher in asymptomatic AD mutation carriers compared to non-carriers and highest in symptomatic carriers. Longitudinal trajectories and correlation analyses indicated that β-synuclein levels start to rise after amyloid deposition preceding axonal degeneration, brain atrophy and hypometabolism, and cognitive decline. β-synuclein levels were associated with cognitive impairment and gradually increased with declining cognition. DISCUSSION: Our study supports the use of blood β-synuclein to track synaptic changes in preclinical AD and as a surrogate marker for cognitive impairment which might be used in early diagnosis and to support patient selection and monitoring of treatment effects in clinical trials. Highlights: Blood β-synuclein levels were already higher in asymptomatic Alzheimer’s disease (AD) mutation carriers. Blood β-synuclein levels were highest in symptomatic AD mutation carriers. Blood β-synuclein levels start to rise 11 years before symptom onset. Rise of β-synuclein precedes axonal degeneration, brain atrophy, and cognitive decline. β-synuclein levels gradually increased with declining cognition. © 2025 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.
Author Keywords
asymptomatic mutation carriers; autosomal dominant Alzheimer´s disease; blood biomarker; preclinical Alzheimer´s disease; synaptic degeneration; β-synuclein
Funding details
Deutsches Zentrum für Neurodegenerative ErkrankungenDZNE
Instituto de Salud Carlos IIIISCIII
Fleni
Fondation Thierry Latran
Biogen
National Institutes of HealthNIH
Roche
Foundation for Barnes-Jewish HospitalFBJH
Ministry of Health and WelfareMOHW
Cure Alzheimer’s FundCAF
Korea Dementia Research CenterKDRC
National Institute on AgingNIA
Korea Health Industry Development InstituteKHIDI
Tau Consortium
Fondation CharcotD.7090
HI21C0066
Michael J. Fox Foundation for Parkinson’s ResearchMJFFMJFF‐020770
Alzheimer’s AssociationAASG-20-690363-DIAN, AARFD‐21‐851415, SG‐20‐690363
European CommissionECMOODMARKER 01EW2008, 01ED2008A
Deutsche ForschungsgemeinschaftDFGSFB1279
K01AG073526
D.5009
Japan Agency for Medical Research and DevelopmentAMEDAMED JP24dk0207066
Bundesministerium für Bildung und ForschungBMBFFTLDc 01GI1007A
Alzheimer Forschung InitiativeAFI23‐PPG‐674‐2, 24‐SGP‐691, 20059CB
Document Type: Article
Publication Stage: Final
Source: Scopus
The associations between attentional control, episodic memory, and Alzheimer’s disease biomarkers of tau and neurodegeneration
(2025) Journal of Alzheimer’s Disease: JAD, 104 (2), pp. 351-363.
Stojanovic, M.a b , Millar, P.R.b , McKay, N.S.c , Aschenbrenner, A.J.b , Balota, D.A.a , Hassenstab, J.b d , Benzinger, T.L.c d , Morris, J.C.b d , Ances, B.M.b d
a Department of Psychological & Brain Sciences, Washington University in St Louis, St. Louis, MO, United States
b Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
c Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
d Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, United States
Abstract
BACKGROUND: While episodic memory decline is the most common cognitive symptom of Alzheimer’s disease (AD), changes in attentional control have also been found to be sensitive to early AD pathology. The relations between longitudinal trajectories of these specific cognitive domains, especially attentional control, and biomarkers of tau and neurodegeneration have not been thoroughly examined. OBJECTIVE: We examined whether baseline tau positron emission tomography (PET) and cortical thickness, relatively later markers within the AD cascade, predicted cross-sectional and longitudinal changes in episodic memory and attentional control. METHODS: Cognitively normal individuals ([Clinical Dementia Rating CDR®] = 0; n = 249) at baseline completed a magnetic resonance imaging (MRI), tau PET, and multiple assessments of episodic memory and attentional control. Generalized additive mixed-effects models examined whether tau PET summary measure and cortical thickness signature predicted cross-sectional and longitudinal trajectories of attentional control and episodic memory. RESULTS: Higher tau PET and lower MRI cortical thickness were generally associated with worse cross-sectional cognitive performance. Our exploratory analyses found cortex-wide associations between tau PET and episodic memory, with limited suggestions of region-specific associations with attentional control. On longitudinal follow-up, higher tau PET was associated with a greater decline in episodic memory. CONCLUSIONS: These results indicate that tau PET is particularly sensitive to detecting longitudinal changes in episodic memory. This further informs relevant endpoints for clinical drug trials in cognitively normal individuals. Future studies might consider longer follow-ups and lag associations between changes in AD biomarkers and changes in cognition.
Author Keywords
Alzheimer’s disease biomarkers; attentional control; cortical thickness; episodic memory; PET-tau
Document Type: Article
Publication Stage: Final
Source: Scopus
Sleep-wake variation in body temperature regulates tau secretion and correlates with CSF and plasma tau
(2025) The Journal of Clinical Investigation, 135 (7), .
Canet, G.a b , Da Gama Monteiro, F.a c , Rocaboy, E.b , Diego-Diaz, S.b , Khelaifia, B.a b , Godbout, K.b , Lachhab, A.b , Kim, J.d , Valencia, D.I.e , Yin, A.d , Wu, H.-T.d , Howell, J.d , Blank, E.d , Laliberté, F.a , Fortin, N.a , Boscher, E.a b , Fereydouni-Forouzandeh, P.b , Champagne, S.b , Guisle, I.a b , Hébert, S.S.a b , Pernet, V.a c f g h , Liu, H.i , Lu, W.i , Debure, L.d , Rapoport, D.M.e , Ayappa, I.e , Varga, A.W.e , Parekh, A.e , Osorio, R.S.d , Lacroix, S.a c , Burns, M.P.j , Lucey, B.P.i , Blessing, E.M.d , Planel, E.a b
a Centre de Recherche du CHU de Québec – Université Laval, Axe Neurosciences, Québec CityQuébec, Canada
b Université Laval, Département de Psychiatrie et Neurosciences, Faculté de MédecineQuébec CityQuébec, Canada
c Université Laval, Département de Médecine Moléculaire, Faculté de MédecineQuébec CityQuébec, Canada
d Department of Psychiatry, NYU Grossman School of MedicineNY, United States
e Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, Sleep Medicine, Icahn School of Medicine at Mount SinaiNY, United States
f Department of Neurology, Inselspital
g Center for Experimental Neurology (ZEN), Bern University Hospital, University of BernBern, Switzerland
h Department of Biomedical Research, University of BernBern, Switzerland
i Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
j Laboratory for Brain Injury and Dementia, Department of Neuroscience, Georgetown University Medical CenterWA, United States
Abstract
Sleep disturbance is bidirectionally associated with an increased risk of Alzheimer’s disease and other tauopathies. While the sleep-wake cycle regulates interstitial and cerebrospinal fluid (CSF) tau levels, the underlying mechanisms remain unknown. Understanding these mechanisms is crucial, given the evidence that tau pathology spreads through neuron-to-neuron transfer, involving the secretion and internalization of pathological tau forms. Here, we combined in vitro, in vivo, and clinical methods to reveal a pathway by which changes in body temperature (BT) over the sleep-wake cycle modulate extracellular tau levels. In mice, a higher BT during wakefulness and sleep deprivation increased CSF and plasma tau levels, while also upregulating unconventional protein secretion pathway I (UPS-I) events including (a) intracellular tau dephosphorylation, (b) caspase 3-mediated cleavage of tau (TauC3), and (c) membrane translocation of tau through binding to phosphatidylinositol 4,5-bisphosphate (PIP2) and syndecan 3. In humans, the increase in CSF and plasma tau levels observed after wakefulness correlated with BT increases during wakefulness. By demonstrating that sleep-wake variation in BT regulates extracellular tau levels, our findings highlight the importance of thermoregulation in linking sleep disturbances to tau-mediated neurodegeneration and the preventative potential of thermal interventions.
Author Keywords
Alzheimer disease; Cell biology; Neuroscience; Proteoglycans
Document Type: Article
Publication Stage: Final
Source: Scopus
Automated Imaging Differentiation for Parkinsonism
(2025) JAMA Neurology, .
Vaillancourt, D.E.a b c d , Barmpoutis, A.e , Wu, S.S.f , DeSimone, J.C.a , Schauder, M.a , Chen, R.d , Parrish, T.B.g h , Wang, W.-E.a , Molho, E.i , Morgan, J.C.j , Simon, D.K.k l , Scott, B.L.m , Rosenthal, L.S.n , Gomperts, S.N.k o , Akhtar, R.S.p , Grimes, D.q r , De Jesus, S.s , Stover, N.t , Bayram, E.u , Ramirez-Zamora, A.b c , Prokop, S.b v , Fang, R.d w , Slevin, J.T.x , Kanel, P.y z , Bohnen, N.I.y aa , Tuite, P.ab , Aradi, S.ac , Strafella, A.P.ad ae , Siddiqui, M.S.af , Davis, A.A.ag , Huang, X.s , Ostrem, J.L.ah , Fernandez, H.ai , Litvan, I.u , Hauser, R.A.ac , Pantelyat, A.n , McFarland, N.R.b c , Xie, T.aj , Okun, M.S.b c , Leader, A.i , Russell, Á.l , Babcock, H.l , White-Tong, K.m , Hua, J.ak al , Goodheart, A.E.o , Peterec, E.C.o , Poon, C.p , Galarce, M.B.p , Thompson, T.r , Collier, A.M.s , Cromer, C.t , Putra, N.u , Costello, R.u , Yilmaz, E.ah , Mercado, C.aj , Mercado, T.aj , Fessenden, A.c , Wagner, R.x , Spears, C.C.aa , Caswell, J.L.y , Bryants, M.ab , Kuzianik, K.ac , Ahmed, Y.ac , Bendahan, N.ae , Njoku, J.O.af , Stiebel, A.ag , Zahed, H.am , Wang, S.S.ah , Hoang, P.T.ah , Seemiller, J.n , Du, G.s , AIDP Study Groupan
a Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, United States
b Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, United States
c Department of Neurology, University of Florida, Gainesville, United States
d J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, United States
e Digital Worlds Institute, College of the Arts, University of Florida, Gainesville, United States
f Department of Biostatistics, University of Florida, Gainesville, United States
g Department of Radiology, Northwestern University, Chicago, IL, United States
h Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
i Parkinson’s Disease and Movement Disorders Center, Albany Medical Center, Albany, NY, United States
j Department of Neurology, Medical College of Georgia, Augusta University, Augusta, United States
k Department of Neurology, Harvard Medical School, Boston, MA, United States
l Beth Israel Deaconess Medical Center, Boston, MA, United States
m Department of Neurology, Duke University Medical Center, Durham, NC, United States
n Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
o Massachusetts General Hospital, Boston, United States
p Ken and Ruth Davee, Department of Neurology, Parkinson’s Disease and Movement Disorders Center, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
q Department of Medicine, University of Ottawa, Ottawa, ON, Canada
r The Ottawa Hospital Research Institute, Ottawa, ON, Canada
s Department of Neurology, College of Medicine, Pennsylvania State University, Hershey, United States
t Department of Neurology, University of Alabama at Birmingham, Birmingham, United States
u Department of Neurosciences, University of California, San Diego, United States
v Department of Pathology, Immunology, and Laboratory Science, University of Florida, Gainesville, United States
w Center for Cognitive Aging, Memory Translational Research Institute, University of Florida, Gainesville, United States
x Department of Neurology, University of Kentucky, Lexington, United States
y Department of Radiology, University of Michigan, Ann Arbor, United States
z Morris K. Udall Center of Excellence for Parkinson’s Disease Research, University of Michigan, Ann Arbor, United States
aa Department of Neurology, University of Michigan, Ann Arbor, United States
ab Department of Neurology, University of Minnesota, Minneapolis, United States
ac Department of Neurology, University of South Florida, Tampa, United States
ad Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
ae Krembil Brain Institute, University Health Network, Toronto, ON, Canada
af Department of Neurology, Wake Forest School of Medicine, Winston-Salem, NC, United States
ag Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO, United States
ah Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, United States
ai Center for Neurological Restoration, Neurological Institute, Cleveland Clinic, Cleveland, OH, United States
aj Department of Neurology, University of Chicago, Chicago, IL, United States
ak F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States
al Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
am Department of Neurology, Stanford Movement Disorders Center, Stanford University, Palo Alto, CA, United States
Abstract
IMPORTANCE Magnetic resonance imaging (MRI) paired with appropriate disease-specific machine learning holds promise for the clinical differentiation of Parkinson disease (PD), multiple system atrophy (MSA) parkinsonian variant, and progressive supranuclear palsy (PSP). A prospective study is needed to test whether the approach meets primary end points to be considered in a diagnostic workup. OBJECTIVE To assess the discriminative performance of Automated Imaging Differentiation for Parkinsonism (AIDP) using 3-T diffusion MRI and support vector machine (SVM) learning. DESIGN, SETTING, AND PARTICIPANTS This was a prospective, multicenter cohort study conducted from July 2021 to January 2024 across 21 Parkinson Study Group sites (US/Canada). Included were patients with PD, MSA, and PSP with established criteria and unanimous agreement in the clinical diagnosis among 3 independent, blinded neurologists who specialize in movement disorders. Patients were assigned to a training set or an independent testing set. EXPOSURE MRI. MAIN OUTCOMES AND MEASURES Area under the receiver operating characteristic curve (AUROC) in the testing set for primary model end points of PD vs atypical parkinsonism, MSA vs PSP, PD vs MSA, and PD vs PSP. AIDP was also paired with antemortem MRI to test against postmortem neuropathology in a subset of autopsy cases. RESULTS A total of 316 patients were screened and 249 patients (mean [SD] age, 67.8 [7.7] years; 155 male [62.2%]) met inclusion criteria. Of these patients, 99 had PD, 53 had MSA, and 97 had PSP. A retrospective cohort of 396 patients (mean [SD] age, 65.8 [8.9] years; 234 male [59.1%]) was also included. Of these patients, 211 had PD, 98 had MSA, and 87 had PSP. Patients were assigned to the training set (78%; 104 prospective, 396 retrospective) or independent testing set, which included 145 (22%; 60 PD, 27 MSA, 58 PSP) prospective patients (mean age, 67.4 [SD 7.7] years; 95 male [65.5%]). The model was robust in differentiating PD vs atypical parkinsonism (AUROC, 0.96; 95% CI, 0.93-0.99; positive predictive value [PPV], 0.91; negative predictive value [NPV], 0.83), MSA vs PSP (AUROC, 0.98; 95% CI, 0.96-1.00; PPV, 0.98; NPV, 0.81), PD vs MSA (AUROC, 0.98; 95% CI, 0.96-1.00; PPV, 0.97; NPV, 0.97), and PD vs PSP (AUROC, 0.98; 95% CI, 0.96-1.00; PPV, 0.92; NPV, 0.98). AIDP predictions were confirmed neuropathologically in 46 of 49 brains (93.9%). CONCLUSIONS AND RELEVANCE This prospective multicenter cohort study of AIDP met its primary end points. Results suggest using AIDP in the diagnostic workup for common parkinsonian syndromes. © 2025 American Medical Association. All rights reserved.
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Approaches to timescale choice in cognitive aging research and potential implications for estimated exposure effects: coordinated analyses in ten cohorts of older adults
(2025) Epidemiology, art. no. 10.1097/EDE.0000000000001859, .
Hayes-Larson, E.a b , Andrews, R.M.c , Kezios, K.L.d , Bercu, A.e , Rouanet, A.f , Helmer, C.f , Crane, P.K.g , Gibbons, L.g , Klinedinst, B.S.g , McEvoy, L.K.h , Nichols, E.i , Weuve, J.c , Rajan, K.B.j , Hwang, P.H.c , Mez, J.k , Farina, M.l , Shaw, C.m , Sims, K.D.c , Therneau, T.n , Petersen, R.C.o , Bouteloup, V.f p , Gross, A.q , Albert, M.r , Morris, J.C.s , Masters, C.L.t , Resnick, S.M.u , Maruff, P.t v , Manly, J.J.w , Turney, I.w x , Vonk, J.M.J.y , Avila-Rieger, J.w , Weigand, A.z , Chen, R.c , Wang, J.aa , Proust-Lima, C.f , Mayeda, E.R.a
a Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, United States
b Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
c Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
d Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, United States
e Bordeaux Population Health Research Center, Inserm CIC 1401-EC, Bordeaux School of Public Health, Bordeaux, France
f Bordeaux Population Health Research Center, Inserm UMR1219, Bordeaux School of Public Health, Bordeaux, France
g University of Washington School of Medicine, Seattle, WA, United States
h Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
i Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States
j Rush Institute for Healthy Aging, Division of Community Epidemiology, Rush University, Chicago, IL, United States
k Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
l Department of Human Development and Family Sciences, University of Texas at Austin, Austin, TX, United States
m Amgen Inc., Thousand Oaks, CA, United States
n Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
o Department of Neurology, Mayo Clinic, Rochester, MN, United States
p Pôle Santé Publique, CIC 1401-EC, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
q Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
r Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
s Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
t Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
u Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, United States
v Cogstate Ltd, Melbourne, VIC, Australia
w Department of Neurology, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, United States
x Health Disparities Research Section, National Institute on Aging, Baltimore, MD, United States
y Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, United States
z San Diego State University, University of San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
aa Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States
Abstract
Background: Cognitive change is an important factor in understanding dementia. Estimating effects of exposures on cognitive change requires choosing an analytical timescale, typically time on study or current age. There is limited consensus regarding timescale choice in epidemiologic cognitive aging research. Methods: Using a coordinated analytic approach in ten cohorts of older adults, we evaluated whether estimated effects of two exposures on memory change differed depending on timescale (time on study or current age). We modeled effects of APOE ε4 genotype (a time-invariant exposure) and diabetes (a potentially time-varying/acquired exposure evaluated at baseline) using mixed-effects models with linear and non-linear time specifications for both timescales. Results: Among APOE ε4 carriers, model-estimated memory scores at baseline (time on study timescale) or at each cohort’s median baseline age (current age timescale) were lower and average rate of decline was faster than among APOE ε4 noncarriers. Model-estimated memory scores at baseline or at median baseline age were generally similar across baseline diabetes status, with variability across cohorts in the diabetes-memory change association. In some cohorts, trends in diabetes-memory change associations differed in direction across timescales. Conclusions: Timescale was largely inconsequential for estimated effects of APOE genotype, but yielded differences in estimated diabetes effects, raising questions about the appropriate scale. Current age scale may be problematic because diabetes was measured heterogeneously in age across individuals, a common issue in aging cohorts. Our work demonstrates approaches to evaluate alternative timescales, including in multi-cohort analyses, and highlights potential implications for estimated exposure effects on cognitive change. © 2025 Wolters Kluwer Health, Inc. All rights reserved.
Author Keywords
Apolipoprotein E; cognitive change; cognitive decline; diabetes; linear mixed effects models; longitudinal analysis; timescale
Document Type: Article
Publication Stage: Article in Press
Source: Scopus