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Record W4408421800 · doi:10.1093/braincomms/fcaf099

Uncovering atrophy progression pattern and mechanisms in individuals at risk of Alzheimer's disease

2025· article· en· W4408421800 on OpenAlexafffundabout
Christina Tremblay, Shady Rahayel, Alexandre Pastor‐Bernier, Frédéric St‐Onge, Andrew Vo, François Rheault, Véronique Daneault, Filip Morys, Sylvia Villeneuve, Alain Dagher, John C.S. Breitner, Sylvain Baillet, Bellec Pierre, Véronique D. Bohbot, M. Mallar Chakravarty, D. Louis Collins, Pierre Étienne, Alan C. Evans, Serge Gauthier, Rick Hoge, Yasser Ituria‐Medina, Gerhard Multhaup, Lisa Marie Munter, Vasavan Nair, Judes Poirier, Pedro Rosa‐Neto, Jean-Paul R. Soucy, Étienne Vachon‐Presseau, Philippe Amouyel, Melissa Appleby, Nicholas J. Ashton, Gülebru Ayrancı, Christophe Bedetti, Jason Brandt, Ann Brinkmalm Westman, A. Claudio Cuello, Mahsa Dadar, Leslie‐Ann Daoust, Samir Das, Marina Dauar‐Tedeschi, Louis De Beaumont, Doris Dea, Maxime Descoteaux, Marianne Dufour, Sarah Farzin, Fabiola Ferdinand, Vladimir Fonov, David Fontaine, Guylaine Gagné, Julie Gonneaud, Justin Kat, Christina Kazazian, Anne Labonté, Marie‐Élyse Lafaille‐Magnan, Marc Lalancette, Jean‐Charles Lambert, Jeannie‐Marie Leoutsakos, Claude Lepage, Cécile Madjar, David Maillet, Jean‐Robert Maltais, Sulantha Mathotaarachchi, Ginette Mayrand, Diane Michaud, Thomas J. Montine, John C. Morris, Véronique Pagé, Tharick A. Pascoal, Sandra Peillieux, Mirela Petkova, Pierre Rioux, Mark A. Sager, Eunice Farah Saint‐Fort, Mélissa Savard, Reisa Sperling, Shirin Tabrizi, Pierre N. Tariot, Eduard Teigner, Ronald G. Thomas, Paule‐Joanne Toussaint, Miranda Tuwaig, Vinod Venugopalan, Sander C.J. Verfaillie, Jacob W. Vogel, Karen Wan, Seqian Wang, Elsa Yu, Ronald C. Petersen, Paul Aisen, Laurel Beckett, Michael Donohue, Anthony Gamst, David J. Harvey, Clifford R. Jack, William J. Jagust, Les Shaw, Arthur W. Toga, John Q. Trojanowski, Myron Weiner

Bibliographic record

VenueBrain Communications · 2025
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill UniversityUniversité de SherbrookeUniversité de MontréalHôpital du Sacré-Cœur de MontréalToronto Metropolitan UniversityMontreal Neurological Institute and Hospital
FundersJohnson and Johnson Pharmaceutical Research and DevelopmentNational Institute on AgingEisai IncorporatedNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchPfizer CanadaGE HealthcareGenentechNational Institutes of HealthH. Lundbeck A/SServierEisaiAlzheimer's Drug Discovery FoundationWeston Brain InstituteNorthern California Institute for Research and EducationMcGill UniversityAlzheimer's AssociationFujirebio USPfizerBioClinicaBiogenNovartis Pharmaceuticals CorporationAlzheimer SocietyU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbGovernment of CanadaMerckJanssen BiotechMeso Scale DiagnosticsIXICOTakeda Pharmaceutical CompanyJanssen Alzheimer Immunotherapy Research And DevelopmentAbbVieMichael J. Fox Foundation for Parkinson's Research
KeywordsAtrophyDiseaseAlzheimer's diseasePathologyAlzheimer's Disease Neuroimaging InitiativeNeuroimagingAmyloid betaMedicineNeuroscienceAmyloid (mycology)Psychology

Abstract

fetched live from OpenAlex

Alzheimer's disease is associated with pre-symptomatic changes in brain morphometry and accumulation of abnormal tau and amyloid-beta pathology. Studying the development of brain changes prior to symptoms onset may lead to early diagnostic biomarkers and a better understanding of Alzheimer's disease pathophysiology. Alzheimer's disease pathology is thought to arise from a combination of protein accumulation and spreading via neural connections, but how these processes influence brain atrophy progression in the pre-symptomatic phases remains unclear. Individuals with a family history of Alzheimer's disease (FHAD) have an elevated risk of Alzheimer's disease, providing an opportunity to study the pre-symptomatic phase. Here, we used structural MRI from three databases (Alzheimer's Disease Neuroimaging Initiative, Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer Disease and Montreal Adult Lifespan Study) to map atrophy progression in FHAD and Alzheimer's disease and assess the constraining effects of structural connectivity on atrophy progression. Cross-sectional and longitudinal data up to 4 years were used to perform atrophy progression analysis in FHAD and Alzheimer's disease compared with controls. PET radiotracers were also used to quantify the distribution of abnormal tau and amyloid-beta protein isoforms at baseline. We first derived cortical atrophy progression maps using deformation-based morphometry from 153 FHAD, 156 Alzheimer's disease and 116 controls with similar age, education and sex at baseline. We next examined the spatial relationship between atrophy progression and spatial patterns of tau aggregates and amyloid-beta plaques deposition, structural connectivity and neurotransmitter receptor and transporter distributions. Our results show that there were similar patterns of atrophy progression in FHAD and Alzheimer's disease, notably in the cingulate, temporal and parietal cortices, with more widespread and severe atrophy in Alzheimer's disease. Both tau and amyloid-beta pathology tended to accumulate in regions that were structurally connected in FHAD and Alzheimer's disease. The pattern of atrophy and its progression also aligned with existing structural connectivity in FHAD. In Alzheimer's disease, our findings suggest that atrophy progression results from pathology propagation that occurred earlier, on a previously intact connectome. Moreover, a relationship was found between serotonin receptor spatial distribution and atrophy progression in Alzheimer's disease. The current study demonstrates that regions showing atrophy progression in FHAD and Alzheimer's disease present with specific connectivity and cellular characteristics, uncovering some of the mechanisms involved in pre-clinical and clinical neurodegeneration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.315
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2025
Admission routes3
Has abstractyes

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