Evidence map›Paper›PMID 40503330›Full record

ArticleBrain communications2025

Brain MRI signatures across sex and CSF Alzheimer's disease biomarkers.

You Cheng, Yingnan He, Karthik Gopinath, Benjamin Billot, Juan Eugenio Iglesias, Chao-Yi Wu, Hiroko Dodge, Anne-Marie Wills, Becky Carlyle, Pia Kivisäkk and 4 more

Abstract read
In one paragraph

Article in Brain communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

You ChengDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
Yingnan HeDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
Karthik GopinathDepartment of Neurology, Harvard Medical School, Boston, MA 02114, USA.
Benjamin BillotComputer Science and AI Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02138, USA.
Juan Eugenio IglesiasDepartment of Neurology, Harvard Medical School, Boston, MA 02114, USA.
Chao-Yi WuDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
Hiroko DodgeDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.ORCID https://orcid.org/0000-0001-7290-8307
Anne-Marie WillsDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
Becky CarlyleDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.ORCID https://orcid.org/0000-0003-1491-3217
Pia KivisäkkDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.ORCID https://orcid.org/0000-0001-9708-7509
Bradley T HymanDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
Steven E ArnoldDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
Sudeshna DasDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.ORCID https://orcid.org/0000-0002-9486-6811
Alzheimer’s Disease Neuroimaging Initiative and for the European Prevention of Alzheimer’s Disease (EPAD) Consortium

Funding

Research Education ComponentP30AG062421 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI STEVEN E ARNOLD · 2019 to 2026
$36.5M
Research Education ComponentP30AG066518 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI Zachary Beattie · 2020 to 2026
$28.6M
NIA NIH HHS P30 AG062421NIA NIH HHS P30 AG066518
6 · The paper itself

Abstract

The relationship between cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease and neurodegenerative effects is not fully understood. This study investigates neurodegeneration patterns across CSF Alzheimer's disease biomarker groups, the association of brain volumes with CSF amyloid and tau status and sex differences in these relationships in a clinical neurology sample. MRI and CSF Alzheimer's disease biomarkers data were analysed in 306 patients of the Mass General Brigham healthcare system aged 50+ (mean age = 68.4 ± 8.8 years; 43.1% female), who had lumbar punctures within 1 year of clinical MRI scans. We first analysed neurodegeneration patterns across four biomarker groups: 60 controls (A-T-&CU; amyloid negative, tau negative, cognitively unimpaired), 25 A+T- (amyloid positive, tau negative), 121 A+T+ (amyloid positive, tau positive) and 100 other dementia (A-T-&CI; amyloid negative, tau negative, cognitively impaired). Second, we examined volumetric associations with amyloid (amyloid positive, tau negative versus control) and tau in the presence of amyloid (amyloid positive, tau positive versus amyloid positive, tau negative) across 52 brain areas. Third, we examined sex differences in these relationships. Finally, we validated core analyses across three independent datasets-NACC (National Alzheimer's Coordinating Center), ADNI (Alzheimer's Disease Neuroimaging Initiative) and EPAD (European Prevention of Alzheimer's Dementia)-totalling 3137 participants, and performed meta-analyses to obtain more robust estimates. We observed distinct neurodegeneration patterns across biomarker groups, with disrupted connectivity (brain volume covariance networks) in amyloid positive and other dementia groups, while amyloid and tau negative, cognitively unimpaired controls exhibited the most connected network. Amyloid was associated with subcortical, cerebellar and brainstem atrophy, with consistent association observations in the thalamus and amygdala across all four datasets. Tau in the presence of amyloid demonstrated general brain shrinkage through enlargement of extracerebral CSF, alongside unexpected ventricle shrinkages. Sex-based analyses revealed that A+T+ (amyloid positive, tau positive) had lower sex differences in connectivity patterns compared with other groups. Sex differences were also noted in amyloid-related ventricular volume changes. This study reveals how amyloid and tau affect brain connectivity and volume across sex and CSF biomarker groups, emphasizing global brain changes and sex differences. By leveraging automated pipelines and advanced MRI and biomarker analyses, we extracted meaningful and replicable findings from heterogeneous clinical samples from real-world data. The meta-analyses across four datasets enhance the generalizability of our results.

Indexed as

brain volumetric changeCSF core Alzheimer’s disease biomarkersmorphometric connectomepredictive modellingsex differences

Identifiers

PMID40503330
PMCPMC12152538

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.