In one paragraphArticle in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed, 6 citations in OpenAlex.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
14 authors at 2 institutions in 1 country.
Sayantan KumarDepartment of Computer Science and Engineering, Washington University in St Louis; 1 Brookings Drive, Saint Louis, MO 63130.ORCID 0000-0001-7213-0734 Tom EarnestMallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110.ORCID 0000-0001-8671-8424 Braden YangMallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110.ORCID 0000-0002-2558-4132 Deydeep KothapalliMallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110.
Andrew J AschenbrennerDepartment of Neurology, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8111, St louis, MO 63110.ORCID 0000-0002-4317-7282 Jason HassenstabDepartment of Neurology, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8111, St louis, MO 63110.ORCID 0000-0002-7802-3371 Chengie XiongInstitute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St Louis; 660 S. Euclid Ave, Campus Box 8132, Saint Louis, MO 63110.
Beau AncesDepartment of Neurology, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8111, St louis, MO 63110.ORCID 0000-0003-3862-7397 John MorrisDepartment of Neurology, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8111, St louis, MO 63110.ORCID 0000-0001-9820-5618 Tammie L S BenzingerMallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110.ORCID 0000-0002-8114-0552 Brian A GordonMallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110.ORCID 0000-0003-2109-2955 Philip PayneDepartment of Computer Science and Engineering, Washington University in St Louis; 1 Brookings Drive, Saint Louis, MO 63130.ORCID 0000-0002-9532-2998 Aristeidis SotirasInstitute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St Louis; 660 S. Euclid Ave, Campus Box 8132, Saint Louis, MO 63110.ORCID 0000-0003-0795-8820 Alzheimer’s Disease Neuroimaging Initiative
Washington University in St. Louis · USMallinckrodt (United States) · US
Funding
Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0MSmartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5MWASHINGTON UNIVERSITY ALZHEIMERS DISEASE RESEARCH CENTERP50AG005681 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 1985 to 2019
$52.1MThe natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5MResearch Education ComponentP30AG066444 · NIA · WASHINGTON UNIVERSITY · PI Susan Lynn Stark · 2020 to 2026
$28.7Mproj 3 - NINDS INSTIUTIONAL CENTER CORE GRANTS TO SUPPORT NEUROSCIENCE RESEARP30NS048056 · NINDS · WASHINGTON UNIVERSITY · PI MINTUN, MARK A. · 2004 to 2014
$7.4MAdvanced machine learning algorithms that integrate multi-modal neuroimaging to quantify the heterogeneity in Alzheimer's DiseaseR01AG067103 · NIA · WASHINGTON UNIVERSITY · PI SOTIRAS, ARISTEIDIS · 2021 to 2025
$2.8MHIGH PERFORMANCE BIOMEDICAL IMAGING COMPUTER RESOURCESS10RR022984 · NCRR · WASHINGTON UNIVERSITY · PI PRIOR, FRED WILLIAM · 2009 to 2009
$1.9MGPU COMPUTING RESOURCE TO ENABLE INNOVATION IN IMAGING AND NETWORK BIOLOGYS10OD018091 · OD · WASHINGTON UNIVERSITY · PI PAPPU, ROHIT V, PRIOR, FRED WILLIAM · 2014 to 2014
$598kAcquisition of a next-generation computing clusterS10OD025200 · OD · WASHINGTON UNIVERSITY · PI TOBIAS, MALCOLM · 2019 to 2019
$597kNCRR NIH HHS S10 RR022984NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P30 AG066444NIA NIH HHS P50 AG005681NIA NIH HHS R01 AG067103NIA NIH HHS U01 AG024904NIH HHS S10 OD018091NIH HHS S10 OD025200NINDS NIH HHS P30 NS048056
6 · The paper itselfAbstract
introductionPrevious studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers.
methodsWe selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN.
resultsGreater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression. DISCUSSION: Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.
Indexed as
abnormal deviationsAlzheimer’s diseaseATN biomarkersAV1451 tau PETAV45 amyloid PETDisease Severity Index (DSI)heterogeneityMRImultimodalnormative modeling
Identifiers
PMID37662280
PMCPMC10473626
OpenAlexW4385932513
What OpenQuestion holds
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LicenceCC BY-NC
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