Evidence map›Paper›PMID 41675432›Full record

ArticleBrain communications2026

Morphometric features enhance phenotype discrimination in frontotemporal lobar degeneration.

Jane K Stocks, Ashley A Heywood, Karteek Popuri, Mirza Faisal Beg, Howard J Rosen, Lei Wang

Abstract read
In one paragraph

Article in Brain communications, 2026. 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
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Jane K StocksDepartment of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.ORCID https://orcid.org/0000-0002-1778-330X
Ashley A HeywoodDepartment of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.ORCID https://orcid.org/0000-0003-0173-990X
Karteek PopuriDepartment of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada A1C5S7.ORCID https://orcid.org/0000-0002-1729-3255
Mirza Faisal BegSchool of Engineering Science, Simon Fraser University, Burnaby, BC, Canada V5A1S6.
Howard J RosenSchool of Medicine, University of California, San Francisco 94143, USA.
Lei WangDepartment of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.ORCID https://orcid.org/0000-0003-3870-3388

Funding

Technology and Remote Assessment CoreU19AG063911 · NIA · MAYO CLINIC ROCHESTER · PI Bradley F Boeve · 2019 to 2026
$120.9M
TDP-43 Loss-of-Function: Biology to BiomarkersP01AG019724 · NIA · UNIVERSITY OF PENNSYLVANIA · PI Jennifer Merrilees · 2002 to 2026
$67.2M
Research Education Core FP30AG072977 · NIA · NORTHWESTERN UNIVERSITY AT CHICAGO · PI ROBERT J VASSAR · 2021 to 2026
$26.3M
Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS)U01AG045390 · NIA · MAYO CLINIC ROCHESTER · PI BOEVE, BRADLEY F, ROSEN, HOWARD J · 2014 to 2018
$16.9M
The Frontotemporal Lobar Degeneration Neuroimaging InitiativeR01AG032306 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ROSEN, HOWARD J · 2009 to 2013
$10.0M
PREDOCTORAL AND POSTDOCTORAL TRAINING PROGRAM IN AGING AND DEMENTIAT32AG020506 · NIA · NORTHWESTERN UNIVERSITY AT CHICAGO · PI ROBERT J VASSAR, SANDRA WEINTRAUB · 2002 to 2026
$9.9M
PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential DiagnosisR01AG055121 · NIA · OHIO STATE UNIVERSITY · PI ROSEN, HOWARD J, WANG, LEI · 2017 to 2020
$2.8M
Multimodal Imaging in Frontotemporal DegenerationK24AG045333 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ROSEN, HOWARD J · 2013 to 2023
$1.9M
PREDICT-FTD: Multimodal Imaging Prediction of FTLD Subtypes.R56AG055121 · NIA · OHIO STATE UNIVERSITY · PI ROSEN, HOWARD J, WANG, LEI · 2023 to 2023
$1.4M
NIA NIH HHS K24 AG045333NIA NIH HHS P01 AG019724NIA NIH HHS P30 AG072977NIA NIH HHS R01 AG032306NIA NIH HHS R01 AG055121NIA NIH HHS R56 AG055121NIA NIH HHS T32 AG020506NIA NIH HHS U01 AG045390NIA NIH HHS U19 AG063911
6 · The paper itself

Abstract

Frontotemporal lobar degeneration is associated with diverse clinical phenotypes underlain by multiple disease pathologies and genetic mutations for which traditional structural magnetic resonance imaging (MRI) analyses lack discriminatory sensitivity and specificity. Here, we use a data-driven multivariate method to extract a concise set of MRI-derived shape morphometric features and cross-sectionally examine the discriminatory capability of their unique combinations in three frontotemporal lobar degeneration clinical phenotypes. Patients with sporadic or familial frontotemporal lobar degeneration clinical syndromes across two cohorts (i.e. behavioral variant (

Indexed as

frontotemporal dementiafrontotemporal lobar degenerationmorphometryMRIphenotype discrimination

Identifiers

PMID41675432
PMCPMC12887737

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.