Article in Journal of Alzheimer's disease : JAD, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing 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.
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
17 authors.
Gengsheng ChenMallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0003-1879-9415
Nicole S McKayMallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0003-4123-2548
Brian A GordonMallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0003-2109-2955
Nelly Joseph-MathurinMallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0002-9735-5152
Jingxia LiuDepartment of Surgery, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0002-9434-7907
Suzanne E SchindlerDepartment of Neurology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0002-1680-1465
Jason HassenstabDepartment of Neurology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0002-7802-3371
Stephanie DoeringMallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0001-5110-0640
Andrew J AschenbrennerKnight Alzheimer's Disease Research Center, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0002-4317-7282
Qing WangMallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0002-1296-7097
Pamela J LaMontagneMallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0002-6752-8518
Sarah J KeefeMallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0002-5943-0940
Parinaz MassoumzadehMallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0001-7510-7273
Carlos CruchagaDepartment of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0002-0276-2899
Chengjie XiongDivison of Biostatistics, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0003-0044-4499
John C MorrisMallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0001-9820-5618
Tammie Ls BenzingerMallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA.ORCID 0000-0002-8114-0552
Funding
WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI CHENGJIE XIONG · 1985 to 2026
$69.5M
WASHINGTON UNIVERSITY ALZHEIMERS DISEASE RESEARCH CENTERP50AG005681 · NIA · WASHINGTON UNIVERSITY · PI FAGAN, ANNE · 1985 to 2019
$52.1M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
Research Education ComponentP30AG066444 · NIA · WASHINGTON UNIVERSITY · PI DAVID M. HOLTZMAN · 2020 to 2026
$28.7M
MORPHOMETRY BIOMEDICAL INFORMATICS RESEARCH NETWORKU24RR021382 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2004 to 2008
$24.1M
STRUCTURE, FUNCTION AND COGNITION IN SCHIZOPHRENIAP50MH071616 · NIMH · WASHINGTON UNIVERSITY · PI BARCH, DEANNA · 2004 to 2008
$11.2M
THE XNAT IMAGING INFORMATICS PLATFORMR01EB009352 · NIBIB · WASHINGTON UNIVERSITY · PI Daniel Scott Marcus · 2009 to 2026
$9.3M
DRIVING PERFORMANCE IN PRECLINICAL ALZHEIMER'S DISEASER01AG043434 · NIA · WASHINGTON UNIVERSITY · PI ROE, CATHERINE M · 2012 to 2016
$2.4M
HIGH PERFORMANCE BIOMEDICAL IMAGING COMPUTER RESOURCESS10RR022984 · NCRR · WASHINGTON UNIVERSITY · PI PRIOR, FRED WILLIAM · 2009 to 2009
$1.9M
Functional-anatomic exploration of cognitive controlR01AG021910 · NIA · WASHINGTON UNIVERSITY · PI BUCKNER, RANDY L · 2005 to 2009
BackgroundAs we transition to disease-modifying treatment for Alzheimer's disease (AD), identifying individuals most at risk for future cognitive decline is crucial. Amyloid PET, cerebrospinal fluid and more recently blood-based biomarkers can identify the first stage of AD. However, changes detectable by PiB-PET may precede the onset of the dementia by 20-30 years. MRI is a widely available tool for detecting longitudinal changes in brain structure, such as cortical thickness and hippocampal volume and may provide additional insight into which patients are at greatest risk to develop cognitive decline.ObjectiveTo determine how well the hippocampal volume and cortical thickness, without specific AD biomarkers, can predict cognitive decline.MethodsMRI data from 344 participants (cognitively unimpaired or mild cognitive impairment, age 50-86) were used to evaluate if changes in cortical thickness and hippocampal volume predict cognitive decline, measured by a global cognitive composite score. A random coefficient model was employed to calculate longitudinal changes in cortical thickness and hippocampal volume and assess their ability to predict cognitive decline.ResultsBaseline cortical thickness as well as hippocampal volume predicted cognitive decline, regardless of baseline cognitive status. In individuals unimpaired at baseline, decreases in cortical thickness and hippocampal volume independently predicted cognitive decline. For participants with baseline mild impairment, decreases in hippocampal volume predicted further cognitive decline.ConclusionsThese findings indicate that MRI could serve as an effective tool for identifying individuals at elevated risk of cognitive decline, a growing public health concern as global populations continue to age.
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.
Baseline and longitudinal changes in cortical thickness and hippocampal volume predict cognitive decline. · full record | OpenQuestion