Evidence map›Paper›PMID 39886010›Full record

ArticleNeuroimage. Reports2024

MRI-guided clustering of patients with mild dementia due to Alzheimer's disease using self-organizing maps.

Kellen K Petersen, Bhargav T Nallapu, Richard B Lipton, Ellen Grober, Ali Ezzati

Abstract read
In one paragraph

Article in Neuroimage. Reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Kellen K PetersenDepartment of Neurology, Washington University in St. Louis, St. Louis, MO, USA.
Bhargav T NallapuThe Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA.
Richard B LiptonThe Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA.
Ellen GroberThe Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA.
Ali EzzatiThe Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA.

Funding

Vascular Structure and Function in Cognitive AgingP01AG003949 · NIA · YESHIVA UNIVERSITY · PI mindy katz · 1985 to 2026
$73.9M
MRI Measures of Cerebrovascular Injury and AD Atrophy in a Study of LatinosRF1AG054548 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI DECARLI, CHARLES, GONZALEZ, HECTOR M · 2017 to 2017
$14.7M
Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA)R01AG048642 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GONZALEZ, HECTOR M · 2015 to 2019
$5.7M
Application of ambulatory methods for assessing short- and long-term associations of sleep health with cognitive decline in older adultsR01AG062622 · NIA · ALBERT EINSTEIN COLLEGE OF MEDICINE · PI BUXTON, ORFEU M, DERBY, CAROL A. · 2019 to 2022
$3.1M
COSMIC: An international consortium to identify risk and protective factors and biomarkers of cognitive ageing and dementia in diverse ethno-racial groups and geographical settingsRF1AG057531 · NIA · UNIVERSITY OF NEW SOUTH WALES · PI SACHDEV, PERMINDER S · 2017 to 2020
$2.9M
The role of personality in cognitive aging and dementiaR01AG060933 · NIA · STATE UNIVERSITY NEW YORK STONY BROOK · PI SCOTT, STACEY BETH · 2019 to 2023
$2.4M
Einstein Center for Excellence for Clinical Trials in NeuroscienceU10NS077308 · NINDS · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI LIPTON, RICHARD B., SHINNAR, SHLOMO · 2011 to 2017
$2.2M
The biological underpinnings of Motoric Cognitive Risk syndrome: a multi-center studyR56AG057548 · NIA · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI VERGHESE, JOE · 2017 to 2017
$1.2M
Predictive analytics for cognitive decline and Alzheimer’s diseaseK23AG063993 · NIA · UNIVERSITY OF CALIFORNIA-IRVINE · PI EZZATI, ALI · 2020 to 2024
$984k
Early Phase Pain Investigation Clinical Network: Greater New York Clinical CenterU24NS113847 · NINDS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI DOAN, LISA, LIPTON, RICHARD B. · 2019 to 2019
$966k
Correction of Bias in Estimating Risk of AD and Cognitive and Mobile Decline Using Auxiliary InformationR21AG056920 · NIA · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI WANG, CUILING · 2017 to 2018
$459k
FDA HHS UG3 FD006795NIA NIH HHS K23 AG063993NIA NIH HHS P01 AG003949NIA NIH HHS R01 AG048642NIA NIH HHS R01 AG060933NIA NIH HHS R01 AG062622NIA NIH HHS R21 AG056920NIA NIH HHS R56 AG057548NIA NIH HHS RF1 AG054548NIA NIH HHS RF1 AG057531NINDS NIH HHS U10 NS077308NINDS NIH HHS U24 NS113847
6 · The paper itself

Abstract

Introduction: Alzheimer's disease (AD) is a phenotypically and pathologically heterogenous neurodegenerative disorder. This heterogeneity can be studied and disentangled using data-driven clustering techniques. Methods: We implemented a self-organizing map clustering algorithm on baseline volumetric MRI measures from nine brain regions of interest (ROIs) to cluster 1041 individuals enrolled in the placebo arm of the EXPEDITION3 trial. Volumetric MRI differences were compared among clusters. Demographics as well as baseline and longitudinal cognitive performance metrics were used to evaluate cluster characteristics. Results: Three distinct clusters, with an overall silhouette coefficient of 0.491, were identified based on MRI volumetrics. Cluster 1 (N = 400) had the largest baseline volumetric measures across all ROIs and the best cognitive performance at baseline. Cluster 2 (N = 269) had larger hippocampal and medial temporal lobe volumes, but smaller parietal lobe volumes in comparison with the third cluster (N = 372). Significant between-group mean differences were observed between Clusters 1 and 2 (difference, 2.38; 95% CI, 1.85 to 2.91; P < 0.001), Clusters 1 and 3 (difference, 1.93; 95% CI, 1.41 to 2.44; P < 0.001), but not between Clusters 2 and 3 (difference, 0.45; 95% CI, -0.11 to 1.02; P = 0.146) in ADAS-14. Conclusions: Volumetric MRI can be used to identify homogenous clusters of amyloid positive individuals with mild dementia. The groups identified differ in baseline and longitudinal characteristics. Cluster 1 shows little ADAS-14 change over the first 40 weeks of study on placebo treatment and may be unsuitable for identifying early benefits of treatment.

Indexed as

Alzheimer’s diseaseMachine learningMild dementiaStructural MRISubtypesUnsupervised learning

Identifiers

PMID39886010
PMCPMC11781377

What Socratic holds

Textmetadata
LicenceCC BY-NC
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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.