Evidence map›Paper›PMID 38375968›Full record

Trial reportHuman brain mapping2024

Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention.

Ofek Finkelstein, Gidon Levakov, Alon Kaplan, Hila Zelicha, Anat Yaskolka Meir, Ehud Rinott, Gal Tsaban, Anja Veronica Witte, Matthias Blüher, Michael Stumvoll and 4 more

Open access · goldAbstract readClinical Trial
In one paragraph

Trial report in Human brain mapping, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.1field-weighted citation impact, top 9% of its field
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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Trial
  2. Review
  3. Article
  4. Review
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 at 3 institutions in 3 countries.

Ofek FinkelsteinDepartment of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.ORCID 0009-0001-4160-9643
Gidon LevakovDepartment of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.
Alon KaplanThe Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
Hila ZelichaThe Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
Anat Yaskolka MeirThe Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
Ehud RinottThe Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
Gal TsabanThe Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
Anja Veronica WitteDepartment of Neurology, Max Planck-Institute for Human Cognitive and Brain Sciences, and Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany.
Matthias BlüherDepartment of Medicine, University of Leipzig, Leipzig, Germany.
Michael StumvollDepartment of Medicine, University of Leipzig, Leipzig, Germany.
Ilan ShelefThe Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
Iris ShaiThe Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
Tammy Riklin RavivThe School of Electrical and Computer Engineering, Ben Gurion University of the Negev, Beer Sheva, Israel.
Galia AvidanDepartment of Psychology, Ben-Gurion University of the Negev, Beer Sheva, Israel.ORCID 0000-0003-2293-3859
Ben-Gurion University of the Negev · ILLeipzig University · DEMax Planck Institute for Human Cognitive and Brain Sciences · DE

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Translational Developmental Neuroscience of AutismK23MH087770 · NIMH · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI DI MARTINO, ADRIANA · 2010 to 2013
$644k
Enhancing the Autism Brain Imaging Data Exchange to Define the Autism ConnectomeR21MH107045 · NIMH · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI DI MARTINO, ADRIANA · 2015 to 2016
$479k
Enhancement of the 1000 Functional Connectome ProjectR03MH096321 · NIMH · NATHAN S. KLINE INSTITUTE FOR PSYCH RES · PI BISWAL, BHARAT BHUSAN, MILHAM, MICHAEL PETER · 2012 to 2013
$158k
Biotechnology and Biological Sciences Research Council BB/H008217/1NIA NIH HHS U01 AG024904NIH HHS U01 AG024904NIMH NIH HHS 5R21MH107045NIMH NIH HHS K23 MH087770NIMH NIH HHS K23MH087770NIMH NIH HHS R03 MH096321NIMH NIH HHS R03MH096321NIMH NIH HHS R21 MH107045
6 · The paper itself

Abstract

Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain morphology. In the DIRECT-PLUS clinical trial, participants with criterion for metabolic syndrome underwent an 18-month lifestyle intervention. Structural brain MRIs were acquired before and after the intervention. We utilized an ensemble learning framework to predict Body-Mass Index (BMI) scores, which correspond to adiposity-related clinical measurements from brain MRIs. We revealed that patient-specific reduction in BMI predictions was associated with actual weight loss and was significantly higher in active diet groups compared to a control group. Moreover, explainable AI (XAI) maps highlighted brain regions contributing to BMI predictions that were distinct from regions associated with age prediction. Our DIRECT-PLUS analysis results imply that predicted BMI and its reduction are unique neural biomarkers for obesity-related brain modifications and weight loss.

Indexed as

Artificial IntelligenceDeep LearningBody Mass IndexBrainHumansLife StyleMagnetic Resonance ImagingObesityOverweightWeight Lossbiomarkerdeep learningMRIobesity

Identifiers

PMID38375968
PMCPMC10878010
OpenAlexW4391966086

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.