ArticleDiabetes, obesity & metabolism2025
Neural pathways to bariatric success: What explainable AI reveals that conventional fMRI methods may miss.
Article in Diabetes, obesity & metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Neural pathways to bariatric success: What explainable AI reveals that conventional fMRI methods may miss.Diabetes, obesity & metabolism · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
aimsMetabolic-bariatric surgery (MBS) remains a cornerstone of obesity treatment, yet 15%-30% of patients fail to achieve its intended benefits. Existing clinical and biochemical markers offer limited value in identifying who will respond favourably to this intervention. We hypothesize that the long-term success of MBS is influenced by individual differences in preoperative brain function. MATERIALS AND
methodsWe collected presurgical resting-state fMRI data from 45 patients undergoing MBS, with the aim of identifying neural patterns associated with achieving at least 50% excess weight loss 12 months post-surgery. The data were analysed using both conventional methods and a high-powered machine learning approach. For the latter, we trained five predictive models on functional connectivity, regional brain activity, and clinical variables. We then applied SHapley Additive exPlanations (SHAP) to the best-performing model to interpret its internal logic, thereby revealing the neural features most strongly linked to treatment success.
resultsConventional methods proved inadequate for this study. A multilayer perceptron model, trained exclusively on functional connectivity data, achieved a noteworthy AUC of 0.85. Its SHAP analysis revealed key neural circuits in the postcentral gyrus, dorsolateral prefrontal cortex, and angular gyrus-regions associated with interoception, executive control, and social-cognitive processes such as theory of mind.
conclusionExplainable AI-powered fMRI analysis uncovered subtle neural patterns that conventional methods failed to detect. These findings suggest that a patient's "neural readiness" for MBS may extend beyond self-regulatory circuits. It may also depend on their capacity to perceive and interpret internal bodily signals and to process emotional information-both personal and social.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.