Multimodal Neuroimaging of Obesity: From Structural-Functional Mechanisms to Precision Interventions
Bibliographic record
Abstract
Purpose: Obesity’s metabolic consequences are well documented; however, its neurobiological underpinnings remain elusive. This systematic review addresses a critical gap by synthesizing evidence on obesity-induced neuroplasticity across structural, functional, and molecular domains through advanced neuroimaging. Methods: According to PRISMA guidelines, we systematically searched (2015–2024) across PubMed/Web of Science, employing MeSH terms: (“Obesity” [Majr]) AND (“Neuroimaging” [Mesh] OR “Magnetic Resonance Imaging” [Mesh]). A total of 104 studies met the inclusion criteria. The inclusion criteria required the following: (1) multimodal imaging protocols (structural MRI/diffusion tensor imaging/resting-state functional magnetic resonance imaging (fMRI)/positron emission tomography (PET)); (2) pre-/post-intervention longitudinal design. Risk of bias was assessed via the Newcastle-Ottawa Scale. Key Findings: 1. Structural alterations: 7.2% mean gray matter reduction in prefrontal cortex (Cohen’s d = 0.81). White matter integrity decline (FA reduction β = −0.33, p < 0.001) across 12 major tracts. 2. Functional connectivity: Resting-state hyperactivity in mesolimbic pathways (fALFF + 23%, p-FDR < 0.05). Impaired fronto–striatal connectivity (r = −0.58 with BMI, 95% CI [−0.67, −0.49]). 3. Interventional reversibility: Bariatric surgery restored prefrontal activation (Δ = +18% vs. controls, p = 0.002). Neurostimulation (transcranial direct current stimulation (tDCS) enhanced cognitive control (post-treatment β = 0.42, p = 0.009). Conclusion: 1. Obesity induces multidomain neural reorganization beyond traditional reward circuits. 2. Neuroimaging biomarkers (e.g., striatal PET-dopamine binding potential) predict intervention outcomes (AUC = 0.79). 3. Precision neuromodulation requires tripartite integration of structural guidance, functional monitoring, and molecular profiling. Findings highlight neuroimaging’s pivotal role in developing stage-specific therapeutic strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".