Helmholtz Gemeinschaft

Search
Browse
Statistics
Feeds

Computational approaches to predicting treatment response to obesity using neuroimaging

[img]
Preview
PDF (Publisher's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
4MB

Item Type:Article
Title:Computational approaches to predicting treatment response to obesity using neuroimaging
Creators Name:Kozarzewski, L. and Maurer, L. and Mähler, A. and Spranger, J. and Weygandt, M.
Abstract:Obesity is a worldwide disease associated with multiple severe adverse consequences and comorbid conditions. While an increased body weight is the defining feature in obesity, etiologies, clinical phenotypes and treatment responses vary between patients. These variations can be observed within individual treatment options which comprise lifestyle interventions, pharmacological treatment, and bariatric surgery. Bariatric surgery can be regarded as the most effective treatment method. However, long-term weight regain is comparably frequent even for this treatment and its application is not without risk. A prognostic tool that would help predict the effectivity of the individual treatment methods in the long term would be essential in a personalized medicine approach. In line with this objective, an increasing number of studies have combined neuroimaging and computational modeling to predict treatment outcome in obesity. In our review, we begin by outlining the central nervous mechanisms measured with neuroimaging in these studies. The mechanisms are primarily related to reward-processing and include "incentive salience" and psychobehavioral control. We then present the diverse neuroimaging methods and computational prediction techniques applied. The studies included in this review provide consistent support for the importance of incentive salience and psychobehavioral control for treatment outcome in obesity. Nevertheless, further studies comprising larger sample sizes and rigorous validation processes are necessary to answer the question of whether or not the approach is sufficiently accurate for clinical real-world application.
Keywords:Personalized Medicine, Obesity Treatment, Machine Learning, Task-fMRI, Resting-State fMRI, Biomarkers
Source:Reviews in Endocrine & Metabolic Disorders
ISSN:1389-9155
Publisher:Springer Nature
Date:23 December 2021
Official Publication:https://doi.org/10.1007/s11154-021-09701-w
PubMed:View item in PubMed

Repository Staff Only: item control page

Downloads

Downloads per month over past year

Open Access
MDC Library