IntroductionObesity presents a significant public health problem. Brain plays a central role in etiology and maintenance of obesity. Prior neuroimaging studies have found that individuals with obesity exhibit altered neural responses to images of food within the brain reward system and related brain networks. However, little is known about the dynamics of these neural responses or their relationship to later weight change. In particular, it is unknown if in obesity, the altered reward response to food images emerges early and automatically, or later, in the controlled stage of processing. It also remains unclear if the pretreatment reward system reactivity to food images is predictive of subsequent weight loss intervention outcome.MethodsIn this study, we presented high-calorie and low-calorie food, and nonfood images to individuals with obesity, who were then prescribed lifestyle changes, and matched normal-weight controls, and examined neural reactivity using magnetoencephalography (MEG). We performed whole-brain analysis to explore and characterize large-scale dynamics of brain systems affected in obesity, and tested two specific hypotheses: (1) in obese individuals, the altered reward system reactivity to food images occurs early and automatically, and (2) pretreatment reward system reactivity predicts the outcome of lifestyle weight loss intervention, with reduced activity associated with successful weight loss.ResultsWe identified a distributed set of brain regions and their precise temporal dynamics that showed altered response patterns in obesity. Specifically, we found reduced neural reactivity to food images in brain networks of reward and cognitive control, and elevated reactivity in regions of attentional control and visual processing. The hypoactivity in reward system emerged early, in the automatic stage of processing (< 150 ms post-stimulus). Reduced reward and attention responsivity, and elevated neural cognitive control were predictive of weight loss after six months in treatment.DiscussionIn summary, we have identified, for the first time with high temporal resolution, the large-scale dynamics of brain reactivity to food images in obese versus normal-weight individuals, and have confirmed both our hypotheses. These findings have important implications for our understanding of neurocognition and eating behavior in obesity, and can facilitate development of novel integrated treatment strategies, including tailored cognitive-behavioral and pharmacological therapies.
Clinical responses to dopamine replacement therapy for individuals with Parkinson’s disease (PD) are often difficult to predict. We characterized changes in MDS-UPDRS motor factor scores resulting from a short-duration L-Dopa response (SDR), and investigated how the inter-subject clinical differences could be predicted from motor cortical magnetoencephalography (MEG). MDS-UPDRS motor factor scores and resting-state MEG recordings were collected during SDR from twenty individuals with a PD diagnosis. We used a novel subject-specific strategy based on linear support vector machines to quantify motor cortical oscillatory frequency profiles that best predicted medication state. Motor cortical profiles differed substantially across individuals and showed consistency across multiple data folds. There was a linear relationship between classification accuracy and SDR of lower limb bradykinesia, although this relationship did not persist after multiple comparison correction, suggesting that combinations of spectral power features alone are insufficient to predict clinical state. Factor score analysis of therapeutic response and novel subject-specific machine learning approaches based on subject-specific neuroimaging provide tools to predict outcomes of therapies for PD.
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