MRI Pattern Recognition in Multiple Sclerosis Normal-Appearing Brain Areas

Objective Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsing­remitting type) in lesioned areas, areas of normal­appearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR techniques. Methods A lesion mapping was carried out by an experienced neurologist for Turbo Inversion Recovery Magnitude (TIRM) images of individual subjects. Combining this mapping with templates from a neuroanatomic atlas, the TIRM images were segmented into three areas of homogenous tissue types (Lesions, NAGM, and NAWM) after spatial standardization. For each area, a linear Support Vector Machine algorithm was used in multiple local classification analyses to determine the diagnostic accuracy in separating MS patients from healthy controls based on voxel tissue intensity patterns extracted from small spherical subregions of these larger areas. To control for covariates, we also excluded group-specific biases in deformation fields as a potential source of information. Results Among regions containing lesions a posterior parietal WM area was maximally informative about the clinical status (96% accuracy, p<10−13). Cerebellar regions were maximally informative among NAGM areas (84% accuracy, p<10−7). A posterior brain region was maximally informative among NAWM areas (91% accuracy, p<10−10). Interpretation We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas. This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes. Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale

The role of neural impulse control mechanisms for dietary success in obesity

Deficits in impulse control are discussed as key mechanisms for major worldwide health problems such as drug addiction and obesity. For example, obese subjects have difficulty controlling their impulses to overeat when faced with food items. Here, we investigated the role of neural impulse control mechanisms for dietary success in middle-aged obese subjects. Specifically, we used a food-specific delayed gratification paradigm and functional magnetic resonance imaging to measure eating-related impulse-control in middle-aged obese subjects just before they underwent a twelve-week low calorie diet. As expected, we found that subjects with higher behavioral impulse control subsequently lost more weight. Furthermore, brain activity before the diet in VMPFC and DLPFC correlates with subsequent weight loss. Additionally, a connectivity analysis revealed that stronger functional connectivity between these regions is associated with better dietary success and impulse control. Thus, the degree to which subjects can control their eating impulses might depend on the interplay between control regions (DLPFC) and regions signaling the reward of food (VMPFC). This could potentially constitute a general mechanism that also extends to other disorders such as drug addiction or alcohol abuse