Objective Traumatic brain injury in contact sports has significant impact on short term neurological and neurosurgical function as well as longer term cognitive disability. In this study, we aim to demonstrate that contact sport participants exhibit differences in Diffusion Tensor Imaging (DTI) caused by repeated physical impact on the brain. We also aim to determine that impact incurred by the contact sports athletes during the season may result in differences between the pre- and post-season DTI scans. Methods DTI data was collected from 10 contact (mean age 20.4 +- 1.36) and 13 age-matched non-contact (mean age 19.5 +- 1.03) sport male athletes, on a 3T MRI scanner. A single shot echo-planar imaging sequence with b-value of 1000s/mm2 and 25 gradient directions was used. Eight of the athletes were again scanned post-season. The b0 non-diffusion weighted image was averaged 5 times. Voxel-wise 2-sample t-tests were run for all group comparisons, and in each case, the positive false discovery rate (pFDR) was computed to assess the whole map, multiple comparison corrected significance. Results There were significant differences in the FA values in the inferior fronto-occipital fasciculus, parts of the superior and posterior coronal radiate and the splenium of the corpus callosum (CC) as well as smaller clusters in the genu and parts of the body of the CC. In addition, the external capsule also shows some difference between the contact and non-contact athlete brains. Additionally, the pre-season and postseason show differences in these regions, however, the post-season p-values show significance in more areas of the CC. Conclusions There are significant DTI changes in the corpus callosum, the external capsule, the inferior fronto-occipital fasciculus as well as regions such as the superior/posterior corona radiata when comparing the pre-season contact versus the non-contact controls and also comparing the post-season contact athletes with the controls. There are also difference in the DTI between the post- vs. pre-season scans.

Understanding the extent to which vascular disease and its risk factors are associated with prodromal dementia, notably Alzheimer's disease (AD), may enhance predictive accuracy as well as guide early interventions. One promising avenue to determine this relationship consists of looking for reliable and sensitive in-vivo imaging methods capable of characterizing the subtle brain alterations before the clinical manifestations. However, little is known from the imaging perspective about how risk factors such as vascular disease influence AD progression. Here, for the first time, we apply an innovative T1 and DTI fusion analysis of 3D corpus callosum (CC) on mild cognitive impairment (MCI) populations with different levels of vascular profile, aiming to de-couple the vascular factor in the prodromal AD stage. Our new fusion method successfully increases the detection power for differentiating MCI subjects with high from low vascular risk profiles, as well as from healthy controls. MCI subjects with high and low vascular risk profiles showed differed alteration patterns in the anterior CC, which may help to elucidate the inter-wired relationship between MCI and vascular risk factors.

IntroductionPrediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi‐task machine learning method (cFSGL) with a novel MR‐based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients.MethodsPrevious work has shown that a multi‐task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer‐based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor‐based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces.ResultsWe combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted.ConclusionsBy combining the power of the cFSGL multi‐task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.

Background Insulin resistance is a link between obesity and the associated disease risk. In addition to its role as an energy regulatory signal to the hypothalamus, insulin also modulates food reward. Objective To examine the relationship of insulin sensitivity (SI) and fasting insulin with cerebral activation in response to food and non-food cues in children. Methods Twelve overweight Hispanic girls (age: 8–11) participated in two study visits, a frequently sampled intravenous glucose tolerance test and a functional neuroimaging (fMRI) session (GE HDxt 3.0Tesla)) with visual stimulation tasks. Blocks of images (high calorie (HC), low calorie (LC) and non-food (NF)) were presented in randomized order. Results Comparing HC with NF, SI was inversely associated with activation in the anterior cingulate (r2 = 0.65; p < 0.05), the insula (r2 = 0.69; p < 0.05), the orbitofrontal cortex (r2 = 0.74; p < 0.05), and the frontal and rolandic operculum (r2 = 0.76; p < 0.001). Associations remained significant after adjustment for BMI. Association of fasting insulin and cerebral activation dissapeared after adjustment for waist circumference. Conclusion In addition to weight loss insulin sensitivity may pose an important target to regulate neural responses to food cues in the prevention of excessive weight gain.