Correlation analysis showed associations between the degree of compression and the level of alcohol exposure. Paper accepted in the 22nd meeting of the Organization of Human Brain Mapping to be held in Geneva, Switzerland from 26-30, June 2016. Between-group differences in caudate nucleus morphology were dispersed across the tail and head regions. Although the exposed and control subjects did not differ significantly in their volumes, the shape analysis showed the hippocampus to be more deformed at the head and tail regions in the alcohol-exposed children. The Mann-Whitney test was performed to predict volume differences between the groups. These were used to compute the volumes and for further statistical analysis. Binary masks of hippocampi and caudate nuclei were generated from the segmented volumes of each brain. Using the localized Hotelling T(2) method, regions of significant shape variations between the control and exposed subjects were identified and mapped onto the mean shapes. A point distribution model was used to quantify the shape variations in terms of a change in co-ordinate positions. An iterative closest point algorithm was used to register the template of one control subject to all other shapes in order to capture the true geometry of the shape with a fixed number of landmark points.
Hippocampi and caudate nuclei were manually segmented, and surface meshes were reconstructed. High-resolution structural magnetic resonance imaging images were acquired for 31 children (19 controls and 12 children diagnosed with fetal alcohol syndrome/partial FAS). Surface deformation-based analysis was used to assess local shape variations in the hippocampi and caudate nuclei of children with fetal alcohol spectrum disorders. In the proposed SVR-LSM, the symptom relation to the entire lesion map as opposed to each isolated voxel is modeled using a nonlinear function, so the intervoxel correlations are intrinsically considered, resulting in a potentially more sensitive way to examine lesion-symptom relationships. The purpose of this artilce was to develop an MLSM using a machine learning-based multivariate regression algorithm: support vector regression (SVR). Although voxel-based lesion-symptom mapping (VLSM) has made substantial contributions to the understanding of brain-behavior relationships, a better understanding of the brain-behavior relationship contributed by multiple brain regions needs a multivariate lesion-symptom mapping (MLSM). Because brain function is a result of coherent activations of a collection of functionally related voxels, lesion-symptom relations are generally contributed by multiple voxels simultaneously. The notion that dysfunctions in neural circuits involved in sharing another’s affect explain these deficits is appealing, but has received only modest experimental support. Lesion analysis is a classic approach to study brain functions. Mindfulness meditation regulates anterior insula activity during empathy for social pain.