Many disorders that affect the brain can cause shape changes in subcortical structures, and these may provide biomarkers for disease detection and progression. Automatic tools are needed to accurately identify and characterize these alterations. In recent work, we developed a surface multivariate tensor-based morphometry analysis (mTBM) to detect morphological group differences in subcortical structures, and we applied this method to study HIV/AIDS, William's syndrome, Alzheimer's disease and prematurity. Here we will focus more specifically on mTBM in neonates, which, in its current form, starts with manually segmented subcortical structures from MRI images of a two subject groups, places a conformal grid on each of their surfaces, registers them to a template through a constrained harmonic map and provides statistical comparisons between the two groups, at each vertex of the template grid. We improve this pipeline in two ways: first by replacing the constrained harmonic map with a new fluid registration algorithm that we recently developed. Secondly, by optimizing the pipeline to study the putamen in newborns. Our analysis is applied to the comparison of the putamen in premature and term born neonates. Recent whole-brain volumetric studies have detected differences in this structure in babies born preterm. Here we add to the literature on this topic by zooming in on this structure, and by generating the first surface-based maps of these changes. To do so, we use a dataset of manually segmented putamens from T1-weighted brain MR images from 17 preterm and 18 term-born neonates. Statistical comparisons between the two groups are performed via four methods: univariate and multivariate tensor-based morphometry, the commonly used medial axis distance, and a combination of the last two statistics. We detect widespread statistically significant differences in morphology between the two groups that are consistent across statistics, but more extensive for multivariate measures.
Empathy is an important aspect of social communication, especially in medical and psychotherapy applications. Measures of empathy can offer insights into the quality of therapy. We use an N-gram language model based maximum likelihood strategy to classify empathic versus non-empathic utterances and report the precision and recall of classification for various parameters. High recall is obtained with unigram while bigram features achieved the highest F1-score. Based on the utterance level models, a group of lexical features are extracted at the therapy session level. The effectiveness of these features in modeling session level annotator perceptions of empathy is evaluated through correlation with expert-coded session level empathy scores. Our combined feature set achieved a correlation of 0.558 between predicted and expert-coded empathy scores. Results also suggest that the longer term empathy perception process may be more related to isolated empathic salient events.
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