Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11492).Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually performed independently in neuroimaging pipelines. However, some brain structures (e.g., globus pallidus, thalamus and its nuclei) can be extracted more accurately by fusing the two modalities. Following the framework of Bayesian segmentation with probabilistic atlases and unsupervised appearance modeling, we present here a novel algorithm to jointly segment multi-modal sMRI/dMRI data. We propose a hierarchical likelihood term for the dMRI defined on the unit ball, which combines the Beta and Dimroth-Scheidegger-Watson distributions to model the data at each voxel. This term is integrated with a mixture of Gaussians for the sMRI data, such that the resulting joint unsupervised likelihood enables the analysis of multi-modal scans acquired with any type of MRI contrast, b-values, or number of directions, which enables wide applicability. We also propose an inference algorithm to estimate the maximum-a-posteriori model parameters from input images, and to compute the most likely segmentation. Using a recently published atlas derived from histology, we apply our method to thalamic nuclei segmentation on two datasets: HCP (state of the art) and ADNI (legacy) – producing lower sample sizes than Bayesian segmentation with sMRI alone.NIH (Grants R21AG050122, P41EB015902

Emotional Intelligence as an Ability: Theory, Challenges, and New Directions

About 25 years ago emotional intelligence (EI) was first introduced to the scientific community. In this chapter, we provide a general framework for understanding EI conceptualized as an ability. We start by identifying the origins of the construct routed in the intelligence literature and the foundational four-branch model of ability EI, then describe the most commonly employed measures of EI as ability, and critically review predictive validity evidence. We further approach current challenges, including the difficulties of scoring answers as “correct” in the emotional sphere, and open a discussion on how to increase the incremental validity of ability EI. We finally suggest new directions by introducing a distinction between a crystallized component of EI, based on knowledge of emotions, and a fluid component, based on the processing of emotion information