Structural brain networks constructed from diffusion MRI are important biomarkers for understanding human brain structure and its relation to cognitive functioning. There is increasing interest in learning differences in structural brain networks between groups of subjects in neuroimaging studies, leading to a variable selection problem in network classification. Traditional methods often use independent edgewise tests or unstructured generalized linear model (GLM) with regularization on vectorized networks to select edges distinguishing the groups, which ignore the network structure and make the results hard to interpret. Apoptosis chemical In this paper, we develop a symmetric bilinear logistic regression (SBLR) with elastic-net penalty to identify a set of small clique subgraphs in network classification. Clique subgraphs, consisting of all the interconnections among a subset of brain regions, have appealing neurological interpretations as they may correspond to some anatomical circuits in the brain related to the outcome. We apply this method to study differences in the structural connectome between adolescents with high and low crystallized cognitive ability, using the crystallized cognition composite score, picture vocabulary and oral reading recognition tests from NIH Toolbox. A few clique subgraphs containing several small sets of brain regions are identified between different levels of functioning, indicating their importance in crystallized cognition.Using machine learning to predict the intensity of pain from fMRI has attracted rapidly increasing interests. However, due to remarkable inter- and intra-individual variabilities in pain responses, the performance of existing fMRI-based pain prediction models is far from satisfactory. The present study proposed a new approach which can design a prediction model specific to each individual or each experimental trial so that the specific model can achieve more accurate prediction of the intensity of nociceptive pain from single-trial fMRI responses. More precisely, the new approach uses a supervised k-means method on nociceptive-evoked fMRI responses to cluster individuals or trials into a set of subgroups, each of which has similar and consistent fMRI activation patterns. Then, for a new test individual/trial, the proposed approach chooses one subgroup of individuals/trials, which has the closest fMRI patterns to the test individual/trial, as training samples to train an individual-specific or a trial-specific pain prediction model. The new approach was tested on a nociceptive-evoked fMRI dataset and achieved significantly higher prediction accuracy than conventional non-specific models, which used all available training samples to train a model. The generalizability of the proposed approach is further validated by training specific models on one dataset and testing these models on an independent new dataset. This proposed individual-specific and trial-specific pain prediction approach has the potential to be used for the development of individualized and precise pain assessment tools in clinical practice.Proton magnetic resonance spectroscopy (1H-MRS) is a noninvasive imaging technique that measures the concentration of metabolites in defined areas of the human brain in vivo. The underlying structure of natural metabolism-emotion relationships is unknown. Further, there is a wide range of between-person differences in metabolite concentration in healthy individuals, but the significance of this variation for understanding emotion in healthy humans is unclear. Here we investigated the relationship of two emotional constructs, agency and flexibility, with the metabolites glutamate and glutamine (Glx), N-acetylaspartate (tNAA), choline (Cho), creatine (tCr), and myo-inositol (Ins) in the right dorsal anterior cingulate cortex (dACC) in medically and psychiatrically healthy volunteers (N = 20, 9 female; mean age = 22.8 years, SD = 3.40). The dACC was selected because this region is an integrative hub involved in multiple brain networks of emotion, cognition and behavior. Emotional traits were assessed using the Mt survive this multiple correction, providing effect sizes for future work. Principal Component Analysis (PCA) revealed a three-factor orthogonal solution indicating specific relationships between 1) Glx and behavioral engagement; 2) Cho and affiliative bonding; and 3) tNAA and a novel dimension that we term neuroaffective reserves. Our results inform the neurobiology of agency and flexibility and lay the groundwork for understanding mechanisms of natural emotion using 1H-MRS.The molecular and cellular basis for cataract development in mice lacking dystrophin, a scaffolding protein that links the cytoskeleton to the extracellular matrix, is poorly understood. In this study, we characterized lenses derived from the dystrophin-deficient mdx3cv mouse model. Expression of Dp71, a predominant isoform of dystrophin in the lens, was induced during lens fiber cell differentiation. Dp71 was found to co-distribute with dystroglycan, connexin-50 and 46, aquaporin-0, and NrCAM as a large cluster at the center of long arms of the hexagonal fibers. Although mdx3cv mouse lenses exhibited dramatically reduced levels of Dp71, only older lenses revealed punctate nuclear opacities compared to littermate wild type (WT) lenses. The levels of dystroglycan, syntrophin, and dystrobrevin which comprise the dystrophin-associated protein complex (DAPC), and NrCAM, connexin-50, and aquaporin-0, were significantly lower in the lens membrane fraction of adult mdx3cv mice compared to WT mice. Additionally, decreases were observed in myosin light chain phosphorylation and lens stiffness together with a significant elevation in the levels of utrophin, a functional homolog of dystrophin in mdx3cv mouse lenses compared to WT lenses. The levels of perlecan and laminin (ligands of α-dystroglycan) remained normal in dystrophin-deficient lens fibers. Taken together, although mdx3cv mouse lenses exhibit only minor defects in lens clarity possibly due to a compensatory increase in utrophin, the noted disruptions of DAPC, stability, and organization of membrane integral proteins of fibers, and stiffness of mdx3cv lenses reveal the importance of dystrophin and DAPC in maintaining lens clarity and function.