Illness related brain effects of neuropsychiatric disorders are not regionally uniform, with some regions showing large pathological effects while others are relatively spared. Presently, Big Data meta-analytic studies tabulate these effects using structural and/or functional brain atlases that are based on the anatomical boundaries, landmarks and connectivity patterns in healthy brains. These patterns are then translated to individual level predictors using approaches such as Regional Vulnerability Index (RVI), which quantifies the agreement between individual brain patterns and the canonical pattern found in the illness.
View Article and Find Full Text PDFUnlabelled: Single molecule fluorescence in situ hybridization (smFISH) can be used to visualize transcriptional activation at the single allele level. We and others have applied this approach to better understand the mechanisms of activation by steroid nuclear receptors. However, there is limited understanding of the interconnection between the activation of target gene alleles inside the same nucleus and within large cell populations.
View Article and Find Full Text PDFObjectives: The aim of this study was to assess mitral valve (MV) remodeling and strain in patients with secondary mitral regurgitation (SMR) compared with primary MR (PMR) and normal valves.
Background: A paucity of data exists on MV strain during the cardiac cycle in humans. Real-time 3-dimensional (3D) echocardiography allows for dynamic MV imaging, enabling computerized modeling of MV function in normal and disease states.
Objectives: The aim of this study was to quantitate patient-specific mitral valve (MV) strain in normal valves and in patients with mitral valve prolapse with and without significant mitral regurgitation (MR) and assess the determinants of MV strain.
Background: Few data exist on MV deformation during systole in humans. Three-dimensional echocardiography allows for dynamic MV imaging, enabling digital modeling of MV function in health and disease.
By computerized analysis of cortical activity recorded via fMRI for pediatric epilepsy patients, we implement algorithmic localization of epileptic seizure focus within one of eight cortical lobes. Our innovative machine learning techniques involve intensive analysis of large matrices of mutual information coefficients between pairs of anatomically identified cortical regions. Drastic selection of pairs of regions with biologically significant inter-connectivity provides efficient inputs for our multi-layer perceptron (MLP) classifier.
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