Publications by authors named "U R Tipnis"
Article Synopsis
- Functional connectomes (FCs) represent brain region interactions using correlation matrices and can be transformed into tangent-FCs for improved models of brain health and aging.
- The study hypothesized that tangent-FCs provide better identification rates (higher fingerprint) than FCs, considering factors like fMRI conditions and regularization techniques.
- Results indicated that minimal regularization (0.01) with a Riemann reference matrix and correlation distance led to the highest identification rates, corroborated by testing on a second dataset.
Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. We present an unsupervised probabilistic model that captures nonlinear relationships between variables over continuous-time.
View Article and Find Full Text PDFModeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different communication processes are taking place on top of a fixed structural topology: path processing score (PPS) estimates how much the brain signal has changed or has been transformed between any two brain regions (source and target); path broadcasting strength (PBS) estimates the propagation of the signal through edges adjacent to the path being assessed. We use PPS and PBS to explore communication dynamics in large-scale brain networks.
View Article and Find Full Text PDFIt has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared.
View Article and Find Full Text PDFBackground: It has been reported previously that the combined loss of chromosomal arms 1p and 19q is a significant predictor of outcome for patients with anaplastic oligodendroglial (AO) tumors and that such chromosomal loss correlates with classic histology in AO. The authors sought to determine whether histology was an equivalent or superior predictor of outcome compared with 1p/19q status in 131 patients with AO tumors.
Methods: The status of 1p and 19q was determined using real-time, quantitative polymerase chain reaction analysis and/or fluorescence in situ hybridization.