HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection

We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available. We propose a new hybrid optimization algorithm that solves the elastic-net support vector machine (SVM) through an alternating direction method of multipliers in the first phase, followed by an interior-point method for the classical SVM in the second phase. Both SVM formulations are adapted to knowledge incorporation. Our proposed algorithm addresses the challenges of automatic feature selection, high optimization accuracy, and algorithmic flexibility for taking advantage of prior knowledge. We demonstrate the effectiveness and efficiency of our algorithm and compare it with existing methods on a collection of synthetic and real-world data.Comment: Proceedings of 8th Learning and Intelligent OptimizatioN (LION8) Conference, 201

Tensor Regression with Applications in Neuroimaging Data Analysis

Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data.Comment: 27 pages, 4 figure

Variational Bayesian causal connectivity analysis for fMRI

The ability to accurately estimate effective connectivity among brain regions from neuroimaging data could help answering many open questions in neuroscience. We propose a method which uses causality to obtain a measure of effective connectivity from fMRI data. The method uses a vector autoregressive model for the latent variables describing neuronal activity in combination with a linear observation model based on a convolution with a hemodynamic response function. Due to the employed modeling, it is possible to efficiently estimate all latent variables of the model using a variational Bayesian inference algorithm. The computational efficiency of the method enables us to apply it to large scale problems with high sampling rates and several hundred regions of interest. We use a comprehensive empirical evaluation with synthetic and real fMRI data to evaluate the performance of our method under various conditions.This work was partially supported by the National Institute of Child Health and Human Development (R01 HD042049). Martin Luessi was partially supported by the Swiss National Science Foundation Early Postdoc Mobility fellowship 148485. This work was supported in part by the Department of Energy under Contract DE-NA0000457, the “Ministerio de Ciencia e Innovación” under Contract TIN2010-15137, and the CEI BioTic with the Universidad de Granada Data were provided (in part) by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University

Alcohol use and its correlates in suicide attempters in a rural tertiary care teaching hospital in south India

Background: The relationship of alcohol and suicide is well documented. The interactions of alcohol use and suicide are complex. Neurobiological, genetic, psychological, social, cultural, and environmental factors are postulated to influence the outcome Aim was to find the frequency of alcohol use in suicide attempters and evaluate the association of alcohol use and its correlates in subjects who use alcohol to facilitate the attempt.Methods: It is a cross sectional observational study set in rural background in south India. Consecutive referrals of suicide attempters (n=175) were selected for the study. Details regarding the socio-demographic profile, suicide related details like lethality, intent, suicidal ideation, previous attempts, and alcohol related details like frequency, quantity and age at onset of alcohol consumption were recorded. Statistical significance of various socio demographic and clinical variables in correlation with use to facilitate attempt were analyzed. Logistic regression was used to determine the predictors in this at risk group of suicide attempters.Results: Over 43.43% of suicide attempters consumed alcohol. Intentional alcohol use prior to attempt to facilitate the attempt group constitutes about 18.29 %. High suicide intent and previous suicide attempt emerged as risk factors when alcohol was used to facilitate the attempt.Conclusions: Determinants, which increase the risk of suicide with alcohol use in rural south India, were identified

Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development

Brain structural and functional development, throughout childhood and into adulthood, underlies the maturation of increasingly sophisticated cognitive abilities. High-level attentional and cognitive control processes rely on the integrity of, and dynamic interactions between, core neurocognitive networks. The right fronto-insular cortex (rFIC) is a critical component of a salience network (SN) that mediates interactions between large-scale brain networks involved in externally oriented attention [central executive network (CEN)] and internally oriented cognition [default mode network (DMN)]. How these systems reconfigure and mature with development is a critical question for cognitive neuroscience, with implications for neurodevelopmental pathologies affecting brain connectivity. Using functional and effective connectivity measures applied to fMRI data, we examine interactions within and between the SN, CEN, and DMN. We find that functional coupling between key network nodes is stronger in adults than in children, as are causal links emanating from the rFIC. Specifically, the causal influence of the rFIC on nodes of the SN and CEN was significantly greater in adults compared with children. Notably, these results were entirely replicated on an independent dataset of matched children and adults. Developmental changes in functional and effective connectivity were related to structural connectivity along these links. Diffusion tensor imaging tractography revealed increased structural integrity in adults compared with children along both within- and between-network pathways associated with the rFIC. These results suggest that structural and functional maturation of rFIC pathways is a critical component of the process by which human brain networks mature during development to support complex, flexible cognitive processes in adulthood

Inter-subject synchronization of brain responses during natural music listening

Music is a cultural universal and a rich part of the human experience. However, little is known about common brain systems that support the processing and integration of extended, naturalistic 'real-world' music stimuli. We examined this question by presenting extended excerpts of symphonic music, and two pseudomusical stimuli in which the temporal and spectral structure of the Natural Music condition were disrupted, to non-musician participants undergoing functional brain imaging and analysing synchronized spatiotemporal activity patterns between listeners. We found that music synchronizes brain responses across listeners in bilateral auditory midbrain and thalamus, primary auditory and auditory association cortex, right-lateralized structures in frontal and parietal cortex, and motor planning regions of the brain [...

Neuropsychophysiological correlates of depression

The neuropsychiatric and cognitive deficits have been shown to exist in various psychiatric disorders. An attempt has been made by authors to evaluate the evidence pertaining to electrophysiological, structural and neuropsychological domains in depression. Renewal of interest in testing patients with depression on a broad range of neuropsychological tasks has revealed distinct pattern of cognitive impairment in cases with depression. The review focuses on structural and neuropsychological evidence of deficit in cases of depression

Multivariate Activation and Connectivity Patterns Discriminate Speech Intelligibility in Wernicke’s, Broca’s, and Geschwind’s Areas

The brain network underlying speech comprehension is usually described as encompassing fronto–temporal–parietal regions while neuroimaging studies of speech intelligibility have focused on a more spatially restricted network dominated by the superior temporal cortex. Here we use functional magnetic resonance imaging with a novel whole-brain multivariate pattern analysis (MVPA) to more fully characterize neural responses and connectivity to intelligible speech. Consistent with previous univariate findings, intelligible speech elicited greater activity in bilateral superior temporal cortex relative to unintelligible speech. However, MVPA identified a more extensive network that discriminated between intelligible and unintelligible speech, including left-hemisphere middle temporal gyrus, angular gyrus, inferior temporal cortex, and inferior frontal gyrus pars triangularis [...