Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.

Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

Variations in neural circuitry, inherited or acquired, may underlie important individual differences in thought, feeling, and action patterns. Here, we used task-free connectivity analyses to isolate and characterize two distinct networks typically coactivated during functional MRI tasks. We identified a "salience network," anchored by dorsal anterior cingulate (dACC) and orbital frontoinsular cortices with robust connectivity to subcortical and limbic structures, and an "executive-control network" that links dorsolateral frontal and parietal neocortices. These intrinsic connectivity networks showed dissociable correlations with functions measured outside the scanner. Prescan anxiety ratings correlated with intrinsic functional connectivity of the dACC node of the salience network, but with no region in the executive-control network, whereas executive task performance correlated with lateral parietal nodes of the executive-control network, but with no region in the salience network. Our findings suggest that task-free analysis of intrinsic connectivity networks may help elucidate the neural architectures that support fundamental aspects of human behavior.

https://www.jneurosci.org/content/jneuro/27/9/2349.full.pdf

Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control

Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.

/pdf/consistent-resting-state-networks-across-healthy-subjects-xd3o1l0nyp.pdf

Consistent resting-state networks across healthy subjects

Presents a new framework for unsupervised NMR image analysis based on hidden MRF modeling and algorithms According to the NMR image statistics, two types of hidden MRF models are introduced and justified in terms of stochastic regularization The image analysis is then formulated as an optimization problem and achieved in two stages: estimate the model parameters to initialize the maximum likelihood solution and conduct finer segmentation through Bayesian decisions using the local context The solution of the new problem formulation is implemented with an efficient multistage procedure The experimental results with real NMR images are provided to demonstrate the promise and effectiveness of the proposed technique >

http://ieeexplore.ieee.org/iel2/3349/10004/00474569.pdf

Hidden MRF model-based algorithms for NMR image analysis

Statistical moments provide us with quantitative measures of the data and estimating these moments in a time-resolved fashion is challenging but important, especially for analyzing human brain data. In this work, we present our attempt at providing a general pipeline for finding different moments (of different orders), which are also resolved in both time and frequency. We showcase the application of our method by analyzing a resting-state functional magnetic resonance imaging dataset. Here we find that individuals with first-episode psychosis (FEP) stay significantly more (compared to controls) in a state which has a very strong pair-wise (two-way) connectivity between salience and default mode networks. That same state also shows strong tri-wise (three-way) connectivity with networks other than the default mode, which points to the fact that different interaction orders have different and complementary information. Our proposed approach can extract meaningful and structured information by calculating (typically ignored) different order moments that are resolved in both time and frequency of the moment space as opposed to sample space.

Going from lines to triangles: A formulation for time-frequency moments of time-series with application to study fMRI

Independent component analysis (ICA) is a powerful method for blind source separation based on the assumption that sources are statistically independent. Though ICA has proven useful and has been employed in many applications, complete statistical independence can be too restrictive an assumption in practice. Additionally, important prior information about the data, such as sparsity, is usually available. Sparsity is a natural property of the data, a form of diversity, which, if incorporated into the ICA model, can relax the independence assumption, resulting in an improvement in the overall separation performance. In this work, we propose a new variant of ICA by entropy bound minimization (ICA-EBM)-a flexible, yet parameter-free algorithm-through the direct exploitation of sparsity. Using this new SparseICA-EBM algorithm, we study the synergy of independence and sparsity through simulations on synthetic as well as functional magnetic resonance imaging (fMRI)-like data.

Enhancing ICA Performance by Exploiting Sparsity: Application to FMRI Analysis

In this paper, we develop an expectation-maximization (EM)-like scheme, called ${\mathcal{K}}$-BM, for iterative numerical computation of the minimum ${\mathcal{K}}$-divergence estimator (M${\mathcal{K}}$DE). This estimator utilizes Parzen’s non-parameteric ${\mathcal{K}}$ernel density estimate to down weight low density areas attributed to outliers. Similarly to the standard EM algorithm, the ${\mathcal{K}}$-BM involves successive Maximizations of lower Bounds on the objective function of the M${\mathcal{K}}$DE. Differently from EM, these bounds do not rely on conditional expectations only. The proposed ${\mathcal{K}}$-BM algorithm is applied to robust parameter estimation of a finite-order multivariate Gaussian mixture model (GMM). Simulation studies illustrate the performance advantage of the ${\mathcal{K}}$-BM as compared to other state-of-the-art robust GMM estimators. 

Robust GMM Parameter Estimation via the K-BM Algorithm

Independent Vector Analysis (IVA) is a popular extension of Independent Component Analysis (ICA) for joint separation of a set of instantaneous linear mixtures, with a direct application in frequency-domain speaker separation or extraction. The mixtures are parameterized by mixing matrices, one matrix per mixture. This means that the IVA mixing model does not account for any relationships between parameters across the mixtures/frequencies. The separation proceeds jointly only through the source model, where statistical dependencies of sources across the mixtures are taken into account. In this paper, we propose a mixing model for joint blind source extraction where the mixing model parameters are linked across the frequencies. This is achieved by constraining the set of feasible parameters to the manifold of half-length separating filters, which has a clear interpretation and application in frequency-domain speaker extraction.

Tulay Adali

Papers

Hidden MRF model-based algorithms for NMR image analysis

Going from lines to triangles: A formulation for time-frequency moments of time-series with application to study fMRI

Enhancing ICA Performance by Exploiting Sparsity: Application to FMRI Analysis

Robust GMM Parameter Estimation via the K-BM Algorithm

Independent Vector Extraction Constrained on Manifold of Half-Length Filters