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

Graphical block diagram simulation tools with highly interactive plotting and visualization capabilities can accelerate and greatly improve DSP and communications education. In this paper, we present several projects given in graduate and senior level undergraduate courses in DSP and communications which use the graphical simulation tool Capsim, and emphasize the importance of true multi-rate signal processing capability for studying DSP. We present a project on integrated design and simulation of network and physical layers in communications systems which also requires asynchronous data flow capability. This project unifies two topics that are traditionally treated separately, and provides important insight for students on error free transmission over noisy channels. >

DSP and communication engineering education with graphical block diagram simulation tools

Examples of complex-valued random phenomena in science and engineering are abound, and joint blind source separation (JBSS) provides an effective way to analyze multiset data. Thus there is a need for flexible JBSS algorithms for efficient data-driven feature extraction in the complex domain. Independent vector analysis (IVA) is a prominent recent extension of independent component analysis to multivariate sources, i.e., to perform JBSS, but its effectiveness is determined by how well the source models used match the true latent distributions and the optimization algorithm employed. The complex multivariate generalized Gaussian distribution (CMGGD) is a simple, yet effective parameterized family of distributions that account for full second- and higher-order statistics including noncircularity, a property that has been often omitted for convenience. In this paper, we marry IVA and CMGGD to derive, IVA-CMGGD, with a number of numerical optimization implementations including steepest descent, the quasi-Newton method Broyden–Fletcher–Goldfarb–Shanno (BFGS), and its limited-memory sibling limited-memory BFGS all in the complex-domain. We demonstrate the performance of our algorithm on simulated data as well as a 14-subject real-world complex-valued functional magnetic resonance imaging dataset against a number of competing algorithms.

/pdf/iva-using-complex-multivariate-ggd-application-to-fmri-ihxfmafd2s.pdf

IVA using complex multivariate GGD: application to fMRI analysis

Brain signals can be measured using multiple imaging modalities, such as magnetic resonance imaging (MRI)-based techniques. Different modalities convey distinct yet complementary information; thus, their joint analyses can provide valuable insight into how the brain functions in both healthy and diseased conditions. Data-driven approaches have proven most useful for multimodal fusion as they minimize assumptions imposed on the data, and there are a number of methods that have been developed to uncover relationships across modalities. However, none of these methods, to the best of our knowledge, can discover “one-to-many associations”, meaning one component from one modality is linked with more than one component from another modality. However, such “one-to-many associations” are likely to exist, since the same brain region can be involved in multiple neurological processes. Additionally, most existing data fusion methods require the signal subspace order to be identical for all modalities—a severe restriction for real-world data of different modalities. Here, we propose a new fusion technique—the consecutive independence and correlation transform (C-ICT) model—which successively performs independent component analysis and independent vector analysis and is uniquely flexible in terms of the number of datasets, signal subspace order, and the opportunity to find “one-to-many associations”. We apply C-ICT to fuse diffusion MRI, structural MRI, and functional MRI datasets collected from healthy controls (HCs) and patients with schizophrenia (SZs). We identify six interpretable triplets of components, each of which consists of three associated components from the three modalities. Besides, components from these triplets that show significant group differences between the HCs and SZs are identified, which could be seen as putative biomarkers in schizophrenia.

Consecutive Independence and Correlation Transform for Multimodal Data Fusion: Discovery of One-to-Many Associations in Structural and Functional Imaging Data

We have previously shown that partial likelihood estimation of finite normal mixtures (FNM) yields very efficient estimators and hence provides a very attractive solution for real-time signal processing. Order selection, however, is a very important factor affecting the overall performance. We address the problem of order selection for the FNM model in applications requiring sequential processing. We pose the problem as the joint determination of the system memory and the observation vector dimension which together determine the number of mixture components. We define penalized partial likelihood as the information theoretic criterion for order selection and propose a sequential order selection procedure. We consider channel equalization as an example, an application for which FNM provides a perfect match to the data generation mechanism and hence is a particularly attractive solution. We study examples in channel equalization showing the effectiveness of the approach and discuss its properties.

Penalized partial likelihood for sequential order selection

Functional connectivity is a widely used measure for finding the relationships between functional entities of the brain. Recently, more focus has been put on the methods that aim to estimate these relationships in a time-resolved fashion. However, the similarities and differences between these methods are not always clear and can result in unfair and incorrect comparisons. Here, we present a framework that provides a unified, systematic view for some of the more well-known methods. Using the proposed unified framework, we explain different methodologies using a unified language and show how they are similar and different conceptually. We give examples how this framework exposes important assumptions made by various methods, which can help clarify differences in results and facilitate reproducibility. We also show how such a framework will enable us to develop methods that improve upon previous methods.

Tulay Adali

Papers

DSP and communication engineering education with graphical block diagram simulation tools

IVA using complex multivariate GGD: application to fMRI analysis

Consecutive Independence and Correlation Transform for Multimodal Data Fusion: Discovery of One-to-Many Associations in Structural and Functional Imaging Data

Penalized partial likelihood for sequential order selection

A Unified Framework for Modularizing and Comparing Time-Resolved Functional Connectivity Methods