Frustrated random walks: A fast method to compute node distances on hypergraphs.

A hypergraph is a generalization of a graph that arises naturally when attribute-sharing among entities is considered. Compared to graphs, hypergraphs have the distinct advantage that they contain explicit communities and are more convenient to manipulate. An open problem in hypergraph research is how to accurately and efficiently calculate node distances on hypergraphs. Estimating node distances enables us to find a node's nearest neighbors, which has important applications in such areas as recommender system, targeted advertising, etc. In this paper, we propose using expected hitting times of random walks to compute hypergraph node distances. We note that simple random walks cannot accurately compute node distances on highly complex real-world hypergraphs, which motivates us to introduce frustrated random walks (FRW) for this task. We further benchmark our method against DeepWalk, and show that while the latter can achieve comparable results, FRW has a distinct computational advantage in cases where the number of targets is fairly small. For such cases, we show that FRW runs in significantly shorter time than DeepWalk. Finally, we analyze the time complexity of our method, and show that for large and sparse hypergraphs, the complexity is approximately linear, rendering it superior to the DeepWalk alternative.

Biomarker identification in psychiatric disorders: from neuroscience to clinical practice.

Patients with psychiatric disorders exhibit several neurobehavioral and neuropsychological alterations compared to healthy controls. However, signature endpoints of these behavioral manifestations have not yet been translated into clinical tests for diagnosis and follow-up measures. Recently, neuroproteomic approaches have been utilized to identify unique signature markers indicative of these disorders. Development of reliable biomarkers has the potential to revolutionize the diagnosis, classification, and monitoring of clinical responses in psychiatric diseases. However, the lack of biological gold standards, the evolving nosology of psychiatric disorders, and the complexity of the nervous system are among the major challenges that have hindered efforts to develop reliable biomarkers in the field of neuropsychiatry and drug abuse. While biomarkers currently have a limited role in the area of neuropsychiatry, several promising biomarkers have been proposed in conditions such as dementia, schizophrenia, depression, suicide, and addiction. One of the primary objectives of this review is to discuss the role of proteomics in the development of biomarkers specific to neuropsychiatry. We discuss and evaluate currently available biomarkers as well as those that are under research for clinical use in the future.

Bioinformatics approach to understanding interacting pathways in neuropsychiatric disorders.

Bioinformatics-based applications have been incorporated into several medical disciplines, including cancer, neuroscience, and recently psychiatry. Both the increasing interest in the molecular aspect of neuropsychiatry and the availability of high-throughput discovery and analysis tools have encouraged the incorporation of bioinformatics and neurosystems biology techniques into psychiatry and neuroscience research. As applied to neuropsychiatry, systems biology involves the acquisition and processing of high-throughput datasets to infer new information. A major component in bioinformatics output is pathway analysis that provides an insight into and prediction of possible underlying pathogenic processes which may help understand disease pathogenesis. In addition, this analysis serves as a tool to identify potential biomarkers implicated in these disorders. In this chapter, we summarize the different tools and algorithms used in pathway analysis along with their applications to the different layers of molecular investigations, from genomics to proteomics.

Weighted-permutation entropy: a complexity measure for time series incorporating amplitude information.

Permutation entropy (PE) has been recently suggested as a novel measure to characterize the complexity of nonlinear time series. In this paper, we propose a simple method to address some of PE's limitations, mainly its inability to differentiate between distinct patterns of a certain motif and the sensitivity of patterns close to the noise floor. The method relies on the fact that patterns may be too disparate in amplitudes and variances and proceeds by assigning weights for each extracted vector when computing the relative frequencies associated with every motif. Simulations were conducted over synthetic and real data for a weighting scheme inspired by the variance of each pattern. Results show better robustness and stability in the presence of higher levels of noise, in addition to a distinctive ability to extract complexity information from data with spiky features or having abrupt changes in magnitude.

Functional dependence in the human brain: a graph theoretical analysis.

In this paper, we propose a graph-theoretical approach to reveal patterns of functional dependencies between different scalp regions. We start by computing pairwise measures of dependence from dense-array scalp electroencephalographic (EEG) recordings. The obtained dependence matrices are then averaged over trials and further statistically processed to provide more reliability. Graph structure information is subsequently extracted using several graph theoretical measures. Simple measures of node degree and clustering strength are shown to be useful to describe the global properties of the analyzed networks. More sophisticated measures, such as betweenness centrality and subgraph centrality tend to provide additional insight into the network structure, and therefore robustly discriminate two cognitive states. We further examine the connected components of the graph to identify the dependent functional regions. The approach supports dynamicity in that all suggested computations can be easily extended to different points in time, thus enabling to monitor dependence evolution and variability with time.

Quantifying cognitive state from EEG using phase synchrony.

Phase synchrony is a powerful amplitudeindependent measure that quantifies linear and nonlinear dynamics between non-stationary signals. It has been widely used in a variety of disciplines including neural science and cognitive psychology. Current time-varying phase estimation uses either the Hilbert transform or the complex wavelet transform of the signals. This paper exploits the concept of phase synchrony as a mean to discriminate face processing from the processing of a simple control stimulus. Dependencies between channel locations were assessed for two separate conditions elicited by distinct pictures (representing a human face and a Gabor patch), both flickering at a rate of 17.5 Hz. Statistical analysis is performed using the Kolmogorov-Smirnov test. Moreover, the phase synchrony measure used is compared with a measure of association that has been previously applied in the same context: the generalized measure of association (GMA). Results show that although phase synchrony works well in revealing regions of high synchronization, and therefore achieves an acceptable level of discriminability, this comes at the expense of sacrificing time resolution.

Quantifying cognitive state from EEG using dependence measures.

The exquisite human ability to perceive facial features has been explained by the activity of neurons particularly responsive to faces, found in the fusiform gyrus and the anterior part of the superior temporal sulcus. This study hypothesizes and demonstrates that it is possible to automatically discriminate face processing from processing of a simple control stimulus based on processed EEGs in an online fashion with high temporal resolution using measures of statistical dependence applied on steady-state visual evoked potentials. Correlation, mutual information, and a novel measure of association, referred to as generalized measure of association (GMA), were applied on filtered current source density data. Dependences between channel locations were assessed for two separate conditions elicited by distinct pictures (a face and a Gabor grating) flickering at a rate of 17.5 Hz. Filter settings were chosen to minimize the distortion produced by bandpassing parameters on dependence estimation. Statistical analysis was performed for automated stimulus classification using the Kolmogorov-Smirnov test. Results show active regions in the occipito-parietal part of the brain for both conditions with a greater dependence between occipital and inferotemporal sites for the face stimulus. GMA achieved a higher performance in discriminating the two conditions. Because no additional face-like stimuli were examined, this study established a basic difference between one particular face and one nonface stimulus. Future work may use additional stimuli and experimental manipulations to determine the specificity of the current connectivity results.

Systems biology, bioinformatics, and biomarkers in neuropsychiatry.

Although neuropsychiatric (NP) disorders are among the top causes of disability worldwide with enormous financial costs, they can still be viewed as part of the most complex disorders that are of unknown etiology and incomprehensible pathophysiology. The complexity of NP disorders arises from their etiologic heterogeneity and the concurrent influence of environmental and genetic factors. In addition, the absence of rigid boundaries between the normal and diseased state, the remarkable overlap of symptoms among conditions, the high inter-individual and inter-population variations, and the absence of discriminative molecular and/or imaging biomarkers for these diseases makes difficult an accurate diagnosis. Along with the complexity of NP disorders, the practice of psychiatry suffers from a "top-down" method that relied on symptom checklists. Although checklist diagnoses cost less in terms of time and money, they are less accurate than a comprehensive assessment. Thus, reliable and objective diagnostic tools such as biomarkers are needed that can detect and discriminate among NP disorders. The real promise in understanding the pathophysiology of NP disorders lies in bringing back psychiatry to its biological basis in a systemic approach which is needed given the NP disorders' complexity to understand their normal functioning and response to perturbation. This approach is implemented in the systems biology discipline that enables the discovery of disease-specific NP biomarkers for diagnosis and therapeutics. Systems biology involves the use of sophisticated computer software "omics"-based discovery tools and advanced performance computational techniques in order to understand the behavior of biological systems and identify diagnostic and prognostic biomarkers specific for NP disorders together with new targets of therapeutics. In this review, we try to shed light on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and illustrate how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients.

An association framework to analyze dependence structure in time series.

The purpose of this paper is two-fold: first, to propose a modification to the generalized measure of association (GMA) framework that reduces the effect of temporal structure in time series; second, to assess the reliability of using association methods to capture dependence between pairs of EEG channels using their time series or envelopes. To achieve the first goal, the GMA algorithm was updated so as to minimize the effect of the correlation inherent in the time structure. The reliability of the modified scheme was then assessed on both synthetic and real data. Synthetic data was generated from a Clayton copula, for which null hypotheses of uncorrelatedness were constructed for the signal. The signal was processed such that the envelope emulated important characteristics of experimental EEG data. Results show that the modified GMA procedure can capture pairwise dependence between generated signals as well as their envelopes with good statistical power. Furthermore, applying GMA and Kendall's tau to quantify dependence using the extracted envelopes of processed EEG data concords with previous findings using the signal itself.

Robust EEG preprocessing for dependence-based condition discrimination.

This paper addresses the robustness of the filtering schemes in processing high resolution electroencephalogram (EEG) data in the context of discriminating two stimuli flickering at a given frequency. The raw data consists of recordings from a 128-channel HydroCell GSN where the subject was visually stimulated with two images flickering at 17.5 Hz, representing two distinct conditions, referred to as Face and Mock. These signals were then passed through a band pass filter to only capture the modulation at the flickering frequency, and a connectivity analysis was performed on the filtered signal using generalized measure of association, to observe if the network connectivity changes from one stimulus to the other. In this paper, we investigate the effect of the bandpass filter on the discriminability of the stimuli over different filter orders and quality factors. We observe that the network connectivity is stable over a significant range of parameter values of the filter, thus establishing the desired robustness.