Adi Maron-Katz

CLICK and EXPANDER: a system for clustering and visualizing gene expression data

MOTIVATION Microarrays have become a central tool in biological research. Their applications range from functional annotation to tissue classification and genetic network inference. A key step in the analysis of gene expression data is the identification of groups of genes that manifest similar expression patterns. This translates to the algorithmic problem of clustering genes based on their expression patterns. RESULTS We present a novel clustering algorithm, called CLICK, and its applications to gene expression analysis. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups (kernels) of highly similar elements, which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clusters. We report on the application of CLICK to a variety of gene expression data sets. In all those applications it outperformed extant algorithms according to several common figures of merit. We also point out that CLICK can be successfully used for the identification of common regulatory motifs in the upstream regions of co-regulated genes. Furthermore, we demonstrate how CLICK can be used to accurately classify tissue samples into disease types, based on their expression profiles. Finally, we present a new java-based graphical tool, called EXPANDER, for gene expression analysis and visualization, which incorporates CLICK and several other popular clustering algorithms. AVAILABILITY http://www.cs.tau.ac.il/~rshamir/expander/expander.html

Expander: from expression microarrays to networks and functions

A major challenge in the analysis of gene expression microarray data is to extract meaningful biological knowledge out of the huge volume of raw data. Expander (EXPression ANalyzer and DisplayER) is an integrated software platform for the analysis of gene expression data, which is freely available for academic use. It is designed to support all the stages of microarray data analysis, from raw data normalization to inference of transcriptional regulatory networks. The microarray analysis described in this protocol starts with importing the data into Expander 5.0 and is followed by normalization and filtering. Then, clustering and network-based analyses are performed. The gene groups identified are tested for enrichment in function (based on Gene Ontology), co-regulation (using transcription factor and microRNA target predictions) or co-location. The results of each analysis step can be visualized in a number of ways. The complete protocol can be executed in ≈1 h.

Functional connectivity dynamics during film viewing reveal common networks for different emotional experiences

Recent theoretical and empirical work has highlighted the role of domain-general, large-scale brain networks in generating emotional experiences. These networks are hypothesized to process aspects of emotional experiences that are not unique to a specific emotional category (e.g., “sadness,” “happiness”), but rather that generalize across categories. In this article, we examined the dynamic interactions (i.e., changing cohesiveness) between specific domain-general networks across time while participants experienced various instances of sadness, fear, and anger. We used a novel method for probing the network connectivity dynamics between two salience networks and three amygdala-based networks. We hypothesized, and found, that the functional connectivity between these networks covaried with the intensity of different emotional experiences. Stronger connectivity between the dorsal salience network and the medial amygdala network was associated with more intense ratings of emotional experience across six different instances of the three emotion categories examined. Also, stronger connectivity between the dorsal salience network and the ventrolateral amygdala network was associated with more intense ratings of emotional experience across five out of the six different instances. Our findings demonstrate that a variety of emotional experiences are associated with dynamic interactions of domain-general neural systems.

A large-scale perspective on stress-induced alterations in resting-state networks

Stress is known to induce large-scale neural modulations. However, its neural effect once the stressor is removed and how it relates to subjective experience are not fully understood. Here we used a statistically sound data-driven approach to investigate alterations in large-scale resting-state functional connectivity (rsFC) induced by acute social stress. We compared rsfMRI profiles of 57 healthy male subjects before and after stress induction. Using a parcellation-based univariate statistical analysis, we identified a large-scale rsFC change, involving 490 parcel-pairs. Aiming to characterize this change, we employed statistical enrichment analysis, identifying anatomic structures that were significantly interconnected by these pairs. This analysis revealed strengthening of thalamo-cortical connectivity and weakening of cross-hemispheral parieto-temporal connectivity. These alterations were further found to be associated with change in subjective stress reports. Integrating report-based information on stress sustainment 20 minutes post induction, revealed a single significant rsFC change between the right amygdala and the precuneus, which inversely correlated with the level of subjective recovery. Our study demonstrates the value of enrichment analysis for exploring large-scale network reorganization patterns, and provides new insight on stress-induced neural modulations and their relation to subjective experience.

Tired and misconnected: A breakdown of brain modularity following sleep deprivation

Sleep deprivation (SD) critically affects a range of cognitive and affective functions, typically assessed during task performance. Whether such impairments stem from changes to the brains intrinsic functional connectivity remain largely unknown. To examine this hypothesis, we applied graph theoretical analysis on resting‐state fMRI data derived from 18 healthy participants, acquired during both sleep‐rested and sleep‐deprived states. We hypothesized that parameters indicative of graph connectivity, such as modularity, will be impaired by sleep deprivation and that these changes will correlate with behavioral outcomes elicited by sleep loss. As expected, our findings point to a profound reduction in network modularity without sleep, evident in the limbic, default‐mode, salience and executive modules. These changes were further associated with behavioral impairments elicited by SD: a decrease in salience module density was associated with worse task performance, an increase in limbic module density was predictive of stronger amygdala activation in a subsequent emotional‐distraction task and a shift in frontal hub lateralization (from left to right) was associated with increased negative mood. Altogether, these results portray a loss of functional segregation within the brain and a shift towards a more random‐like network without sleep, already detected in the spontaneous activity of the sleep‐deprived brain. Hum Brain Mapp 38:3300–3314, 2017.

A hierarchical Bayesian model for flexible module discovery in three-way time-series data.

Motivation: Detecting modules of co-ordinated activity is fundamental in the analysis of large biological studies. For two-dimensional data (e.g. genes × patients), this is often done via clustering or biclustering. More recently, studies monitoring patients over time have added another dimension. Analysis is much more challenging in this case, especially when time measurements are not synchronized. New methods that can analyze three-way data are thus needed. Results: We present a new algorithm for finding coherent and flexible modules in three-way data. Our method can identify both core modules that appear in multiple patients and patient-specific augmentations of these core modules that contain additional genes. Our algorithm is based on a hierarchical Bayesian data model and Gibbs sampling. The algorithm outperforms extant methods on simulated and on real data. The method successfully dissected key components of septic shock response from time series measurements of gene expression. Detected patient-specific module augmentations were informative for disease outcome. In analyzing brain functional magnetic resonance imaging time series of subjects at rest, it detected the pertinent brain regions involved. Availability and implementation: R code and data are available at http://acgt.cs.tau.ac.il/twigs/. Contact: rshamir@tau.ac.il Supplementary information: Supplementary data are available at Bioinformatics online.

RichMind: A Tool for Improved Inference from Large-Scale Neuroimaging Results

As the use of large-scale data-driven analysis becomes increasingly common, the need for robust methods for interpreting a large number of results increases. To date, neuroimaging attempts to interpret large-scale activity or connectivity results often turn to existing neural mapping based on previous literature. In case of a large number of results, manual selection or percent of overlap with existing maps is frequently used to facilitate interpretation, often without a clear statistical justification. Such methodology holds the risk of reporting false positive results and overlooking additional results. Here, we propose using enrichment analysis for improving the interpretation of large-scale neuroimaging results. We focus on two possible cases: position group analysis, where the identified results are a set of neural positions; and connection group analysis, where the identified results are a set of neural position-pairs (i.e. neural connections). We explore different models for detecting significant overrepresentation of known functional brain annotations using simulated and real data. We implemented our methods in a tool called RichMind, which provides both statistical significance reports and brain visualization. We demonstrate the abilities of RichMind by revisiting two previous fMRI studies. In both studies RichMind automatically highlighted most of the findings that were reported in the original studies as well as several additional findings that were overlooked. Hence, RichMind is a valuable new tool for rigorous inference from neuroimaging results.

Tracing the Neural Carryover Effects of Interpersonal Anger on Resting-State fMRI in Men and Their Relation to Traumatic Stress Symptoms in a Subsample of Soldiers

Uncontrolled anger may lead to aggression and is common in various clinical conditions, including post traumatic stress disorder. Emotion regulation strategies may vary with some more adaptive and efficient than others in reducing angry feelings. However, such feelings tend to linger after anger provocation, extending the challenge of coping with anger beyond provocation. Task-independent resting-state (rs) fMRI may be a particularly useful paradigm to reveal neural processes of spontaneous recovery from a preceding negative emotional experience. We aimed to trace the carryover effects of anger on endogenous neural dynamics by applying a data-driven examination of changes in functional connectivity (FC) during rs-fMRI between before and after an interpersonal anger induction (N = 44 men). Anger was induced based on unfair monetary offers in a previously validated decision-making task. We calculated a common measure of global FC (gFC) which captures the level of FC between each region and all other regions in the brain, and examined which brain regions manifested changes in this measure following anger. We next examined the changes in all functional connections of each individuated brain region with all other brain regions to reveal which connections underlie the differences found in the gFC analysis of the previous step. We subsequently examined the relation of the identified neural modulations in the aftermath of anger with state- and trait- like measures associated with anger, including brain structure, and in a subsample of designated infantry soldiers (N = 21), with levels of traumatic stress symptoms (TSS) measured 1 year later following combat-training. The analysis pipeline revealed an increase in right amygdala gFC in the aftermath of anger and specifically with the right inferior frontal gyrus (IFG).We found that the increase in FC between the right amygdala and right IFG following anger was positively associated with smaller right IFG volume, higher trait-anger level and among soldiers with more TSS. Moreover, higher levels of right amygdala gFC at baseline predicted less reported anger during the subsequent anger provocation. The results suggest that increased amygdala-IFG connectivity following anger is associated with maladaptive recovery, and relates to long-term development of stress symptomatology in a subsample of soldiers.

Information flow and coherence of EEG during awake, meditation and drowsiness.

A comparison of coupling (information flow) and coherence (connectedness) of the brain regions between human awake, meditation and drowsiness states was carried out in this study. The Directed Transfer Function (DTF) method was used to estimate the coupling or brains flow of information between different regions during each condition. Welch and Minimum Variance Distortionless Response (MVDR) methods were utilised to estimate the coherence between brain areas. Analysis was conducted using the EEG data of 30 subjects (10 awake, 10 drowsiness and 10 meditating) with 6 EEG electrodes. The EEG data was recorded for each subject during 5 minutes baseline and 15 minutes of three specific conditions (awake, meditation or drowsiness). Statistical analysis was carried out which consisted of the Kruskal-Wallis (KW) non-parametric analysis of variance followed by post-hoc tests with Bonferroni alpha-correction. The results of this study revealed that a change in external awareness led to substantial differences in the spectral profile of the brains information flow as well as its connectedness.