Analysis of Regular Patterns in Un-weighted Directed Graphs

Time evolving networks tend to have an element of regularity. This regularity is characterized by existence of repetitive patterns in the data sequences of the graph metrics. As per our research, the relevance of such regular patterns to the network has not been adequately explored. Such patterns in certain data sequences are indicative of properties like popularity, activeness etc. which are of vital significance for any network. These properties are closely indicated by data sequences of graph metrics - degree prestige, degree centrality and occurrence. In this paper, (a) an improved mining algorithm has been used to extract regular patterns in these sequences, and (b) a methodology has been proposed to quantitatively analyse the behavior of the obtained patterns. To analyze this behavior, a quantification measure coined as "Sumscore" has been defined to compare the relative significance of such patterns. The patterns are ranked according to their Sumscores and insights are then drawn upon it. The efficacy of this method is demonstrated by experiments on two real world datasets.

Multi-classification of action intention understanding brain signals based on thresholding graph metric

BACKGROUND: Classification of action intention understanding is extremely important for human computer interaction. Many studies on the action intention understanding classification mainly focus on binary classification, while the classification accuracy is often unsatisfactory, not to mention multi-classification. METHOD: To complete the multi-classification task of action intention understanding brain signals effectively, we propose a novel feature extraction procedure based on thresholding graph metrics. RESULTS: Both the alpha frequency band and full-band obtained considerable classification accuracies. Compared with other methods, the novel method has better classification accuracy. CONCLUSIONS: Brain activity of action intention understanding is closely related to the alpha band. The new feature extraction procedure is an effective method for the multi-classification of action intention understanding brain signals.

Network analysis of trauma in patients with early-stage psychosis

Childhood trauma (ChT) is a risk factor for psychosis. Negative lifestyle factors such as rumination, negative schemas, and poor diet and exercise are common in psychosis. The present study aimed to perform a network analysis of interactions between ChT and negative lifestyle in patients and controls. We used data of patients with early-stage psychosis (n = 500) and healthy controls (n = 202). Networks were constructed using 12 nodes from five scales: the Brief Core Schema Scale (BCSS), Brooding Scale (BS), Dietary Habits Questionnaire, Physical Activity Rating, and Early Trauma Inventory Self Report-Short Form (ETI). Graph metrics were calculated. The nodes with the highest predictability and expected influence in both patients and controls were cognitive and emotional components of the BS and emotional abuse of the ETI. The emotional abuse was a mediator in the shortest pathway connecting the ETI and negative lifestyle for both groups. The negative others and negative self of the BCSS mediated emotional abuse to other BCSS or BS for patients and controls, respectively. Our findings suggest that rumination and emotional abuse were central symptoms in both groups and that negative others and negative self played important mediating roles for patients and controls, respectively.Trial Registration: ClinicalTrials.gov identifier: CUH201411002.

Distinct Modes of Ongoing Antigen Interactions Shape Intraclonal Dynamics in Splenic Marginal Zone Lymphoma

Almost one-third of all splenic marginal zone lymphoma (SMZL) cases express B cell receptor immunoglobulin (BcR IG) encoded by the IGHV1-2*04 gene. Such cases display a distinctive profile of genomic aberrations (e.g. higher incidence of NOTCH2 and KLF2 mutations) and a more aggressive clinical course compared to SMZL cases utilizing other IGHV genes. Such skewing of the BcR IG gene repertoire implicates antigen selection in SMZL ontogeny. Although the supportive evidence is compelling, it mostly derives from low-throughput approaches, which are inherently limited in their capacity to capture the complexity of the BcR IG gene repertoire. This hinders the comprehensive assessment of the subclonal architecture of SMZL that could offer insight into the dynamics of antigen-IG interactions. Here, we sought to overcome this limitation through a high-throughput immunogenetic investigation of SMZL, focusing on the detailed characterization of somatic hypermutation (SHM) and intraclonal diversification (ID) profiles. Our study included 22 cases utilizing the IGHV1-2*04 gene and 36 cases utilizing other IGHV genes. IGHV-IGHD-IGHJ (IGH) gene rearrangements were PCR-amplified and libraries were sequenced on the Illumina MiSeq platform. Data was analyzed with the IMGT/HighV-QUEST and TRIP software as well as a novel bioinformatics/biostatistics pipeline. Clonotypes were defined as unique combinations of a given IGHV gene+VH CDR3 amino acid (aa) sequence. Only IGH gene rearrangement sequences assigned to the dominant clonotypes of each case were assessed. In detail, all nucleotide variants (nt vars, i.e. all sequences clustered in the same dominant clonotype yet displaying distinct SHM profiles) were identified and further analyzed. Starting from the most abundant nt var, a network was built representing its connections with all other nt vars. For this analysis, we introduce the terms 'most relevant pathway' (MRP) corresponding to the pathway including connected nt vars with the highest total number of IGH sequences; and 'longest mutational pathways' (LMP) corresponding to the pathways with the highest number of nt vars (Fig. 1). Different graph metrics assessed the impact of ID in different SMZL subgroups: the first one focuses on the 'most relevant pathway' and quantifies SHM convergence [ratio of the total number of IGH sequences corresponding to the nt vars of this pathway to the number of IGH sequences in the most abundant nt var]; while the second refers to the length of the 'longest mutational pathways'. Cases lacking additional connected nt vars [length of the LMP=1; 3 IGHV1-2*04 cases and 4 non-IGHV1-2*04 cases] were excluded. Consequently, the analysis included 19 IGHV1-2*04 cases and 32 non-IGHV1-2*04 cases. Significant differences were noted in the SHM and ID profiles between groups; the IGHV1-2*04 group had significantly (p<0.01) higher convergence values ranging from 0.009 to 1.243 (median: 0.102), as opposed to the non-IGHV1-2*04 group (range: 0.002-1.13, median: 0.014), overall suggesting that stronger selective pressures act in SMZL cases expressing the IGHV1-2*04 versus others. Moreover, IGHV1-2*04 cases displayed significantly (p<0.01) longer mutational pathways (length range: 2-6, median: 3) compared to the other group (range: 2-5, median: 2), alluding to more pronounced ID arising due to ongoing SHM. Finally, all mutations leading to aa changes were analyzed in the context of ID networks. More recurrent aa mutations were identified amongst cases with higher levels of convergence. For instance, the VH FR2 M39I change, one of the most prominent recurrent SHMs in the IGHV1-2*04 group, was found in the most abundant nt var in 13/19 IGHV1-2*04 cases, while it was identified in nt vars with extra mutations in another 3 cases. Of interest, it was present at the end of the mutational pathways in these 16 cases, whilst in the other group it was present only in one case using the IGHV1-2*02 gene, and absent in the rest (p<0.01). In conclusion, in the first large-scale high-throughput immunogenetic analysis of SMZL we provide strong evidence for more pronounced antigenic pressure in cases utilizing IGHV1-2*04 versus other IGHV genes. Our findings highlight a unique subclonal architecture for IGHV1-2*04 SMZL and corroborate the hypothesis that this group may represent a distinct molecular variant of SMZL. Figure 1 Figure 1. Disclosures Rossi: Abbvie: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Gilead: Honoraria, Research Funding; Verastem: Honoraria, Research Funding; Roche: Honoraria, Research Funding; Cellestia: Honoraria, Research Funding. Chatzidimitriou: Janssen: Honoraria, Research Funding; Abbvie: Honoraria, Research Funding. Stamatopoulos: Janssen: Honoraria, Research Funding; Gilead: Honoraria, Research Funding; Abbvie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding.

CNN for a Connectivity Based Epilepsy Diagnosis with Resting-state EEG

Objective: This study investigates the performance of a CNN algorithm on epilepsy diagnosis. Without pathology, diagnosis involves long and costly electroencephalographic (EEG) monitoring. Novel approaches may overcome this by comparing brain connectivity using graph metrics. This study, however, uses deep learning to learn connectivity patterns directly from easily acquired EEG data. Approach: A convolutional neural network (CNN) algorithm was applied on directed Granger causality (GC) connectivity measures, derived from 50 seconds of resting-state surface EEG recordings from 30 subjects with epilepsy and a 30 subject control group. Main results: The learned CNN filters reflected reduced delta band connectivity in frontal regions and increased left lateralized frontal-posterior gamma band connectivity. A diagnosis accuracy of 85% (F1-score 85%) was achieved by an ensemble of CNN models, each trained on differently prepared data from different electrode combinations. Conclusions: Appropriate preparation of connectivity data enables generic CNN algorithms to be used for detection of multiple discriminative epileptic features. Differential patterns revealed in this study may help to shed light on underlying altered cognitive abilities in epilepsy patients. Significance: The accuracy achieved in this study shows that, in combination with other methods, this approach could prove a valuable clinical decision support system for epilepsy diagnosis.

The structure and behaviour of hierarchical infrastructure networks

Critical services depend on infrastructure networks for their operation and any disruption to these networks can have significant impacts on society, the economy, and quality of life. Such networks can be characterised as graphs which can be used to understand their structural properties, and the effect on their behaviour and robustness to hazards. Using a suite of graphs and critical infrastructure networks, this study aims to show that networks which exhibit a hierarchical structure are more likely to be less robust comparatively to non-hierarchical networks when exposed to failures, including those which supply critical services. This study investigates the properties of a hierarchical structure through identifying a set of key characteristics from an ensemble of graph models which are then used in a comparative analysis against a suite of spatial critical infrastructure networks. A failure model is implemented and applied to understand the implications of hierarchical structures in real world networks for their robustness to perturbations. The study concludes that a set of three graph metrics, cycle basis, maximum betweenness centrality and assortativity coefficient, can be used to identify the extent of a hierarchy in graphs, where a lack of robustness is linked to the hierarchical structure, a feature exhibited in both graph models and infrastructure networks.

Spinal Cord Resting State Activity in Individuals With Fibromyalgia Who Take Opioids

Chronic pain coincides with myriad functional alterations throughout the brain and spinal cord. While spinal cord mechanisms of chronic pain have been extensively characterized in animal models and in vitro, to date, research in patients with chronic pain has focused only very minimally on the spinal cord. Previously, spinal cord functional magnetic resonance imaging (fMRI) identified regional alterations in spinal cord activity in patients (who were not taking opioids) with fibromyalgia, a chronic pain condition. Here, in patients with fibromyalgia who take opioids (N = 15), we compared spinal cord resting-state fMRI data vs. patients with fibromyalgia not taking opioids (N = 15) and healthy controls (N = 14). We hypothesized that the opioid (vs. non-opioid) patient group would show greater regional alterations in spinal cord activity (i.e., the amplitude of low frequency fluctuations or ALFF, a measure of regional spinal cord activity). However, we found that regional spinal cord activity in the opioid group was more similar to healthy controls, while regional spinal cord activity in the non-opioid group showed more pronounced differences (i.e., ventral increases and dorsal decreases in regional ALFF) vs. healthy controls. Across patient groups, self-reported fatigue correlated with regional differences in spinal cord activity. Additionally, spinal cord functional connectivity and graph metrics did not differ among groups. Our findings suggest that, contrary to our main hypothesis, patients with fibromyalgia who take opioids do not have greater alterations in regional spinal cord activity. Thus, regional spinal cord activity may be less imbalanced in patients taking opioids compared to patients not taking opioids.

Network Analysis of Trauma in Patients With Early-Stage Psychosis

Objectives: Childhood trauma (ChT) is a risk factor for psychosis. Negative lifestyle factors such as rumination, negative schemas, and poor diet and exercise are common in psychosis. The present study aimed to perform a network analysis of interactions between ChT and negative lifestyle in patients and controls.Methods: We used data of patients with early-stage psychosis (n = 500) and healthy controls (n = 202). Networks were constructed using 12 nodes from five scales: the Brief Core Schema Scale (BCSS), Brooding Scale (BS), Dietary Habits Questionnaire, Physical Activity Rating, and Early Trauma Inventory Self Report-Short Form (ETI). Graph metrics were calculated.Results: The nodes with the highest predictability and expected influence in both patients and controls were cognitive (Co) and emotional domains (Em) of the BS and emotional abuse (EMO) of the ETI. The EMO was a mediator in the shortest pathway connecting the ETI and negative lifestyle for both groups. The negative other (NO) and negative self (NS) of the BCSS mediated EMO to other BCSS or BS for patients and controls, respectively.Conclusion: Our findings suggest that rumination and EMO were central symptoms in both groups and that NO and NS played important mediating roles for patients and controls, respectively.Trial Registration: ClinicalTrials.gov identifier: CUH201411002