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Emotion dysregulation is common among individuals with autism spectrum disorder (ASD). This study examined the relationship between emotion dysregulation and resting heart rate variability (HRV), a marker of the autonomic nervous system, in ASD adolescents. Resting HRV data were collected from ASD (n = 23) and typically developing (TD) adolescents (n = 32) via short-term electrocardiogram. Parents/caregivers reported participants' level of emotion dysregulation with the Emotion Dysregulation Inventory (EDI). Controlling for the effects of age and gender, regression analyses revealed moderating effects of group, suggesting that lower resting HRV was more strongly associated with greater emotion dysregulation in ASD than TD adolescents. The results support the view that disruptions in autonomic functioning may contribute to emotion dysregulation in ASD., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Motivation: Many real-world problems can be modeled as annotated graphs. Scalable graph algorithms that extract actionable information from such data are in demand since these graphs are large, varying in topology, and have diverse node/edge annotations. When these graphs change over time they create dynamic graphs, and open the possibility to find patterns across different time points. In this article, we introduce a scalable algorithm that finds unique dense regions across time points in dynamic graphs. Such algorithms have applications in many different areas, including the biological, financial, and social domains., Results: There are three important contributions to this manuscript. First, we designed a scalable algorithm, USNAP, to effectively identify dense subgraphs that are unique to a time stamp given a dynamic graph. Importantly, USNAP provides a lower bound of the density measure in each step of the greedy algorithm. Second, insights and understanding obtained from validating USNAP on real data show its effectiveness. While USNAP is domain independent, we applied it to four non-small cell lung cancer gene expression datasets. Stages in non-small cell lung cancer were modeled as dynamic graphs, and input to USNAP. Pathway enrichment analyses and comprehensive interpretations from literature show that USNAP identified biologically relevant mechanisms for different stages of cancer progression. Third, USNAP is scalable, and has a time complexity of O(m+mc log nc+nc log nc), where m is the number of edges, and n is the number of vertices in the dynamic graph; mc is the number of edges, and nc is the number of vertices in the collapsed graph., Availability and Implementation: The code of USNAP is available at https://www.cs.utoronto.ca/~juris/data/USNAP22., (© The Author(s) 2023. Published by Oxford University Press.)

Gene expression microarrays are one of the most widely used high-throughput technologies in molecular biology, with applications such as identification of disease mechanisms and development of diagnostic and prognostic gene signatures. However, the success of these tasks is often limited because microarray analysis does not account for the complex relationships among genes, their products, and overall signaling and regulatory cascades. Incorporating protein-protein interaction data into microarray analysis can help address these challenges. This chapter reviews how protein-protein interactions can help with microarray analysis, leading to benefits such as better explanations of disease mechanisms, more complete gene annotations, improved prioritization of genes for future experiments, and gene signatures that generalize better to new data., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Apolipoprotein E ɛ4 allele (ApoE4) is the most common gene polymorphism related to Alzheimer's disease (AD). Impaired synaptic dysfunction occurs in ApoE4 carriers before any clinical symptoms. It remains unknown whether ApoE4 status affects the hippocampal neuromodulation, which further influences brain network topology., Purpose: To study the relationship of regional and global network properties by using graph theory analysis and glutamatergic (Glx) neuromodulation in the ApoE isoforms., Study Type: Prospective., Subjects: Eighty-four cognitively normal adults (26 ApoE4 and 58 non-ApoE4 carriers)., Field Strength/sequence: Gradient-echo echo-planar and point resolved spectroscopy sequence at 3 T., Assessment: Glx concentration in bilateral hippocampi were processed with jMRUI (4.0), and graph theory metrics (global: γ, λ, small-worldness in whole brain; regional: nodal clustering coefficient (C i ) and nodal characteristic path length (L i )) in top 20% highly connected hubs of subgroups (low-risk: non-ApoE4; high-risk: APOE4) were calculated and compared., Statistical Tests: Two-sample t test was used to compare metrics between subgroups. Correlations between regional properties and Glx by Pearson's partial correlation with false discovery rate correction., Results: Significant differences (P < 0.05) in C i between subgroups were found in hubs of left inferior frontal, bilateral inferior temporal, and bilateral precentral gyri, right parahippocampus, and bilateral precuneus. In addition, there was a significant correlation between Glx in the left hippocampus and C i in inferior frontal gyrus (r = -0.537, P = 0.024), right inferior temporal (r = -0.478, P = 0.043), right parahippocampus (r = -0.629, P = 0.016), left precentral (r = -0.581, P = 0.022), right precentral (r = -0.651, P = 0.003), left precuneus (r = -0.545, P = 0.024), and right precuneus (r = -0.567, P = 0.022); and L i in left precuneus (r = 0.575, P = 0.032) and right precuneus (r = 0.586, P = 0.032) in the high-risk group, but not in the low-risk group., Data Conclusion: Our results suggested that healthy ApoE4 carriers exhibit poorer local interconnectivity. Moreover, the close relationship between glutamate and small-world network properties in ApoE4 carriers might reflect a compensatory response to the impaired network efficiency., Evidence Level: 2 TECHNICAL EFFICACY: Stage 3., (© 2021 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC. on behalf of International Society for Magnetic Resonance in Medicine.)

The glutamatergic cycle is essential in modulating memory processing by the hippocampal circuitry. Our combined proton magnetic resonance spectroscopy ( 1 H-MRS) and task-based functional magnetic resonance imaging (fMRI) study (using face-name paired-associates encoding and retrieval task) of a cognitively normal cohort of 67 healthy adults (18 ApoE4 carriers and 49 non-ApoE4 carriers) found altered patterns of relationships between glutamatergic-modulated synaptic signalling and neuronal activity or functional hyperaemia in the ApoE4 isoforms. Our study highlighted the asymmetric left-right hippocampal glutamatergic system in modulating neuronal activities in ApoE4 carriers versus non-carriers. Such brain differentiation might be developmental cognitive advantages or compensatory due to impaired synaptic integrity and plasticity in ApoE4 carriers. As there was no difference in myoinositol levels measured by MRS between the ApoE4 and non-ApoE4 subgroups, the mechanism is unlikely to be a response to neuroinflammation., (© 2021 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Background: Owing to the prevalence of neuromyths in education, there has been a call for more teacher training in neuroscience. However, neuroscience is rarely featured in teacher education. This study investigated the neuroscience literacy and perceptions of neuroscience in education among preservice teachers in order to inform future development of initial teacher education., Method: Neuroscience literacy of 968 preservice teachers and their perceptions towards applying neuroscience in education were examined using survey items adapted from studies addressing similar constructs. Rasch item response theory and classical test theory techniques were employed for data analysis., Results: Most of the preservice teachers had limited brain knowledge and subscribed to many common neuromyths but were positive towards applying neuroscience in education. General brain knowledge was the only predictor for ability to identify neuromyths (β = .564)., Conclusion: Neuroscience knowledge can help safeguard preservice teachers against neuromyths. Neuroscience training deserves a place in teacher education., (Copyright © 2020. Published by Elsevier GmbH.)

Can we use graph mining algorithms to find patterns in tumor molecular mechanisms? Can we model disease progression with multiple time-specific graph comparison algorithms? In this paper, we will focus on this area. Our main contributions are 1) we proposed the Temporal-Omics (Temp-O) workflow to model tumor progression in non-small cell lung cancer (NSCLC) using graph comparisons between multiple stage-specific graphs, and 2) we showed that temporal structures are meaningful in the tumor progression of NSCLC. Other identified temporal structures that were not highlighted in this paper may also be used to gain insights to possible novel mechanisms. Importantly, the Temp-O workflow is generic; while we applied it on NSCLC, it can be applied in other cancers and diseases. We used gene expression data from tumor samples across disease stages to model lung cancer progression, creating stage-specific tumor graphs. Validating our findings in independent datasets showed that differences in temporal network structures capture diverse mechanisms in NSCLC. Furthermore, results showed that structures are consistent and potentially biologically important as we observed that genes with similar protein names were captured in the same cliques for all cliques in all datasets. Importantly, the identified temporal structures are meaningful in the tumor progression of NSCLC as they agree with the molecular mechanism in the tumor progression or carcinogenesis of NSCLC. In particular, the identified major histocompatibility complex of class II temporal structures capture mechanisms concerning carcinogenesis; the proteasome temporal structures capture mechanisms that are in early or late stages of lung cancer; the ribosomal cliques capture the role of ribosome biosynthesis in cancer development and sustainment. Further, on a large independent dataset we validated that temporal network structures identified proteins that are prognostic for overall survival in NSCLC adenocarcinoma., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Using a wireless single channel EEG device, we investigated the feasibility of using short-term frontal EEG as a means to evaluate the dynamic changes of mental workload. Frontal EEG signals were recorded from twenty healthy subjects performing four cognitive and motor tasks, including arithmetic operation, finger tapping, mental rotation and lexical decision task. Our findings revealed that theta activity is the common EEG feature that increases with difficulty across four tasks. Meanwhile, with a short-time analysis window, the level of mental workload could be classified from EEG features with 65%-75% accuracy across subjects using a SVM model. These findings suggest that frontal EEG could be used for evaluating the dynamic changes of mental workload.