Graph Theoretical Network Research Articles

Brain network changes after the first seizure: an insight into medication response?

After a first epileptic seizure, anti-seizure medications (ASMs) can change the likelihood of having a further event. This prospective study aimed to quantify brain network changes associated with taking ASM monotherapy. We applied graph theoretical network analysis to longitudinal resting-state functional MRI (fMRI) data from 28 participants who had recently experienced their first seizure. Participants were imaged before and during long-term ASM therapy, with a mean inter-scan interval of 6.9 months. After commencing ASM, we observed an increase in the clustering coefficient and a decrease in network path length. Brain changes after ASM treatment were most prominent in the superior frontoparietal and inferior fronto-temporal regions. Participants with recurrent seizures display the most pronounced network changes after ASM treatment. This study shows changes in brain network function after ASM administration, particularly in participants with recurrent seizures. Larger studies that ideally include control cohorts are required to understand further the connection between ASM-related brain network changes and longer-term seizure status.

The effect of cognitive reserve on the cognitive connectome in healthy ageing.

During ageing, different cognitive functions decline at different rates. Additionally, cognitive reserve may influence inter-individual variability in age-related cognitive decline. These complex relationships can be studied by constructing a so-called cognitive connectome and characterising it with advanced graph-theoretical network analyses. This study examined the effect of cognitive reserve on the cognitive connectome across age. A total of 334 cognitively healthy participants were stratified into early middle age (37-50years; n = 110), late middle age (51-64years; n = 106), and elderly (65-78years; n = 118) groups. Within each age group, individuals were subdivided into high and low cognitive reserve. For each subgroup, a cognitive connectome was constructed based on correlations between 47 cognitive variables. Applying graph theory, different global network measures were compared between the groups. Graph-theoretical network analyses revealed that individuals with high cognitive reserve were characterized by a stable cognitive connectome across age groups. High cognitive reserve groups only differed in modularity. In contrast, individuals with low cognitive reserve showed a marked reconfiguration of cognitive connectomes across age groups with differences extending over a variety of network measures including network strength, global efficiency, modularity, and small-worldness. Our results suggest a stabilizing effect of cognitive reserve on the cognitive connectome. Gaining further insights into these findings and underlying mechanisms will contribute to our understanding of age-related cognitive decline and guide the development of strategies to preserve cognitive function in ageing.

Representation and Analysis of Kinship, Based on the Naming Pattern

In this study, we develop a graph-theoretic network model for genealogical data. It is based on the naming system used by the Galo tribe in the Indian state of Arunachal Pradesh. P-graphs are used to visually represent the genealogy. According to the analysis of the network on social network metrics, the genealogical network is a sparsely connected, scale-free network with an average path length of 11.615 and a graph density of 0.002. Only a small portion of its nodes have high centrality values. The network has three communities, twenty authority nodes, two hub nodes, and observed no relinking marriages.

Regression and alignment for functional data and network topology.

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

Beyond the chronic pain stage: default mode network perturbation depends on years lived with back pain.

Research has indicated that the default mode network (DMN) is perturbated in patients with chronic pain when compared with healthy controls, and this perturbation is correlated with the duration of pain during the chronic pain stage. It remains unclear whether DMN adaptations manifest during the subacute pain stage and progress over time because of the duration of pain experience, rather than being a specific correlate of the chronic pain stage. Furthermore, information regarding whether these adaptations are related to cognitive processes of adaptation is lacking. To this end, we examined the DMN in 31 patients with chronic back pain (CBP), 77 patients with subacute back pain (SBP), as well as 39 healthy pain-free controls (HC) applying a graph-theoretic network approach on functional resting-state magnetic resonance imaging. Beyond the comparison between groups, we used a linear analysis considering the years lived with pain (YLP) across all patients with back pain and additionally performed a mediation analysis of the role of cognitive pain coping. In line with previous studies, we found significant DMN perturbation in CBP compared with HC. However, this did not apply to the comparison of CBP with SBP. Instead, we observed a positive correlation between DMN perturbation and YLP. This was significantly mediated by coping attitudes towards pain. Default mode network perturbation may thus reflect neural adaptation processes to pain experience rather than a single correlate of the chronic pain stage and be modulated by cognitive adaption. This points to potentially underinvestigated significant adaptation processes that could enable more fine-grained patient stratification.

Hippocampal hub failure is linked to long-term memory impairment in anti-NMDA-receptor encephalitis: insights from structural connectome graph theoretical network analysis

BackgroundAnti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is characterized by distinct structural and functional brain alterations, predominantly affecting the medial temporal lobes and the hippocampus. Structural connectome analysis with graph-based investigations of network properties allows for an in-depth characterization of global and local network changes and their relationship with clinical deficits in NMDAR encephalitis.MethodsStructural networks from 61 NMDAR encephalitis patients in the post-acute stage (median time from acute hospital discharge: 18 months) and 61 age- and sex-matched healthy controls (HC) were analyzed using diffusion-weighted imaging (DWI)-based probabilistic anatomically constrained tractography and volumetry of a selection of subcortical and white matter brain volumes was performed. We calculated global, modular, and nodal graph measures with special focus on default-mode network, medial temporal lobe, and hippocampus. Pathologically altered metrics were investigated regarding their potential association with clinical course, disease severity, and cognitive outcome.ResultsPatients with NMDAR encephalitis showed regular global graph metrics, but bilateral reductions of hippocampal node strength (left: p = 0.049; right: p = 0.013) and increased node strength of right precuneus (p = 0.013) compared to HC. Betweenness centrality was decreased for left-sided entorhinal cortex (p = 0.042) and left caudal middle frontal gyrus (p = 0.037). Correlation analyses showed a significant association between reduced left hippocampal node strength and verbal long-term memory impairment (p = 0.021). We found decreased left (p = 0.013) and right (p = 0.001) hippocampal volumes that were associated with hippocampal node strength (left p = 0.009; right p < 0.001).ConclusionsFocal network property changes of the medial temporal lobes indicate hippocampal hub failure that is associated with memory impairment in NMDAR encephalitis at the post-acute stage, while global structural network properties remain unaltered. Graph theory analysis provides new pathophysiological insight into structural network changes and their association with persistent cognitive deficits in NMDAR encephalitis.

Hemisphere lateralization of graph theoretical network in end-stage knee osteoarthritis patients

Hemisphere functional lateralization is a prominent feature of the human brain. However, it is not known whether hemispheric lateralization features are altered in end-stage knee osteoarthritis (esKOA). In this study, we performed resting-state functional magnetic imaging on 46 esKOA patients and 31 healthy controls (HCs) and compared with the global and inter-hemisphere network to clarify the hemispheric functional network lateralization characteristics of patients. A correlation analysis was performed to explore the relationship between the inter-hemispheric network parameters and clinical features of patients. The node attributes were analyzed to explore the factors changing in the hemisphere network function lateralization in patients. We found that patients and HCs exhibited “small-world” brain network topology. Clustering coefficient increased in patients compared with that in HCs. The hemisphere difference in inter-hemispheric parameters including assortativity, global efficiency, local efficiency, clustering coefficients, small-worldness, and shortest path length. The pain course and intensity of esKOA were positively correlated with the right hemispheric lateralization in local efficiency, clustering coefficients, and the small-worldness, respectively. The significant alterations of several nodal properties were demonstrated within group in pain-cognition, pain-emotion, and pain regulation circuits. The abnormal lateralization inter-hemisphere network may be caused by the destruction of regional network properties.

Dissociative Experiences, Borderline Personality Disorder Features, and Childhood Trauma: Generating Hypotheses from Data-Driven Network Analysis in an International Sample

ABSTRACT Dissociation is a multifaceted phenomenon that occurs in various mental disorders, including borderline personality disorder (BPD), but also in non-clinical populations. Severity of childhood trauma (abuse, neglect) plays an important role in the development of dissociation and BPD. However, the complex interplay of different dissociative symptoms, BPD features, and self-reported childhood trauma experiences is not yet fully understood. Graph-theoretical network analysis can help to better understand such multivariate interrelations. Objective: This study aimed to investigate associations between self-reported dissociation, BPD features, and childhood trauma experiences using a graph-theoretical approach. Data was collected online via international mental health platforms and research sites. N = 921 individuals (77.4% female) were included; 40% reported pathological levels of dissociation. Variables were assessed with established psychometric scales (Dissociative Experiences Scale; Personality Assessment Inventory Borderline Features Scale; Childhood Trauma Questionnaire) and analyzed within a partial correlation network. Positive bivariate correlations between all variables were found. When accounting for their mutual influence on each other, dissociation was predominantly connected to BPD features with effect sizes between rp = .028 and rp = .126, while still showing a slight unique relationship with physical neglect (rp = .044). Findings suggest close associations between dissociative experiences and BPD features. While childhood trauma plays an important role in the development of dissociation and BPD, its recall may not fully explain their current co-occurrence. Prospective studies are needed to shed more light on causal pathways to better understand which factors contribute to dissociation and its link to BPD (features).

Resting network architecture of theta oscillations reflects hyper-learning of sensorimotor information in Gilles de la Tourette syndrome.

Gilles de la Tourette syndrome is a neurodevelopmental disorder characterized by motor and vocal tics. It is associated with enhanced processing of stimulus-response associations, including a higher propensity to learn probabilistic stimulus-response contingencies (i.e. statistical learning), the nature of which is still elusive. In this study, we investigated the hypothesis that resting-state theta network organization is a key for the understanding of superior statistical learning in these patients. We investigated the graph-theoretical network architecture of theta oscillations in adult patients with Gilles de la Tourette syndrome and healthy controls during a statistical learning task and in resting states both before and after learning. We found that patients with Gilles de la Tourette syndrome showed a higher statistical learning score than healthy controls, as well as a more optimal (small-world-like) theta network before the task. Thus, patients with Gilles de la Tourette syndrome had a superior facility to integrate and evaluate novel information as a trait-like characteristic. Additionally, the theta network architecture in Gilles de la Tourette syndrome adapted more to the statistical information during the task than in HC. We suggest that hyper-learning in patients with Gilles de la Tourette syndrome is likely a consequence of increased sensitivity to perceive and integrate sensorimotor information leveraged through theta oscillation-based resting-state dynamics. The study delineates the neural basis of a higher propensity in patients with Gilles de la Tourette syndrome to pick up statistical contingencies in their environment. Moreover, the study emphasizes pathophysiologically endowed abilities in patients with Gilles de la Tourette syndrome, which are often not taken into account in the perception of this common disorder but could play an important role in destigmatization.

Network analysis and molecular modeling studies of pinocembrin a bioactive phytochemical of Dodonaea viscosa against Parkinson's disease.

Parkinson's disease, a neurodegenerative disorder, is quickly progressing and accounts for 15% of dementia cases. Parkinson's disease is the second most frequent form of neuronal degeneration after Alzheimer's, with an average age of 55 years for individuals exhibiting neuropsychiatric and physiological symptoms. Due to the effectiveness, low toxicity, and low side effects, bioactive compounds from plants have received increased attention recently as therapeutic drugs. In the current study, effective anti-neurodegenerative phytochemicals from Dodonaea viscosa were screened using in silico methods and have been proposed to be further investigated for the treatment of Parkinson's disease. The structures of twenty bioactive chemicals were screened and graph theoretical network analysis revealed alpha-synuclein as a potent therapeutic target. Based on docking scores, an effective bioactive molecule was selected, and its energy values, electrostatic potential surface and drug-like qualities were examined using molecular orbitals, pharmacokinetics and toxicity studies. Pinocembrin was found as a superior binder based on molecular docking as it demonstrated stronger binding with - 10.2kcal/mol. An investigation using Ramachandran plot validated the protein-ligand complex secondary structure's stability. Pinocembrin, a bioactive phytochemical from Dodonaea viscosa, may be a viable lead molecule that may be developed as a candidate medicine for anti-neurodegenerative therapy against Parkinson's disease.

Construction of ecological network with protected areas as the main region in Guangzhou City, China

In the context of the national ecological security development strategy, constructing regional ecological networks centered on protected areas and ecological corridors has become an urgent issue in protected areas system development of China. We focused on strengthening ecological connections between protected areas in Guangzhou, identified the ecological resource patches, ecological corridors, and ecological nodes by using Invest model, connectivity analysis, circuit theory models, and graph-theoretical network structure evaluation, and constructed an ecological network for the Guangzhou with nature reserves as the core. The results showed that 52 ecological resource patches were identified in the study area, covering a total area of 1450.01 km2. Nature reserves accounted for 76.4% of the total area, forming the main part of the ecological resource patches. 115 ecological corridors were identified, with a total length of 900.56 km and an average length of 7.83 km. Furthermore, 72 ecological key points were identified, covering a total area of 17.57 km2, and 81 ecological breakpoints, with a total area of 35.9 km2. The network structure indices (α=0.65, β=2.21, and γ=0.77) indicated a reasonably structured and well-connected network. By exploring pathways for constructing regional ecological networks under the protected areas system and enriching the application of circuit theory models in ecological network construction, this study provides scientific basis for regional ecological security and biodiversity conservation.

Aging effects on dual-route speech processing networks during speech perception in noise.

Healthy aging leads to complex changes in the functional network of speech processing in a noisy environment. The dual-route neural architecture has been applied to the study of speech processing. Although evidence suggests that senescent increases activity in the brain regions across the dorsal and ventral stream regions to offset reduced periphery, the regulatory mechanism of dual-route functional networks underlying such compensation remains largely unknown. Here, by utilizing functional near-infrared spectroscopy (fNIRS), we investigated the compensatory mechanism of the dual-route functional connectivity, and its relationship with healthy aging by using a speech perception task at varying signal-to-noise ratios (SNR) in healthy individuals (young adults, middle-aged adults, and older adults). Results showed that the speech perception scores showed a significant age-related decrease with the reduction of the SNR. The analysis results of dual-route speech processing networks showed that the functional connection of Wernicke's area and homolog Wernicke's area were age-related increases. Further to clarify the age-related characteristics of the dual-route speech processing networks, graph-theoretical network analysis revealed an age-related increase in the efficiency of the networks, and the age-related differences in nodal characteristics were found both in Wernicke's area and homolog Wernicke's area under noise environment. Thus, Wernicke's area might be a key network hub to maintain efficient information transfer across the speech process network with healthy aging. Moreover, older adults would recruit more resources from the homologous Wernicke's area in a noisy environment. The recruitment of the homolog of Wernicke's area might provide a means of compensation for older adults for decoding speech in an adverse listening environment. Together, our results characterized dual-route speech processing networks at varying noise environments and provided new insight for the compensatory theories of how aging modulates the dual-route speech processing functional networks.

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury: predictors of patient prognosis.

Abstract JOURNAL/nrgr/04.03/01300535-202407000-00035/figure1/v/2023-11-20T171125Z/r/image-tiff Patients with mild traumatic brain injury have a diverse clinical presentation, and the underlying pathophysiology remains poorly understood. Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neurobiological markers after mild traumatic brain injury. This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury. Graph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function. However, most previous mild traumatic brain injury studies using graph theory have focused on specific populations, with limited exploration of simultaneous abnormalities in structural and functional connectivity. Given that mild traumatic brain injury is the most common type of traumatic brain injury encountered in clinical practice, further investigation of the patient characteristics and evolution of structural and functional connectivity is critical. In the present study, we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury. In this longitudinal study, we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 weeks of injury, as well as 36 healthy controls. Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis. In the acute phase, patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network. More than 3 months of follow-up data revealed signs of recovery in structural and functional connectivity, as well as cognitive function, in 22 out of the 46 patients. Furthermore, better cognitive function was associated with more efficient networks. Finally, our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury. These findings highlight the importance of integrating structural and functional connectivity in understanding the occurrence and evolution of mild traumatic brain injury. Additionally, exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.

Graph Theoretical Network Analysis and Pharmacoinformatics-Based Investigation of Bioactive Compounds from Semecarpus anacardium Linn. for Alzheimer’s Disease

Alzheimer’s disease is a highly prevalent disease that has affected over 50 million people worldwide. It is mainly caused by the continuous deposition of amyloid-[Formula: see text] peptide (A[Formula: see text]. Acetylcholinesterase (AChE), N-methyl D-aspartate (NMDA), Tau-Tubulin kinase 1 (TTBK1) and [Formula: see text]-secretase 1 (BACE-1) are potential targets to treat Alzheimer’s disease and to reduce the pathogenesis; amongst, BACE-1 is preferred owing to its capacity in minimizing the accumulation of A[Formula: see text]. This study explored Semecarpus anacardium Linn.-derived phytocompounds and their interaction with BACE-1. The phytochemicals of S. anacardiumLinn. were retrieved from the IMPPAT database. The factual targets (AChE, BACE-1, NMDA and TTBK1) of Alzheimer’s disease are identified by the graph theoretical network analysis, and are docked with a total of eight bioactive compounds from S. anacardium Linn. Interestingly, the docking score was significant (−12 kcal × mol[Formula: see text] for the interactions of amentoflavone with BACE-1. The co-crystallized ligand of BACE-1 (2-imino-3-methyl-5,5-diphenylimidazolidin-4-one) was considered for further studies and revealed a superior binding affinity (−8.7 kcal × mol[Formula: see text]. Molecular dynamics (MD) simulation and Density Functional Theory (DFT) analyzed the conformational stability of amentoflavone and BACE-1 complex. Furthermore, S. anacardium Linn. seemed to be nontoxic against U87-MG cells in vitro. These results from in silico prediction suggest that the derivatives of S. anacardium Linn. possess a significant interaction with the BACE-1 and provide a favorable direction to prevent aggregation of A[Formula: see text] peptides.

Network Analysis and In Silico Molecular Modeling of Bioactive Compounds from Sida cordifolia Against NMDA Receptor

Epilepsy, one of the most common neurological disorders, is characterized by continuing predisposition of epileptic seizures. Abnormal discharges of seizures burst the electrical functions in the brain leading to unprompted behaviors of brain activity. Recently, more attention has been focused on bioactive substances from plants as therapeutic agents for neurodegenerative disorders because of their efficacy, low toxicity, and less side-effects. This study has utilized in silico molecular modeling as a tool for screening effective antiepileptic compounds from Sida cordifolia which could be further explored for the treatment of epilepsy. Twenty-six bioactive compounds were identified through data repository and their structures were retrieved from PubChem database. Based on the Graph theoretical network analysis, NMDA protein was identified as an ideal drug target. The effective compound was chosen based on the docking scores and its drug-likeness properties were analyzed by in silico prediction of pharmacokinetic and physicochemical properties. Based on molecular docking, 5,7-dihydroxy-3-isoprenyl flavone was identified as a better binder as this exhibited higher binding towards the target with lower binding energy (−10.5 kcal × mol[Formula: see text]. The stability of the protein–ligand complex was confirmed by molecular dynamics simulation study. The intermolecular interaction assessed during dynamic conditions indicated that the bioactive compound, 5,7-dihydroxy-3-isoprenyl flavone, from Sida cordifolia could be a potential lead molecule that can be developed as a candidate drug for anti-epileptic therapy.

Pharmacoinformatics-based identification of phytochemicals from Solanum torvum Swartz. fruits as potential inhibitors for MAPK14 protein

Plants and phytocompounds gained more attention because of their unrivalled variety of chemical diversity. In this view, the present study was executed to predict the anticancer potential of Solanum torvum Swartz. fruits derived phytocompounds against one of the breast cancer target proteins (MAPK14, PDB ID: 5ETA, resolution: 2.80 Å) through pharmacoinformatics-based screening and molecular dynamics simulation tools. Initially, a graph theoretical network approach was used to visualize the genes, enzymes, and proteins involved in the signalling pathway of breast cancer and identify the significant target protein (MAPK14). A total of thirty-three active compounds were selected from S. torvum sw. through the IMPPAT database, and their structures were drawn by Chemsketch software. The drug-like behaviours of the compounds were assessed through pharmacokinetics and physicochemical characterization studies. Five compounds, namely chlorogenin (−10.90 kcal × mol−1), corosolic acid (−10.80 kcal × mol−1), solaspigenin (−10.80 kcal × mol−1), paniculogenin (−10.70 kcal × mol−1), spirostane-3,6-dione (−10.70 kcal × mol−1) exhibited top binding score against MAPK14, these are higher than that of the standard drug (Doxorubicin) (−8.60 kcal × mol−1). Additionally, the five top-binding compounds revealed better drug-likeness traits and the lowest toxicity profiles. MD simulation studies confirmed the stability of the top five scored compounds with the MAPK14 binding pockets. According to these findings, the selected five compounds might be used as significant MAPK14 inhibitors and can be used as new medicines for the treatment of breast cancer. Communicated by Ramaswamy H. Sarma

Functional connectivity varies across scales in a fragmented landscape.

Species of different sizes interact with the landscape differently because ecological structure varies with scale, as do species movement capabilities and habitat requirements. As such, landscape connectivity is dependent upon the scale at which an animal interacts with its environment. Analyses of landscape connectivity must incorporate ecologically relevant scales to address scale-specific differences. Many evaluations of landscape connectivity utilize incrementally increasing buffer distances or other arbitrary spatial delineations as scales of analysis. Instead, we used a mammalian body mass discontinuity analysis to objectively identify scales in the Central Platte River Valley (CPRV) of Nebraska, U.S.A. We implemented a graph-theoretic network analysis to evaluate the connectivity of two wetland land cover types in the CPRV, wet meadow and emergent marsh, at multiple scales represented by groupings of species with similar body mass. Body mass is allometric with multiple traits of species, including dispersal distances. The landscape was highly connected at larger scales but relatively unconnected at smaller scales. We identified a threshold at which the landscape becomes highly connected between 500 m and 6,500 m dispersal distances. The presence of a connectivity threshold suggests that species with dispersal distances close to the threshold may be most vulnerable to habitat loss or reconfiguration and management should account for the connectivity threshold. Furthermore, we propose that a multiscale approach to management will be necessary to ensure landscape connectivity for diverse species.

A network perspective on real-life threat, anxiety, and avoidance.

Anxiety, approach, and avoidance motivation crucially influence mental and physical health, especially when environments are stressful. The interplay between anxiety and behavioral motivation is modulated by multiple individual factors. This proof-of-concept study applies graph-theoretical network analysis to explore complex associations between self-reported trait anxiety, approach and avoidance motivation, situational anxiety, stress symptoms, perceived threat, perceived positive consequences of approach, and self-reported avoidance behavior in real-life threat situations. A total of 436 participants who were matched on age and gender (218 psychotherapy patients, 218 online-recruited nonpatients) completed an online survey assessing these factors in response to the COVID-19 pandemic. The resulting cross-sectional psychological network revealed a complex pattern with multiple positive (e.g., between trait anxiety, avoidance motivation, and avoidance behavior) and negative associations (e.g., between approach and avoidance motivation). The patient and online subsample networks did not differ significantly, however, descriptive differences may inform future research.

Psychological Factors Linked to Intimate Partner Violence and Childhood Maltreatment: On Dissociation as a Possible Bridge Symptom.

Intimate partner violence (IPV) is a serious health concern, occurring worldwide in various forms and settings. Over the past years, multiple sources reported an increase of IPV globally, partly related to COVID-19 restrictions. Childhood maltreatment enhances the risk of IPV, possibly via alterations in emotion regulation, attachment, maladaptive core beliefs, dissociation, and psychopathological symptoms. However, studies investigating these associations simultaneously are still needed. This study aimed to investigate association between IPV, childhood maltreatment severity, maladaptive schemata (mistrust, alienation, enmeshment), attachment anxiety, social support, emotion regulation, dissociation, posttraumatic stress disorder (PTSD), and borderline personality disorder (BPD) symptoms. We further explored the complex interplay of all factors, accounting for their shared associations. An anonymous online survey was posted on international online platforms for people experiencing domestic violence and on research platforms. Regression analyses and graph-theoretical network analysis were used to explore associations between all variables. N = 434 participants (40% in treatment) completed the survey. IPV perpetration and victimization were highly correlated. Both were significantly associated with childhood maltreatment severity, early maladaptive schemata, dissociation, BPD features, and PTSD symptoms. When including all variables in one model, IPV was associated with dissociation, which indirectly linked it to childhood maltreatment experiences, PTSD symptoms, withdrawal, and self-blame. Our findings suggest that IPV perpetration and victimization often co-occur. Dissociation may be an important bridge symptom, linking IPV to childhood maltreatment experiences, PTSD symptoms, and maladaptive coping. Prospective studies are needed to corroborate these findings and to establish psychological mechanisms underlying IPV.

Abnormal gray matter volume and functional connectivity patterns in social cognition-related brain regions of young children with autism spectrum disorder.

Autism spectrum disorder (ASD) is associated with abnormal brain imaging findings, but descriptions thereof are inconsistent. The aim of the present study was to investigate brain abnormalities in young children with ASD using a combination of structural and functional brain magnetic resonance imaging (MRI). Structural and resting-state functional MRI was performed in 67 children with ASD (aged 2-7 years) and 39 age-matched typically developing (TD) controls. Voxel-based morphometry was used to evaluate differences in brain structure between groups. Topologic parameters of the functional brain network were compared by graph theoretic analysis and network connectomes were compared with network-based statistics. A support vector machine (SVM) was used to discriminate between ASD and TD groups. Results demonstrated young children with ASD had increased gray matter volumes (GMVs) in the right medial superior frontal gyrus and left fusiform gyrus compared with the TD group. The ASD group had altered subnetwork connectivity in frontal and temporal lobes and other social cognition-related brain regions. Functional connectivity in the left superior temporal gyrus and left temporal pole of the middle temporal gyrus was positively correlated with adaptability and language developmental quotient (DQ) in children with ASD. The combination of the brain structural and functional features had 86.2% accuracy in discriminating between ASD and TD. The present study shows that young children with ASD have altered GMVs and functional networks in social cognition-related brain regions, which are potential neuroimaging biomarkers for ASD.