Distribution of centrality measures on undirected random networks via the cavity method.

The Katz centrality of a node in a complex network is a measure of the node's importance as far as the flow of information across the network is concerned. For ensembles of locally tree-like undirected random graphs, this observable is a random variable. Its full probability distribution is of interest but difficult to handle analytically because of its "global" character and its definition in terms of a matrix inverse. Leveraging a fast Gaussian Belief Propagation-Cavity algorithm to solve linear systems on tree-like structures, we show that i) the Katz centrality of a single instance can be computed recursively in a very fast way, and ii) the probability [Formula: see text] that a random node in the ensemble of undirected random graphs has centrality [Formula: see text] satisfies a set of recursive distributional equations, which can be analytically characterized and efficiently solved using a population dynamics algorithm. We test our solution on ensembles of Erdos-Rényi and Scale Free networks in the locally tree-like regime, with excellent agreement. The analytical distribution of centrality for the configuration model conditioned on the degree of each node can be employed as a benchmark to identify nodes of empirical networks with over- and underexpressed centrality relative to a null baseline. We also provide an approximate formula based on a rank-[Formula: see text] projection that works well if the network is not too sparse, and we argue that an extension of our method could be efficiently extended to tackle analytical distributions of other centrality measures such as PageRank for directed networks in a transparent and user-friendly way.

Human adolescent brain similarity development is different for paralimbic versus neocortical zones.

Adolescent development of human brain structural and functional networks is increasingly recognized as fundamental to emergence of typical and atypical adult cognitive and emotional proodal magnetic resonance imaging (MRI) data collected from N [Formula: see text] 300 healthy adolescents (51%; female; 14 to 26 y) each scanned repeatedly in an accelerated longitudinal design, to provide an analyzable dataset of 469 structural scans and 448 functional MRI scans. We estimated the morphometric similarity between each possible pair of 358 cortical areas on a feature vector comprising six macro- and microstructural MRI metrics, resulting in a morphometric similarity network (MSN) for each scan. Over the course of adolescence, we found that morphometric similarity increased in paralimbic cortical areas, e.g., insula and cingulate cortex, but generally decreased in neocortical areas, and these results were replicated in an independent developmental MRI cohort (N [Formula: see text] 304). Increasing hubness of paralimbic nodes in MSNs was associated with increased strength of coupling between their morphometric similarity and functional connectivity. Decreasing hubness of neocortical nodes in MSNs was associated with reduced strength of structure-function coupling and increasingly diverse functional connections in the corresponding fMRI networks. Neocortical areas became more structurally differentiated and more functionally integrative in a metabolically expensive process linked to cortical thinning and myelination, whereas paralimbic areas specialized for affective and interoceptive functions became less differentiated, as hypothetically predicted by a developmental transition from periallocortical to proisocortical organization of the cortex. Cytoarchitectonically distinct zones of the human cortex undergo distinct neurodevelopmental programs during typical adolescence.

Evolution of system connectivity to support food production in the Indus Basin in Pakistan.

Sustainability challenges related to food production arise from multiple nature-society interactions occurring over long time periods. Traditional methods of quantitative analysis do not represent long-term changes in the networks of system components, including institutions and knowledge that affect system behavior. Here, we develop an approach to study system structure and evolution by combining a qualitative framework that represents sustainability-relevant human, technological, and environmental components, and their interactions, mediated by knowledge and institutions, with network modeling that enables quantitative metrics. We use this approach to examine the water and food system in the Punjab province of the Indus River Basin in Pakistan, exploring how food production has been sustained, despite high population growth, periodic floods, and frequent political and economic disruptions. Using network models of five periods spanning 75 y (1947 to 2022), we examine how quantitative metrics of network structure relate to observed sustainability-relevant outcomes and how potential interventions in the system affect these quantitative metrics. We find that the persistent centrality of some and evolving centrality of other key nodes, coupled with the increasing number and length of pathways connecting them, are associated with sustaining food production in the system over time. Our assessment of potential interventions shows that regulating groundwater pumping and phasing out fossil fuels alters network pathways, and helps identify potential vulnerabilities for future food production.

Altered brain function in classical trigeminal neuralgia patients: ALFF, ReHo, and DC static- and dynamic-frequency study.

The present study aimed to clarify the brain function of classical trigeminal neuralgia (CTN) by analyzing 77 CTN patients and age- and gender-matched 73 healthy controls (HCs) based on three frequency bands of the static and dynamic amplitude of low-frequency fluctuation, regional homogeneity, and degree centrality (sALFF, sReHo, sDC, dALFF, dReHo, and dDC). Compared to HCs, the number of altered brain regions was different in three frequency bands, and the classical frequency band was most followed by slow-4 in CTN patients. Cerrelellum_8_L (sReHo), Cerrelellum_8_R (sDC), Calcarine_R (sDC), and Caudate_R (sDC) were found only in classical frequency band, while Precuneus_L (sALFF) and Frontal_Inf_Tri_L (sReHo) were found only in slow-4 frequency band. Except for the above six brain regions, the others overlapped in the classical and slow-4 frequency bands. CTN seriously affects the mental health of patients, and some different brain regions are correlated with clinical parameters. The static and dynamic indicators of brain function were complementary in CTN patients, and the changing brain regions showed frequency specificity. Compared to slow-5 frequency band, slow-4 is more consistent with the classical frequency band, which could be valuable in exploring the pathophysiology of CTN.

Aberrant morphometric networks in Alzheimer's disease have hemispheric asymmetry and age dependence.

Alzheimer's disease (AD) is associated with abnormal accumulations of hyperphosphorylated tau and amyloid-ß proteins, resulting in unique patterns of atrophy in the brain. We aimed to elucidate some characteristics of the AD's morphometric networks constructed by associating different morphometric features among brain areas and evaluating their relationship to Mini-Mental State Examination total score and age. Three-dimensional T1-weighted (3DT1) image data scanned by the same 1.5T magnetic resonance imaging (MRI) were obtained from 62 AD patients and 41 healthy controls (HCs) and were analysed by using FreeSurfer. The associations of the extracted six morphometric features between regions were estimated by correlation coefficients. The global and local graph theoretical measures for this network were evaluated. Associations between graph theoretical measures and age, sex and cognition were evaluated by multiple regression analysis in each group. Global measures of integration: global efficiency and mean information centrality were significantly higher in AD patients. Local measures of integration: node global efficiency and information centrality were significantly higher in the entorhinal cortex, fusiform gyrus and posterior cingulate cortex of AD patients but only in the left hemisphere. All global measures were correlated with age in AD patients but not in HCs. The information centrality was associated with age in AD's broad brain regions. Our results showed that altered morphometric networks due to AD are left-hemisphere dominant, suggesting that AD pathogenesis has a left-right asymmetry. Ageing has a unique impact on the morphometric networks in AD patients. The information centrality is a sensitive graph theoretical measure to detect this association.

Identification of hub genes and potential networks by centrality network analysis of PCR amplified Fusarium oxysporum f. sp. lycopersici EF1α gene.

BACKGROUND: Fusarium wilt is a devastating soil-borne fungal disease of tomato across the world. Conventional method of disease prevention including usage of common pesticides and methods like soil solarisation are usually ineffective in the treatment of this disease. Therefore, there is an urgent need to identify virulence related genes in the pathogen which can be targeted for fungicide development. RESULTS: Pathogenicity testing and phylogenetic classification of the pathogen used in this study confirmed it as Fusarium oxysporum f. sp. lycopersici (Fol) strain. A recent discovery indicates that EF1α, a protein with conserved structural similarity across several fungal genera, has a role in the pathogenicity of Magnaporthe oryzae, the rice blast fungus. Therefore, in this study we have done structural and functional classification of EF1α to understand its role in pathogenicity of Fol. The protein model of Fol EF1α was created using the template crystal structure of the yeast elongation factor complex EEF1A:EEF1BA which showed maximum similarity with the target protein. Using the STRING online database, the interactive information among the hub genes of EF1α was identified and the protein-protein interaction network was recognized using the Cytoscape software. On combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 4 hub genes including Fol EF1α were selected for further investigation. The three interactors of Fol EF1α showed maximum similarity with homologous proteins found in Neurospora crassa complexed with the known fungicide, cycloheximide. Through the sequence similarity and PDB database analysis, homologs of Fol EF1α were found: EEF1A:EEF1BA in complex with GDPNP in yeast and EF1α in complex with GDP in Sulfolobus solfataricus. The STITCH database analysis suggested that EF1α and its other interacting partners interact with guanosine diphosphate (GDPNP) and guanosine triphosphate (GTP). CONCLUSIONS: Our study offers a framework for recognition of several hub genes network in Fusarium wilt that can be used as novel targets for fungicide development. The involvement of EF1α in nucleocytoplasmic transport pathway suggests that it plays role in GTP binding and thus apart from its use as a biomarker, it may be further exploited as an effective target for fungicide development. Since, the three other proteins that were found to be tightly associated Fol EF1α have shown maximum similarity with homologous proteins of Neurospora crassa that form complex with fungicide- Cycloheximide. Therefore, we suggest that cycloheximide can also be used against Fusarium wilt disease in tomato. The active site cavity of Fol EF1α can also be determined for computational screening of fungicides using the homologous proteins observed in yeast and Sulfolobus solfataricus. On this basis, we also suggest that the other closely associated genes that have been identified through STITCH analysis, they can also be targeted for fungicide development.

Predicting associations between CircRNA and diseases through structure-aware graph transformer and path-integral convolution.

A series of biological experiments has demonstrated that circular RNAs play a crucial regulatory role in cellular processes and may be potentially associated with diseases. Uncovering these connections helps in understanding potential disease mechanisms and advancing the development of treatment strategies. However, in biology, traditional experiments face limitations in terms of efficiency and cost, especially when enumerating possible associations. To address these limitations, several computational methods have been proposed, but existing methods only measure from a nodal perspective and cannot capture structural similarities between edges. In this study, we introduce an advanced computational method called SATPIC2CD for analyzing potential associations between circular RNAs and diseases. Specifically, we first employ an Structure-Aware Graph Transformer (SAT), which extracts five predefined metapath representations before calculating attention. This adaptive network integrates structural information into the original self-attention by aggregating information within and between paths. Subsequently, we use Path Integral Convolutional Networks (PACN) to integrate feature information for all path weights between two nodes. Afterward, we complement the network node features with feature loss and feature smoothing using Gated Recurrent Units (GRU) and node centrality. Finally, a Multi-Layer Perceptron (MLP) is employed to obtain the ultimate prediction scores for each circular RNA-disease pair. SATPIC2CD performs remarkably well, with an accuracy of up to 0.9715 measured by the Area Under the Curve (AUC) in a 5-fold cross-validation, surpassing other comparative models. Case studies further emphasize the high precision of our method in identifying circular RNA-disease associations, laying a solid foundation for guiding future biological research efforts.

Centrality statistics of symptom networks of schizophrenia: a systematic review.

The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.

Estimating the prognostic value of cross-sectional network connectivity for treatment response in depression.

BACKGROUND: Tightly connected symptom networks have previously been linked to treatment resistance, but most findings come from small-sample studies comparing single responder v. non-responder networks. We aimed to estimate the association between baseline network connectivity and treatment response in a large sample and benchmark its prognostic value against baseline symptom severity and variance. METHODS: N = 40 518 patients receiving treatment for depression in routine care in England from 2015-2020 were analysed. Cross-sectional networks were constructed using the Patient Health Questionnaire-9 (PHQ-9) for responders and non-responders (N = 20 259 each). To conduct parametric tests investigating the contribution of PHQ-9 sum score mean and variance to connectivity differences, networks were constructed for 160 independent subsamples of responders and non-responders (80 each, n = 250 per sample). RESULTS: The baseline non-responder network was more connected than responders (3.15 v. 2.70, S = 0.44, p < 0.001), but effects were small, requiring n = 750 per group to have 85% power. Parametric analyses revealed baseline network connectivity, PHQ-9 sum score mean, and PHQ-9 sum score variance were correlated (r = 0.20-0.58, all p < 0.001). Both PHQ-9 sum score mean (ß = -1.79, s.e. = 0.07, p < 0.001), and PHQ-9 sum score variance (ß = -1.67, s.e. = 0.09, p < 0.001) had larger effect sizes for predicting response than connectivity (ß = -1.35, s.e. = 0.12, p < 0.001). The association between connectivity and response disappeared when PHQ-9 sum score variance was accounted for (ß = -0.28, s.e. = 0.19, p = 0.14). We replicated these results in patients completing longer treatment (8-12 weeks, N = 22 952) and using anxiety symptom networks (N = 70 620). CONCLUSIONS: The association between baseline network connectivity and treatment response may be largely due to differences in baseline score variance.

Quantifying hubness to predict surgical outcomes in epilepsy: Assessing resection-hub alignment in interictal intracranial EEG networks.

OBJECTIVE: Intracranial EEG can identify epilepsy-related networks in patients with focal epilepsy; however, the association between network organization and post-surgical seizure outcomes remains unclear. Hubness serves as a critical metric to assess network organization by identifying brain regions that are highly influential to other regions. In this study, we tested the hypothesis that favorable post-operative seizure outcomes are associated with the surgical removal of interictal network hubs, measured by the novel metric "Resection-Hub Alignment Degree (RHAD)." METHODS: We analyzed Phase II interictal intracranial EEG from 69 patients with epilepsy who were seizure-free (n = 45) and non-seizure-free (n = 24) 1 year post-operatively. Connectivity matrices were constructed from intracranial EEG recordings using imaginary coherence in various frequency bands, and centrality metrics were applied to identify network hubs. The RHAD metric quantified the congruence between hubs and resected/ablated areas. We used a logistic regression model, incorporating other clinical factors, and evaluated the association of this alignment regarding post-surgical seizure outcomes. RESULTS: There was a significant difference in RHAD in fast gamma (80-200 Hz) interictal network between patients with favorable and unfavorable surgical outcomes (p = .025). This finding remained similar across network definitions (i.e., channel-based or region-based network) and centrality measurements (Eigenvector, Closeness, and PageRank). The alignment between surgically removed areas and other commonly used clinical quantitative measures (seizure-onset zone, irritative zone, high-frequency oscillations zone) did not reveal significant differences in post-operative outcomes. This finding suggests that the hubness measurement may offer better predictive performance and finer-grained network analysis. In addition, the RHAD metric showed explanatory validity both alone (area under the curve [AUC] = .66) and in combination with surgical therapy type (resection vs ablation, AUC = .71). SIGNIFICANCE: Our findings underscore the role of network hub surgical removal, measured through the RHAD metric of interictal intracranial EEG high gamma networks, in enhancing our understanding of seizure outcomes in epilepsy surgery.

Community connectivity and local heterogeneity explain animal species co-occurrences within pond communities.

Metacommunity processes have the potential to determine most features of the community structure. However, species diversity has been the dominant focus of studies. Nestedness, modularity and checkerboard distribution of species occurrences are main components of biodiversity organisation. Within communities, these patterns emerge from the interaction between functional diversity, spatial heterogeneity and resource availability. Additionally, the connectivity determines the pool of species for community assembly and, eventually, the pattern of species co-occurrence within communities. Despite the recognised theoretical expectations, the change in occurrence patterns within communities along ecological gradients has seldom been considered. Here, we analyse the spatial occurrence of animal species along sampling units within 18 temporary ponds and its relationship with pond environments and geographic isolation. Isolated ponds presented a nested organisation of species with low spatial segregation-modularity and checkerboard-and the opposite was found for communities with high connectivity. A pattern putatively explained by high functional diversity in ponds with large connectivity and heterogeneity, which determines that species composition tracks changes in microhabitats. On the contrary, nestedness is promoted in dispersal-limited communities with low functional diversity, where microhabitat filters mainly affect richness without spatial replacement between functional groups. Vegetation biomass promotes nestedness, probably due to the observed increase in spatial variance in biomass with the mean biomass. Similarly, the richness of vegetation reduced the spatial segregation of animals within communities. This result may be due to the high plant diversity of the pond that is observed similarly along all sampling units, which promotes the spatial co-occurrence of species at this scale. In the study system, the spatial arrangement of species within communities is related to local drivers as heterogeneity and metacommunity processes by means of dispersal between communities. Patterns of species co-occurrence are interrelated with community biodiversity and species interactions, and consequently with most functional and structural properties of communities. These results indicate that understanding the interplay between metacommunity processes and co-occurrence patterns is probably more important than previously thought to understand biodiversity assembly and functioning.

Los procesos metacomunitarios tienen el potencial de determinar la mayoría de las características de la estructura de las comunidades. Sin embargo, los trabajos se han enfocado principalmente en los patrones de diversidad de especies. El anidamiento, la modularidad y la distribución en damero de la ocurrencia espacial de las especies son propiedades básicas de las comunidades. Estos patrones surgen de la interacción entre la diversidad funcional, la heterogeneidad espacial y la disponibilidad de recursos dentro de las comunidades. Además, el pool de especies disponibles para el ensamblaje está determinado por la conectividad de la comunidad, afectando así su patrón de co­ocurrencia de especies. A pesar de las reconocidas expectativas teóricas, el cambio en los patrones de ocurrencia dentro de las comunidades a lo largo de gradientes ecológicos ha sido poco considerado. Aquí, analizamos la ocurrencia espacial de especies animales dentro de 18 charcos temporales y su relación con las características ambientales y el aislamiento geográfico de los charcos. Los charcos aislados presentaron alto anidamiento espacial mientras que los charcos de alta conectividad una distribución de ocurrencias modular y en damero. Por un lado, la baja diversidad funcional en charcos aislados, determinaría que los filtros microambientales afecten la riqueza de especies sin reemplazo espacial entre grupos funcionales, promoviendo un arreglo anidado de ocurrencias. Por otro lado, la alta diversidad funcional en charcos con alta conectividad y heterogeneidad permitiría el reemplazo espacial de especies en gradientes microambientales, determinando los patrones de segregación observados. La biomasa vegetal promueve el anidamiento, probablemente debido al aumento observado en la variación espacial de la biomasa con la biomasa media. La riqueza vegetal también redujo la segregación espacial de los animales dentro de las comunidades. Este resultado puede deberse a que la alta diversidad de plantas de los charcos es también observada a nivel de unidades muestreales, favoreciendo esto la coexistencia espacial de especies. El arreglo espacial de especies dentro de las comunidades estudiadas estaría determinado tanto por factores locales como la heterogeneidad, como por procesos regionales operando a través de la dispersión de individuos entre comunidades. Los patrones de co­ocurrencia de especies están interrelacionados con la diversidad comunitaria y las interacciones bióticas, y consecuentemente con la mayoría de las propiedades estructurales y funcionales de las comunidades. Este estudio evidencia la importancia de la conexión entre procesos metacomunitarios y la co­ocurrencia espacial de especies para comprender el ensamblaje y funcionamiento de la biodiversidad.

Social control is associated with increased reproductive skew in a wild mammal.

In group-living species, reproductive variation among individuals of the same sex is widespread. By identifying the mechanisms underlying this reproductive skew, we gain fundamental insights into the evolution and maintenance of sociality. A common mechanism, social control, is typically studied by quantifying dominance, which is one of many attributes of sociality that describes how individuals exert influence on others and is an incomprehensive measure of social control as it accounts only for direct relationships. Here, we use the global reaching centrality (GRC), which quantifies the degree of hierarchy in a social network by accounting for both direct and indirect social relationships. Using a wild, free-living population of adult female yellow-bellied marmots (Marmota flaviventris), we found a positive relationship between the reproductive skew index and GRC: more despotic social groups have higher reproductive skew. The GRC was stronger predictor for skew than traditional measures of social control (i.e. dominance). This allows deeper insights into the diverse ways individuals control other group members' reproduction, a core component in the evolution of sociality. Future studies of skew across taxa may profit by using more comprehensive, network-based measures of social control.

Alteration of dynamical degree centrality in brain functional network and its association with metabolic disorder in minimal hepatic encephalopathy.

PURPOSE: To investigate dynamical degree centrality (dDC) alteration and its association with metabolic disturbance and cognitive impairment in minimal hepatic encephalopathy (MHE). METHODS: Fifty-eight cirrhotic patients (22 with MHE, 36 without MHE [NHE]) and 25 healthy controls underwent resting-state functional magnetic resonance imaging, 1H-magnetic resonance spectroscopy, and neurocognitive examination based on the Psychometric Hepatic Encephalopathy Score (PHES). We obtained metabolite ratios in the bilateral posterior cingulate cortex and precuneus, including glutamate and glutamine (Glx)/total creatine (tCr), myo-inositol (mI)/tCr, total choline/tCr, and N-acetyl aspartate/tCr. For each voxel, degree centrality was calculated as the sum of its functional connectivity with other voxels in the brain; and sliding-window correlation was used to calculate dDC per voxel. RESULTS: We observed a stepwise increase in Glx/tCr and a decrease in mI/tCr from NHE to MHE. The intergroup dDC differences were observed in the bilateral posterior cingulate cortex and precuneus (region of interest [ROI1]), bilateral superior-medial frontal gyrus and anterior cingulate cortex (ROI2), and left caudate head. The dDC in ROI2 (r = 0.450, P < 0.001) and mI/tCr (r = 0.297, P = 0.024) was correlated with PHES. Significant correlations were found between dDC in ROI1 and Glx/tCr (r = - 0.413, P = 0.001) and mI/tCr (r = 0.554, P < 0.001). The dDC in ROI2, Glx/tCr, and mI/tCr showed potential for distinguishing NHE from MHE (areas under the curve = 0.859, 0.655, and 0.672, respectively). CONCLUSION: Our findings suggested dynamic brain network disorganization in MHE, which was associated with metabolic derangement and neurocognitive impairment.

Aberrant brain dynamics in major depressive disorder during working memory task.

Working memory (WM) is a distributed and dynamic process, and WM deficits are recognized as one of the top-ranked endophenotype candidates for major depressive disorders (MDD). However, there is a lack of knowledge of brain temporal-spatial profile of WM deficits in MDD. We used the dynamical degree centrality (dDC) to investigate the whole-brain temporal-spatial profile in 40 MDD and 40 controls during an n-back task with 2 conditions (i.e., '0back' and '2back'). We explored the dDC temporal variability and clustered meta-stable states in 2 groups during different WM conditions. Pearson's correlation analysis was used to evaluate the relationship between the altered dynamics with clinical symptoms and WM performance. Compared with controls, under '2back vs. 0back' contrast, patients showed an elevated dDC variability in wide range of brain regions, including the middle frontal gyrus, orbital part of inferior frontal gyrus (IFGorb), hippocampus, and middle temporal gyrus. Furthermore, the increased dDC variability in the hippocampus and IFGorb correlated with worse WM performance. However, there were no significant group-related differences in the meta-stable states were observed. This study demonstrated the increased WM-related instability (i.e., the elevated dDC variability) was represented in MDD, and enhancing stability may help patients achieve better WM performance.

Development of neonatal brain functional centrality and alterations associated with preterm birth.

Formation of the functional connectome in early life underpins future learning and behavior. However, our understanding of how the functional organization of brain regions into interconnected hubs (centrality) matures in the early postnatal period is limited, especially in response to factors associated with adverse neurodevelopmental outcomes such as preterm birth. We characterized voxel-wise functional centrality (weighted degree) in 366 neonates from the Developing Human Connectome Project. We tested the hypothesis that functional centrality matures with age at scan in term-born babies and is disrupted by preterm birth. Finally, we asked whether neonatal functional centrality predicts general neurodevelopmental outcomes at 18 months. We report an age-related increase in functional centrality predominantly within visual regions and a decrease within the motor and auditory regions in term-born infants. Preterm-born infants scanned at term equivalent age had higher functional centrality predominantly within visual regions and lower measures in motor regions. Functional centrality was not related to outcome at 18 months old. Thus, preterm birth appears to affect functional centrality in regions undergoing substantial development during the perinatal period. Our work raises the question of whether these alterations are adaptive or disruptive and whether they predict neurodevelopmental characteristics that are more subtle or emerge later in life.

Connectomic disturbances in Duchenne muscular dystrophy with mild cognitive impairment.

Duchenne muscular dystrophy (DMD) is frequently associated with mild cognitive deficits. However, the underlying disrupted brain connectome and the neural basis remain unclear. In our current study, 38 first-episode, treatment-naive patients with DMD and 22 matched healthy controls (HC) were enrolled and received resting-sate functional magnetic resonance imaging scans. Voxel-based degree centrality (DC), seed-based functional connectivity (FC), and clinical correlation were performed. Relative to HC, DMD patients had lower height, full Intellectual Quotients (IQ), and IQ-verbal comprehension. Significant increment of DC of DMD patients were found in the left dorsolateral prefrontal cortex (DLPFC.L) and right dorsomedial prefrontal cortex (DMPFC.R), while decreased DC were found in right cerebellum posterior lobe (CPL.R), right precentral/postcentral gyrus (Pre/Postcentral G.R). DMD patients had stronger FC in CPL.R-bilateral lingual gyrus, Pre/Postcentral G.R-Insular, and DMPFC.R-Precuneus.R, had attenuated FC in DLPFC.L-Insular. These abnormally functional couplings were closely associated with the extent of cognitive impairment, suggested an over-activation of default mode network and executive control network, and a suppression of primary sensorimotor cortex and cerebellum-visual circuit. The findings collectively suggest the distributed brain connectome disturbances maybe a neuroimaging biomarker in DMD patients with mild cognitive impairment.

Decreased dynamic variability of the cerebellum in the euthymic patients with bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is a complex mental illness characterized by different mood states, including depression, mania/hypomania, and euthymia. This study aimed to comprehensively evaluate dynamic changes in intrinsic brain activity by using dynamic fractional amplitude of low-frequency fluctuations (dfALFF) and dynamic degree centrality (dDC) in patients with BD euthymia or depression and healthy individuals. METHODS: The resting-state functional magnetic resonance imaging data were analyzed from 37 euthymic and 28 depressed patients with BD, as well as 85 healthy individuals. Using the sliding-window method, the dfALFF and dDC were calculated for each participant. These values were compared between the 3 groups using one-way analysis of variance (ANOVA). Additional analyses were conducted using different window lengths, step width, and window type to ensure the reliability of the results. RESULTS: The euthymic group showed significantly lower dfALFF and dDC values of the left and right cerebellum posterior lobe compared with the depressed and control groups (cluster level PFWE < 0.05), while the latter two groups were comparable. Brain regions showing significant group differences in the dfALFF analysis overlapped with those with significant differences in the dDC analysis. These results were consistent across different window lengths, step width, and window type. CONCLUSIONS: These findings suggested that patients with euthymic BD exhibit less flexibility of temporal functional activities in the cerebellum posterior lobes compared to either depressed patients or healthy individuals. These results could contribute to the development of neuropathological models of BD, ultimately leading to improved diagnosis and treatment of this complex illness.

Event centrality in social anxiety disorder and major depressive disorder.

Event centrality is defined by the extent to which a memory of an event has become central to an individual's identity and life story. Previous research predominantly focused on the link between event centrality and trauma-related symptomatology. Nevertheless, it can be argued that the perception of (adverse) events as central to one's self is not exclusive to Posttraumatic Stress Disorder (PTSD). Other disorders where adverse events are linked to the onset of symptoms might also be related to event centrality. This study examined the relevance of event centrality for Social Anxiety Disorder (SAD) and for Major Depressive Disorder (MDD) separately. Moreover, we examined which cognitive and emotion regulation variables (i.e., trait anxiety, rumination, worry, intrusions and avoidance, and posttraumatic cognitions) mediated these relationships. No significant correlation was found between event centrality and social anxiety. However, a significant positive correlation was found between event centrality and depression. In a combined group, this relation was mediated by all cognitive and emotion regulation variables except for worry.

The mnemonic effect of central and peripheral misinformation on social media.

The increasing use of social media has amplified the spread of false information. Yet little is known about the mnemonic consequences associated with exposure to different types of false information online. In two studies, we examined in a simulated online context how exposure to false information either central or peripheral in events affected memory. European American and Asian/Asian American college students (Study 1 N = 200; Study 2 N = 225) were presented with GIFs of daily life events and read tweets about the events that included four types of information: central true information, central false information, peripheral true information, and peripheral false information. They then took a True/False recognition test that included tweeted and untweeted true and false information and indicated how confident they were in their responses. Regardless of cultural background, participants in both studies demonstrated the misinformation effect, whereby they falsely recognised more and resisted less tweeted than untweeted false information. Furthermore, they showed higher susceptibility to peripheral than central false information exposed via tweets. Asian participants were less influenced by misinformation than European Americans in Study 2. These findings have important implications to combat misinformation in online environments.

Network hubs cease to be influential in the presence of low levels of advertising.

Attempts to find central "influencers," "opinion leaders," "hubs," "optimal seeds," or other important people who can hasten or slow diffusion or social contagion has long been a major research question in network science. We demonstrate that opinion leadership occurs only under conventional but implausible scope conditions. We demonstrate that a highly central node is a more effective seed for diffusion than a random node if nodes can only learn via the network. However, actors are also subject to external influences such as mass media and advertising. We find that diffusion is noticeably faster when it begins with a high centrality node, but that this advantage only occurs in the region of parameter space where external influence is constrained to zero and collapses catastrophically even at minimal levels of external influence. Importantly, nearly all prior agent-based research on choosing a seed or seeds implicitly occurs in the network influence only region of parameter space. We demonstrate this effect using preferential attachment, small world, and several empirical networks. These networks vary in how large the baseline opinion leadership effect is, but in all of them it collapses with the introduction of external influence. This implies that, in marketing and public health, advertising broadly may be underrated as a strategy for promoting network-based diffusion.