Global Topological Properties Research Articles

Dynamical changes of interaction across functional brain communities during propofol-induced sedation.

It is crucial to understand how anesthetics disrupt information transmission within the whole-brain network and its hub structure to gain insight into the network-level mechanisms underlying propofol-induced sedation. However, the influence of propofol on functional integration, segregation, and community structure of whole-brain networks were still unclear. We recruited 12 healthy subjects and acquired resting-state functional magnetic resonance imaging data during 5 different propofol-induced effect-site concentrations (CEs): 0, 0.5, 1.0, 1.5, and 2.0μg/ml. We constructed whole-brain functional networks for each subject under different conditions and identify community structures. Subsequently, we calculated the global and local topological properties of whole-brain network to investigate the alterations in functional integration and segregation with deepening propofol sedation. Additionally, we assessed the alteration of key nodes within the whole-brain community structure at each effect-site concentrations level. We found that global participation was significantly increased at high effect-site concentrations, which was mediated by bilateral postcentral gyrus. Meanwhile, connector hubs appeared and were located in posterior cingulate cortex and precentral gyrus at high effect-site concentrations. Finally, nodal participation coefficients of connector hubs were closely associated to the level of sedation. These findings provide valuable insights into the relationship between increasing propofol dosage and enhanced functional interaction within the whole-brain networks.

Uhlmann phase of a thermal spin-1 system with zero field splitting

We study the Uhlmann geometric phase of a spin-1 particle subjected to zero-field splitting (ZFS) interaction, modulated by a dimensionless parameter α, under the effect of an external magnetic field with a tilting angle θ. We show that the ZFS term induces a transition in the geometrical phase behavior, characterized by a critical parameter value, α=αc. For α<αc, this phase displays two critical temperatures at θ=π/2, similar to spin-1 systems without ZFS, but with a separation that varies with α. In contrast, for α>αc, the phase exhibits two singularities at a single critical temperature but at different field orientations θ≠π/2. The phase disappears for significantly large |α|, regardless of the values of the Hamiltonian parameters. This behavior clearly departs from the usual thermal Uhlmann phase observed in SU(2) systems. In addition, we analytically calculate the heat capacity, which, for θ=π/2 and nearby values, displays two different regimes according to the sign of α. For α<0, it develops two peaks associated to the multilevel nature of the system, while for α>0 only a single Schottky-anomaly like peak appears as in two level systems. Interestingly, when θ=π/2, the temperature centroids of the Uhlmann phase and the heat capacity coincide in the region between critical temperatures for a given value of α<αc. Furthermore, we demonstrate that when α=0, the Uhlmann phase, a global topological property of the system, can be expressed as a function of the thermal component of the Bures metric, a local geometric property related to the heat capacity.

Topological alterations in white matter anatomical networks in cervical dystonia

BackgroundAccumulating neuroimaging evidence indicates that patients with cervical dystonia (CD) have changes in the cortico-subcortical white matter (WM) bundle. However, whether these patients’ WM structural networks undergo reorganization remains largely unclear. We aimed to investigate topological changes in large-scale WM structural networks in patients with CD compared to healthy controls (HCs), and explore the network changes associated with clinical manifestations.MethodsDiffusion tensor imaging (DTI) was conducted in 30 patients with CD and 30 HCs, and WM network construction was based on the BNA-246 atlas and deterministic tractography. Based on the graph theoretical analysis, global and local topological properties were calculated and compared between patients with CD and HCs. Then, the AAL-90 atlas was used for the reproducibility analyses. In addition, the relationship between abnormal topological properties and clinical characteristics was analyzed.ResultsCompared with HCs, patients with CD showed changes in network segregation and resilience, characterized by increased local efficiency and assortativity, respectively. In addition, a significant decrease of network strength was also found in patients with CD relative to HCs. Validation analyses using the AAL-90 atlas similarly showed increased assortativity and network strength in patients with CD. No significant correlations were found between altered network properties and clinical characteristics in patients with CD.ConclusionOur findings show that reorganization of the large-scale WM structural network exists in patients with CD. However, this reorganization is attributed to dystonia-specific abnormalities or hyperkinetic movements that need further identification.

Electroacupuncture modulates abnormal brain connectivity after ischemia reperfusion injury in rats: A graph theory-based approach.

Electroacupuncture (EA) has been shown to facilitate brain plasticity-related functional recovery following ischemic stroke. The functional magnetic resonance imaging technique can be used to determine the range and mode of brain activation. After stroke, EA has been shown to alter brain connectivity, whereas EA's effect on brain network topology properties remains unclear. An evaluation of EA's effects on global and nodal topological properties in rats with ischemia reperfusion was conducted in this study. There were three groups of adult male Sprague-Dawley rats: sham-operated group (sham group), middle cerebral artery occlusion/reperfusion (MCAO/R) group, and MCAO/R plus EA (MCAO/R+EA) group. The differences in global and nodal topological properties, including shortest path length, global efficiency, local efficiency, small-worldness index, betweenness centrality (BC), and degree centrality (DC) were estimated. Graphical network analyses revealed that, as compared with the sham group, the MCAO/R group demonstrated a decrease in BC value in the right ventral hippocampus and increased BC in the right substantia nigra, accompanied by increased DC in the left nucleus accumbens shell (AcbSh). The BC was increased in the right hippocampus ventral and decreased in the right substantia nigra after EA intervention, and MCAO/R+EA resulted in a decreased DC in left AcbSh compared to MCAO/R. The results of this study provide a potential basis for EA to promote cognitive and motor function recovery after ischemic stroke.

Machine learning models for diagnosis of essential tremor and dystonic tremor using grey matter morphological networks

BackgroundEssential tremor (ET) and dystonic tremor (DT) are the two most common tremor disorders, and misdiagnoses are very common due to similar tremor symptoms. In this study, we explore the structural network mechanisms of ET and DT using brain grey matter (GM) morphological networks and combine those with machine learning models. Methods3D-T1 structural images of 75 ET patients, 71 DT patients, and 79 healthy controls (HCs) were acquired. We used voxel-based morphometry to obtain GM images and constructed GM morphological networks based on the Kullback-Leibler divergence-based similarity (KLS) method. We used the GM volumes, morphological relations, and global topological properties of GM-KLS morphological networks as input features. We employed three classifiers to perform the classification tasks. Moreover, we conducted correlation analysis between discriminative features and clinical characteristics. Results16 morphological relations features and 1 global topological metric were identified as the discriminative features, and mainly involved the cerebello-thalamo-cortical circuits and the basal ganglia area. The Random Forest (RF) classifier achieved the best classification performance in the three-classification task, achieving a mean accuracy (mACC) of 78.7%, and was subsequently used for binary classification tasks. Specifically, the RF classifier demonstrated strong classification performance in distinguishing ET vs. HCs, ET vs. DT, and DT vs. HCs, with mACCs of 83.0 %, 95.2 %, and 89.3 %, respectively. Correlation analysis demonstrated that four discriminative features were significantly associated with the clinical characteristics. ConclusionThis study offers new insights into the structural network mechanisms of ET and DT. It demonstrates the effectiveness of combining GM-KLS morphological networks with machine learning models in distinguishing between ET, DT, and HCs.

Altered Functional Connectivity of Temporoparietal Lobe in Obstructive Sleep Apnea: A Resting-State fNIRS Study.

Obstructive Sleep Apnea (OSA), a sleep disorder with high prevalence, is normally accompanied by affective, autonomic, and cognitive abnormalities, and is deemed to be linked to functional brain alterations. To investigate alterations in brain functional connectivity properties in patients with OSA, a comparative analysis of global and local topological properties of brain networks was conducted between patients with OSA and healthy controls (HCs), utilizing functional near-infrared spectroscopy (fNIRS) imaging. A total of 148 patients with OSA and 150 healthy individuals were involved. Firstly, quantitative alterations in blood oxygen concentration, changes in functional connectivity, and variations in graph theory-based network topological characteristics were assessed. Then, with Mann-Whitney statistics, this study compared whether there are significant differences in the above characteristics between patients with OSA and HCs. Lastly, the study further examined the correlation between the altered characteristics and the apnea hypopnea index (AHI) using linear regression. Results revealed a higher mean and standard deviation of hemoglobin concentration in the superior temporal gyrus among patients with OSA compared to HCs. Resting-state functional connectivity (RSFC) exhibited a slight increase between the superior temporal gyrus and other specific areas in patients with OSA. Notably, neither patients with OSA nor HCs demonstrated significant small-world network properties. Patients with OSA displayed an elevated clustering coefficient (p < 0.05) and local efficiency (p < 0.05). Additionally, patients with OSA exhibited a tendency towards increased nodal betweenness centrality (p < 0.05) and degree centrality (p < 0.05) in the right supramarginal gyrus, as well as a trend towards higher betweenness centrality (p < 0.05) in the right precentral gyrus. The results of multiple linear regressions indicate that the influence of the AHI on RSFC between the right precentral gyrus and right superior temporal gyrus (p < 0.05), as well as between the right precentral gyrus and right supramarginal gyrus (p < 0.05), are statistically significant. These findings suggest that OSA may compromise functional brain connectivity and network topological properties in affected individuals, serving as a potential neurological mechanism underlying the observed abnormalities in brain function associated with OSA.

Low and high-order topological disruption of functional networks in multiple system atrophy with freezing of gait: A resting-state study

ObjectiveFreezing of gait (FOG), a specific survival-threatening gait impairment, needs to be urgently explored in patients with multiple system atrophy (MSA), which is characterized by rapid progression and death within 10 years of symptom onset. The objective of this study was to explore the topological organisation of both low- and high-order functional networks in patients with MAS and FOG. MethodLow-order functional connectivity (LOFC) and high-order functional connectivity FC (HOFC) networks were calculated and further analysed using the graph theory approach in 24 patients with MSA without FOG, 20 patients with FOG, and 25 healthy controls. The relationship between brain activity and the severity of freezing symptoms was investigated in patients with FOG. ResultsRegarding global topological properties, patients with FOG exhibited alterations in the whole-brain network, dorsal attention network (DAN), frontoparietal network (FPN), and default network (DMN), compared with patients without FOG. At the node level, patients with FOG showed decreased nodal centralities in sensorimotor network (SMN), DAN, ventral attention network (VAN), FPN, limbic regions, hippocampal network and basal ganglia network (BG), and increased nodal centralities in the FPN, DMN, visual network (VIN) and, cerebellar network. The nodal centralities of the right inferior frontal sulcus, left lateral amygdala and left nucleus accumbens (NAC) were negatively correlated with the FOG severity. ConclusionThis study identified a disrupted topology of functional interactions at both low and high levels with extensive alterations in topological properties in MSA patients with FOG, especially those associated with damage to the FPN. These findings offer new insights into the dysfunctional mechanisms of complex networks and suggest potential neuroimaging biomarkers for FOG in patients with MSA.

A Learnable Counter-Condition Analysis Framework for Functional Connectivity-Based Neurological Disorder Diagnosis.

To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis.

Self–dual solutions of a field theory model of two linked rings

In this work the connection established in [7,8] between a model of two linked polymers rings with fixed Gaussian linking number forming a 4-plat and the statistical mechanics of non-relativistic anyon particles is explored. The excluded volume interactions have been switched off and only the interactions of entropic origin arising from the topological constraints are considered. An interpretation from the polymer point of view of the field equations that minimize the energy of the model in the limit in which one of the spatial dimensions of the 4-plat becomes very large is provided. It is shown that the self-dual contributions are responsible for the long-range interactions that are necessary for preserving the global topological properties of the system during the thermal fluctuations. The non self-dual part is also related to the topological constraints, and takes into account the local interactions acting on the monomers in order to prevent the breaking of the polymer lines. It turns out that the energy landscape of the two linked rings is quite complex. Assuming as a rough approximation that the monomer densities of half of the 4-plat are constant, at least two points of energy minimum are found. Classes of non-trivial self-dual solutions of the self-dual field equations are derived. One of these classes is characterized by densities of monomers that are the squared modulus of holomorphic functions. The second class is obtained under some assumptions that allow to reduce the self-dual equations to an analog of the Gouy-Chapman equation for the charge distribution of ions in a double layer capacitor. In the present case, the spatial distribution of the electric potential of the ions is replaced by the spatial distribution of the fictitious magnetic fields associated with the presence of the topological constraints. In the limit in which two of the spatial dimensions are large in comparison with the third one, we provide exact formulas for the conformations of the monomer densities of the 4-plat by using the elliptic, hyperbolic and trigonometric solutions of the sinh-Gordon and cosh-Gordon equations which have been used for instance in the construction of classical string solutions in AdS3 and dS3 [9].

Effects of electroconvulsive therapy on functional brain networks in patients with schizophrenia

BackgroundSchizophrenia is a kind of intractable brain disorder. Electroconvulsive therapy (ECT) has been used to rapidly improve the clinical symptoms of patients with schizophrenia, but the effect of ECT on topological attributes of brain functional network in patients with schizophrenia has not been clear. The purpose of this study was to investigate the brain functional network mechanism of ECT against schizophrenia.MethodsThirty-one patients with schizophrenia and fifty healthy controls matching age, gender, and years of education were included. All participants underwent general data collection and magnetic resonance imaging scanning before ECT, and clinical symptoms were assessed using the Positive And Negative Syndrome Scale (PANSS). MRI and clinical symptoms were collected again after the first and eighth ECT application. The functional brain network was constructed on the basis of magnetic resonance imaging, and the global and node topological properties were analyzed. Repeated measure variance analysis was used to explore the changes of the topological attribute values and clinical symptom scores before and after ECT, and Bonferroni post hoc analysis was performed. The independent sample t-test was used to compare the differences in the topological attribute values between patients and healthy controls at three time points before and after ECT. Partial correlation analysis was performed for topological attribute values and clinical symptom scores of abnormal brain regions in the patient groups and their changes during ECT. A general linear regression model was used to predict the outcome after the final eighth ECT using the patient's response to the first ECT.Results(1) One ECT can restore the gamma(γ), lamuda(λ), sigma(σ), nodal global efficiency (Ne) of right insular gyrus ventral agranular insula (INS_R_vIa) and nodal local efficiency (NLe) of bilateral fusiform gyrus medioventral area37 (FuG_A37mv). Eight ECT can also restore the NLe of cortex rostral lingual gyrus (MVOcC _R_rLinG). Eight ECT did not improve the Ne of right superior parietal lobule rostral area 7 (SPL_R_A7r) and NLe of left superior frontal gyrus medial area 6 (SFG_L_A6m). (2) Even after only the first use of ECT, total PANSS scores began to decrease (mean ΔPANSSECT1 was 11.7%; Range, 2%-32.8%), decreased significantly after the eighth application (mean ΔPANSSECT8 was 86.0%; Range,72.5% to 97.9%). Five patients met the response criteria after ECT1 (20% reduction in PANSS total score), and all patients met the response criteria after ECT8. (3) Linear regression analysis showed that ΔPANSSECT1 was a significant predictor of ΔPANSSECT8 (F=5.387, P=0.028), and ΔPANSSECT1 explained 15.7% of the variance of ΔPANSSECT8 (R2=0.157).ConclusionsECT was able to normalize γ, λ, σ, Ne of INS_R_vIa, NLe of bilateral FuG_A37mv in SZ patients after the first treatment, and NLe of MVOcC_R_rLinG after the eighth ECT. ECT significantly alleviates psychotic symptoms in patients with SZ, and its efficacy after eight sessions can be predicted by the patient's response to the first session of ECT.

Disrupted topological properties of structural brain networks present a glutamatergic neuropathophysiology in people with narcolepsy.

Growing evidences have documented various abnormalities of the white matter bundles in people with narcolepsy. We sought to evaluate topological properties of brain structural networks, and their association with symptoms and neuropathophysiological features in people with narcolepsy. Diffusion tensor imaging was conducted for people with narcolepsy (n = 30) and matched healthy controls as well as symptoms assessment. Structural connectivity for each participant was generated to analyze global and regional topological properties and their correlations with narcoleptic features. Further human brain transcriptome was extracted and spatially registered for connectivity vulnerability. Genetic functional enrichment analysis was performed and further clarified using in vivo emission computed tomography data. A wide and dramatic decrease in structural connectivities was observed in people with narcolepsy, with descending network degree and global efficiency. These metrics were not only correlated with sleep latency and awakening features, but also reflected alterations of sleep macrostructure in people with narcolepsy. Network-based statistics identified a small hyperenhanced subnetwork of cingulate gyrus that was closely related to rapid eye movement sleep behavior disorder (RBD) in narcolepsy. Further imaging genetics analysis suggested glutamatergic signatures were responsible for the preferential vulnerability of connectivity alterations in people with narcolepsy, while additional PET/SPECT data verified that structural alteration was significantly correlated with metabotropic glutamate receptor 5 (mGlutR5) and N-methyl-D-aspartate receptor (NMDA). People with narcolepsy endured a remarkable decrease in the structural architecture, which was not only closely related to narcolepsy symptoms but also glutamatergic signatures.

Altered small-world and disrupted topological properties of functional connectivity networks in patients with nonarteritic anterior ischemic optic neuropathy

BackgroundNonarteritic anterior ischemic optic neuropathy (NAION) is a disease of the optic nerve, but its effect on brain network topology is still unclear.This study aimed to investigate brain network alterations in NAION patients and to explore their relationship with functional impairment. MethodsResting-state functional MRI data were collected from 23 NAION patients and 23 matched healthy control subjects.We used graph theory analysis to investigate the global and nodal network topological properties,and network-based statistical (NBS) methods were used to explore intergroup differences in functional connectivity (FC) strength. ResultsCompared to the control group, NAION patients had lower global efficiency, normalized clustering coefficient and small-world values and higher characteristic path length (P < 0.05). In the hub distributions of functional networks, the NAION group had one hub region disappearing and four hub regions appearing in nodal degree centrality (Dc), and two hubs disappearing and one hub region appearing in nodal betweenness centrality (Bc). The NAION group also had enhanced brain FC primarily associated with the frontal, prefrontal, parietal lobes and cerebellum. Furthermore, the right temporal pole, superior temporal gyrus (r = –0.424), the right inferior temporal gyrus (r = –0.414), the right cerebellar lobule Ⅵ (r = 0.450), and the left cerebellar lobule crus Ⅰ (r = 0.584) were significantly correlated with clinical severity. ConclusionNAION patients show disruption and redistribution of FC in specific regions of the brain network, which may be associated with visual impairment.

Diffusion kurtosis imaging tractography reveals disrupted white matter structural networks in children with obstructive sleep apnea syndrome.

To assess the disruptions of brain white matter (WM) structural network in children with obstructive sleep apnea (OSA) using diffusion kurtosis imaging (DKI). We use DKI tractography to construct individual whole-brain, region-level WM networks in 40 OSA and 28 healthy children. Then, we apply graph theory approaches to analyze whether OSA children would show altered global and regional network topological properties and whether these alterations would significantly correlate with the clinical characteristics of OSA. We found that both OSA and healthy children showed an efficient small-world organization and highly similar hub distributions in WM networks. However, characterized by kurtosis fractional anisotropy (KFA) weighted networks, OSA children exhibited decreased global and local efficiency, increased shortest path length compared with healthy children. For regional topology, OSA children exhibited significant decreased nodal betweenness centrality (BC) in the bilateral medial orbital superior frontal gyrus (ORBsupmed), right orbital part superior frontal gyrus (ORBsup), insula, postcentral gyrus, left middle temporal gyrus (MTG), and increased nodal BC in the superior parietal gyrus, pallidum. Intriguingly, the altered nodal BC of multiple regions (right ORBsupmed, ORBsup and left MTG) within default mode network showed significant correlations with sleep parameters for OSA patients. Our results suggest that children with OSA showed decreased global integration and local specialization in WM networks, typically characterized by DKI tractography and KFA metric. This study may advance our current understanding of the pathophysiological mechanisms of impaired cognition underlying OSA.

Alterations in the topological organization of the default-mode network in Tourette syndrome

BackgroundThe exact pathophysiology of TS is still elusive. Previous studies have identified default mode networks (DMN) abnormalities in patients with TS. However, these literatures investigated the neural activity during the tic suppression, not a true resting-state. Therefore, this study aimed to reveal the neural mechanism of Tourette's syndrome (TS) from the perspective of topological organization and functional connectivity within the DMN by electroencephalography (EEG) in resting-state.MethodsThe study was conducted by analyzing the EEG data of TS patients with graph theory approaches. Thirty children with TS and thirty healthy controls (HCs) were recruited, and all subjects underwent resting-state EEG data acquisition. Functional connectivity within the DMN was calculated, and network properties were measured.ResultsA significantly lower connectivity in the neural activity of the TS patients in the β band was found between the bilateral posterior cingulate cortex/retrosplenial cortex (t = -3.02, p < 0.05). Compared to HCs, the TS patients’ local topological properties (degree centrality) in the left temporal lobe in the γ band were changed, while the global topological properties (global efficiency and local efficiency) in DMN exhibited no significant differences. It was also demonstrated that the degree centrality of the left temporal lobe in the γ band was positively related to the Yale Global Tic Severity Scale scores (r = 0.369, p = 0.045).ConclusionsThe functional connectivity and topological properties of the DMN of TS patients were disrupted, and abnormal DMN topological property alterations might affect the severity of tic in TS patients. The abnormal topological properties of the DMN in TS patients may be due to abnormal functional connectivity alterations. The findings provide novel insight into the neural mechanism of TS patients.

Pre-COVID brain network topology prospectively predicts social anxiety alterations during the COVID-19 pandemic

BackgroundSocial anxiety (SA) is a negative emotional response that can lead to mental health issues, which some have experienced during the coronavirus disease 2019 (COVID-19) pandemic. Little attention has been given to the neurobiological mechanisms underlying inter-individual differences in SA alterations related to COVID-19. This study aims to identify neurofunctional markers of COVID-specific SA development. Methods110 healthy participants underwent resting-state magnetic resonance imaging and behavioral tests before the pandemic (T1, October 2019 to January 2020) and completed follow-up behavioral measurements during the pandemic (T2, February to May 2020). We constructed individual functional networks and used graph theoretical analysis to estimate their global and nodal topological properties, then used Pearson correlation and partial least squares correlations examine their associations with COVID-specific SA alterations. ResultsIn terms of global network parameters, SA alterations (T2-T1) were negatively related to pre-pandemic brain small-worldness and normalized clustering coefficient. In terms of nodal network parameters, SA alterations were positively linked to a pronounced degree centrality pattern, encompassing both the high-level cognitive networks (dorsal attention network, cingulo-opercular task control network, default mode network, memory retrieval network, fronto-parietal task control network, and subcortical network) and low-level perceptual networks (sensory/somatomotor network, auditory network, and visual network). These findings were robust after controlling for pre-pandemic general anxiety, other stressful life events, and family socioeconomic status, as well as by treating SA alterations as categorical variables. ConclusionsThe individual functional network associated with SA alterations showed a disrupted topological organization with a more random state, which may shed light on the neurobiological basis of COVID-related SA changes at the network level.

Abnormalities in modular connectivity of functional brain networks and cognitive changes in patients with anti -N-methyl-D-aspartate receptor encephalitis

ObjectiveTo explore potential mechanisms of cognitive changes in patients with anti-NMDAR encephalitis (ANMDARE) from intramodule and intermodule effects of brain functional networks. MethodsResting-state functional MRI(rs-fMRI) imaging data was collected from 30 ANMDARE and 30 healthy controls (HCs). A brain functional matrix was constructed, and sparsity was established by module similarity. For both groups, changes in functional connectivity (FC) within and between modules was calculated, and whole-brain functional topology was analyzed. Finally, the association of brain functional with cognitive function in ANMDARE was further analyzed. ResultsCompared to HCs, ANMDARE had enhanced connectivity within the modules that included the occipito-parietal-temporal and parahippocampal gyri. ANMDARE had significantly higher participation coefficients (PC) in the right inferior frontal gyrus than HCs and significantly lower PC in the left superior parietal lobule, left caudate nucleus, and right putamen. No statistically significant differences in global topological properties were found between the two groups. No correlations were found between functional and structural brain indicators and the Cognitive Assessment Scale and the Emotional Deficit Scale. ConclusionsPatients with ANMDARE are manifested by enhanced intramodular FC and intermodular connectivity changes in the brain. This may help to understand the pathophysiological mechanisms of the disease from a global perspective.

Persistent homology of collider observations: When (w)hole matters

Topological invariants have played a fundamental role in the advancement of theoretical high energy physics. Physicists have used several kinematic techniques to distinguish new physics predictions from the Standard Model (SM) of particle physics at Large Hadron Collider (LHC). However, the study of global topological invariants of the collider signals has not yet attracted much attention. In this article, we present a novel approach to study collider signals using persistent homology. The global topological properties of the ensemble of events as expressed by measures like persistent entropy, Betti area, etc. are worth considering in addition to the traditional approach of using kinematic variables event by event. In this exploratory study, we first explore the characteristic topological signature of a few SM electroweak resonant productions. Next, we use the framework to distinguish global properties of the invisible Higgs decay processes in the SM and a real singlet extension of the SM featuring stable singlet scalar dark matter.

Link prediction in complex network using information flow

Link prediction in complex networks has recently attracted a great deal of attraction in diverse scientific domains, including social and biological sciences. Given a snapshot of a network, the goal is to predict links that are missing in the network or that are likely to occur in the near future. This problem has both theoretical and practical significance; it not only helps us to identify missing links in a network more efficiently by avoiding the expensive and time consuming experimental processes, but also allows us to study the evolution of a network with time. To address the problem of link prediction, numerous attempts have been made over the recent years that exploit the local and the global topological properties of the network to predict missing links in the network. In this paper, we use parametrised matrix forest index (PMFI) to predict missing links in a network. We show that, for small parameter values, this index is linked to a heat diffusion process on a graph and therefore encodes geometric properties of the network. We then develop a framework that combines the PMFI with a local similarity index to predict missing links in the network. The framework is applied to numerous networks obtained from diverse domains such as social network, biological network, and transport network. The results show that the proposed method can predict missing links with higher accuracy when compared to other state-of-the-art link prediction methods.

Different topological patterns in structural covariance networks between high and low delay discounters.

People prefer immediate over future rewards because they discount the latter's value (a phenomenon termed "delay discounting," used as an index of impulsivity). However, little is known about how the preferences are implemented in brain in terms of the coordinated pattern of large-scale structural brain networks. To examine this question, we classified high discounting group (HDG) and low discounting group (LDG) in young adults by assessing their propensity for intertemporal choice. We compared global and regional topological properties in gray matter volume-based structural covariance networks between two groups using graph theoretical analysis. HDG had less clustering coefficient and characteristic path length over the wide sparsity range than LDG, indicating low network segregation and high integration. In addition, the degree of small-worldness was more significant in HDG. Locally, HDG showed less betweenness centrality (BC) in the parahippocampal gyrus and amygdala than LDG. These findings suggest the involvement of structural covariance network topology on impulsive choice, measured by delay discounting, and extend our understanding of how impulsive choice is associated with brain morphological features.

Altered structural connectivity in olfactory disfunction after mild COVID-19 using probabilistic tractography

We aimed to investigate changes in olfactory bulb volume and brain network in the white matter (WM) in patients with persistent olfactory disfunction (OD) following COVID-19. A cross-sectional study evaluated 38 participants with OD after mild COVID-19 and 24 controls, including Sniffin' Sticks identification test (SS-16), MoCA, and brain magnetic resonance imaging. Network-Based Statistics (NBS) and graph theoretical analysis were used to explore the WM. The COVID-19 group had reduced olfactory bulb volume compared to controls. In NBS, COVID-19 patients showed increased structural connectivity in a subnetwork comprising parietal brain regions. Regarding global network topological properties, patients exhibited lower global and local efficiency and higher assortativity than controls. Concerning local network topological properties, patients had reduced local efficiency (left lateral orbital gyrus and pallidum), increased clustering (left lateral orbital gyrus), increased nodal strength (right anterior orbital gyrus), and reduced nodal strength (left amygdala). SS-16 test score was negatively correlated with clustering of whole-brain WM in the COVID-19 group. Thus, patients with OD after COVID-19 had relevant WM network dysfunction with increased connectivity in the parietal sensory cortex. Reduced integration and increased segregation are observed within olfactory-related brain areas might be due to compensatory plasticity mechanisms devoted to recovering olfactory function.