Network Connectivity Strength Research Articles

The cumulative impact of clinical risk on brain networks and associations with executive function impairments in adolescents with congenital heart disease.

Patients with congenital heart disease (CHD) demonstrate altered structural brain network connectivity. However, there is large variability between reported results and little information is available to identify those patients at highest risk for brain alterations. Thus, we aimed to investigate if network connectivity measures were associated with the individual patient's cumulative load of clinical risk factors and with family-environmental factors in a cohort of adolescents with CHD. Further, we investigated associations with executive function impairments. In 53 adolescents with CHD who underwent open-heart surgery during infancy, and 75 healthy controls, diffusion magnetic resonance imaging and neuropsychological assessment was conducted at a mean age of 13.2 ± 1.3 years. Structural connectomes were constructed using constrained spherical deconvolution tractography. Graph theory and network-based statistics were applied to investigate network connectivity measures. A cumulative clinical risk (CCR) score was built by summing up binary risk factors (neonatal, cardiac, neurologic) based on clinically relevant thresholds. The role of family-environmental factors (parental education, parental mental health, and family function) was investigated. An age-adjusted executive function summary score was built from nine neuropsychological tests. While network integration and segregation were preserved in adolescents with CHD, they showed lower edge strength in a dense subnetwork. A higher CCR score was associated with lower network segregation, edge strength, and executive function performance. Edge strength was particularly reduced in a subnetwork including inter-frontal and fronto-parietal-thalamic connections. There was no association with family-environmental factors. Poorer executive functioning was associated with lower network integration and segregation. We demonstrated evidence for alterations of network connectivity strength in adolescents with CHD - particularly in those patients who face a cumulative exposure to multiple clinical risk factors over time. Quantifying the cumulative load of risk early in life may help to better predict trajectories of brain development in order to identify and support the most vulnerable patients as early as possible.

Network excitability of stimulation-induced spectral responses helps localize the seizure onset zone

ObjectiveWhile evoked potentials elicited by single pulse electrical stimulation (SPES) may assist seizure onset zone (SOZ) localization during intracranial EEG (iEEG) monitoring, induced high frequency activity has also shown promising utility. We aimed to predict SOZ sites using induced cortico-cortical spectral responses (CCSRs) as an index of excitability within epileptogenic networks. MethodsSPES was conducted in 27 epilepsy patients undergoing iEEG monitoring and CCSRs were quantified by significant early (10–200 ms) increases in power from 10 to 250 Hz. Using response power as CCSR network connection strengths, graph centrality measures (metrics quantifying each site’s influence within the network) were used to predict whether sites were within the SOZ. ResultsAcross patients with successful surgical outcomes, greater CCSR centrality predicted SOZ sites and SOZ sites targeted for surgical treatment with median AUCs of 0.85 and 0.91, respectively. We found that the alignment between predicted and targeted SOZ sites predicted surgical outcome with an AUC of 0.79. ConclusionsThese findings indicate that network analysis of CCSRs can be used to identify increased excitability of SOZ sites and discriminate important surgical targets within the SOZ. SignificanceCCSRs may supplement traditional passive iEEG monitoring in seizure localization, potentially reducing the need for recording numerous seizures.

Epilepsy lesion localization method based on brain function network.

In the past, the localization of seizure onset zone (SOZ) primarily relied on traditional EEG signal analysis methods. However, due to their limited spatial and temporal resolution, accurately pinpointing neural activity was challenging, thereby restricting their clinical applicability. Compared with traditional EEG signals, SEEG signals have superior spatial and temporal resolution, and can more accurately record neural activity near epileptic foci, making them better suited for studying SOZ. In addition, the traditional EEG signal analysis methods still have limitations, mainly focusing on the analysis of local signal features, while ignoring the complexity and interconnection of the overall brain network. How to more accurately locate SOZ is still not well resolved. The purpose of this study is to develop an effective positioning method for more accurate positioning. To overcome these limitations, this study proposed a model integrating brain functional network analysis with nonlinear dynamics. We utilized weighted phase lag index (WPLI) to construct brain functional network, epilepic network connectivity strength (ENCS) as the feature, and introduced persistence entropy (PE) for feature fusion, subsequently employing support vector machine (SVM) classification. The proposed method was verified on the HUP-iEEG dataset, our solution identified the SOZ with 0.9440 accuracy, 0.9848 precision, 0.8974 recall rate, 0.9340 F1 score and 0.9697 area under the ROC curve across patients, which outperforms the existing approaches. It exhibits a 2.30 percentage point enhancement in localisation accuracy along with a 2.97 percentage points in AUC compared to others. Our method consider the interactions between nodes in brain network connections, as well as the inherent nonlinear and non-stationary properties of neural signals, to be more robust.

Disrupted Functional Brain Network Architecture in Sufferers with Boxing-Related Repeated Mild Traumatic Brain Injury: A Resting-State EEG Study.

Repetitive mild traumatic brain injury (rmTBI) often occurs in individuals engaged in contact sports, particularly boxing. This study aimed to elucidate the effects of rmTBI on phase-locking value (PLV)-based graph theory and functional network architecture in individuals with boxing-related injuries in five frequency bands by employing resting-state electroencephalography (EEG). Twenty-fore professional boxers and 25 matched healthy controls were recruited to perform a resting-state task, and their noninvasive scalp EEG data were collected simultaneously. Based on the construction of PLV matrices for boxers and controls, phase synchronization and graph-theoretic characteristics were identified in each frequency band. The significance of the calculated functional brain networks between the two populations was analyzed using a network-based statistical (NBS) approach. Compared to controls, boxers exhibited an increasing trend in PLV synchronization and notable differences in the distribution of functional centers, especially in the gamma frequency band. Additionally, attenuated nodal network parameters and decreased small-world measures were observed in the theta, beta, and gamma bands, suggesting that the functional network efficiency and small-world characteristics were significantly weakened in boxers. NBS analysis revealed that boxers exhibited a significant increase in network connectivity strength compared to controls in the theta, beta, and gamma frequency bands. The functional connectivity of the significance subnetworks exhibited an asymmetric distribution between the bilateral hemispheres, indicating that the optimized organization of information integration and segregation for the resting-state networks was imbalanced and disarranged for boxers. This is the first study to investigate the underlying deficits in PLV-based graph-theoretic characteristics and NBS-based functional networks in patients with rmTBI from the perspective of whole-brain resting-state EEG. Joint analyses of distinctive graph-theoretic representations and asymmetrically hyperconnected subnetworks in specific frequency bands may serve as an effective method to assess the underlying deficiencies in resting-state network processing in patients with sports-related rmTBI.

Efficient excitation-transfer across fully connected networks via local-energy optimization

We study the excitation transfer across a fully connected quantum network whose sites energies can be artificially designed. Starting from a simplified model of a broadly-studied physical system, we systematically optimize its local energies to achieve high excitation transfer for various environmental conditions, using an adaptive Gradient Descent technique and Automatic Differentiation. We show that almost perfect transfer can be achieved with and without local dephasing, provided that the dephasing rates are not too large. We investigate our solutions in terms of resilience against variations in either the network connection strengths, or size, as well as coherence losses. We highlight the different features of a dephasing-free and dephasing-driven transfer. Our work gives further insight into the interplay between coherence and dephasing effects in excitation-transfer phenomena across fully connected quantum networks. In turn, this will help designing optimal transfer in artificial open networks through the simple manipulation of local energies.

The effects of mental fatigue on fine motor performance in humans and its neural network connectivity mechanism: a dart throwing study.

While it is well known that mental fatigue impairs fine motor performance, the investigation into its neural basis remains scant. Here, we investigate the impact of mental fatigue on fine motor performance and explore its underlying neural network connectivity mechanisms. A total of 24 healthy male university students were recruited and randomly divided into two groups: a mental fatigue group (MF) and a control group (Control). Both groups completed 50 dart throws, while electroencephalography (EEG) data were collected. Following the Stroop intervention, participants in the MF group exhibited a decrease in Stroop task accuracy and throwing performance, and an increase in reaction time along with VAS and NASA scores. The EEG data during dart-throwing revealed that the network connectivity strength of theta oscillations in the frontal and left central regions was significantly higher in the MF group compared with the Control group, while the network connectivity strength of alpha oscillations in the left parietal region was significantly enhanced. The interregional connectivity within the theta and alpha rhythm bands, particularly in the frontal-central-parietal network connections, also showed a significant increase in the MF group. Mental fatigue impairs dart throwing performance and is accompanied by increased connectivity in alpha and theta.

Using a longitudinal network structure to subgroup depressive symptoms among adolescents

BackgroundNetwork modeling has been proposed as an effective approach to examine complex associations among antecedents, mediators and symptoms. This study aimed to investigate whether the severity of depressive symptoms affects the multivariate relationships among symptoms and mediating factors over a 2-year longitudinal follow-up.MethodsWe recruited a school-based cohort of 1480 primary and secondary school students over four semesters from January 2020 to December 2021. The participants (n = 1145) were assessed at four time points (ages 10–13 years old at baseline). Based on a cut-off score of 5 on the 9-item Patient Health Questionnaire at each time point, the participants were categorized into the non-depressive symptom (NDS) and depressive symptom (DS) groups. We conducted network analysis to investigate the symptom-to-symptom influences in these two groups over time.ResultsThe global network metrics did not differ statistically between the NDS and DS groups at four time points. However, network connection strength varied with symptom severity. The edge weights between learning anxiety and social anxiety were prominently in the NDS group over time. The central factors for NDS and DS were oversensitivity and impulsivity (3 out of 4 time points), respectively. Moreover, both node strength and closeness were stable over time in both groups.ConclusionsOur study suggests that interrelationships among symptoms and contributing factors are generally stable in adolescents, but a higher severity of depressive symptoms may lead to increased stability in these relationships.

Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics.

In the realm of cognitive science, the phenomenon of "successful cognitive aging" stands as a hallmark of individuals who exhibit cognitive abilities surpassing those of their age-matched counterparts. However, it is paramount to underscore a significant gap in the current research, which is marked by a paucity of comprehensive inquiries that deploy substantial sample sizes to methodically investigate the cerebral biomarkers and contributory elements underpinning this cognitive success. It is within this context that our present study emerges, harnessing data derived from the UK Biobank. In this study, a highly selective cohort of 1060 individuals aged 65 and above was meticulously curated from a larger pool of 17,072 subjects. The selection process was guided by their striking cognitive resilience, ascertained via rigorous evaluation encompassing both generic and specific cognitive assessments, compared to their peers within the same age stratum. Notably, the cognitive abilities of the chosen participants closely aligned with the cognitive acumen commonly observed in middle-aged individuals. Our study leveraged a comprehensive array of neuroimaging-derived metrics, obtained from three Tesla MRI scans (T1-weighted images, dMRI, and resting-state fMRI). The metrics included image-derived phenotypes (IDPs) that addressed grey matter morphology, the strength of brain network connectivity, and the microstructural attributes of white matter. Statistical analyses were performed employing ANOVA, Mann-Whitney U tests, and chi-square tests to evaluate the distinctive aspects of IDPs pertinent to the domain of successful cognitive aging. Furthermore, these analyses aimed to elucidate lifestyle practices that potentially underpin the maintenance of cognitive acumen throughout the aging process. Our findings unveiled a robust and compelling association between heightened cognitive aptitude and the integrity of white matter structures within the brain. Furthermore, individuals who exhibited successful cognitive aging demonstrated markedly enhanced activity in the cerebral regions responsible for auditory perception, voluntary motor control, memory retention, and emotional regulation. These advantageous cognitive attributes were mirrored in the health-related lifestyle choices of the surveyed cohort, characterized by elevated educational attainment, a lower incidence of smoking, and a penchant for moderate alcohol consumption. Moreover, they displayed superior grip strength and enhanced walking speeds. Collectively, these findings furnish valuable insights into the multifaceted determinants of successful cognitive aging, encompassing both neurobiological constituents and lifestyle practices. Such comprehensive comprehension significantly contributes to the broader discourse on aging, thereby establishing a solid foundation for the formulation of targeted interventions aimed at fostering cognitive well-being among aging populations.

Metabolic interactions between organs in overweight and obesity using total-body positron emission tomography.

Overweight and obesity is a complex condition resulting from unbalanced energy homeostasis among various organs. However, systemic abnormalities in overweight and obese people are seldom explored in vivo by metabolic imaging techniques. The aim of this study was to determine metabolic abnormities throughout the body in overweight and obese adults using total-body positron emission tomography (PET) glucose uptake imaging. Thirty normal weight subjects [body mass index (BMI) < 25 kg/m2, 55.47 ± 13.94 years, 16 men and 14 women], and 26 overweight and obese subjects [BMI ≥ 25 kg/m2, 52.38 ± 9.52 years, 21 men and 5 women] received whole-body 18F-fluorodeoxyglucose PET imaging using the uEXPLORER. Whole-body standardized uptake value normalized by lean body mass (SUL) images were calculated. Metabolic networks were constructed based on the whole-body SUL images using covariance network approach. Both group-level and individual-level network differences between normal weight and overweight/obese subjects were evaluated. Correlation analysis was conducted between network properties and BMI for the overweight/obese subjects. Compared with normal weight subjects, overweight/obese subjects exhibited altered network connectivity strength in four network nodes, namely the pancreas (p = 0.033), spleen (p = 0.021), visceral adipose tissue (VAT) (p = 1.12 × 10-5) and bone (p = 0.021). Network deviations of overweight/obese subjects from the normal weight were positively correlated with BMI (r = 0.718, p = 3.64 × 10-5). In addition, overweight/obese subjects experienced altered connections between organs, and some of the altered connections, including pancreas-right lung and VAT-bilateral lung connections were significantly correlated with BMI. Overweight/obese individuals exhibit metabolic alterations in organ level, and altered metabolic interactions at the systemic level. The proposed approach using total-body PET imaging can reveal individual metabolic variability and metabolic deviations between organs, which would open up a new path for understanding metabolic alterations in overweight and obesity.

Cognitive function and cardiovascular health in the elderly: network analysis based on hypertension, diabetes, cerebrovascular disease, and coronary heart disease.

Cognitive decline in the elderly population is a growing concern, and vascular factors, such as hypertension, diabetes, cerebrovascular disease, and coronary heart disease, have been associated with cognitive impairments. This study aims to provide deeper insights into the structure of cognitive function networks under these different vascular factors and explore their potential associations with specific cognitive domains. Cognitive function was assessed using a modified Chinese version of the mini-mental state examination (MMSE) scale, and intensity centrality and side weights were estimated by network modeling. The network structure of cognitive function was compared across subgroups by including vascular factors as subgroup variables while controlling for comorbidities and confounders. The results revealed that cerebrovascular disease and coronary heart disease had a more significant impact on cognitive function. Cerebrovascular disease was associated with weaker centrality in memory and spatial orientation, and a sparser cognitive network structure. Coronary heart disease was associated with weaker centrality in memory, repetition, executive function, recall, attention, and calculation, as well as a sparser cognitive network structure. The NCT analyses further highlighted significant differences between the cerebrovascular disease and coronary heart disease groups compared to controls in terms of overall network structure and connection strength. Our findings suggest that specific cognitive domains may be more vulnerable to impairments in patients with cerebrovascular disease and coronary heart disease. These insights could be used to improve the accuracy and sensitivity of cognitive screening in these patient populations, inform personalized cognitive intervention strategies, and provide a better understanding of the potential mechanisms underlying cognitive decline in patients with vascular diseases.

Partial synchronization and community switching in phase-oscillator networks and its analysis based on a bidirectional, weighted chain of three oscillators.

Complex networks often possess communities defined based on network connectivity. When dynamics undergo in a network, one can also consider dynamical communities, i.e., a group of nodes displaying a similar dynamical process. We have investigated both analytically and numerically the development of a dynamical community structure, where the community is referred to as a group of nodes synchronized in frequency, in networks of phase oscillators. We first demonstrate that using a few example networks, the community structure changes when network connectivity or interaction strength is varied. In particular, we found that community switching, i.e., a portion of oscillators change the group to which they synchronize, occurs for a range of parameters. We then propose a three-oscillator model: a bidirectional, weighted chain of three Kuramoto phase oscillators, as a theoretical framework for understanding the community formation and its variation. Our analysis demonstrates that the model shows a variety of partially synchronized patterns: oscillators with similar natural frequencies tend to synchronize for weak coupling, while tightly connected oscillators tend to synchronize for strong coupling. We obtain approximate expressions for the critical coupling strengths by employing a perturbative approach in a weak coupling regime and a geometric approach in strong coupling regimes. Moreover, we elucidate the bifurcation types of transitions between different patterns. Our theory might be useful for understanding the development of partially synchronized patterns in a wider class of complex networks than community structured networks.

Composition-dependent properties and network structure of Ge-Se-Te chalcogenide glasses

Ge12.5Se87.5-xTex (0≤x≤45) glasses were selected for elucidating the composition-dependent properties and network structure of Te-containing glasses. With increasing Te content (x), Vickers hardness (Hv) and glass transition temperature (Tg) initially increased and then decreased, showing a compositional threshold at x=27.5. It is found that the compositional trend of Hv and Tg is in good accordance with the structural evolution studied by Raman spectra. The results suggest that the introduction of Te leads to the evolution of the network connectivity and average bond strength of Ge12.5Se87.5-xTex glass structure, which imposes an opposite impact on the structural properties (Hv and Tg). This work provides a new insight to the structure-property correlation of Ge-Se-Te, which would facilitate the understanding of the structural role of Te in ChGs.

Exploration of Urban Network Spatial Structure Based on Traffic Flow, Migration Flow and Information Flow: A Case Study of Shanxi Province, China

Urban coordinated development is an important aspect of regional development. The high-quality development of the Yellow River Basin cannot be separated from the coordinated and sustainable development of its inner cities. However, the network connection and spatial structure of cities in the Yellow River Basin have not received sufficient attention. Therefore, this study considered 11 prefecture-level cities in Shanxi Province, an underdeveloped region in the Yellow River Basin, as case areas and selected data on traffic, migration, and information flow that can better represent the urban spatial network structure and depict the spatial connection between cities. Based on the flow intensity calculation, flow direction judgment, spatial structure index, and social network analysis, the spatial structural characteristics of Shanxi Province were comprehensively analyzed from the perspective of flow space. The results showed the following: (1) Cities in Shanxi Province present a development trend of “one core and multiple centers.” The strong connection concerns mostly Taiyuan and radiates outward and presents a Chinese character “大”—shaped spatial connection pattern. (2) Taiyuan is the first connecting city of most cities in Shanxi Province, and the element flows particularly towards the central city and geographical proximity. (3) The urban spatial pattern of Shanxi Province presents an obvious unipolar development trend, where the network structure is an “absence-type pyramid.” The imbalance of the urban network connection strength is prominent in Shanxi Province, which is strong and numerous in the south but opposite in the north. (4) The overall network element flow density is low, the network connection is weak, Taiyuan agglomeration and radiation are the strongest, and Changzhi centrality ranks second, but the gap between Changzhi and Taiyuan is wide, and the polarization phenomenon is serious. Future research should focus on the rapidly developing provincial capital city of Taiyuan, coordinating the steady development of the central Shanxi city cluster, and driving the common development of neighboring cities.

White matter connectivity networks predict early dementia conversion in Parkinson’s disease

AbstractBackgroundSeveral clinical and neuroimaging biomarkers have been proposed to identify individuals at high risk for dementia conversion in Parkinson’s disease (PD). This study aimed to explore whether white matter (WM) connectivity disruption can predict the dementia conversion in patients with newly diagnosed PD with mild cognitive impairment (PD‐MCI).MethodNeuroimaging analyses of WM structural connectivity were performed in 75 patients with drug‐naïve PD‐MCI who underwent serial cognitive assessments during the follow‐up period (&gt;5 years). The patients were classified into either the PD with dementia (PDD) high‐risk group (PDD‐H, n = 38) or low‐risk group (PDD‐L, n = 37), depending on whether they converted to dementia within 5 years of PD diagnosis. We conducted degree‐based statistics (DBS) analyses based on a graph‐theoretical concept to identify the subnetworks whose WM connectivity was disrupted in the PDD‐H group compared with the PDD‐L group. We also performed partial correlation analyses to investigate whether the network connectivity strength was correlated with either the cognitive composite scores or the estimated risk score for PDD conversion within 5 years.ResultThe PDD‐H group showed poorer cognitive performance on frontal/executive, visual memory/visuospatial, and attention/working memory/language function than the PDD‐L group at baseline assessment, even though there was no difference in parkinsonian motor deficits severity, disease duration, and years of education. The PDD‐H group exhibited more severely disrupted WM connectivity in both frontal and posterior cortical regions with eight hub nodes in the DBS analysis. The strength of structural connectivity within the identified subnetworks was correlated with the composite scores of the frontal/executive function domain (γ = 0.393, p = 0.010) and the risk score of PDD conversion within 5 years (γ = ‐0.480, p = 0.001).ConclusionThe present study demonstrated that disrupted WM connectivity in frontal and posterior cortical regions, which was correlated with frontal/executive dysfunction, could be used as a marker for early dementia conversion in patients with PD‐MCI.

Modulation of resting-state functional connectivity in default mode network is associated with the long-term treatment outcome in major depressive disorder.

Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD. Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD. Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up. Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.

Innovation Network Reconfiguration Makes Infrastructure Megaprojects More Resilient.

Innovation management of infrastructure megaprojects is a challenging task. There are many risks in the process of innovation in engineering technology, such as shortage of funds, policy fluctuations, and difficulties in the transformation of achievements. Meanwhile, innovation organizations involve multiple participants, which makes cooperation complicated. Therefore, resilient innovation is proposed and considered as a tool that can optimize innovation management. The resilience of innovation depends largely on partnerships at the organizational level, which is rarely explored in current studies. This research aims to examine the relationship between organizational resilience and innovation network characteristics. Based on a survey of 164 participants in infrastructure innovation projects, the structural equation model (SEM) is used to explore the factors that influence organizational resilience. The findings show that there is a positive correlation between network characteristics and organizational resilience. Furthermore, the strength of network connections has a direct impact on the preventive and resistance ability of resilience. Network heterogeneity has an impact on the dual ability of resilience. Finally, a case study of the Qinghai-Tibet Railway innovation network shows that based on the above influence paths, we can find a strategy to reconstruct the network to improve resilience.

Abnormal Information Flow in Schizophrenia Is Linked to Psychosis.

Prior research has shown that patients with schizophrenia (SZ) show disruption in brain network connectivity that is thought to underlie their cognitive and psychotic symptoms. However, most studies examining functional network disruption in schizophrenia have focused on the temporally correlated coupling of the strength of network connections. Here, we move beyond correlative metrics to assay causal computations of connectivity changes in directed neural information flow, assayed from a neural source to a target in SZ. This study describes a whole-brain magnetoencephalography-imaging approach to examine causal computations of connectivity changes in directed neural information flow between brain regions during resting states, quantified by phase-transfer entropy (PTE) metrics, assayed from a neural source to an endpoint, in 21 SZ compared with 21 healthy controls (HC), and associations with cognitive and clinical psychotic symptoms in SZ. We found that SZ showed significant disruption in information flow in alpha (8-12 Hz) and beta (12-30 Hz) frequencies, compared to HC. Reduced information flow in alpha frequencies from the precuneus to the medio-ventral occipital cortex was associated with more severe clinical psychopathology (ie, positive psychotic symptoms), while reduced information flow between insula and middle temporal gyrus was associated with worsening cognitive symptoms. The present findings highlight the importance of delineating dysfunction in neural information flow in specific oscillatory frequencies between distinct regions that underlie the cognitive and psychotic symptoms in SZ, and provide potential neural biomarkers that could lead to innovations in future neuromodulation treatment development.

Critical dynamics in the spread of focal epileptic seizures: Network connectivity, neural excitability and phase transitions.

Focal epileptic seizures can remain localized or, alternatively, spread across brain areas, often resulting in impairment of cognitive function and loss of consciousness. Understanding the factors that promote spread is important for developing better therapeutic approaches. Here, we show that: (1) seizure spread undergoes “critical” phase transitions in models (epileptor-networks) that capture the neural dynamics of spontaneous seizures while incorporating patient-specific brain network connectivity, axonal delays and identified epileptogenic zones (EZs). We define a collective variable for the spreading dynamics as the spread size, i.e. the number of areas or nodes in the network to which a seizure has spread. Global connectivity strength and excitability in the surrounding non-epileptic areas work as phase-transition control parameters for this collective variable. (2) Phase diagrams are predicted by stability analysis of the network dynamics. (3) In addition, the components of the Jacobian’s leading eigenvector, which tend to reflect the connectivity strength and path lengths from the EZ to surrounding areas, predict the temporal order of network-node recruitment into seizure. (4) However, stochastic fluctuations in spread size in a near-criticality region make predictability more challenging. Overall, our findings support the view that within-patient seizure-spread variability can be characterized by phase-transition dynamics under transient variations in network connectivity strength and excitability across brain areas. Furthermore, they point to the potential use and limitations of model-based prediction of seizure spread in closed-loop interventions for seizure control.

Identifying the white matter structural network of motor reserve in early Parkinson's disease

IntroductionMotor reserve refers to the individual capacity to cope with nigrostriatal dopamine depletion in Parkinson's disease (PD). This study aimed to explore the white matter structural network associated with motor reserve in patients with newly diagnosed PD. MethodsA total of 238 patients with early-stage drug-naïve PD who underwent 18F-FP-CIT PET and brain MRI scans at initial assessment were enrolled. We estimated individual motor reserve based on the Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) scores and dopamine transporter availability in the posterior putamen using a residual model. Then, we performed threshold-free network-based statistics (TFNBS) analysis to identify the structural brain network associated with the estimated motor reserve. We also assessed the effect of the network connectivity strength on the longitudinal increase in levodopa-equivalent dose (LED). ResultsThe mean age at PD symptom onset was 69.10 ± 9.03 years and the mean UPDRS-III score at the time of PD diagnosis was 22.44 ± 9.72. TFNBS analysis identified a motor reserve-associated structural network whose nodes were mainly in the frontal region and cerebellum. Higher network strength (i.e., greater motor reserve) was associated with a slower longitudinal increase in LED during a 3-year follow-up period. ConclusionThe structural brain network is associated with motor reserve in patients with PD. Connectivity strength within the identified network indicates the individual's capacity to tolerate PD-related pathologies, which is maintained with disease progression and affects the long-term motor prognosis of PD.

An efficient supply management in water flow network using graph spectral techniques.

An efficient way of distributing water in urban cities is one of the major global challenges of our time. A Water Distribution Network (WDN) is a vital infrastructure, intended to provide fresh water to households within a city or designated boundary. Given the WDN's complexity, effective numerical techniques are required to aid in the development of an ideal monitoring system. Flow meters and gate valves should be placed in low-connected areas of the WDN with water flow reaching several regions of the network. This study proposes a general strategy for assisting water utilities in making decisions for effective water supply. The aim of the research is to use weighted spectral clustering algorithms to outline water districts while addressing hydraulic restrictions via weighted adjacency and Laplacian matrices of the weighted network. This work aims to identify influenced nodes in the network based on Eigen centrality and effectively distribute water across those nodes. This project also looks at how to measure the network's connection strength to avoid water leaks. The best clusters are found using Eigenvalues and Eigenvectors of weighted adjacency matrices and Laplacian matrices of the water network in the proposed graph spectral framework. In order to establish the optimum water network division approach, topological and graph metrics were used to compare multiple spectral clustering techniques. The proposed graph spectral approach is tested using a genuine water distribution network serving an urban area of Coimbatore city in India and offering a method for partitioning complex networks that employ the spectral graph partitioning algorithm.