Journal articles: 'Static connectivity'

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de Lacy, Nina, and Vince D. Calhoun. "Dynamic connectivity and the effects of maturation in youth with attention deficit hyperactivity disorder." Network Neuroscience 3, no. 1 (January 2019): 195–216. http://dx.doi.org/10.1162/netn_a_00063.

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The analysis of time-varying connectivity by using functional MRI has gained momentum given its ability to complement traditional static methods by capturing additional patterns of variation in human brain function. Attention deficit hyperactivity disorder (ADHD) is a complex, common developmental neuropsychiatric disorder associated with heterogeneous connectivity differences that are challenging to disambiguate. However, dynamic connectivity has not been examined in ADHD, and surprisingly few whole-brain analyses of static functional network connectivity (FNC) using independent component analysis (ICA) exist. We present the first analyses of time-varying connectivity and whole-brain FNC using ICA in ADHD, introducing a novel framework for comparing local and global dynamic connectivity in a 44-network model. We demonstrate that dynamic connectivity analysis captures robust motifs associated with group effects consequent on the diagnosis of ADHD, implicating increased global dynamic range, but reduced fluidity and range localized to the default mode network system. These differentiate ADHD from other major neuropsychiatric disorders of development. In contrast, static FNC based on a whole-brain ICA decomposition revealed solely age effects, without evidence of group differences. Our analysis advances current methods in time-varying connectivity analysis, providing a structured example of integrating static and dynamic connectivity analysis to further investigation into functional brain differences during development.

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Dumkrieger, Gina, Catherine D. Chong, Katherine Ross, Visar Berisha, and Todd J. Schwedt. "Static and dynamic functional connectivity differences between migraine and persistent post-traumatic headache: A resting-state magnetic resonance imaging study." Cephalalgia 39, no. 11 (May 1, 2019): 1366–81. http://dx.doi.org/10.1177/0333102419847728.

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Introduction Although migraine and persistent post-traumatic headache often share phenotypic characteristics, few studies have interrogated the pathophysiological differences underlying these headache types. While there is now some indication of differences in brain structure between migraine and persistent post-traumatic headache, differences in brain function have not been adequately investigated. The objective of this study was to compare static and dynamic functional connectivity patterns in migraine versus persistent post-traumatic headache using resting-state magnetic resonance imaging. Methods This case-control study interrogated the static functional connectivity and dynamic functional connectivity patterns of 59 a priori selected regions of interest involved in pain processing. Pairwise connectivity (region of interest to region of interest) differences between migraine (n = 33) and persistent post-traumatic headache (n = 44) were determined and compared to healthy controls (n = 36) with ANOVA and subsequent t-tests. Pearson partial correlations were used to explore the relationship between headache burden (headache frequency; years lived with headache) and functional connectivity and between pain intensity at the time of imaging and functional connectivity for migraine and persistent post-traumatic headache groups, separately. Results Significant differences in static functional connectivity between migraine and persistent post-traumatic headache were found for 17 region pairs that included the following regions of interest: Primary somatosensory, secondary somatosensory, posterior insula, hypothalamus, anterior cingulate, middle cingulate, temporal pole, supramarginal gyrus, superior parietal, middle occipital, lingual gyrus, pulvinar, precuneus, cuneus, somatomotor, ventromedial prefrontal cortex, and dorsolateral prefrontal cortex. Significant differences in dynamic functional connectivity between migraine and persistent post-traumatic headache were found for 10 region pairs that included the following regions of interest: Secondary somatosensory, hypothalamus, middle cingulate, temporal pole, supramarginal gyrus, superior parietal, lingual gyrus, somatomotor, precentral, posterior cingulate, middle frontal, fusiform gyrus, parieto-occiptal, and amygdala. Although there was overlap among the regions demonstrating static functional connectivity differences and those showing dynamic functional connectivity differences between persistent post-traumatic headache and migraine, there was no overlap in the region pair functional connections. After controlling for sex and age, there were significant correlations between years lived with headache with static functional connectivity of the right dorsolateral prefrontal cortex with the right ventromedial prefrontal cortex in the migraine group and with static functional connectivity of right primary somatosensory with left supramarginal gyrus in the persistent post-traumatic headache group. There were significant correlations between headache frequency with static functional connectivity of left secondary somatosensory with right cuneus in the migraine group and with static functional connectivity of left middle cingulate with right pulvinar and right posterior insula with left hypothalamus in the persistent post-traumatic headache group. Dynamic functional connectivity was significantly correlated with headache frequency, after controlling for sex and age, in the persistent post-traumatic headache group for one region pair (right middle cingulate with right supramarginal gyrus). Dynamic functional connectivity was correlated with pain intensity at the time of imaging for the migraine cohort for one region pair (right posterior cingulate with right amygdala). Conclusions Resting-state functional imaging revealed static functional connectivity and dynamic functional connectivity differences between migraine and persistent post-traumatic headache for regions involved in pain processing. These differences in functional connectivity might be indicative of distinctive pathophysiology associated with migraine versus persistent post-traumatic headache.

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Scott, J. A., M. Pujol, D. Györe, F. M. Stuart, and S. M. V. Gilfillan. "Determining static reservoir connectivity using noble gases." Chemical Geology 582 (November 2021): 120410. http://dx.doi.org/10.1016/j.chemgeo.2021.120410.

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Jin, Changfeng, Hao Jia, Pradyumna Lanka, D. Rangaprakash, Lingjiang Li, Tianming Liu, Xiaoping Hu, and Gopikrishna Deshpande. "Dynamic brain connectivity is a better predictor of PTSD than static connectivity." Human Brain Mapping 38, no. 9 (June 12, 2017): 4479–96. http://dx.doi.org/10.1002/hbm.23676.

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Riazi, Amir Hosein, Hossein Rabbani, and Rahele Kafieh. "Dynamic Brain Connectivity in Resting-State FMRI Using Spectral ICA and Graph Approach: Application to Healthy Controls and Multiple Sclerosis." Diagnostics 12, no. 9 (September 19, 2022): 2263. http://dx.doi.org/10.3390/diagnostics12092263.

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Multiple sclerosis (MS) is a neuroinflammatory disease that involves structural and functional damage to the brain. It changes the functional connectivity of the brain between and within networks. Resting-state functional magnetic resonance imaging (fMRI) enables us to measure functional correlation and independence between different brain regions. In recent years, statistical methods, including independent component analysis (ICA) and graph-based analysis, have been widely used in fMRI studies. Furthermore, topological properties of the brain have been appeared as significant features of neuroscience studies. Most studies are focused on graph analysis and ICA methods, rather than considering spectral approaches. Here, we developed a new framework to measure brain connectivity (in static and dynamic formats) and incorporate it to study fMRI data from MS patients and healthy controls (HCs). For this purpose, a spectral ICA method is proposed to extract the nodes of the brain graph. Spectral ICA extracts more reliable components and decreases the processing time in calculation of the static brain connectivity. Compared to Infomax ICA, dynamic range and low-frequency to high-frequency power ratio (fALFF) show better results using the proposed ICA. It is also helpful in selection of the states for dynamic connectivity. Furthermore, the dynamic connectivity-based extracted components from spectral ICA are estimated using a mutual information method and based on correlation of sliding time-windowed on selected IC time courses. First-level and second-level connectivity states are calculated using correlations of connectivity strength between graph nodes (spectral ICA components). Finally, static and dynamic connectivity are analyzed based on correlation nodes percolated by an anatomical automatic labeling (AAL) atlas. Despite static and dynamic connectivity results of AAL correlations not showing any significant changes between MS and HC, our results based on spectral ICA in static and dynamic connectivity showed significantly decreased connectivity in MS patients in the anterior cingulate cortex, whereas it was significantly weaker in the core but stronger at the periphery of the posterior cingulate cortex.

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IMAI, NOBORU. "Dynamic Resting-State Functional Connectivity in Migraineurs." OBM Neurobiology 06, no. 04 (October 26, 2022): 1–11. http://dx.doi.org/10.21926/obm.neurobiol.2204143.

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Functional magnetic resonance imaging (fMRI) is widely used to detect changes in the resting-state brain networks of migraine patients. Functional connectivity fMRI analysis examines the functional organization of the brain based on temporal correlations of blood oxygen level-dependent signal changes in different brain regions. Most previous resting-state fMRI studies have assumed that functional connectivity between brain regions remains relatively stable over time. However, it is now known that the brain is a complex system that undergoes time-dependent dynamics. Therefore, functional connectivity may change over time. In recent years, resting-state fMRI analysis has evolved from the detection of static coupling to the study of dynamic connectivity. However, studies of dynamic functional connectivity in migraine patients are limited. Related studies have shown that dynamic functional connectivity analysis reveals significant changes in connectivity and abnormal networks not found in static functional connectivity analysis.

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White, Tonya, and Vince D. Calhoun. "Dissecting Static and Dynamic Functional Connectivity: Example From the Autism Spectrum." Journal of Experimental Neuroscience 13 (January 2019): 117906951985180. http://dx.doi.org/10.1177/1179069519851809.

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The ability to measure the intrinsic functional architecture of the brain has grown exponentially over the last 2 decades. Measures of intrinsic connectivity within the brain, typically measured using resting-state functional magnetic resonance imaging (MRI), have evolved from primarily “static” approaches, to include dynamic measures of functional connectivity. Measures of dynamic functional connectivity expand the assumptions to allow brain regions to have temporally different patterns of communication between different regions. That is, connections within the brain can differentially fire between different regions at different times, and these differences can be quantified. Applying approaches that measure the dynamic characteristics of functional brain connectivity have been fruitful in identifying differences during brain development and psychopathology. We provide a brief overview of static and dynamic measures of functional connectivity and illustrate the synergy in applying these approaches to identify both age-related differences in children and differences between typically developing children and children with autistic symptoms.

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Hsieh, Hsinyu, Qiang Xu, Fang Yang, Qirui Zhang, Jingru Hao, Gaoping Liu, Ruoting Liu, et al. "Distinct Functional Cortico-Striato-Thalamo-Cerebellar Networks in Genetic Generalized and Focal Epilepsies with Generalized Tonic-Clonic Seizures." Journal of Clinical Medicine 11, no. 6 (March 15, 2022): 1612. http://dx.doi.org/10.3390/jcm11061612.

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This study aimed to delineate cortico-striato-thalamo-cerebellar network profiles based on static and dynamic connectivity analysis in genetic generalized and focal epilepsies with generalized tonic-clonic seizures, and to evaluate its potential for distinguishing these two epilepsy syndromes. A total of 342 individuals participated in the study (114 patients with genetic generalized epilepsy with generalized tonic-clonic seizures (GE-GTCS), and 114 age- and sex-matched patients with focal epilepsy with focal to bilateral tonic-clonic seizure (FE-FBTS), 114 healthy controls). Resting-state fMRI data were examined through static and dynamic functional connectivity (dFC) analyses, constructing cortico-striato-thalamo-cerebellar networks. Network patterns were compared between groups, and were correlated to epilepsy duration. A pattern-learning algorithm was applied to network features for classifying both epilepsy syndromes. FE-FBTS and GE-GTCS both presented with altered functional connectivity in subregions of the motor/premotor and somatosensory networks. Among these two groups, the connectivity within the cerebellum increased in the static, while the dFC variability decreased; conversely, the connectivity of the thalamus decreased in FE-FBTS and increased in GE-GTCS in the static state. Connectivity differences between patient groups were mainly located in the thalamus and cerebellum, and correlated with epilepsy duration. Support vector machine (SVM) classification had accuracies of 66.67%, 68.42%, and 77.19% when using static, dynamic, and combined approaches to categorize GE-GTCS and FE-GTCS. Network features with high discriminative ability predominated in the thalamic and cerebellar connectivities. The network embedding of the thalamus and cerebellum likely plays an important differential role in GE-GTCS and FE-FBTS, and could serve as an imaging biomarker for differential diagnosis.

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Zhang, Dongmei, Wenbin Jiang, Zhijiang Kang, Anzhong Hu, and Ruiqi Wang. "Automatic Evaluation of an Interwell-Connected Pattern for Fractured-Vuggy Reservoirs Based on Static and Dynamic Analysis." Energies 16, no. 1 (January 3, 2023): 569. http://dx.doi.org/10.3390/en16010569.

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The types of fractured-vuggy reservoirs are diverse, with dissolution holes and fractures of different scales as the main reservoir spaces. Clarifying the connectivity between wells is crucial for improving the recovery rate of fractured-vuggy reservoirs and avoiding problems of poor water- flooding balance and serious water channeling. A traditional dynamic connected model hardly describes the geological characteristics of multiple media, such as karst caves and fractures, which cause multiple solutions from the calculation. The static analysis is the basis for connectivity evaluation. In this study, we designed an intelligent search strategy based on an improved A* algorithm to automatically find a large-scale fractured-vuggy connected path by seismic multi-attribute analysis. The algorithm automatically evaluates the interwell-connected mode and clarifies the relationship between the static connected channel and the fractured-vuggy space configuration. Restricted by various factors, such as seismic identification accuracy, a static connectivity study can hardly determine the filling and half-filling inside the channel effectively, even if it can identify the main connectivity channels. An injection-production response analysis based on dynamic production data can more accurately reflect the reservoir’s actual connectivity and degree of filling. This paper further studies dynamic response characteristics based on multifractals combined with production data. To reduce the evaluation uncertainty, we combined the static and dynamic connected analysis results to comprehensively evaluate the main connected modes, such as large fracture connectivity, cavern connectivity, and fractured-vuggy compound connectivity. We use the Tahe oilfield as an example to carry out an automatic evaluation of the connected pattern. The comprehensive evaluation results of the new algorithm were basically consistent with the tracer test results and can better reflect the interwell space-configuration relationship. Our model has certain guiding significance for the adjustment of working measures during waterflooding in fractured-vuggy reservoirs.

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Zou, Yan, Weijun Tang, Xiangyang Qiao, and Ji Li. "Aberrant modulations of static functional connectivity and dynamic functional network connectivity in chronic migraine." Quantitative Imaging in Medicine and Surgery 11, no. 6 (June 2021): 2253–64. http://dx.doi.org/10.21037/qims-20-588.

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Nyatega, Charles Okanda, Li Qiang, Mohammed Jajere Adamu, Ayesha Younis, and Halima Bello Kawuwa. "Altered Dynamic Functional Connectivity of Cuneus in Schizophrenia Patients: A Resting-State fMRI Study." Applied Sciences 11, no. 23 (December 1, 2021): 11392. http://dx.doi.org/10.3390/app112311392.

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Objective: Schizophrenia (SZ) is a functional mental condition that has a significant impact on patients’ social lives. As a result, accurate diagnosis of SZ has attracted researchers’ interest. Based on previous research, resting-state functional magnetic resonance imaging (rsfMRI) reported neural alterations in SZ. In this study, we attempted to investigate if dynamic functional connectivity (dFC) could reveal changes in temporal interactions between SZ patients and healthy controls (HC) beyond static functional connectivity (sFC) in the cuneus, using the publicly available COBRE dataset. Methods: Sliding windows were applied to 72 SZ patients’ and 74 healthy controls’ (HC) rsfMRI data to generate temporal correlation maps and, finally, evaluate mean strength (dFC-Str), variability (dFC-SD and ALFF) in each window, and the dwelling time. The difference in functional connectivity (FC) of the cuneus between two groups was compared using a two-sample t-test. Results: Our findings demonstrated decreased mean strength connectivity between the cuneus and calcarine, the cuneus and lingual gyrus, and between the cuneus and middle temporal gyrus (TPOmid) in subjects with SZ. Moreover, no difference was detected in variability (standard deviation and the amplitude of low-frequency fluctuation), the dwelling times of all states, or static functional connectivity (sFC) between the groups. Conclusions: Our verdict suggest that dynamic functional connectivity analyses may play crucial roles in unveiling abnormal patterns that would be obscured in static functional connectivity, providing promising impetus for understanding schizophrenia disease.

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Jennings, Megan K., Katherine A. Zeller, and Rebecca L. Lewison. "Dynamic Landscape Connectivity Special Issue Editorial." Land 10, no. 6 (May 25, 2021): 555. http://dx.doi.org/10.3390/land10060555.

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Until fairly recently, the majority of landscape connectivity analyses have considered connectivity as a static landscape feature, despite the widespread recognition that landscapes and the abiotic and biotic processes that influence them are dynamic [...]

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Zeller, Katherine, David Wattles, Javan Bauder, and Stephen DeStefano. "Forecasting Seasonal Habitat Connectivity in a Developing Landscape." Land 9, no. 7 (July 18, 2020): 233. http://dx.doi.org/10.3390/land9070233.

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Connectivity and wildlife corridors are often key components to successful conservation and management plans. Connectivity for wildlife is typically modeled in a static environment that reflects a single snapshot in time. However, it has been shown that, when compared with dynamic connectivity models, static models can underestimate connectivity and mask important population processes. Therefore, including dynamism in connectivity models is important if the goal is to predict functional connectivity. We incorporated four levels of dynamism (individual, daily, seasonal, and interannual) into an individual-based movement model for black bears (Ursus americanus) in Massachusetts, USA. We used future development projections to model movement into the year 2050. We summarized habitat connectivity over the 32-year simulation period as the number of simulated movement paths crossing each pixel in our study area. Our results predict black bears will further colonize the expanding part of their range in the state and move beyond this range towards the greater Boston metropolitan area. This information is useful to managers for predicting and addressing human–wildlife conflict and in targeting public education campaigns on bear awareness. Including dynamism in connectivity models can produce more realistic models and, when future projections are incorporated, can ensure the identification of areas that offer long-term functional connectivity for wildlife.

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Mutlu, Ali Yener, Edward Bernat, and Selin Aviyente. "A Signal-Processing-Based Approach to Time-Varying Graph Analysis for Dynamic Brain Network Identification." Computational and Mathematical Methods in Medicine 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/451516.

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In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on first identifying key event intervals by quantifying the change in the connectivity patterns across time and then summarizing the activity in each event interval by extracting the most informative network using principal component decomposition. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the error-related negativity (ERN) component related to cognitive control. The statistically significant connectivity patterns for each interval are presented to illustrate the dynamic nature of functional connectivity.

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Chen, Jing, Dalong Sun, Yonghui Shi, Wei Jin, Yanbin Wang, Qian Xi, and Chuancheng Ren. "Alterations of static functional connectivity and dynamic functional connectivity in motor execution regions after stroke." Neuroscience Letters 686 (November 2018): 112–21. http://dx.doi.org/10.1016/j.neulet.2018.09.008.

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Chadderdon, George L., Ashutosh Mohan, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Joseph T. Francis, Gordon M. G. Shepherd, and William W. Lytton. "Motor Cortex Microcircuit Simulation Based on Brain Activity Mapping." Neural Computation 26, no. 7 (July 2014): 1239–62. http://dx.doi.org/10.1162/neco_a_00602.

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The deceptively simple laminar structure of neocortex belies the complexity of intra- and interlaminar connectivity. We developed a computational model based primarily on a unified set of brain activity mapping studies of mouse M1. The simulation consisted of 775 spiking neurons of 10 cell types with detailed population-to-population connectivity. Static analysis of connectivity with graph-theoretic tools revealed that the corticostriatal population showed strong centrality, suggesting that would provide a network hub. Subsequent dynamical analysis confirmed this observation, in addition to revealing network dynamics that cannot be readily predicted through analysis of the wiring diagram alone. Activation thresholds depended on the stimulated layer. Low stimulation produced transient activation, while stronger activation produced sustained oscillations where the threshold for sustained responses varied by layer: 13% in layer 2/3, 54% in layer 5A, 25% in layer 5B, and 17% in layer 6. The frequency and phase of the resulting oscillation also depended on stimulation layer. By demonstrating the effectiveness of combined static and dynamic analysis, our results show how static brain maps can be related to the results of brain activity mapping.

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Jakab, Noémi. "Uncertainty assessment based on scenarios derived from static connectivity metrics." Open Geosciences 8, no. 1 (January 1, 2016): 799–807. http://dx.doi.org/10.1515/geo-2016-0057.

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AbstractThe approach presented in this paper characterises the uncertainty related to the outputs of a sequential Gaussian simulation. The input data set was a random subset of a complete CT slice. To outline possible, characteristically different groups of realisations within the outputs, we performed a distance-based classification of the realisations based on their derived connectivity features. Global metrics of connectivity also called geo-body or geoobject connectivity is derivative properties related to the overall structure of the simulated field. Based on these attributes stochastic images, which show the same characteristics from a statistical point of view become distinguishable. The scenarios generated this way are able to bridge the gap of information content between the individual stochastic images and the entirety of the pooled realisations. Scenarios are also capable of highlighting the groups of most probable outcomes from the realisations while screening the effect of ergodic fluctuations of the individual stochastic images. They yield a more realistic representation of the smaller scale heterogeneities than the individual stochastic images. In this sense, our approach is able to resolve the question of how many realisations to choose for the assessment of uncertainty. Besides, it eliminates subjectivity and supports reproducible decision-making when the task is to select stochastic images for dynamic simulation.

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Flavell, Steven W., and Andrew Gordus. "Dynamic functional connectivity in the static connectome of Caenorhabditis elegans." Current Opinion in Neurobiology 73 (April 2022): 102515. http://dx.doi.org/10.1016/j.conb.2021.12.002.

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Mayer, Andrew R., Josef M. Ling, Elena A. Allen, Stefan D. Klimaj, Ronald A. Yeo, and Faith M. Hanlon. "Static and Dynamic Intrinsic Connectivity following Mild Traumatic Brain Injury." Journal of Neurotrauma 32, no. 14 (July 15, 2015): 1046–55. http://dx.doi.org/10.1089/neu.2014.3542.

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Feng, Han, and Javad Lavaei. "Connectivity Properties of the Set of Stabilizing Static Decentralized Controllers." SIAM Journal on Control and Optimization 58, no. 5 (January 2020): 2790–820. http://dx.doi.org/10.1137/19m123765x.

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Pang, Yajing, Huangbin Zhang, Qian Cui, Qi Yang, Fengmei Lu, Heng Chen, Zongling He, Yifeng Wang, Jiaojian Wang, and Huafu Chen. "Combined static and dynamic functional connectivity signatures differentiating bipolar depression from major depressive disorder." Australian & New Zealand Journal of Psychiatry 54, no. 8 (May 26, 2020): 832–42. http://dx.doi.org/10.1177/0004867420924089.

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Objective: Bipolar disorder in the depressive phase (BDd) may be misdiagnosed as major depressive disorder (MDD), resulting in poor treatment outcomes. To identify biomarkers distinguishing BDd from MDD is of substantial clinical significance. This study aimed to characterize specific alterations in intrinsic functional connectivity (FC) patterns in BDd and MDD by combining whole-brain static and dynamic FC. Methods: A total of 40 MDD and 38 BDd patients, and 50 age-, sex-, education-, and handedness-matched healthy controls (HCs) were included in this study. Static and dynamic FC strengths (FCSs) were analyzed using complete time-series correlations and sliding window correlations, respectively. One-way analysis of variance was performed to test group effects. The combined static and dynamic FCSs were then used to distinguish BDd from MDD and to predict clinical symptom severity. Results: Compared with HCs, BDd patients showed lower static FCS in the medial orbitofrontal cortex and greater static FCS in the caudate, while MDD patients exhibited greater static FCS in the medial orbitofrontal cortex. BDd patients also demonstrated greater static and dynamic FCSs in the thalamus compared with both MDD patients and HCs, while MDD patients exhibited greater dynamic FCS in the precentral gyrus compared with both BDd patients and HCs. Combined static and dynamic FCSs yielded higher accuracy than either static or dynamic FCS analysis alone, and also predicted anhedonia severity in BDd patients and negative mood severity in MDD patients. Conclusion: Altered FC within frontal–striatal–thalamic circuits of BDd patients and within the default mode network/sensorimotor network of MDD patients accurately distinguishes between these disorders. These unique FC patterns may serve as biomarkers for differential diagnosis and provide clues to the pathogenesis of mood disorders.

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ABE, SUMIYOSHI, and STEFAN THURNER. "HIERARCHICAL AND MIXING PROPERTIES OF STATIC COMPLEX NETWORKS EMERGING FROM FLUCTUATING CLASSICAL RANDOM GRAPHS." International Journal of Modern Physics C 17, no. 09 (September 2006): 1303–11. http://dx.doi.org/10.1142/s0129183106009837.

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The Erdös–Rényi classical random graph is characterized by a fixed linking probability for all pairs of vertices. Here, this concept is generalized by drawing the linking probability from a certain distribution. Such a procedure is found to lead to a static complex network with an arbitrary connectivity distribution. In particular, a scale-free network with the hierarchical organization is constructed without assuming any knowledge about the global linking structure, in contrast to the preferential attachment rule for a growing network. The hierarchical and mixing properties of the static scale-free network thus constructed are studied. The present approach establishes a bridge between a scalar characterization of individual vertices and topology of an emerging complex network. The result may offer a clue for understanding the origin of a few abundance of connectivity distributions in a wide variety of static real-world networks.

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Chiang, Sharon, Emilian R. Vankov, Hsiang J. Yeh, Michele Guindani, Marina Vannucci, Zulfi Haneef, and John M. Stern. "Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity." PLOS ONE 13, no. 1 (January 10, 2018): e0190220. http://dx.doi.org/10.1371/journal.pone.0190220.

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Henzinger, Monika Rauch. "A Static 2-Approximation Algorithm for Vertex Connectivity and Incremental Approximation Algorithms for Edge and Vertex Connectivity." Journal of Algorithms 24, no. 1 (July 1997): 194–220. http://dx.doi.org/10.1006/jagm.1997.0855.

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Barss, Trevor S., Behdad Parhizi, and Vivian K. Mushahwar. "Transcutaneous spinal cord stimulation of the cervical cord modulates lumbar networks." Journal of Neurophysiology 123, no. 1 (January 1, 2020): 158–66. http://dx.doi.org/10.1152/jn.00433.2019.

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It has been established that coordinated arm and leg (A&L) cycling facilitates corticospinal drive and modulation of cervico-lumbar connectivity and ultimately improves overground walking in people with incomplete spinal cord injury or stroke. This study examined the effect of noninvasive transcutaneous spinal cord stimulation (tSCS) on the modulation of cervico-lumbar connectivity. Thirteen neurologically intact adults participated in the study. The excitability of the Hoffmann (H) reflex elicited in the soleus muscle was examined under multiple conditions involving either the arms held in a static position or rhythmic arm cycling while tSCS was applied to either the cervical or lumbar cord. As expected, soleus H-reflex amplitude was significantly suppressed by 19.2% during arm cycling (without tSCS) relative to arms static (without tSCS). Interestingly, tSCS of the cervical cord with arms static significantly suppressed the soleus H-reflex (−22.9%), whereas tSCS over the lumbar cord did not suppress the soleus H-reflex (−3.8%). The combination of arm cycling with cervical or lumbar tSCS did not yield additional suppression of the soleus H-reflex beyond that obtained with arm cycling alone or cervical tSCS alone. The results demonstrate that activation of the cervical spinal cord through both rhythmic arm cycling and tonic tSCS significantly modulates the activity of lumbar networks. This highlights the potential for engaging cervical spinal cord networks through tSCS during rehabilitation interventions to enhance cervico-lumbar connectivity. This connectivity is influential in facilitating improvements in walking function after neurological impairment. NEW & NOTEWORTHY This is the first study to investigate the modulatory effects of transcutaneous spinal cord stimulation (tSCS) on cervico-lumbar connectivity. We report that both rhythmic activation of the cervical spinal cord through arm cycling and tonic activation of the cervical cord through tSCS significantly modulate the activity of lumbar networks. This suggests that engaging cervical spinal cord networks through tSCS during locomotor retraining interventions may not only enhance cervico-lumbar connectivity but also further improve walking capacity.

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Bonkhoff, Anna K., Flor A. Espinoza, Harshvardhan Gazula, Victor M. Vergara, Lukas Hensel, Jochen Michely, Theresa Paul, et al. "Acute ischaemic stroke alters the brain’s preference for distinct dynamic connectivity states." Brain 143, no. 5 (May 1, 2020): 1525–40. http://dx.doi.org/10.1093/brain/awaa101.

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Abstract Acute ischaemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for the loss of specialized neural tissue and function. Static resting state functional MRI studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced changes in network connectivity aggregated over the duration of several minutes. In this study, we used dynamic resting state functional MRI analyses to increase temporal resolution to seconds and explore transient configurations of motor network connectivity in acute stroke. To this end, we collected resting state functional MRI data of 31 patients with acute ischaemic stroke and 17 age-matched healthy control subjects. Stroke patients presented with moderate to severe hand motor deficits. By estimating dynamic functional connectivity within a sliding window framework, we identified three distinct connectivity configurations of motor-related networks. Motor networks were organized into three regional domains, i.e. a cortical, subcortical and cerebellar domain. The dynamic connectivity patterns of stroke patients diverged from those of healthy controls depending on the severity of the initial motor impairment. Moderately affected patients (n = 18) spent significantly more time in a weakly connected configuration that was characterized by low levels of connectivity, both locally as well as between distant regions. In contrast, severely affected patients (n = 13) showed a significant preference for transitions into a spatially segregated connectivity configuration. This configuration featured particularly high levels of local connectivity within the three regional domains as well as anti-correlated connectivity between distant networks across domains. A third connectivity configuration represented an intermediate connectivity pattern compared to the preceding two, and predominantly encompassed decreased interhemispheric connectivity between cortical motor networks independent of individual deficit severity. Alterations within this third configuration thus closely resembled previously reported ones originating from static resting state functional MRI studies post-stroke. In summary, acute ischaemic stroke not only prompted changes in connectivity between distinct networks, but it also caused characteristic changes in temporal properties of large-scale network interactions depending on the severity of the individual deficit. These findings offer new vistas on the dynamic neural mechanisms underlying acute neurological symptoms, cortical reorganization and treatment effects in stroke patients.

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Tsurugizawa, Tomokazu, and Daisuke Yoshimaru. "Impact of anesthesia on static and dynamic functional connectivity in mice." NeuroImage 241 (November 2021): 118413. http://dx.doi.org/10.1016/j.neuroimage.2021.118413.

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Hovadik, Joseph M., and David K. Larue. "Static characterizations of reservoirs: refining the concepts of connectivity and continuity." Petroleum Geoscience 13, no. 3 (August 2007): 195–211. http://dx.doi.org/10.1144/1354-079305-697.

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Thompson, William Hedley, Per Brantefors, and Peter Fransson. "From static to temporal network theory: Applications to functional brain connectivity." Network Neuroscience 1, no. 2 (June 2017): 69–99. http://dx.doi.org/10.1162/netn_a_00011.

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Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

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Jamadar, Sharna D., Phillip G. D. Ward, Emma X. Liang, Edwina R. Orchard, Zhaolin Chen, and Gary F. Egan. "Metabolic and Hemodynamic Resting-State Connectivity of the Human Brain: A High-Temporal Resolution Simultaneous BOLD-fMRI and FDG-fPET Multimodality Study." Cerebral Cortex 31, no. 6 (February 3, 2021): 2855–67. http://dx.doi.org/10.1093/cercor/bhaa393.

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Abstract Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the capacity to image 2 sources of energetic dynamics in the brain—glucose metabolism and the hemodynamic response. fMRI connectivity has been enormously useful for characterizing interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease, but FDG-sPET cannot be used to estimate subject-level measures of “connectivity,” only across-subject “covariance.” Here, we applied high-temporal resolution constant infusion functional positron emission tomography (fPET) to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterized by frontoparietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with FDG-sPET metabolic covariance, indicating that metabolic brain connectivity is a nonergodic process whereby individual brain connectivity cannot be inferred from group-level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain and open up the opportunity for novel fundamental studies of human brain connectivity as well as multimodality biomarkers of neurological diseases.

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Lang, E. W., A. M. Tomé, I. R. Keck, J. M. Górriz-Sáez, and C. G. Puntonet. "Brain Connectivity Analysis: A Short Survey." Computational Intelligence and Neuroscience 2012 (2012): 1–21. http://dx.doi.org/10.1155/2012/412512.

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This short survey the reviews recent literature on brain connectivity studies. It encompasses all forms of static and dynamic connectivity whether anatomical, functional, or effective. The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments. Resting state conditions have become a dominant experimental paradigm, and a number of resting state networks, among them the prominent default mode network, have been identified. Graphical models represent a convenient vehicle to formalize experimental findings and to closely and quantitatively characterize the various networks identified. Underlying these abstract concepts are anatomical networks, the so-called connectome, which can be investigated by functional imaging techniques as well. Future studies have to bridge the gap between anatomical neuronal connections and related functional or effective connectivities.

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Segura, David, Emil J. Khatib, and Raquel Barco. "Dynamic Packet Duplication for Industrial URLLC." Sensors 22, no. 2 (January 13, 2022): 587. http://dx.doi.org/10.3390/s22020587.

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The fifth-generation (5G) network is presented as one of the main options for Industry 4.0 connectivity. To comply with critical messages, 5G offers the Ultra-Reliable and Low latency Communications (URLLC) service category with a millisecond end-to-end delay and reduced probability of failure. There are several approaches to achieve these requirements; however, these come at a cost in terms of redundancy, particularly the solutions based on multi-connectivity, such as Packet Duplication (PD). Specifically, this paper proposes a Machine Learning (ML) method to predict whether PD is required at a specific data transmission to successfully send a URLLC message. This paper is focused on reducing the resource usage with respect to pure static PD. The concept was evaluated on a 5G simulator, comparing between single connection, static PD and PD with the proposed prediction model. The evaluation results show that the prediction model reduced the number of packets sent with PD by 81% while maintaining the same level of latency as a static PD technique, which derives from a more efficient usage of the network resources.

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Salles, Tristan, Patrice Rey, and Enrico Bertuzzo. "Mapping landscape connectivity as a driver of species richness under tectonic and climatic forcing." Earth Surface Dynamics 7, no. 4 (October 1, 2019): 895–910. http://dx.doi.org/10.5194/esurf-7-895-2019.

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Abstract. Species distribution and richness ultimately result from complex interactions between biological, physical, and environmental factors. It has been recently shown for a static natural landscape that the elevational connectivity, which measures the proximity of a site to others with similar habitats, is a key physical driver of local species richness. Here we examine changes in elevational connectivity during mountain building using a landscape evolution model. We find that under uniform tectonic and variable climatic forcing, connectivity peaks at mid-elevations when the landscape reaches its geomorphic steady state and that the orographic effect on geomorphic evolution tends to favour lower connectivity on leeward-facing catchments. Statistical comparisons between connectivity distribution and results from a metacommunity model confirm that to the 1st order, landscape elevation connectivity explains species richness in simulated mountainous regions. Our results also predict that low-connectivity areas which favour isolation, a driver for in situ speciation, are distributed across the entire elevational range for simulated orogenic cycles. Adjustments of catchment morphology after the cessation of tectonic activity should reduce speciation by decreasing the number of isolated regions.

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Zhang, Feifei, Zhipeng Yang, Kun Qin, John A. Sweeney, Neil Roberts, Zhiyun Jia, and Qiyong Gong. "Effect of jet lag on brain white matter functional connectivity." Psychoradiology 1, no. 2 (May 24, 2021): 55–65. http://dx.doi.org/10.1093/psyrad/kkaa003.

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Abstract Background A long-haul flight across more than five time zones may produce a circadian rhythm sleep disorder known as jet lag. Little is known about the effect of jet lag on white matter (WM) functional connectivity (FC). Objective The present study is to investigate changes in WM FC in subjects due to recovery from jet lag after flying across six time zones. Methods Here, resting-state functional magnetic resonance imaging was performed in 23 participants within 24 hours of flying and again 50 days later. Gray matter (GM) and WM networks were identified by k-means clustering. WM FC and functional covariance connectivity (FCC) were analyzed. Next, a sliding window method was used to establish dynamic WM FC. WM static and dynamic FC and FCC were compared between when participants had initially completed their journey and 50 days later. Emotion was assessed using the Positive and Negative Affect Schedule and the State Anxiety Inventory. Results All participants were confirmed to have jet lag symptoms by the Columbian Jet Lag Scale. The static FC strengthes of cingulate network (WM7)- sensorimotor network and ventral frontal network- visual network were lower after the long-haul flight compared with recovery. Corresponding results were obtained for the dynamic FC analysis. The analysis of FCC revealed weakened connections between the WM7 and several other brain networks, especially the precentral/postcentral network. Moreover, a negative correlation was found between emotion scores and the FC between the WM7 and sensorimotor related regions. Conclusions The results of this study provide further evidence for the existence of WM networks and show that jet lag is associated with alterations in static and dynamic WM FC and FCC, especially in sensorimotor networks. Jet lag is a complex problem that not only is related to sleep rhythm but also influences emotion.

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Jun, Tackseung, and Jeong-Yoo Kim. "A Note on Connectivity and Stability in Dynamic Network Formation." Games 11, no. 4 (October 29, 2020): 49. http://dx.doi.org/10.3390/g11040049.

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We consider the dynamic network formation problem under the requirement that the whole network be connected and remain connected after q nodes are destroyed. We propose the concept of dynamic Cq-stability and characterize dynamic Cq-stable networks for any q≥0. Comparison with the outcome in the static model is also discussed.

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Dhulipala, Laxman, Changwan Hong, and Julian Shun. "ConnectIt." Proceedings of the VLDB Endowment 14, no. 4 (December 2020): 653–67. http://dx.doi.org/10.14778/3436905.3436923.

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Connected components is a fundamental kernel in graph applications. The fastest existing multicore algorithms for solving graph connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.

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Jiang, Sisi, Hechun Li, Haonan Pei, Linli Liu, Zhiliang Li, Yan Chen, Xiangkui Li, Qifu Li, Dezhong Yao, and Cheng Luo. "Connective profiles and antagonism between dynamic and static connectivity underlying generalized epilepsy." Brain Structure and Function 226, no. 5 (March 17, 2021): 1423–35. http://dx.doi.org/10.1007/s00429-021-02248-1.

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Jeong, H., S. Srinivasan, and S. Bryant. "Uncertainty Quantification of CO2 Plume Migration Using Static Connectivity of Geologic Features." Energy Procedia 37 (2013): 3771–79. http://dx.doi.org/10.1016/j.egypro.2013.06.273.

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Liu, Aiping, Xun Chen, Xiaojuan Dan, Martin J. McKeown, and Z. Jane Wang. "A Combined Static and Dynamic Model for Resting-State Brain Connectivity Networks." IEEE Journal of Selected Topics in Signal Processing 10, no. 7 (October 2016): 1172–81. http://dx.doi.org/10.1109/jstsp.2016.2594949.

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Dretsch, Michael N., D. Rangaprakash, Jeffrey S. Katz, Thomas A. Daniel, Adam M. Goodman, Thomas S. Denney, and Gopikrishna Deshpande. "Strength and Temporal Variance of the Default Mode Network to Investigate Chronic Mild Traumatic Brain Injury in Service Members with Psychological Trauma." Journal of Experimental Neuroscience 13 (January 2019): 117906951983396. http://dx.doi.org/10.1177/1179069519833966.

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Background: There is a significant number of military personnel with a history of mild traumatic brain injury (mTBI) who suffer from comorbid posttraumatic stress symptoms (PTS). Although there is evidence of disruptions of the default mode network (DMN) associated with PTS and mTBI, previous studies have only studied static connectivity while ignoring temporal variability of connectivity. Objective: To assess DMN disrupted or dysregulated neurocircuitry, cognitive functioning, and psychological health of active-duty military with mTBI and PTS. Method: U.S. Army soldiers with PTS (n = 14), mTBI + PTS (n = 25), and healthy controls (n = 21) voluntarily completed a cognitive and symptom battery. In addition, participants had magnetic resonance imaging (MRI) to assess both static functional connectivity (SFC) and variance of dynamic functional connectivity (vDFC) of the DMN. Results: Both the PTS and mTBI + PTS groups had significant symptoms, but only the comorbid group had significant decrements in cognitive functioning. Both groups showed less stable and disrupted neural signatures of the DMN, mainly constituting the cingulate-frontal-temporal-parietal attention network. Specifically, the PTS group showed a combination of both reduced contralateral strength and reduced unilateral variability of frontal- cingulate- temporal connectivities, as well as increased variability of frontal- parietal connectivities. The mTBI + PTS group had fewer abnormal connectives than the PTS group, all of which included reduced strength of frontal- temporal regions and reduced variability frontal- cingulate- temporal regions. Greater SFC and vDFC connectivity of the left dorsolateral prefrontal cortex (dlPFC) ↔ precuneus was associated with higher cognitive scores and lower symptom scores. Conclusions: Findings suggest that individuals with PTS and mTBI + PTS have a propensity for accentuated generation of thoughts, feelings, sensations, and/or images while in a resting state. Compared with controls, only the PTS group was associated with accentuated variability of the frontal- parietal attention network. While there were no significant differences in DMN connectivity strength between the mTBI + PTS and PTS groups, variability of connectivity was able to distinguish them.

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Rubino, J. Germán, Eva Caspari, Tobias M. Müller, and Klaus Holliger. "Fracture connectivity can reduce the velocity anisotropy of seismic waves." Geophysical Journal International 210, no. 1 (April 26, 2017): 223–27. http://dx.doi.org/10.1093/gji/ggx159.

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Abstract The degree of connectivity of fracture networks is a key parameter that controls the hydraulic properties of fractured rock formations. The current understanding is that this parameter does not alter the effective elastic properties of the probed medium and, hence, cannot be inferred from seismic data. However, this reasoning is based on static elasticity, which neglects dynamic effects related to wave-induced fluid pressure diffusion (FPD). Using a numerical upscaling procedure based on the theory of quasi-static poroelasticity, we provide the first evidence to suggest that fracture connectivity can reduce significantly velocity anisotropy in the seismic frequency band. Analyses of fluid pressure fields in response to the propagation of seismic waves demonstrate that this reduction of velocity anisotropy is not due to changes of the geometrical characteristics of the probed fracture networks, but rather related to variations of the stiffening effect of the fracture fluid in response to FPD. These results suggest that accounting for FPD effects may not only allow for improving estimations of geometrical and mechanical properties of fracture networks, but may also provide information with regard to the effective hydraulic properties.

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Jamil, Amirul Hakim, and Sharifah Salwa Syed Mahdzar. "VISUAL CONNECTIVITY AND STREET NODES LIVEABILITY: A CASE STUDY OF JOHOR BAHRU HERITAGE DISTRICT, MALAYSIA." Journal of Tourism, Hospitality and Environment Management 7, no. 27 (March 8, 2022): 77–93. http://dx.doi.org/10.35631/jthem.727007.

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In Malaysia, streets could be identified as public spaces for various activities especially at the heritage district. This study aims to promote street node as a potential liveable and sociable public space between buildings focussing on Johor Bahru city. The paper objectively proposes a method on identifying street nodes liveability pattern and introduce a relationship between occupied socio-physical and visual connectivity. It is argued that people sitting and standing at the sidewalks can also be affected by the visual connection amongst the pedestrians in addition to the condition to the street itself. The distribution of static activities of people are measured according to socio-physical elements and visual graph analysis (VGA) in Depthmap software. The results show simple relationship between the socio-physical element and the presence of static activities of people in the walkway. It is suggested that street designers, business operator and the authority could be able to create liveable spaces according to the environmental conditions and types of existing static activity.

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Karampasi, Aikaterini S., Antonis D. Savva, Vasileios Ch Korfiatis, Ioannis Kakkos, and George K. Matsopoulos. "Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network." Applied Sciences 11, no. 13 (July 5, 2021): 6216. http://dx.doi.org/10.3390/app11136216.

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Effective detection of autism spectrum disorder (ASD) is a complicated procedure, due to the hundreds of parameters suggested to be implicated in its etiology. As such, machine learning methods have been consistently applied to facilitate diagnosis, although the scarcity of potent autism-related biomarkers is a bottleneck. More importantly, the variability of the imported attributes among different sites (e.g., acquisition parameters) and different individuals (e.g., demographics, movement, etc.) pose additional challenges, eluding adequate generalization and universal modeling. The present study focuses on a data-driven approach for the identification of efficacious biomarkers for the classification between typically developed (TD) and ASD individuals utilizing functional magnetic resonance imaging (fMRI) data on the default mode network (DMN) and non-physiological parameters. From the fMRI data, static and dynamic connectivity were calculated and fed to a feature selection and classification framework along with the demographic, acquisition and motion information to obtain the most prominent features in regard to autism discrimination. The acquired results provided high classification accuracy of 76.63%, while revealing static and dynamic connectivity as the most prominent indicators. Subsequent analysis illustrated the bilateral parahippocampal gyrus, right precuneus, midline frontal, and paracingulate as the most significant brain regions, in addition to an overall connectivity increment.

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Martinelli, Daniele, Gloria Castellazzi, Roberto De Icco, Ana Bacila, Marta Allena, Arianna Faggioli, Grazia Sances, et al. "Thalamocortical Connectivity in Experimentally-Induced Migraine Attacks: A Pilot Study." Brain Sciences 11, no. 2 (January 27, 2021): 165. http://dx.doi.org/10.3390/brainsci11020165.

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In this study we used nitroglycerin (NTG)-induced migraine attacks as a translational human disease model. Static and dynamic functional connectivity (FC) analyses were applied to study the associated functional brain changes. A spontaneous migraine-like attack was induced in five episodic migraine (EM) patients using a NTG challenge. Four task-free functional magnetic resonance imaging (fMRI) scans were acquired over the study: baseline, prodromal, full-blown, and recovery. Seed-based correlation analysis (SCA) was applied to fMRI data to assess static FC changes between the thalamus and the rest of the brain. Wavelet coherence analysis (WCA) was applied to test time-varying phase-coherence changes between the thalamus and salience networks (SNs). SCA results showed significantly FC changes between the right thalamus and areas involved in the pain circuits (insula, pons, cerebellum) during the prodromal phase, reaching its maximal alteration during the full-blown phase. WCA showed instead a loss of synchronisation between thalami and SN, mainly occurring during the prodrome and full-blown phases. These findings further support the idea that a temporal change in thalamic function occurs over the experimentally induced phases of NTG-induced headache in migraine patients. Correlation of FC changes with true clinical phases in spontaneous migraine would validate the utility of this model.

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Bendjeddou, Amira, Nacira Ghoualmi, and Congduc Pham. "A New Method for Prolonging Network Lifetime and Maintaining the Connectivity in Wireless Sensor Network Through Controlling the Transmission Power." International Journal of Embedded and Real-Time Communication Systems 5, no. 1 (January 2014): 1–14. http://dx.doi.org/10.4018/ijertcs.2014010101.

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Energy preservation constitutes a very critical challenge in wireless sensor network surveillance applications. On one hand, transmitting data by using additional transmission power is among the biggest sources of energy consumption. On the other hand, using a small transmission power degrades the connectivity between nodes. In this paper, a Distributed transmission Power Control Method (DPCM) to minimize the consumed energy is proposed. Moreover, it aims to keep a good connectivity between nodes. These purposes are accomplished by adjusting, dynamically, the transmission power taking into account the connectivity information of the neighbors at one and two-hop. Simulation experiences are carried out to measure the performance improvements of the presented method in both static and mobile networks by using Castalia simulator. According to the obtained results, the authors' method minimizes the consumed energy without penalizing the connectivity between nodes compared to DPCS and farthest neighbor methods. In addition, DPCM achieves good performances in spite of node mobility.

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Hu, Guanjie, Honglin Ge, Kun Yang, Dongming Liu, Yong Liu, Zijuan Jiang, Xiao Hu, et al. "Altered Static and Dynamic Voxel-mirrored Homotopic Connectivity in Patients with Frontal Glioma." Neuroscience 490 (May 2022): 79–88. http://dx.doi.org/10.1016/j.neuroscience.2022.03.006.

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Untergehrer, Gisela, Denis Jordan, Eberhard F. Kochs, Rüdiger Ilg, and Gerhard Schneider. "Fronto-Parietal Connectivity Is a Non-Static Phenomenon with Characteristic Changes during Unconsciousness." PLoS ONE 9, no. 1 (January 27, 2014): e87498. http://dx.doi.org/10.1371/journal.pone.0087498.

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Lechner, Alex Mark, Rebecca M. B. Harris, Veronica Doerr, Erik Doerr, Michael Drielsma, and Edward C. Lefroy. "From static connectivity modelling to scenario-based planning at local and regional scales." Journal for Nature Conservation 28 (November 2015): 78–88. http://dx.doi.org/10.1016/j.jnc.2015.09.003.

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Park, Ji Eun, Seung Chai Jung, Kyeoung Hwa Ryu, Joo Young Oh, Ho Sung Kim, Choong-Gon Choi, Sang Joon Kim, and Woo Hyun Shim. "Differences in dynamic and static functional connectivity between young and elderly healthy adults." Neuroradiology 59, no. 8 (July 8, 2017): 781–89. http://dx.doi.org/10.1007/s00234-017-1875-2.

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Althaus, E., G. Călinescu, I. I. Măndoiu, S. Prasad, N. Tchervenski, and A. Zelikovsky. "Power Efficient Range Assignment for Symmetric Connectivity in Static Ad Hoc Wireless Networks." Wireless Networks 12, no. 3 (December 30, 2005): 287–99. http://dx.doi.org/10.1007/s11276-005-5275-x.

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Journal articles on the topic 'Static connectivity'

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