Network Connectivity Parameters Research Articles

Acceptance and Commitment Therapy and white matter plasticity in individuals with subclinical depression and psychotic experiences: A Randomised Controlled Trial

BackgroundResearch indicates that Acceptance and Commitment Therapy in Daily Life (ACT-DL) is effective in reducing symptoms of depression, anxiety and psychosis. During adolescence, vulnerability to psychopathology peaks, creating a window for early interventions, while white matter development is ongoing. This study aims to examine microstructural white matter after ACT-DL intervention in youngsters with mild psychopathology. MethodsForty-five individuals with mild psychopathology were randomly allocated to ACT-DL (n=20) or topic discussion control (TD, n=25). Symptomatology was assessed with the Community Assessment of Psychic Experiences (CAPE), Montgomery–Åsberg Depression Rating Scale (MADRS) and the Experience Sampling Method (ESM). Diffusion Weighted Imaging (DWI) and network-connectivity parameters were obtained and compared before and after the intervention/control condition. Interactions between microstructural white matter change and condition were examined in models of CAPE positive symptoms and ESM subclinical psychotic experiences (PE) and negative affect (NA) levels. ResultsACT-DL, compared to TD, was associated with changes on subclinical depressive and psychotic symptom levels. There was no significant change in DWI or network connectivity in either condition and no significant difference between both conditions. In the model of NA, several regional interactions between condition and network measures were significant, but stratification per condition provided no significant associations. There were no significant interactions between DWI or network connectivity parameters and condition in the models of the CAPE positive symptoms, MADRS and PE. ConclusionsThe findings suggest that behavioral (symptom) changes are more sensitive to a five-week psychological training than microstructural white matter changes which did not show significant changes over time.

Synaptic propagation in neuronal networks with finite-support space-dependent coupling.

Traveling waves of electrical activity are ubiquitous in biological neuronal networks. Traveling waves in the brain are associated with sensory processing, phase coding, and sleep. The neuron and network parameters that determine traveling waves' evolution are the synaptic space constant, synaptic conductance, membrane time constant, and synaptic decay time constant. We used an abstract neuron model in a one-dimensional network to investigate the propagation characteristics of traveling wave activity. We formulate a set of evolution equationsbased on the network connectivity parameters. Using a combination of numerical and analytical approaches, we show that these traveling waves are stable to a series of perturbations with biological relevance.

Graph approaches for analysis of brain connectivity during dexmedetomidine sedation

Sedation is commonly used to relieve fear and anxiety during procedures. Dexmedetomidine (DEX), approved by the US Food and Drug Administration in 1999 for short-term sedation, is a selective alpha2-adrenoreceptor agonist. The use of DEX is increasing due to minimal respiratory depression and easy and quick awakening from sedation. Its sedative mechanisms are suggested to be related to changes in the interaction between brain regions. In this study, we used graph theory to investigate whether the altered network connection is associated with sedation. Electroencephalogram (EEG) recordings of 32 channels were acquired during awake and DEX-induced sedation for 20 participants. We extracted EEG epochs from the awake and the DEX sedation state. Using the graph theory, we compared the changes in the network connection parameters with the awake state. We observed that the slopes in 1/f dynamics, which indicate overall brain network characteristics, were greater during DEX-induced sedation compared to the awake state, suggesting a transition towards a random network behavior. In addition, network connections from the perspective of information processing were significantly disturbed in the alpha frequency band, unlike other frequency bands augmenting network connections. The alpha frequency band plays a prominent role in the function and interaction of cognitive activities. These results collectively indicate that changes in the brain network critical to cognition during DEX administration may also be related to the mechanism of sedation.

Alertness Estimation Using Connection Parameters of the Brain Network

Alertness mechanism of unmanned monitoring vehicles to environment is important. Especially, the vigilance modeling of underground security robots has a particularly significance because the underground is a dangerous environment. However, there is no a mature methodology for the alertness computation. In this work, four parts of the alertness estimation are focused. First, an autonomous robot alertness mechanism framework is proposed by using the deep reinforcement learning model of the human alertness mechanism. Second, a fast K-T filtering algorithm is developed to eliminate the multiple noises of the electroencephalograph (EEG) signals by the blind source separation and the adjustable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> factor wavelet transform. Third, the description problem of the directed interaction stability of the cortical EEG signals is solved by the ensemble empirical mode decomposition and the directional transfer function. Fourth, the human alertness estimation is explored by using the support vector regression of the dynamically spatial-temporal brain network connection parameters. Experiments show that, the mean square error and the determination coefficient of the explored alertness estimation are respectively 0.115 and 0.8337. Compared with the scalp EEG alertness estimation, it has a better performance because the mean square error is decreased by 0.0684, and the determination coefficient is increased by 0.023.

Integrating Data From In Vitro New Approach Methodologies for Developmental Neurotoxicity.

In vivo developmental neurotoxicity (DNT) testing is resource intensive and lacks information on cellular processes affected by chemicals. To address this, DNT new approach methodologies (NAMs) are being evaluated, including: the microelectrode array neuronal network formation assay; and high-content imaging to evaluate proliferation, apoptosis, neurite outgrowth, and synaptogenesis. This work addresses 3 hypotheses: (1) a broad screening battery provides a sensitive marker of DNT bioactivity; (2) selective bioactivity (occurring at noncytotoxic concentrations) may indicate functional processes disrupted; and, (3) a subset of endpoints may optimally classify chemicals with in vivo evidence for DNT. The dataset was comprised of 92 chemicals screened in all 57 assay endpoints sourced from publicly available data, including a set of DNT NAM evaluation chemicals with putative positives (53) and negatives (13). The DNT NAM battery provides a sensitive marker of DNT bioactivity, particularly in cytotoxicity and network connectivity parameters. Hierarchical clustering suggested potency (including cytotoxicity) was important for classifying positive chemicals with high sensitivity (93%) but failed to distinguish patterns of disrupted functional processes. In contrast, clustering of selective values revealed informative patterns of differential activity but demonstrated lower sensitivity (74%). The false negatives were associated with several limitations, such as the maximal concentration tested or gaps in the biology captured by the current battery. This work demonstrates that this multi-dimensional assay suite provides a sensitive biomarker for DNT bioactivity, with selective activity providing possible insight into specific functional processes affected by chemical exposure and a basis for further research.

Research Status and Future Development of Endpoint Detection Algorithms Based on Computer Science Language Signals

Today is an information technology era, language signal recognition technology, language signal coding technology and a variety of new language technology will be widely used in all areas of our life, such as: security field, human-computer interaction, communication field. We can accurately analyze pure speech signals and silent segments in a speech by using language signal endpoint detection technology, which will bring decisive effects to the efficiency of ASR and ASC. Three steps can be used to represent the language endpoint detection model, namely, the language signal preprocessing step, the extraction of the characteristic vector of the whole language flow, and the establishment of the language endpoint discrimination model. Finally, the speech endpoint discrimination model is established. The traditional speech endpoint detection algorithms include the two threshold method based on time domain, the universal entropy method based on frequency domain and the invert characteristic method. Aiming at the low SNR and complex noise environment, in order to obtain satisfactory endpoint detection effect, this paper proposes an endpoint detection model based on optimized Extreme Learning machine (ELM), and makes up for the deficiencies of the algorithm itself by optimizing network connection parameters[1-2].

Multi‐Drone Edge Intelligence and SAR Smart Wearable Devices for Emergency Communication

Disasters, either manmade or natural, call for rapid and timely actions. Due to disaster, all of the communication infrastructures are destroyed, and there is no way for connection between people in disaster and others outside the disaster range. Drone technology is the critical technology for delivering communication services and guiding people and monitoring the unwanted effects of a disaster. The collaboration of advanced technologies can reduce life losses, save people’s lives, and manage the disaster crisis. The network performance of collaboration between the Internet of Things (IoT) and drone edge intelligence can help gather and process data, extend the wireless coverage area, deliver medical emergencies, provide real‐time information about the emergency, and gather data from areas that are impossible for humans to reach. In this paper, we focus on the network performance for efficient collaboration of drone edge intelligence and smart wearable devices for disaster management. We focus mainly on network connectivity parameters for improving real‐time data sharing between the drone edge intelligence and smart wearable devices. The relevant parameters that are considered in this study include delay, throughput, and the load from drone edge intelligence. It is further shown that network performance can have significant improvement when the abovementioned parameters are correctly optimised, and the improved performance can significantly improve the guiding/coordinating of search and rescue (SAR) teams effectively and efficiently.

Disrupted white matter integrity and network connectivity are related to poor motor performance

Motor impairment is common in the elderly population. Disrupted white matter tracts and the resultant loss of connectivity between cortical regions play an essential role in motor control. Using diffusion tensor imaging (DTI), we investigated the effect of white matter microstructure on upper-extremity and lower-extremity motor function in a community-based sample. A total of 766 participants (57.3 ± 9.2 years) completed the assessment of motor performance, including 3-m walking speed, 5-repeat chair-stand time, 10-repeat hand pronation-supination time, and 10-repeat finger-tapping time. Fractional anisotropy (FA), mean diffusivity (MD), and structural network connectivity parameters were calculated based on DTI. Lower FA and higher MD were associated with poor performance in walking, chair-stand, hand pronation-supination, and finger-tapping tests, independent of the presence of lacunes, white matter hyperintensities volume, and brain atrophy. Reduced network density, network strength, and global efficiency related to slower hand pronation-supination and finger-tapping, but not related to walking speed and chair-stand time. Disrupted white matter integrity and reduced cerebral network connectivity were associated with poor motor performance. Diffusion-based methods provide a more in-depth insight into the neural basis of motor dysfunction.

Analysis of brain connectivity during nitrous oxide sedation using graph theory

Nitrous oxide, the least potent inhalation anesthetic, is widely used for conscious sedation. Recently, it has been reported that the occurrence of anesthetic-induced loss of consciousness decreases the interconnection between brain regions, resulting in brain network changes. However, few studies have investigated these changes in conscious sedation using nitrous oxide. Therefore, the present study aimed to use graph theory to analyze changes in brain networks during nitrous oxide sedation. Participants were 20 healthy volunteers (10 men and 10 women, 20–40 years old) with no history of systemic disease. We acquired electroencephalogram (EEG) recordings of 32 channels during baseline, nitrous oxide inhalation sedation, and recovery. EEG epochs from the baseline and the sedation state (50% nitrous oxide) were extracted and analyzed with the network connection parameters of graph theory. Analysis of 1/f dynamics, revealed a steeper slope while in the sedation state than during the baseline. Network connectivity parameters showed significant differences between the baseline and sedation state, in delta, alpha1, alpha2, and beta2 frequency bands. The most pronounced differences in functional distance during nitrous oxide sedation were observed in the alpha1 and alpha2 frequency bands. Change in 1/f dynamics indicates that changes in brain network systems occur during nitrous oxide administration. Changes in network parameters imply that nitrous oxide interferes with the efficiency of information integration in the frequency bands important for cognitive processes and attention tasks. Alteration of brain network during nitrous oxide administration may be associated to the sedative mechanism of nitrous oxide.

Transcriptional Networks in Single Perivascular Cells Sorted from Human Adipose Tissue Reveal a Hierarchy of Mesenchymal Stem Cells.

Adipose tissue is a rich source of multipotent mesenchymal stem-like cells, located in the perivascular niche. Based on their surface markers, these have been assigned to two main categories: CD31- /CD45- /CD34+ /CD146- cells (adventitial stromal/stem cells [ASCs]) and CD31- /CD45- /CD34- /CD146+ cells (pericytes [PCs]). These populations display heterogeneity of unknown significance. We hypothesized that aldehyde dehydrogenase (ALDH) activity, a functional marker of primitivity, could help to better define ASC and PC subclasses. To this end, the stromal vascular fraction from a human lipoaspirate was simultaneously stained with fluorescent antibodies to CD31, CD45, CD34, and CD146 antigens and the ALDH substrate Aldefluor, then sorted by fluorescence-activated cell sorting. Individual ASCs (n = 67) and PCs (n = 73) selected from the extremities of the ALDH-staining spectrum were transcriptionally profiled by Fluidigm single-cell quantitative polymerase chain reaction for a predefined set (n = 429) of marker genes. To these single-cell data, we applied differential expression and principal component and clustering analysis, as well as an original gene coexpression network reconstruction algorithm. Despite the stochasticity at the single-cell level, covariation of gene expression analysis yielded multiple network connectivity parameters suggesting that these perivascular progenitor cell subclasses possess the following order of maturity: (a) ALDHbr ASC (most primitive); (b) ALDHdim ASC; (c) ALDHbr PC; (d) ALDHdim PC (least primitive). This order was independently supported by specific combinations of class-specific expressed genes and further confirmed by the analysis of associated signaling pathways. In conclusion, single-cell transcriptional analysis of four populations isolated from fat by surface markers and enzyme activity suggests a developmental hierarchy among perivascular mesenchymal stem cells supported by markers and coexpression networks. Stem Cells 2017;35:1273-1289.

Functional connectivity analysis using whole brain and regional network metrics in MS patients.

In the present study we investigated brain network connectivity differences between patients with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC) as derived from functional resonance magnetic imaging (fMRI) using graph theory. Resting state fMRI data of 18 RRMS patients (12 female, mean age ± SD: 42 ± 12.06 years) and 25 HC (8 female, 29.2 ± 5.38 years) were analyzed. In order to obtain information of differences in entire brain network, we focused on both, local and global network connectivity parameters. And the regional connectivity differences were assessed using regional network parameters. RRMS patients presented a significant increase of modularity in comparison to HC, pointing towards a network structure with densely interconnected nodes within one module, while the number of connections with other modules outside decreases. This higher decomposable network favours cost-efficient local information processing and promotes long-range disconnection. In addition, at the regional anatomical level, the network parameters clustering coefficient and local efficiency were increased in the insula, the superior parietal gyrus and the temporal pole. Our study indicates that modularity as derived from fMRI can be seen as a characteristic connectivity feature that is increased in MS patients compared to HC. Furthermore, specific anatomical regions linked to perception, motor function and cognition were mainly involved in the enhanced local information processing.

The Relationship between Frontotemporal Effective Connectivity during Picture Naming, Behavior, and Preserved Cortical Tissue in Chronic Aphasia.

While several studies of task-based effective connectivity of normal language processing exist, little is known about the functional reorganization of language networks in patients with stroke-induced chronic aphasia. During oral picture naming, activation in neurologically intact individuals is found in “classic” language regions involved with retrieval of lexical concepts [e.g., left middle temporal gyrus (LMTG)], word form encoding [e.g., left posterior superior temporal gyrus, (LpSTG)], and controlled retrieval of semantic and phonological information [e.g., left inferior frontal gyrus (LIFG)] as well as domain-general regions within the multiple demands network [e.g., left middle frontal gyrus (LMFG)]. After stroke, lesions to specific parts of the left hemisphere language network force reorganization of this system. While individuals with aphasia have been found to recruit similar regions for language tasks as healthy controls, the relationship between the dynamic functioning of the language network and individual differences in underlying neural structure and behavioral performance is still unknown. Therefore, in the present study, we used dynamic causal modeling (DCM) to investigate differences between individuals with aphasia and healthy controls in terms of task-induced regional interactions between three regions (i.e., LIFG, LMFG, and LMTG) vital for picture naming. The DCM model space was organized according to exogenous input to these regions and partitioned into separate families. At the model level, random effects family wise Bayesian Model Selection revealed that models with driving input to LIFG best fit the control data whereas models with driving input to LMFG best fit the patient data. At the parameter level, a significant between-group difference in the connection strength from LMTG to LIFG was seen. Within the patient group, several significant relationships between network connectivity parameters, spared cortical tissue, and behavior were observed. Overall, this study provides some preliminary findings regarding how neural networks for language reorganize for individuals with aphasia and how brain connectivity relates to underlying structural integrity and task performance.

Weakly Circadian Cells Improve Resynchrony

The mammalian suprachiasmatic nuclei (SCN) contain thousands of neurons capable of generating near 24-h rhythms. When isolated from their network, SCN neurons exhibit a range of oscillatory phenotypes: sustained or damping oscillations, or arrhythmic patterns. The implications of this variability are unknown. Experimentally, we found that cells within SCN explants recover from pharmacologically-induced desynchrony by re-establishing rhythmicity and synchrony in waves, independent of their intrinsic circadian period We therefore hypothesized that a cell's location within the network may also critically determine its resynchronization. To test this, we employed a deterministic, mechanistic model of circadian oscillators where we could independently control cell-intrinsic and network-connectivity parameters. We found that small changes in key parameters produced the full range of oscillatory phenotypes seen in biological cells, including similar distributions of period, amplitude and ability to cycle. The model also predicted that weaker oscillators could adjust their phase more readily than stronger oscillators. Using these model cells we explored potential biological consequences of their number and placement within the network. We found that the population synchronized to a higher degree when weak oscillators were at highly connected nodes within the network. A mathematically independent phase-amplitude model reproduced these findings. Thus, small differences in cell-intrinsic parameters contribute to large changes in the oscillatory ability of a cell, but the location of weak oscillators within the network also critically shapes the degree of synchronization for the population.

System design and network requirements for interactive multimedia

In recent years, there has been a strong interest in transmitting compressed video over packet switched networks, such as asynchronous transfer mode (ATM). Previous work has dealt with transmitting MPEG over constant bit rate (CBR) and variable bit rate (VBR) services of ATM. The available bit rate (ABR) service of ATM is expected to be much more cost effective than CBR or VBR. However, there hasn't been much work done on running interactive client/server applications (for example, video on demand) over ABR. We have developed a framework to design interactive video systems transmitting MPEG video over the ATM ABR service. We have developed models to determine the network connection parameters required to run interactive client/server multimedia applications over an ATM network using the ABR service. We solve our model using real-time dynamic equilation (RTDE) analysis. We conclude that by proper dimensioning of the buffers at the client and the server, it is possible to run interactive video over the ATM ABR service.

System design and network requirements for interactive multimedia

In recent years, there has been a strong interest in transmitting compressed video over packet switched networks, such as asynchronous transfer mode (ATM). Previous work has dealt with transmitting MPEG over constant bit rate (CBR) and variable bit rate (VBR) services of ATM. The available bit rate (ABR) service of ATM is expected to be much more cost effective than CBR or VBR. However, there hasn't been much work done on running interactive client/server applications (for example, video on demand) over ABR. We have developed a framework to design interactive video systems transmitting MPEG video over the ATM ABR service. We have developed models to determine the network connection parameters required to run interactive client/server multimedia applications over an ATM network using the ABR service. We solve our model using real-time dynamic equilation (RTDE) analysis. We conclude that by proper dimensioning of the buffers at the client and the server, it is possible to run interactive video over the ATM ABR service.

Self-organized synchronous oscillations in a network of excitable cells coupled by gap junctions

Recent evidence suggests that electrical coupling plays a role in generating oscillatory behaviour in networks of neurons; however, the underlying mechanisms have not been identified. Using a cellular automata model proposed by Traub et al (Traub R D, Schmitz D, Jefferys J G and Draguhn A 1999 High-frequency population oscillations are predicted to occur in hippocampal pyramidal neural networks interconnected by axo-axonal gap junctions Neuroscience 92 407–26), we describe a novel mechanism for self-organized oscillations in networks that have strong, sparse random electrical coupling via gap junctions. The network activity is generated by random spontaneous activity that is moulded into regular population oscillations by the propagation of activity through the network. We explain how this activity gives rise to particular dependences of mean oscillation frequency on network connectivity parameters and on the rate of spontaneous activity, and we derive analytical expressions to approximate the mean frequency and variance of the oscillations. In doing so, we provide insight into possible mechanisms for frequency control and modulation in networks of neurons.

Self-organized synchronous oscillations in a network of excitable cells coupled by gap junctions

Recent evidence suggests that electrical coupling plays a role in generating oscillatory behaviour in networks of neurons; however, the underlying mechanisms have not been identified. Using a cellular automata model proposed by Traub et al (Traub R D, Schmitz D, Jefferys J G and Draguhn A 1999 High-frequency population oscillations are predicted to occur in hippocampal pyramidal neural networks interconnected by axo-axonal gap junctions Neuroscience 92 407–26), we describe a novel mechanism for self-organized oscillations in networks that have strong, sparse random electrical coupling via gap junctions. The network activity is generated by random spontaneous activity that is moulded into regular population oscillations by the propagation of activity through the network. We explain how this activity gives rise to particular dependences of mean oscillation frequency on network connectivity parameters and on the rate of spontaneous activity, and we derive analytical expressions to approximate the mean frequency and variance of the oscillations. In doing so, we provide insight into possible mechanisms for frequency control and modulation in networks of neurons.