scholarly journals Creative destruction: Sparse activity emerges on the mammal connectome under a simulated communication strategy with collisions and redundancy

2020 ◽  
Author(s):  
Yan Hao ◽  
Daniel Graham
Keyword(s):  
Time Window ◽  
Brain Network ◽  
Communication Strategy ◽  
Neural Systems ◽  
Highly Active ◽  
Average Activity ◽  
Empirical Networks ◽  
Randomized Networks ◽  
Dynamics Characteristic ◽  
Routing Models

ABSTRACTSignal interactions in brain network communication have been little studied. We describe how nonlinear collision rules on simulated mammal brain networks can result in sparse activity dynamics characteristic of mammalian neural systems. We tested the effects of collisions in “information spreading” (IS) routing models and in standard random walk (RW) routing models. Simulations employed synchronous agents on tracer-based mesoscale mammal connectomes at a range of signal loads. We find that RW models have high average activity that increases with load. Activity in RW models is also densely distributed over nodes: a substantial fraction is highly active in a given time window, and this fraction increases with load. Surprisingly, while IS models make many more attempts to pass signals, they show lower net activity due to collisions compared to RW, and activity in IS increases little as function of load. Activity in IS also shows greater sparseness than RW, and sparseness decreases slowly with load. Results hold on two networks of the monkey cortex and one of the mouse whole-brain. We also find evidence that activity is lower and more sparse for empirical networks compared to degree-matched randomized networks under IS, suggesting that brain network topology supports IS-like routing strategies.

scholarly journals Creative destruction: Sparse activity emerges on the mammal connectome under a simulated communication strategy with collisions and redundancy

2020 ◽  
Vol 4 (4) ◽  
pp. 1055-1071
Author(s):  
Yan Hao ◽  
Daniel Graham
Keyword(s):  
Time Window ◽  
Brain Network ◽  
Communication Strategy ◽  
Neural Systems ◽  
Highly Active ◽  
Average Activity ◽  
Empirical Networks ◽  
Randomized Networks ◽  
Dynamics Characteristic ◽  
Routing Models

Signal interactions in brain network communication have been little studied. We describe how nonlinear collision rules on simulated mammal brain networks can result in sparse activity dynamics characteristic of mammalian neural systems. We tested the effects of collisions in “information spreading” (IS) routing models and in standard random walk (RW) routing models. Simulations employed synchronous agents on tracer-based mesoscale mammal connectomes at a range of signal loads. We find that RW models have high average activity that increases with load. Activity in RW models is also densely distributed over nodes: a substantial fraction is highly active in a given time window, and this fraction increases with load. Surprisingly, while IS models make many more attempts to pass signals, they show lower net activity due to collisions compared to RW, and activity in IS increases little as function of load. Activity in IS also shows greater sparseness than RW, and sparseness decreases slowly with load. Results hold on two networks of the monkey cortex and one of the mouse whole-brain. We also find evidence that activity is lower and more sparse for empirical networks compared to degree-matched randomized networks under IS, suggesting that brain network topology supports IS-like routing strategies.

scholarly journals Development of Brain Network Connectivity in Risky Drinkers During Adolescence

2020 ◽  
Author(s):  
Simon T. E. Baker ◽  
Murat Yücel ◽  
Alex Fornito ◽  
Andrew Zalesky ◽  
Sarah Whittle ◽  
...  
Keyword(s):  
Structural Connectivity ◽  
Mean Diffusivity ◽  
Network Connectivity ◽  
Brain Network ◽  
Neural Systems ◽  
Risky Drinking ◽  
Drinking Alcohol ◽  
Brain Network Connectivity ◽  
Weighted Magnetic Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

AbstractAlcohol consumption is common in adolescence, a time when the human brain undergoes substantial development, raising concerns about the neurodevelopmental impact of drinking alcohol, especially at high levels. Risky drinking may adversely affect the developing white matter, comprised of axonal fibre pathways that integrate anatomically distributed, and functionally specialised, neural systems. We used diffusion-weighted magnetic resonance imaging (MRI) to perform the first prospective, comprehensive and regionally unbiased connectome-wide analysis of longitudinal changes in inter-regional structural connectivity between 16.5 and 18.8 years of age, comparing adolescents who reported frequent risky drinking (n = 20) with those who reported limited risky drinking (n = 19) across this developmental period. We found no difference in the development of structural connectivity between these groups, regardless of whether inter-regional connections were quantified using streamline count, fractional anisotropy, mean diffusivity, axial diffusivity, or radial diffusivity. These findings suggest that risky drinking may have limited adverse effects on the development of inter-regional structural connectivity during mid to late adolescence.

scholarly journals Application of Multi Network Alignment Algorithms for Connectomes Study

2017 ◽  
Author(s):  
Marianna Milano ◽  
Pietro Hiram Guzzi ◽  
Mario Cannataro
Keyword(s):  
Brain Networks ◽  
Brain Network ◽  
Network Alignment ◽  
Neural Systems ◽  
Image Domain ◽  
Modeling And Analysis ◽  
Network Nodes ◽  
Alignment Algorithms ◽  
Anatomical Image ◽  
Anatomical Space

A growing area in neurosciences is focused on the modeling and analysis the complex system of connections in neural systems, i.e. the connectome. Here we focus on the representation of connectomes by using graph theory formalisms. The human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. Recently, it has been proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space to define network nodes of individual brain networks. In the network domain, the question of comparison of the structure of networks arises. Such question is tackled by modeling the comparison of brain network as a network alignment (NA) problem. In this paper, we first defined the NA problem formally, then we applied three existing state of the art of multiple alignment algorithms (MNA) on diffusion MRI-derived brain networks and we compared the performances. The results confirm that MNA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes.

scholarly journals Detecting brain connectivity changes during seizures using Granger causality measures on scalp EEG

2015 ◽  
Author(s):  
Christos Koutlis
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Window ◽  
Brain Connectivity ◽  
Characteristic Curve ◽  
Multivariate Time Series ◽  
Brain Regions ◽  
Brain Network ◽  
Network Feature ◽  
The Brain

In this work the objective is to detect brain connectivity changes during epileptic seizures using methods of multivariate time series analysis on scalp multi-channel EEG. Different brain regions represented by the electrode positions interact in terms of Granger causality and these directed connections formulate the brain network at a certain time window. The numerous proposed network features are believed to capture the information of many network characteristics. The ability of a single network feature of the brain network to detect the transition of brain activity from preictal to ictal is examined. The connectivity of the brain is estimated by 13 Granger causality indices on 7 epochs from multivariate time series (19 channels per epoch) at 15 time windows of 20 seconds (5 min in total) before seizure and during the seizure. The characteristics of the networks are estimated by 379 network features. Finally, the discrimination task (preictal vs. ictal) for each network feature is evaluated by the area under receiver operating characteristic curve (AUROC).

Intra- and crossmodal refractory effects in auditory and somatosensory ERPs

2012 ◽  
Vol 25 (0) ◽  
pp. 156
Author(s):  
Boukje Habets ◽  
Marlene Hense ◽  
Davide Bottari ◽  
Brigitte Roeder
Keyword(s):  
Time Window ◽  
Event Related Potentials ◽  
Neural Systems ◽  
Auditory Stimuli ◽  
Preceding Stimulus ◽  
Period Effects ◽  
Tactile Stimuli ◽  
Related Potentials ◽  
Random Manner ◽  
Main Effects

Refractory period effects are defined as a temporal decrement in neural response due to a previous activation of the same system. We varied the ISI and modality of a preceding stimulus to investigate overlapping and distinct neural systems processing auditory and tactile stimuli. Auditory stimuli and tactile stimuli were presented in a sequential, random manner with a duration of 50 ms and an ISI of 1000 or 2000 ms. The P1–N1–P2 complex of event-related potentials (ERP) was analyzed separately for auditory and tactile stimuli, as a function of preceding ISI and modality. Main effects of ISI and modality were found within the time-window of the P1 and P2 (auditory) and P1, N1 and P2 (tactile). These results suggest an overlap in underlying neural systems when stimuli from different modalities are being processed.

scholarly journals Special Patterns of Dynamic Brain Networks Discriminate Between Face and Non-face Processing: A Single-Trial EEG Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Zhongliang Yin ◽  
Yue Wang ◽  
Minghao Dong ◽  
Shenghan Ren ◽  
Haihong Hu ◽  
...  
Keyword(s):  
Human Brain ◽  
Face Processing ◽  
Information Transfer ◽  
Minimum Spanning Tree ◽  
Time Window ◽  
Time Windows ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
The Face

Face processing is a spatiotemporal dynamic process involving widely distributed and closely connected brain regions. Although previous studies have examined the topological differences in brain networks between face and non-face processing, the time-varying patterns at different processing stages have not been fully characterized. In this study, dynamic brain networks were used to explore the mechanism of face processing in human brain. We constructed a set of brain networks based on consecutive short EEG segments recorded during face and non-face (ketch) processing respectively, and analyzed the topological characteristic of these brain networks by graph theory. We found that the topological differences of the backbone of original brain networks (the minimum spanning tree, MST) between face and ketch processing changed dynamically. Specifically, during face processing, the MST was more line-like over alpha band in 0–100 ms time window after stimuli onset, and more star-like over theta and alpha bands in 100–200 and 200–300 ms time windows. The results indicated that the brain network was more efficient for information transfer and exchange during face processing compared with non-face processing. In the MST, the nodes with significant differences of betweenness centrality and degree were mainly located in the left frontal area and ventral visual pathway, which were involved in the face-related regions. In addition, the special MST patterns can discriminate between face and ketch processing by an accuracy of 93.39%. Our results suggested that special MST structures of dynamic brain networks reflected the potential mechanism of face processing in human brain.

scholarly journals Improving Biocatalyst Performance by Integrating Statistical Methods into Protein Engineering

2010 ◽  
Vol 76 (19) ◽  
pp. 6397-6403 ◽  
Author(s):  
Moran Brouk ◽  
Yuval Nov ◽  
Ayelet Fishman
Keyword(s):  
Statistical Analysis ◽  
Initial Data ◽  
Rational Design ◽  
Point Mutations ◽  
Enzymatic System ◽  
Wild Type ◽  
Initial Oxidation ◽  
Data Set ◽  
Highly Active ◽  
Average Activity

ABSTRACT Directed evolution and rational design were used to generate active variants of toluene-4-monooxygenase (T4MO) on 2-phenylethanol (PEA), with the aim of producing hydroxytyrosol, a potent antioxidant. Due to the complexity of the enzymatic system—four proteins encoded by six genes—mutagenesis is labor-intensive and time-consuming. Therefore, the statistical model of Nov and Wein (J. Comput. Biol. 12:247-282) was used to reduce the number of variants produced and evaluated in a lab. From an initial data set of 24 variants, with mutations at nine positions, seven double or triple mutants were identified through statistical analysis. The average activity of these mutants was 4.6-fold higher than the average activity of the initial data set. In an attempt to further improve the enzyme activity to obtain PEA hydroxylation, a second round of statistical analysis was performed. Nine variants were considered, with 3, 4, and 5 point mutations. The average activity of the variants obtained in the second statistical round was 1.6-fold higher than in the first round and 7.3-fold higher than that of the initial data set. The best variant discovered, TmoA I100A E214G D285Q, exhibited an initial oxidation rate of 4.4 ± 0.3 nmol/min/mg protein, which is 190-fold higher than the rate obtained by the wild type. This rate was also 2.6-fold higher than the activity of the wild type on the natural substrate toluene. By considering only 16 preselected mutants (out of ∼13,000 possible combinations), a highly active variant was discovered with minimum time and effort.

scholarly journals Less Is Not More: Neural Responses to Missing and Superfluous Accents in Context

2012 ◽  
Vol 24 (12) ◽  
pp. 2400-2418 ◽  
Author(s):  
Diana V. Dimitrova ◽  
Laurie A. Stowe ◽  
Gisela Redeker ◽  
John C. J. Hoeks
Keyword(s):  
Semantic Processing ◽  
Time Window ◽  
Neural System ◽  
Judgment Task ◽  
Neural Systems ◽  
Neural Responses ◽  
Making Sense ◽  
Negative Effect ◽  
The Brain ◽  
Preceding Question

Prosody, particularly accent, aids comprehension by drawing attention to important elements such as the information that answers a question. A study using ERP registration investigated how the brain deals with the interpretation of prosodic prominence. Sentences were embedded in short dialogues and contained accented elements that were congruous or incongruous with respect to a preceding question. In contrast to previous studies, no explicit prosodic judgment task was added. Robust effects of accentuation were evident in the form of an “accent positivity” (200–500 msec) for accented elements irrespective of their congruity. Our results show that incongruously accented elements, that is, superfluous accents, activate a specific set of neural systems that is inactive in case of incongruously unaccented elements, that is, missing accents. Superfluous accents triggered an early positivity around 100 msec poststimulus, followed by a right-lateralized negative effect (N400). This response suggests that redundant information is identified immediately and leads to the activation of a neural system that is associated with semantic processing (N400). No such effects were found when contextually expected accents were missing. In a later time window, both missing and superfluous accents triggered a late positivity on midline electrodes, presumably related to making sense of both kinds of mismatching stimuli. These results challenge previous findings of greater processing for missing accents and suggest that the natural processing of prosody involves a set of distinct, temporally organized neural systems.

scholarly journals Solid-State Transformations in Inner Coordination Sphere of [Co(NH3)6][Fe(C2O4)3]∙3H2O as a Route to Access Catalytically Active Co-Fe Materials

Materials ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 221 ◽  
Author(s):  
Denis Domonov ◽  
Sophia Pechenyuk ◽  
Yulia Semushina ◽  
Kirill Yusenko
Keyword(s):  
Thermal Decomposition ◽  
Intermediate Phase ◽  
Catalytic Properties ◽  
Decomposition Reaction ◽  
Argon Atmosphere ◽  
Air Oxidation ◽  
Crystal Water ◽  
Highly Active ◽  
Catalytically Active ◽  
Average Activity

Thermal decomposition of [Co(NH3)6][Fe(C2O4)3]∙3H2O in argon atmosphere, at a low heating rate (3°/min), and in large amounts of the initial complex (~0.1 mole), has been studied. It was possible to distinguish four decomposition steps upon heating: In the temperature range of 50–100 °C—the loss of crystal water; 100–190 °C—stability region of dehydrated complex; 230–270 °C—the range of stability of intermediate phase with the formula CoFe(NH3)2(C2O4)2; 270–350 °C—thermal decomposition of the intermediate with the formation of metallic products and further air oxidation with the formation of Co1.5Fe1.5O4. Catalytic properties of thermolysis products were tested in the decomposition reaction of H2O2 (inactive), oxidation of acetone (average activity), and decomposition of ammonium perchlorate (highly active).

Moving on Time: Brain Network for Auditory-Motor Synchronization is Modulated by Rhythm Complexity and Musical Training

2008 ◽  
Vol 20 (2) ◽  
pp. 226-239 ◽  
Author(s):  
Joyce L. Chen ◽  
Virginia B. Penhune ◽  
Robert J. Zatorre
Keyword(s):  
Prefrontal Cortex ◽  
Musical Training ◽  
Temporal Structure ◽  
Premotor Cortex ◽  
Brain Network ◽  
Neural Systems ◽  
Functional Magnetic Resonance ◽  
Movement Execution ◽  
Musical Rhythm ◽  
Simple Movement

Much is known about the motor system and its role in simple movement execution. However, little is understood about the neural systems underlying auditory-motor integration in the context of musical rhythm, or the enhanced ability of musicians to execute precisely timed sequences. Using functional magnetic resonance imaging, we investigated how performance and neural activity were modulated as musicians and nonmusicians tapped in synchrony with progressively more complex and less metrically structured auditory rhythms. A functionally connected network was implicated in extracting higher-order features of a rhythm's temporal structure, with the dorsal premotor cortex mediating these auditory-motor interactions. In contrast to past studies, musicians recruited the prefrontal cortex to a greater degree than nonmusicians, whereas secondary motor regions were recruited to the same extent. We argue that the superior ability of musicians to deconstruct and organize a rhythm's temporal structure relates to the greater involvement of the prefrontal cortex mediating working memory.

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