scholarly journals Assessing The Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher’s Choice Paths

2021 ◽  
Author(s):  
Stavros I. Dimitriadis
Keyword(s):  
Spatial Scales ◽  
Single Layer ◽  
Brain Network ◽  
Brain Disorders ◽  
Sources Of Information ◽  
Multilayer Networks ◽  
Network Graph ◽  
Graph Theoretic ◽  
Functional Relationships ◽  
Network Topologies

AbstractThere is a growing interest in the neuroscience community on the advantages of multimodal neuroimaging modalities. Functional and structural interactions between brain areas can be represented as a network (graph) allowing us to employ graph-theoretic tools in multiple research directions. Researchers usually treated brain networks acquired from different modalities or different frequencies separately. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. We can incorporate this information from different modalities (multi-modal case), from different frequencies (multi-frequency case), or a single modality following a dynamic functional connectivity analysis (multi-layer,dynamic case). Researchers already used multi-layer networks to model brain disorders, to detect key hubs related to a specific function, to reveal structural-functional relationships, and to define more precise connectomic biomarkers related to brain disorders. However, the construction of a multilayer network depends on the selection of multiple preprocessing steps that can affect the final network topology. Here, we analyzed the fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total). We focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, we untangled specific combinations of researchers’ choices that yield repeatable topologies, giving us the chance to recommend best practices over consistent topologies.

scholarly journals Brain pathology recapitulates physiology: A network meta-analysis

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Thomas J. Vanasse ◽  
Peter T. Fox ◽  
P. Mickle Fox ◽  
Franco Cauda ◽  
Tommaso Costa ◽  
...  
Keyword(s):  
Network Architecture ◽  
Spatial Scales ◽  
Meta Analysis ◽  
Metabolic Cost ◽  
Common Mechanism ◽  
Brain Disorders ◽  
Brain Pathology ◽  
Structural Network ◽  
Meta Analyses ◽  
Function Network

AbstractNetwork architecture is a brain-organizational motif present across spatial scales from cell assemblies to distributed systems. Structural pathology in some neurodegenerative disorders selectively afflicts a subset of functional networks, motivating the network degeneration hypothesis (NDH). Recent evidence suggests that structural pathology recapitulating physiology may be a general property of neuropsychiatric disorders. To test this possibility, we compared functional and structural network meta-analyses drawing upon the BrainMap database. The functional meta-analysis included results from >7,000 experiments of subjects performing >100 task paradigms; the structural meta-analysis included >2,000 experiments of patients with >40 brain disorders. Structure-function network concordance was high: 68% of networks matched (pFWE < 0.01), confirming the broader scope of NDH. This correspondence persisted across higher model orders. A positive linear association between disease and behavioral entropy (p = 0.0006;R2 = 0.53) suggests nodal stress as a common mechanism. Corroborating this interpretation with independent data, we show that metabolic ‘cost’ significantly differs along this transdiagnostic/multimodal gradient.

scholarly journals Using Tertiary Sulci to Map the “Cognitive Globe” of Prefrontal Cortex

2021 ◽  
pp. 1-18
Author(s):  
Jacob A. Miller ◽  
Mark D'Esposito ◽  
Kevin S. Weiner
Keyword(s):  
Prefrontal Cortex ◽  
Frontal Lobe ◽  
Spatial Scales ◽  
Structure And Function ◽  
Future Research ◽  
Functional Relationships ◽  
Theoretical Perspectives ◽  
Individual Participant ◽  
And Function

Stuss considered the human prefrontal cortex (pFC) as a “cognitive globe” [Stuss, D. T., & Benson, D. F. Neuropsychological studies of the frontal lobes. Psychological Bulletin, 95, 3–28, 1984] on which functions of the frontal lobe could be mapped. Here, we discuss classic and recent findings regarding the evolution, development, function, and cognitive role of shallow indentations or tertiary sulci in pFC, with the goal of using tertiary sulci to map the “cognitive globe” of pFC. First, we discuss lateral pFC (LPFC) tertiary sulci in classical anatomy and modern neuroimaging, as well as their development, with a focus on those within the middle frontal gyrus. Second, we discuss tertiary sulci in comparative neuroanatomy, focusing on primates. Third, we summarize recent findings showing the utility of tertiary sulci for understanding structural–functional relationships with functional network insights in ventromedial pFC and LPFC. Fourth, we revisit and update unresolved theoretical perspectives considered by C. Vogt and O. Vogt (Allgemeinere ergebnisse unserer hirnforschung. Journal für Psychologie und Neurologie, 25, 279–462, 1919) and F. Sanides (Structure and function of the human frontal lobe. Neuropsychologia, 2, 209–219, 1964) that tertiary sulci serve as landmarks for cortical gradients. Together, the consideration of these classic and recent findings indicate that tertiary sulci are situated in a unique position within the complexity of the “cognitive globe” of pFC: They are the smallest and shallowest of sulci in pFC, yet can offer insights that bridge spatial scales (microns to networks), modalities (functional connectivity to behavior), and species. As such, the map of tertiary sulci within each individual participant serves as a coordinate system specific to that individual on which functions may be further mapped. We conclude with new theoretical and methodological questions that, if answered in future research, will likely lead to mechanistic insight regarding the structure and function of human LPFC.

scholarly journals Sensing the Same Fishing Fleet with AIS and VIIRS: A Seven-Year Assessment of Squid Jiggers in FAO Major Fishing Area 41

2019 ◽  
Vol 12 (1) ◽  
pp. 32
Author(s):  
Javier Ruiz ◽  
Isabel Caballero ◽  
Gabriel Navarro
Keyword(s):  
Infrared Imaging ◽  
Spatial Scales ◽  
Fishing Effort ◽  
Automatic Identification ◽  
Identification System ◽  
Sources Of Information ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
High Degree ◽  
Fishing Area

Global Fishing Watch and VIIRS-DNB (visible infrared imaging radiometer suite day/night band) signals are compared for the jigger fleet in FAO (Food and Agriculture Organization of the United Nations) Major Fishing Area 41 during the maximum feasible time span (2012–2018). Both signals have shown a high degree of consistency at all temporal and spatial scales analyzed, including seasonal cycles, lack of signal for some years and interannual tendencies. This indicates that both signals are a fair representation of the fishing effort exerted by the jigger fleet in this zone. The high degree of consistency does not support views questioning satellite AIS (automatic identification system) as a reliable tool to survey fishing activities. Instead, our results add evidence supporting the value of remote sensing, in particular, when independent sources of information (such as VIIRS-DNB and AIS) are combined, as a relevant tool to add transparency and support compliance of fishing activities in vast and distant regions of the ocean.

Experts EEG Cognitive Analysis and Substance Classification in Security Inspection

2019 ◽  
Vol 33 (04) ◽  
pp. 1950006 ◽  
Author(s):  
Yue Yuan ◽  
Hong Wang ◽  
Peixin Yuan
Keyword(s):  
Phase Locking ◽  
Low Frequency ◽  
Brain Network ◽  
Eeg Signals ◽  
Graph Theoretic ◽  
Security Officers ◽  
Detection Technology ◽  
Functional Brain Network ◽  
New Perspective ◽  
Security Inspection

In recent years, the workload of security offers has increased along with the requirement of anti-terrorism. In the paper, a series of evaluation index of security inspection based on the EEG signals of the security officers were proposed to improve the accuracy of dangerous instances detection and decrease the workload of the officers. We performed an experiment to record the EEG data of security officers when they were watching the picture with or without the dangerous item in the uncovered and obscured scenes. Brain network analysis based on graph theory was applied to generate the indexes from the EEG induced by the parcel picture of security inspection, and is a new perspective on the classification of the parcel composition. The paper studied the low-frequency, multi-channel experts EEG signals, calculated the phase locking value (PLV) between every two channels to construct the topological functional brain network (FBN). The appropriate binary FBNs were obtained by setting the thresholds, and then the complex brain network parameters were estimated by the graph-theoretic methods, which were used for classification with 10-fold cross-validation and the average accuracy was 83.3[Formula: see text][Formula: see text][Formula: see text]97.78%. The method was effectively applied to the substance classification and would further improve the recognition accuracy of the target by combining this method with the existing detection technology.

A PSO based investigation of research fields of researchers

Kybernetes ◽  
2019 ◽  
Vol 49 (6) ◽  
pp. 1767-1782
Author(s):  
Goldina Ghosh ◽  
C.B. Akki ◽  
Nivedita Kasturi
Keyword(s):  
Social Networking Sites ◽  
Design Methodology ◽  
Theoretic Approach ◽  
Network Graph ◽  
Content Type ◽  
Graph Theoretic ◽  
Research Fields ◽  
Graph Theoretic Approach ◽  
Hidden Knowledge ◽  
Extended Work

Purpose The purpose of this study is data generated from any social networking sites may provide some hidden knowledge on a particular domain. Based on this concept the previous paper had proved that social connectivity enhancement takes place through triadic closure and embeddedness in terms of social network graph-theoretic approach. Further, the work was justified by genetic algorithm (GA) where observation showed how interdisciplinary work can occur because of crossover, and therefore, different groups of researchers could be identified. Further enhancement of the work has been focused on in this paper. Design/methodology/approach In continuation with the previous work, this paper detects other possible fields related to “high graded researchers” who can share the information with the other group of researchers (“imminent high graded” and “new researchers”) using particle swarm optimization (PSO) technique. Findings While exploitation was done using GA in the previous work, exploration is done in the current work based on PSO using the same grade score value to the objective function. Both the velocity and direction of high graded researchers in this extended work could be derived, which was not possible using GA. Originality/value This could help the next two levels of researchers (“imminent high graded researchers” and “new researchers”) in expanding their research fields in line with the fields of high graded researchers.

Vulnerability of sewer network – graph theoretic approach

2020 ◽  
Vol 196 ◽  
pp. 370-376
Author(s):  
Balaraman Ganesan ◽  
Sundareswaran Raman ◽  
Sujatha Ramalingam ◽  
Mustafa Erkan Turan ◽  
Goksen Bacak-Turan
Keyword(s):  
Theoretic Approach ◽  
Network Graph ◽  
Graph Theoretic ◽  
Sewer Network ◽  
Graph Theoretic Approach

Optimization of Decision Making in Multilayer Networks: The Role of Locus Coeruleus

2008 ◽  
Vol 20 (12) ◽  
pp. 2863-2894 ◽  
Author(s):  
Eric Shea-Brown ◽  
Mark S. Gilzenrat ◽  
Jonathan D. Cohen
Keyword(s):  
Decision Making ◽  
Decision Process ◽  
Single Layer ◽  
Theoretical Work ◽  
Layer Model ◽  
Multilayer Networks ◽  
Reward Rate ◽  
Threshold Crossing ◽  
Single Layer Model

Previous theoretical work has shown that a single-layer neural network can implement the optimal decision process for simple, two-alternative forced-choice (2AFC) tasks. However, it is likely that the mammalian brain comprises multilayer networks, raising the question of whether and how optimal performance can be approximated in such an architecture. Here, we present theoretical work suggesting that the noradrenergic nucleus locus coeruleus (LC) may help optimize 2AFC decision making in the brain. This is based on the observations that neurons of the LC selectively fire following the presentation of salient stimuli in decision tasks and that the corresponding release of norepinephrine can transiently increase the responsivity, or gain, of cortical processing units. We describe computational simulations that investigate the role of such gain changes in optimizing performance of 2AFC decision making. In the tasks we model, no prior cueing or knowledge of stimulus onset time is assumed. Performance is assessed in terms of the rate of correct responses over time (the reward rate). We first present the results of a single-layer model that accumulates (integrates) sensory input and implements the decision process as a threshold crossing. Gain transients, representing the modulatory effect of the LC, are driven by separate threshold crossings in this layer. We optimize over all free parameters to determine the maximum reward rate achievable by this model and compare it to the maximum reward rate when gain is held fixed. We find that the dynamic gain mechanism yields no improvement in reward for this single-layer model. We then examine a two-layer model, in which competing sensory accumulators in the first layer (capable of implementing the task relevant decision) pass activity to response accumulators in a second layer. Again, we compare a version in which threshold crossing in the first (decision) layer elicits an LC response (and a concomitant increase in gain) with a fixed-gain version of the model. Here, we find that gain transients modeling the LC phasic response yield an improvement in reward rate of 12% to 24%. Furthermore, we show that the timing characteristics of these gain transients agree with observations concerning LC firing patterns reported in recent experimental studies. This provides converging evidence for the hypothesis that the LC optimizes processes underlying 2AFC decision making in multilayer networks.

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