3/18/2023 0 Comments Graphs about thebrainResults: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. 2Department of Psychology, University of Central Florida, Orlando, FL, United Statesīackground: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders.1Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States.Farahani 1, Waldemar Karwowski 1 * and Nichole R. MNET : A fully automated all-in-one network analysis toolbox for fMRI and DTI.ĮEGNET : A toolbox for analyzing and visualizing M/EEG connectivity.īRAPH : Brain analysis using graph theory.įastFC : Efficient computation of functional brain networks.Farzad V. GAT/bnets : Graph Analysis Toolbox of functional and structural brain networks.īASCO : Inter-regional functional connectivity analysis in event-related fMRI data. MIBCA : Automated all-in-one connectivity toolbox with batch processing. GRETNA : A toolbox for comprehensive analyses of topology of the brain connectome. Graph Theory GLM Toolbox : A GLM toolbox of brain-network graph-analysis properties.īrainnetome Toolkit : A MATLAB GUI toolkit of complex network measures.īioNeCT : A cohesive platform for analyzing brain network connectivity in EEG recordings. Neuroimaging Analysis Kit : A library of modules and pipelines for fMRI processing. WFU_MMNET : A toolbox for multivariate modeling of brain networks Network Based Statistic Toolbox : A toolbox for testing hypotheses about the connectome. GraphVar : A user-friendly GUI-based toolbox for graph-analyses of brain connectivity. Virtual Brain Project : A consortium for simulation of primate brain-network dynamics.įieldTrip : Advanced analysis toolbox of MEG, EEG, and invasive electrophysiological data.ĬONN : Cross-platform software for the computation, display, and analysis of fcMRI data.ĭSI-Studio : A tractography software toolbox for diffusion MRI analysis. Human Connectome Project : An NIH consortium for mapping brain white-matter pathways. It has been ported to, included in, or modified in, the following projects:īctpy : Brain Connectivity Toolbox for Python.īct-cpp : Brain Connectivity Toolbox in C++. The Brain Connectivity Toolbox codebase is widely used by brain-imaging researchers. Brain Connectivity Toolbox in other projects
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