Fundamentals of Brain Network Analysis

By Alex Fornito, Andrew Zalesky, E. Bullmore. 2016

844 citations recorded by the source corpus.

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In Cognitive Neuroscience

Citations in the corpus

An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI (2017, background): “Evaluating observed component sizes with respect to a null distribution of maximal sizes ensures that the resulting inferences on network components are corrected for multiple comparisons (Fornito et al., 2016).”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, background): “…theoretic analysis on partial correlation networks, since correlation coefficient captures the effect of both direct and indirect paths minimizing the information added by the analysis (e.g. the shortest path length between two nodes is already captured by full correlation (Fornito et al., 2016)).”
Morphometric Similarity Networks Detect Microscale Cortical Organisation And Predict Inter-Individual Cognitive Variation (2017, neutral): “…MSNs consistently demonstrated a repertoire of complex topological features shared by diverse naturally-occurring networks (Barabási, 2016; Fornito et al., 2016) including: a fat-tailed (i.e., right- or positive-skewed) degree distribution, implying the existence of hub nodes;…”
Generative modeling of brain maps with spatial autocorrelation (2020, neutral): “Surrogate data instantiate an explicitly formulated null hypothesis, and thereby reproduce the expected distribution of a statistic under that hypothesis (Fornito et al., 2016).”
Graph theory methods: applications in brain networks (2018, background): “…when applied properly, can offer important new insights into the structure and function of networked brain systems, including their architecture architecture, evolution, development, and clinical disorders coverage of the topic is beyond the scope of this article (see a recent textbook 9 ).”
Small-World Brain Networks Revisited (2016, background): “We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex.”
Graph Neural Networks in Network Neuroscience (2021, background): “Particularly, neuroscientists modeled the brain as a graph where nodes represent the anatomical brain regions connected by edges referring to morphological, functional, or structural connectivities relating pairwise nodes [4], [5], [6].”