Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data

Schwarz, Adam J.; McGonigle, John

doi:10.1016/j.neuroimage.2010.12.047

Cited by 214 publications

(238 citation statements)

References 71 publications

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“…Another caveat related to weighted networks, especially in functional networks, is that links are based on positive connections, whereas negative connections are ignored. Although some studies include negative links (Chen et al, 2011;Schwarz and McGonigle, 2011), there is still a problem with the conceptualization of negative links in relation to different graph metrics, such as clustering coefficient. In a recent study by Rubinov and Sporns (2011), a weighted network including negative links was analyzed without an applied threshold.…”

Section: Discussionmentioning

confidence: 99%

“…Assuming this relationship holds true, the size of the network may determine what threshold is best to achieve a desired edge density. Although most research focuses on the strong positive links in the network, there are newer models that incorporate strong negative (anticorrelated) links (Chen et al, 2011;Schwarz and McGonigle, 2011). Nonetheless, the choice of a threshold is still a point of debate within the field with some studies reporting results across the spectrum of a chosen thresholding approach.…”

Section: Network Thresholdmentioning

confidence: 99%

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The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain

et al. 2011

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Although graph theory has been around since the 18th century, the field of network science is more recent and continues to gain popularity, particularly in the field of neuroimaging. The field was propelled forward when Watts and Strogatz introduced their small-world network model, which described a network that provided regional specialization with efficient global information transfer. This model is appealing to the study of brain connectivity, as the brain can be viewed as a system with various interacting regions that produce complex behaviors. In practice, graph metrics such as clustering coefficient, path length, and efficiency measures are often used to characterize system properties. Centrality metrics such as degree, betweenness, closeness, and eigenvector centrality determine critical areas within the network. Community structure is also essential for understanding network organization and topology. Network science has led to a paradigm shift in the neuroscientific community, but it should be viewed as more than a simple ''tool du jour.'' To fully appreciate the utility of network science, a greater understanding of how network models apply to the brain is needed. An integrated appraisal of multiple network analyses should be performed to better understand network structure rather than focusing on univariate comparisons to find significant group differences; indeed, such comparisons, popular with traditional functional magnetic resonance imaging analyses, are arguably no longer relevant with graph-theory based approaches. These methods necessitate a philosophical shift toward complexity science. In this context, when correctly applied and interpreted, network scientific methods have a chance to revolutionize the understanding of brain function.

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Section: Discussionmentioning

confidence: 99%

Section: Network Thresholdmentioning

confidence: 99%

The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain

et al. 2011

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“…Network characterizations based on binary networks gained by thresholding approaches are likely to neglect at least some information, such as the role of negative correlations. However, since the biological relevance of anticorrelations in fMRI networks is still under debate (Chang and Glover, 2009;Fox et al, 2009) and relies to some degree on the choice of fMRI preprocessing strategy (Murphy et al, 2009;Weissenbacher et al, 2009;Schwarz and McGonigle, 2011), we ground our main functional network analysis on graphs that are fully connected and maximally sparse (i.e., in the described range of connection probabilities).…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Reconfiguration of Large-Scale Brain Functional Networks during Propofol-Induced Loss of Consciousness

et al. 2012

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Applying graph theoretical analysis of spontaneous BOLD fluctuations in functional magnetic resonance imaging (fMRI), we investigated whole-brain functional connectivity of 11 healthy volunteers during wakefulness and propofol-induced loss of consciousness (PI-LOC). After extraction of regional fMRI time series from 110 cortical and subcortical regions, we applied a maximum overlap discrete wavelet transformation and investigated changes in the brain's intrinsic spatiotemporal organization. During PI-LOC, we observed a breakdown of subcortico-cortical and corticocortical connectivity. Decrease of connectivity was pronounced in thalamocortical connections, whereas no changes were found for connectivity within primary sensory cortices. Graph theoretical analyses revealed significant changes in the degree distribution and local organization metrics of brain functional networks during PI-LOC: compared with a random network, normalized clustering was significantly increased, as was small-worldness. Furthermore we observed a profound decline in long-range connections and a reduction in whole-brain spatiotemporal integration, supporting a topological reconfiguration during PI-LOC. Our findings shed light on the functional significance of intrinsic brain activity as measured by spontaneous BOLD signal fluctuations and help to understand propofol-induced loss of consciousness.

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“…Consider correlation: most often correlation analyses will result in nearly all brain areas considered being correlated to some extent (Schwarz and McGonigle, 2011;Tohka et al, 2012) so how does one decide which of these correlations are significant? Statistical tests exist to determine the statistical significance of interactions measures, however in order to achieve real statistical significance, while accounting for multiple comparisons, avoiding circularity, and satisfying assumptions, many of these tests end up being unusable or impractical.…”

Section: Statistical Interpretationsmentioning

confidence: 99%

Evaluation and calibration of functional network modeling methods based on known anatomical connections

et al. 2013

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1-Abstract EnglishRecent studies have identified large scale brain networks based on the spatio-temporal structure of spontaneous fluctuations in resting-state fMRI data. It is expected that functional connectivity based on resting-state data is reflective of-but not identical to the underlying anatomical connectivity.However, which functional connectivity analysis methods reliably predict the network structure remains unclear. Here we tested and compared network connectivity analysis methods by applying them to fMRI restingstate time-series obtained from the human visual cortex. The methods evaluated here are those previously tested against simulated data in Smith et al. (Neuroimage, 2011).To this end, we defined regions within retinotopic visual areas V1, V2, and V3 according to their eccentricity in the visual field, delineating central, intermediate, and peripheral eccentricity regions of interest (ROIs). These ROIs served as nodes in the models we study. We based our evaluation -anatomical connectivity in the monkey visual cortex. For each evaluated method, we computed the fractional rate of detecting connections known to -th percentile of the 4 distribution of interaction magnitudes of those connections not expected to exist.Under optimal conditions, including session duration of 68 minutes, a relatively small network consisting of 9 nodes and artifact-free regression of the global effect, each of the top methods predicted the expected connections with 75%-83% c-sensitivity. Partial Correlation performed best (PCorr; 83%), followed by Regularized Inverse Covariance (ICOV; 79%), Bayesian Network methods (BayesNet; 77%), Correlation (75%), andGeneral Synchronization measures (75%). With decreased session duration, these top methods saw decreases in c-sensitivities, achieving 66%-78% and 60%-70% for 34 and 17 minute sessions, respectively. With a short resting-state fMRI scan of 8.5 minutes (TR = 2s), none of the methods predicted the real network well, with ICOV (53%) and PCorr (51%) performing best. With increased complexity of the network from 9 to 36 nodes, multivariate methods including PCorr and BayesNet saw a decrease in performance. However, this decrease became small when using data from a long (68 minutes) session. Artifact-free regression of the global effect significantly increased the c-sensitivity of all top-performing methods. In an overall evaluation across all tests we performed, PCorr, ICOV and BayesNet set themselves somewhat above all other methods. 5We propose that data-based calibration based on known anatomical connections be integrated into future network studies, in order to maximize sensitivity and reduce false positives. Studies have also demonstrated changes in fMRI-measured brain activity when the subjects are in a state of rest. Importantly, it has been shown that these spontaneous (i.e. without any external stimulation)fluctuations in fMRI signals demonstrate functional connectivity over large parts of the human brain (Xiong et al., 1999). These large parts of the brain correspo...

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Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data

Cited by 214 publications

References 71 publications

The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain

The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain

Spatiotemporal Reconfiguration of Large-Scale Brain Functional Networks during Propofol-Induced Loss of Consciousness

Evaluation and calibration of functional network modeling methods based on known anatomical connections

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