BSMART: A Matlab/C toolbox for analysis of multichannel neural time series

Cui, Jie; Xu, Lei; Bressler, Steven L.; Ding, Mingzhou; Liang, Hualou

doi:10.1016/j.neunet.2008.05.007

Cited by 182 publications

(157 citation statements)

References 22 publications

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“…We look forward to a scenario in which neuroscientists are readily able to select the connectivity analysis method best suited for their specific question, and for the data they have at hand. Several freely available software toolboxes can now facilitate the application of G-causality (Cui et al, 2008;Barnett and Seth, 2014). Of these, the…”

Section: Discussionmentioning

confidence: 99%

Granger Causality Analysis in Neuroscience and Neuroimaging

2015

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Editor's Note: Toolboxes are intended to briefly highlight and evaluate an emerging approach or a resource that is becoming widely used in neuroscience. For more information, see http://www.jneurosci.org/misc/itoa.shtml. Granger Causality Analysis in Neuroscience and NeuroimagingAnil K. Seth, Adam B. Barrett, and Lionel Barnett Sackler Centre for Consciousness Science, School of Engineering and Informatics, University of Sussex, Brighton BN1 9QJ, United Kingdom Introduction A key challenge in neuroscience and, in particular, neuroimaging, is to move beyond identification of regional activations toward the characterization of functional circuits underpinning perception, cognition, behavior, and consciousness. Granger causality (G-causality) analysis provides a powerful method for achieving this, by identifying directed functional ("causal") interactions from time-series data. G-causality implements a statistical, predictive notion of causality whereby causes precede, and help predict, their effects. It is defined in both the time and frequency domains, and it allows for the conditioning out of common causal influences. In this paper we explain the theoretical basis and computational implementation of G-causality analysis in neuroimaging and, more broadly, in neurophysiology, noting bothitsexcitingpotentialandtheassumptions that govern its application and interpretation.Concepts of brain connectivity are becoming increasingly prevalent as neuroscientists seek to unravel the detailed circuitry underlying perception, cognition, and behavior. Efforts to characterize

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Section: Discussionmentioning

confidence: 99%

Granger Causality Analysis in Neuroscience and Neuroimaging

2015

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“…Coherence and Granger causality spectral analyses: To study connectivity and directional influences between BF and VC, coherence and Granger causality spectral analyses were performed by using the BSMART toolbox (Cui et al, 2008). After data normalizing and detrending, we fitted an autoregressive model (AR) to the time series.…”

Section: Methodsmentioning

confidence: 99%

Gamma band directional interactions between basal forebrain and visual cortex during wake and sleep states

Nair

Klaassen

Poirot

et al. 2016

Journal of Physiology-Paris

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The basal forebrain (BF) is an important regulator of cortical excitability and responsivity to sensory stimuli, and plays a major role in wake-sleep regulation. While the impact of BF on cortical EEG or LFP signals has been extensively documented, surprisingly little is known about LFP activity within BF. Based on bilateral recordings from rats in their home cage, we describe endogenous LFP oscillations in the BF during quiet wakefulness, rapid eye movement (REM) and slow wave sleep (SWS) states. Using coherence and Granger causality methods, we characterize directional influences between BF and visual cortex (VC) during each of these states. We observed pronounced BF gamma activity particularly during wakefulness, as well as to a lesser extent during SWS and REM. During wakefulness, this BF gamma activity exerted a directional influence on VC that was associated with cortical excitation. During SWS but not REM, there was also a robust directional gamma band influence of BF on VC. In all three states, directional influence in the gamma band was only present in BF to VC direction and tended to be regulated specifically within each brain hemisphere. Locality of gamma band LFPs to the BF was confirmed by demonstration of phase locking of local spiking activity to the gamma cycle. We report novel aspects of endogenous BF LFP oscillations and their relationship to cortical LFP signals during sleep and wakefulness. We link our findings to known aspects of GABAergic BF networks that likely underlie gamma band LFP activations, and show that the Granger causality analyses can faithfully recapitulate many known attributes of these networks.

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“…After this conversion of the spike trains, signals in each trial were sampled at 200 Hz. From this point, we conducted parametric Granger causality analysis by using the open-source MATLAB package (BSMART) (Cui et al, 2008). In brief, the converted neuronal signals were first normalized by subtracting the point-by-point ensemble mean across trials and dividing by the point-by-point SD across trials.…”

Section: Methodsmentioning

confidence: 99%

Triphasic Dynamics of Stimulus-Dependent Information Flow between Single Neurons in Macaque Inferior Temporal Cortex

Hirabayashi¹,

Takeuchi²,

Tamura³

et al. 2010

J. Neurosci.

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The functional connectivity between cortical neurons is not static and is known to exhibit contextual modulations in terms of the coupling strength. Here we hypothesized that the information flow in a cortical local circuit exhibits complex forward-and-back dynamics, and conducted Granger causality analysis between the neuronal spike trains that were simultaneously recorded from macaque inferior temporal (IT) cortex while the animals performed a visual object discrimination task. Spikes from neuron pairs with a displaced peak on the cross-correlogram (CCG) showed Granger causality in the gamma-frequency range (30 -80 Hz) with the dominance in the direction consistent with the CCG peak (forward direction). Although, in a classical view, the displaced CCG peak has been interpreted as an indicative of a pauci-synaptic serial linkage, temporal dynamics of the gamma Granger causality after stimulus onset exhibited a more complex triphasic pattern,withatransientforwardcomponentfollowedbyaslowlydevelopingbackwardcomponentandsubsequentreappearanceoftheforward component. These triphasic dynamics of causality were not explained by the firing rate dynamics and were not observed for cell pairs that exhibited a center peak on the CCG. Furthermore, temporal dynamics of Granger causality depended on the feature configuration within the presented object. Together, these results demonstrate that the classical view of functional connectivity could be expanded to incorporate more complex forward-and-back dynamics and also imply that multistage processing in the recognition of visual objects might be implemented by multiphasic dynamics of directional information flow between single neurons in a local circuit in the IT cortex.

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BSMART: A Matlab/C toolbox for analysis of multichannel neural time series

Cited by 182 publications

References 22 publications

Granger Causality Analysis in Neuroscience and Neuroimaging

Granger Causality Analysis in Neuroscience and Neuroimaging

Gamma band directional interactions between basal forebrain and visual cortex during wake and sleep states

Triphasic Dynamics of Stimulus-Dependent Information Flow between Single Neurons in Macaque Inferior Temporal Cortex

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