Functional connectivity: studying nonlinear, delayed interactions between BOLD signals

Lahaye, Pierre-Jean; Poline, Jean‐Baptiste; Flandin, Guillaume; Dodel, Silke; Garnero, Line

doi:10.1016/s1053-8119(03)00340-9

Cited by 83 publications

(68 citation statements)

References 31 publications

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“…Effective connectivity approaches that are based on instantaneous regression equations relating only concurrent values, such as psychophysiological interactions and structural equation modeling, discard possibly important temporal information in the data. It has been shown (Lahaye et al, 2003) that even in the context of functional connectivity, incorporating lagged values increases the sensitivity to detect relationships between typical fMRI time-series. Many of the more recent effective connectivity approaches are based on stochastic or deterministic dynamic models, capable of capturing temporal structure.…”

Section: Discussionmentioning

confidence: 99%

Mapping directed influence over the brain using Granger causality and fMRI

2005

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We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the concept of Granger causality to define the existence and direction of influence from information in the data. Temporal precedence information is exploited to compute Granger causality maps that identify voxels that are sources or targets of directed influence for any selected region-of-interest. We investigated the method by simulations and by application to fMRI data of a complex visuomotor task. The presented exploratory approach of mapping influences between a region of interest and the rest of the brain can form a useful complement to existing models of effective connectivity. D 2004 Elsevier Inc. All rights reserved.

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Section: Discussionmentioning

confidence: 99%

Mapping directed influence over the brain using Granger causality and fMRI

2005

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“…Note that methods based on the entire time courses, such as the residuals method, have the advantage of using all the temporal information, and can lead to functional connectivity measures that take into account the signal history as proposed by Lahaye et al [2003] and Sato et al [2006].…”

Section: Discussionmentioning

confidence: 99%

Functional connectivity estimation in fMRI data: Influence of preprocessing and time course selection

Stuart

Rossell

Henshall

et al. 2007

Human Brain Mapping

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A number of techniques have been used to provide functional connectivity estimates for a given fMRI data set. In this study we compared two methods: a 'rest-like' method where the functional connectivity was estimated for the whitened residuals after regressing out the task-induced effects, and a within-condition method where the functional connectivity was estimated separately for each experimental condition. In both cases four pre-processing strategies were used: 1) time courses extracted from standard pre-processed data (standard); 2) adjusted time courses extracted using the volume of interest routines in SPM2 from standard pre-processed data (spm); 3) time courses extracted from ICA denoised data (standard denoised); and 4) adjusted time courses extracted from ICA denoised data (spm denoised). The temporal correlation between time series extracted from two cortical regions were statistically compared with the temporal correlation between a time series extracted from a cortical region and a time series extracted form a region placed in CSF. Since the later correlation is due to physiological noise and other artifacts, we used this comparison to investigate whether rest-like and task modulated connectivity could be estimated from the same data set. The pre-processing strategy had a significant effect on the connectivity estimates with the standard time courses providing larger connectivity values than the spm time courses for both estimation methods. The CSF comparison indicated that for our data set only rest-like connectivity could be estimated. The rest-like connectivity values were similar with connectivity estimated from resting state data.

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“…Thus, functional connectivity does not necessarily imply a causal link, whereas effective connectivity does. As pointed out by Friston (see Lee et al 2003), effective connectivity is model dependent, whereas functional connectivity is not (at least not explicitly; note, however, that in the evaluation of functional connectivity as a simple correlation coefficient, it is implicitly assumed that the interactions are linear and instantaneous; see Lahaye et al (2003) and Roebroeck et al (2005) for approaches that avoid these assumptions). It should be noted that different investigators have, over the years, used different terms to represent these two notions, but the functional neuroimaging community seems to have settled on these general designations (Horwitz 2003).…”

Section: Functional and Effective Connectivitymentioning

confidence: 99%

“…Hampson et al 2002) versus time-shifted (e.g. Lahaye et al 2003); (iii) resting state (e.g. Biswal et al 1995) versus an active experimental condition (e.g.…”

Section: Functional and Effective Connectivitymentioning

confidence: 99%

Investigating the neural basis for functional and effective connectivity. Application to fMRI

Horwitz

Warner

Fitzer

et al. 2005

Phil. Trans. R. Soc. B

114

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Viewing cognitive functions as mediated by networks has begun to play a central role in interpreting neuroscientific data, and studies evaluating interregional functional and effective connectivity have become staples of the neuroimaging literature. The neurobiological substrates of functional and effective connectivity are, however, uncertain. We have constructed neurobiologically realistic models for visual and auditory object processing with multiple interconnected brain regions that perform delayed match-to-sample (DMS) tasks. We used these models to investigate how neurobiological parameters affect the interregional functional connectivity between functional magnetic resonance imaging (fMRI) time-series. Variability is included in the models as subject-to-subject differences in the strengths of anatomical connections, scan-to-scan changes in the level of attention, and trialto-trial interactions with non-specific neurons processing noise stimuli. We find that time-series correlations between integrated synaptic activities between the anterior temporal and the prefrontal cortex were larger during the DMS task than during a control task. These results were less clear when the integrated synaptic activity was haemodynamically convolved to generate simulated fMRI activity. As the strength of the model anatomical connectivity between temporal and frontal cortex was weakened, so too was the strength of the corresponding functional connectivity. These results provide a partial validation for using fMRI functional connectivity to assess brain interregional relations.

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Functional connectivity: studying nonlinear, delayed interactions between BOLD signals

Cited by 83 publications

References 31 publications

Mapping directed influence over the brain using Granger causality and fMRI

Mapping directed influence over the brain using Granger causality and fMRI

Functional connectivity estimation in fMRI data: Influence of preprocessing and time course selection

Investigating the neural basis for functional and effective connectivity. Application to fMRI

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