Dataset Information

Detecting Functional Connectivity During Audiovisual Integration with MEG: A Comparison of Connectivity Metrics.

ABSTRACT: In typical magnetoencephalography and/or electroencephalography functional connectivity analysis, researchers select one of several methods that measure a relationship between regions to determine connectivity, such as coherence, power correlations, and others. However, it is largely unknown if some are more suited than others for various types of investigations. In this study, the authors investigate seven connectivity metrics to evaluate which, if any, are sensitive to audiovisual integration by contrasting connectivity when tracking an audiovisual object versus connectivity when tracking a visual object uncorrelated with the auditory stimulus. The authors are able to assess the metrics' performances at detecting audiovisual integration by investigating connectivity between auditory and visual areas. Critically, the authors perform their investigation on a whole-cortex all-to-all mapping, avoiding confounds introduced in seed selection. The authors find that amplitude-based connectivity measures in the beta band detect strong connections between visual and auditory areas during audiovisual integration, specifically between V4/V5 and auditory cortices in the right hemisphere. Conversely, phase-based connectivity measures in the beta band as well as phase and power measures in alpha, gamma, and theta do not show connectivity between audiovisual areas. The authors postulate that while beta power correlations detect audiovisual integration in the current experimental context, it may not always be the best measure to detect connectivity. Instead, it is likely that the brain utilizes a variety of mechanisms in neuronal communication that may produce differential types of temporal relationships.

SUBMITTER: Ard T

PROVIDER: S-EPMC4533088 | biostudies-other | 2015 Aug

REPOSITORIES: biostudies-other

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Detecting Functional Connectivity During Audiovisual Integration with MEG: A Comparison of Connectivity Metrics.

Ard Tyler T Carver Frederick W FW Holroyd Tom T Horwitz Barry B Coppola Richard R

Brain connectivity 20150226 6

In typical magnetoencephalography and/or electroencephalography functional connectivity analysis, researchers select one of several methods that measure a relationship between regions to determine connectivity, such as coherence, power correlations, and others. However, it is largely unknown if some are more suited than others for various types of investigations. In this study, the authors investigate seven connectivity metrics to evaluate which, if any, are sensitive to audiovisual integration ...[more]

PMID: 25599264

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Project description:Despite advances in the field of dynamic connectivity, fixed sliding window approaches for the detection of fluctuations in functional connectivity are still widely used. The use of conventional connectivity metrics in conjunction with a fixed sliding window comes with the arbitrariness of the chosen window lengths. In this paper we use multivariate autoregressive and neural mass models with a priori defined ground truths to systematically analyze the sensitivity of conventional metrics in combination with different window lengths to detect genuine fluctuations in connectivity for various underlying state durations. Metrics of interest are the coherence, imaginary coherence, phase lag index, phase locking value and the amplitude envelope correlation. We performed analysis for two nodes and at the network level. We demonstrate that these metrics show indeed higher variability for genuine temporal fluctuations in connectivity compared to a static connectivity state superimposed by noise. Overall, the error of the connectivity estimates themselves decreases for longer state durations (order of seconds), while correlations of the connectivity fluctuations with the ground truth was higher for longer state durations. In general, metrics, in combination with a sliding window, perform poorly for very short state durations. Increasing the SNR of the system only leads to a moderate improvement. In addition, at the network level, only longer window widths were sufficient to detect plausible resting state networks that matched the underlying ground truth, especially for the phase locking value, amplitude envelope correlation and coherence. The length of these longer window widths did not necessarily correspond to the underlying state durations. For short window widths resting state network connectivity patterns could not be retrieved. We conclude that fixed sliding window approaches for connectivity can detect modulations of connectivity, but mostly if the underlying dynamics operate on moderate to slow timescales. In practice, this can be a drawback, as state durations can vary significantly in empirical data.

Project description:At the sensor level many aspects, such as spectral power, functional and effective connectivity as well as relative-power-ratio ratio (RPR) and spatial resolution have been comprehensively investigated through both electroencephalography (EEG) and magnetoencephalography (MEG). Despite this, differences between both modalities have not yet been systematically studied by direct comparison. It remains an open question as to whether the integration of EEG and MEG data would improve the information obtained from the above mentioned parameters. Here, EEG (64-channel system) and MEG (275 sensor system) were recorded simultaneously in conditions with eyes open (EO) and eyes closed (EC) in 29 healthy adults. Spectral power, functional and effective connectivity, RPR, and spatial resolution were analyzed at five different frequency bands (delta, theta, alpha, beta and gamma). Networks of functional and effective connectivity were described using a spatial filter approach called the dynamic imaging of coherent sources (DICS) followed by the renormalized partial directed coherence (RPDC). Absolute mean power at the sensor level was significantly higher in EEG than in MEG data in both EO and EC conditions. At the source level, there was a trend towards a better performance of the combined EEG+MEG analysis compared with separate EEG or MEG analyses for the source mean power, functional correlation, effective connectivity for both EO and EC. The network of coherent sources and the spatial resolution were similar for both the EEG and MEG data if they were analyzed separately. Results indicate that the combined approach has several advantages over the separate analyses of both EEG and MEG. Moreover, by a direct comparison of EEG and MEG, EEG was characterized by significantly higher values in all measured parameters in both sensor and source level. All the above conclusions are specific to the resting state task and the specific analysis used in this study to have general conclusion multi-center studies would be helpful.

Dataset Information

Detecting Functional Connectivity During Audiovisual Integration with MEG: A Comparison of Connectivity Metrics.

Publications

Detecting Functional Connectivity During Audiovisual Integration with MEG: A Comparison of Connectivity Metrics.

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