Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

Abstract

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.

Assessing cortical synchronization during transcranial direct current stimulation: A graph-theoretical analysis

“…The results show that both anodal and cathodal stimulation affected mainly the low (theta and alpha) frequency EEG bands, modulating specific cortical connections in intra-and interhemispheric networks. Considering that the resting state patterns of brain connectivity are the result of robust and specific intrinsic neural activity (Cabral et al, 2014;Deligianni et al, 2014), our results highlight that tDCS is able to affect selectively the spontaneous neuronal fluctuations, modulating the dynamics of the brain at rest. Although tDCS is able to change network properties, it doesn't seem to affect the topological organization of the brain activity at a global level.…”

Joint prediction of multiple scores captures better individual traits from brain images

“…Neuroimaging studies often use low-rank multi-output models like CCA (Hotelling, 1936) and partial least squares (PLS) (Krishnan et al, 2011) to link imaging based features to other 115 blocks of data: to cross-predict fMRI and EEG (Deligianni et al, 2014), to explain genetic outcomes (Floch et al, 2012), behavioral and clinical scores (Monteiro et al, 2016;Miller et al, 2016;Smith et al, 2015), or a different imaging modality (Avants et al, 2010;Sui et al, 2012). Reduced rank regression 120 (Vounou et al, 2010;Izenman, 1975) is a related multi-output linear-regression.…”

Partial covariance based functional connectivity computation using Ledoit–Wolf covariance regularization

“…Nevertheless, we observed that partial covariance estimates were more reliable than full covariance estimates both at the individual and group level. This work adds to previous studies which have applied Ledoit-Wolf shrinkage to calculate partial correlation values (Varoquaux et al, 2012, Deligianni et al, 2014). …”

“…Covariance matrix shrinkage has been recently used in the investigation of functional connectivity (Varoquaux et al, 2012, Deligianni et al, 2014, Shou et al, 2014). Stein’s paradox (Stein, 1956) asserts that a shrunken estimate of the mean outperforms the sample mean in predicting the true mean of a multivariate distribution.…”

“…Ledoit-Wolf regularization has previously been used to condition resting-state BOLD covariance matrices (Varoquaux et al, 2012, Deligianni et al, 2014) but the properties and consequences of this approach to functional connectivity have not been thoroughly investigated. In order to introduce this approach to the broader neuroimaging community, we first demonstrate the favorable properties of the partial covariance quantity.…”