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.
Highlights
Large scale networks with correlated time courses have been consistently identified in the resting brain with functional Magnetic Resonance Imaging (Beckmann and Smith, 2004; Varoquaux et al, 2010b), and electroencephalography (EEG) (Tagliazucchi et al, 2012) and magnetoencephalography (MEG) (Brookes et al, 2011a,b)
We extend the sparse Canonical Correlation Analysis (sCCA) framework with the principle of randomized Lasso (Meinshausen and Buhlmann, 2010) to identify the most prominent connections that contribute to this relationship
We present results based on brain connectomes derived from the whole time series of EEG (WTS) as well as corresponding results derived based on brain connectomes estimated from averaging the Hilbert transformed EEG signal within each epoch (AWE)
Summary
Large scale networks with correlated time courses have been consistently identified in the resting brain with functional Magnetic Resonance Imaging (fMRI) (Beckmann and Smith, 2004; Varoquaux et al, 2010b), and electroencephalography (EEG) (Tagliazucchi et al, 2012) and magnetoencephalography (MEG) (Brookes et al, 2011a,b). Spontaneous neural fluctuations exhibit consistent correlation structures over a wide range of spatial and temporal scales and they constitute a prominent energy-consuming feature of the brain (Schölvinck et al, 2013; Smith et al, 2013). Several studies highlight their significance in modulating brain function and task efficiency (Bonnelle et al, 2012). FMRI data are acquired as series of volumetric images, typically obtained every few seconds, that represent blood oxygen level-dependent (BOLD) contrast This mechanism is related to the delivery of blood to active neuronal tissue and it allows indirect inference on brain function. Temporal resolution of EEG is on the scale of milliseconds, the localization of the signal involves sophisticated algorithms and a priori models for both the source and the volume conductor and yet it only achieves accuracy in the range of 1–2 cm (Kaiboriboon et al, 2012)
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