Elsevier

NeuroImage

Volume 80, 15 October 2013, Pages 190-201
NeuroImage

Adding dynamics to the Human Connectome Project with MEG

https://doi.org/10.1016/j.neuroimage.2013.05.056Get rights and content

Highlights

  • Millisecond resolution dynamic connectivity will be provided to the HCP using MEG

  • MEG data will be collected at rest and during motor and cognitive task performance

  • Task paradigms are matched to tfMRI designs to provide cross-modal information

  • A number of connectivity metrics will be implemented at both sensor and source levels

  • Automated processing pipelines will be made publically available

Abstract

The Human Connectome Project (HCP) seeks to map the structural and functional connections between network elements in the human brain. Magnetoencephalography (MEG) provides a temporally rich source of information on brain network dynamics and represents one source of functional connectivity data to be provided by the HCP. High quality MEG data will be collected from 50 twin pairs both in the resting state and during performance of motor, working memory and language tasks. These data will be available to the general community. Additionally, using the cortical parcellation scheme common to all imaging modalities, the HCP will provide processing pipelines for calculating connection matrices as a function of time and frequency. Together with structural and functional data generated using magnetic resonance imaging methods, these data represent a unique opportunity to investigate brain network connectivity in a large cohort of normal adult human subjects. The analysis pipeline software and the dynamic connectivity matrices that it generates will all be made freely available to the research community.

Introduction

The connectome was conceived as a comprehensive structural description of the network elements and connections comprising the human brain (Sporns et al., 2005). This connectome theoretically constitutes the structural support for brain function. While such a connectome could be conceived at multiple scales, the Human Connectome Project (HCP) has chosen to work at the macroscale level, in which distinct brain regions comprising large neuronal populations are defined as network nodes (whether based on individual voxels or on voxel clusters derived from functional imaging studies) between which both structural and functional connections are defined (Van Essen et al., 2013, Van Essen et al., 2012).

For macroscopic connectome representation, the primary source of structural connection data lies in diffusion weighted magnetic resonance imaging (dMRI) methods (Sporns, 2011) which return a static map of resolvable anatomical connections between brain regions. While brain activity is underpinned by anatomical connectivity, it cannot be understood in those terms alone. Thus, for example, functional connectivity may be seen in the absence of direct anatomical connections (Deco et al., 2011, Honey et al., 2009, Vincent et al., 2007). The value of correlating structural and functional connectivity has been recognized for many years (Rubinov et al., 2009, Sporns et al., 2005). Functional information is provided to the HCP from two sources, which differ in their spatial and temporal resolution as well as on the basis of their signal generation (see Table 1). While fMRI provides a vascular surrogate for neural activity that broadly correlates with anatomical connectivity (Honey et al., 2010, Rubinov et al., 2009, Skudlarski et al., 2008), MEG represents population neuronal activity, which is likely to have a less straightforward correspondence to dMRI-derived anatomical mappings due to the rapid transit of information through indirect pathways (Rubinov et al., 2009). Inclusion of MEG data provides a measure of brain network connectivity at time scales used in neural communication, an important adjunct to that provided by the more static maps provided by anatomical and BOLD functional connectivity data.

As a macroscale measure, MEG represents the activity of neuronal populations in which communication has been shown to be accomplished in part via synchronized oscillatory activity which has been related to binding of relevant, or inhibition of irrelevant, information during cognitive activity (Buzsaki, 2009, Fries, 2009, Singer, 1993, Stanley, 2013, Varela et al., 2001). The goal of the MEG component of the HCP is to integrate the temporal information provided by electrophysiological methods with the structural data inherent in anatomical connectome and fMRI functional connectivity studies to better understand the way in which brain networks transmit and process information.

Owing to substantial differences in spatial resolution, temporal resolution and coverage, relating electromagnetic brain activity to the anatomical and fMRI connectome is challenging (Table 1). In the spatial domain, the anatomical connectome is resolvable to submillimeter precision (Calamante et al., 2011, Calamante et al., 2010, Uğurbil et al., 2013). The spatial resolution of fMRI in the HCP will be 2 mm isotropic voxels (see Ugerbil et al., 2013). In contrast, the spatial resolution of electrophysiological methods is, at best, 10 mm at the cortical surface (Lin et al., 2006). In the temporal domain, the anatomical connectome is, by definition, static. For fMRI, the upper frequency limit of physiologically meaningful signal plausibly is ~ 0.1 Hz (Hathout et al., 1999) while that for MEG could be up to 1 kHz (Xiang et al., 2009). This difference in spectral content translates to a substantial difference in information acquired over a typical recording epoch. Thus, MEG supports a wide variety of analytic strategies for characterizing signal interactions between regional brain pairs that are not accessible to fMRI.

The HCP will deliver multimodal neuroimaging and behavioral data on a large cohort of subjects with the goal of characterizing normal brain connectivity patterns in healthy adult human subjects (Barch et al., 2013, Van Essen et al., 2012). In keeping with initial definitions of the human connectome, a major focus of the HCP will lie in characterization of structural brain connections. Expansion of the concept of connectome to investigation of functional networks will include both fMRI and MEG. Here we present the first full description of the MEG component of the HCP. While the expectation is that this effort will lead to substantial new contributions in the study of dynamic brain network connectivity, the following will focus primarily on the fundamental approach taken in creating an electrophysiological complement to the human connectome.

Section snippets

The electrophysiological approach to connectomics

A major goal of the HCP, in addition to data acquisition, is to provide automated pipelines for processing of the data made publicly available via Connectome DB, with visualization tools provided by the Connectome Workbench (Glasser et al., 2013, Marcus et al., 2013, Marcus et al., 2011). The MEG component of the HCP will provide one set of utility pipelines and two connectivity pipelines (see section on Utility pipelines: quality control and artifact identification up to section on Task MEG

HCP MEG deliverables

The ConnectomeDB is the public face of the HCP (Marcus et al., 2013). The ConnectomeDB will manage all MEG data and analysis results and be the primary site from which raw and processed data may be downloaded (Marcus et al., 2011). All data will be shared in standardized formats or well-defined custom formats should standard formats not be readily available. Imaging data will be shared in the original DICOM or in NIfTI format, with dense and sparse connectivity matrices shared in the CIFTI

Methodological considerations

Inverse MEG source modeling is fundamentally an ill-posed problem (Gramfort et al., 2012, Helmholtz, 1853). The relative advantages and disadvantages of the various methods for computing source–space activity are discussed above (see section on Utility pipelines: anatomy processing and source reconstruction). Each method is associated with a point-spread function (PSF) that varies across the brain (Hauk et al., 2011). It is important to emphasize that these PSFs limit the capacity to correctly

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