Adding dynamics to the Human Connectome Project with MEG.

TLDR: The Human Connectome Project (HCP) as discussed by the authors aims to map the structural and functional connections between network elements in the human brain using magnetoencephalography (MEG) data.

About: This article is published in NeuroImage.The article was published on 2013-10-15 and is currently open access. It has received 190 citations till now. The article focuses on the topics: Connectome & Resting state fMRI.

Figures

Fig. 4. The working memory paradigm is designed to match the N-back task performed during task fMRI. For MEG studies, only two categories are presented (A) in a block design of 16 task blocks in each of two runs. (B) illustrates the design for a 2-back face task.

Table 1 Connectivity characteristics of imaging modalities.

Fig. 1. Data acquisition. Data will be collected on a whole head 4D neuroimaging system housed in a magnetically shielded room at Saint Louis University (SLU). The MEG system includes 248 magnetometer channels together with 23 reference channels. Data will be sampled at 2 kHz and delta encoding is used to increase readout resolution, providing a quantization of ~0.3 fT. Spatial digitization of head shape and fiducials will be accomplished using a Polhemus FASTRAK-III system. A Sun Blade running 64 bit Solaris 8 Unix and communicating with a dedicated UltraAX-12 system is used for data collection and sensor configuration. Following data acquisition, data will be uploaded via https to the Washington University in St. Louis (WashU) internal HCP database. Public data releases will be accomplished through the external ConnectomeDB interface.

Fig. 5. Resting state network analysis. Upper: Seed-based approach allowing the study of network segregation/integration. Upper left: segregated RSNs obtained in the related MCWs; upper right: 3D plot of covariance across RSN nodes showing the central role of DMN. Lower: Comparison of brain networks obtained using ICA independently on MEG [lower] and fMRI [upper] data. (A) DMN; (B) left lateral frontoparietal network; (C) right lateral frontoparietal network; (D) sensorimotor network; (E) medial parietal regions; (F) visual network; (G) frontal lobes including anterior cingulate cortex; (H) cerebellum.

Fig. 2. MEG analysis pipelines. The upper part of the pipeline labeled as “raw data” operates on the entire data set and is shared between the task and rest pipelines. For TASK (left panel), the box labeled as “all trials” operates on the concatenated epochs from all conditions; the box labeled “contrasts” operates on epochs of individual conditions and contrasts between conditions. For REST (right panel), the box labeled “single subject ICA” operates on temporal ICs at the individual level and the box labeled “group level ICA” operates on spatial ICA at the group level.

Fig. 6. Frequency specific source localization (A) and connectivity map based on Talairach atlas labeling scheme (Lancaster et al., 2000). (A) DICS sourcemodeling in the beta band for LH movement in the motor task (−0.5–0 s before movement onset). Beta band power is greatest inmotor andmore posterior regions. (B) Seed regions of interestwere placed bilaterally in Brodmann's area 4, and imaginary coherence calculated in the beta band−0.5–0 s before movement onset. Note high levels of connectivity cross-hemispherically to homolateral motor cortex and medial prefrontal regions. Line thickness is proportional to the value of imaginary coherence, blue lines represent connections originating in the left hemisphere, and red lines originate in the right hemisphere.

Frequently asked questions

Q1. What are the contributions mentioned in the paper "Adding dynamics to the human connectome project with meg" ?

The Human Connectome Project ( HCP ) this paper uses diffusion weighted magnetic resonance imaging ( Dwyer et al., 2013 ) to measure the structural support for brain function. 

Q2. What future works have the authors mentioned in the paper "Adding dynamics to the human connectome project with meg" ?

In the future, more elaborate connectivity metrics are likely to become available. Metrics based on non-parametric spectral factorization can be adopted to study directionality, such as spectrally resolved Granger causality ( Bosman et al., 2012 ). DCM may be implemented if indicated by initial results. 

Q3. What is the argument that parcellations should be used in MEG analysis?

It might be argued that high spatial resolution modalities, e.g., resting-state fMRI, should be used to define the parcels used in MEG functional connectivity analyses. 

Q4. What can be used to produce MEG data-derived network parcellations?

Source level group-ICA can be used to produce MEG data-derived network parcellations, which can be used to interrogate different subpopulations of the HCP database. 

Q5. What are the main drawbacks of a seed based approach?

The main drawbacks are reliance on prior knowledge of seed locations based on fMRI, the assumption that ROIs are correctly chosen and are concordant between modalities, and the dependence on accurate inverse source modeling together with information on neurovascular coupling to register BOLD data with electrophysiological sources. 

Q6. What format will be used to share the data?

Small files, such as lists of electrode positions and the specification of bad channels and time segments, will be shared in ASCII format. 

Q7. What is the connection time between the ConnectomeDB servers and the requester?

Total download time depends on the round-trip latency between the ConnectomeDB servers and requester and is throttled by the lowest bandwidth link between sender and receiver. 

Q8. What is the primary source of structural data for the macroscopic connectome?

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. 

Q9. How long can a complete MEG data release be downloaded?

Based on measurements conducted between ConnectomeDB and requesters at the University of Minnesota and Oxford University, the authors estimate that a complete quarterly MEG raw data release can be downloaded in approximately 3–3.5 h. 

Q10. How many GB of data will be available to the users?

Preliminary estimates indicate that the rawMEGdata,metadata and preprocessing information for each quarterly data release will total approximately 328 GB. 

Q11. What is the frequency range for which the inverse solution isillustrated?

The frequency range for which the inverse solution isillustrated is the upper beta band, which can clearly be seen to peak in motor and posterior areas. 

Q12. What is the temporal range of BLP correlations?

the temporal frequencies over which BLP correlations are defined are in the infraslow range (b0.1 Hz), i.e., comparable to frequencies accessed by fMRI.