Adding dynamics to the Human Connectome Project with MEG - PubMed (original) (raw)

. 2013 Oct 15:80:190-201.

doi: 10.1016/j.neuroimage.2013.05.056. Epub 2013 May 20.

R Oostenveld, S Della Penna, G Michalareas, F Prior, A Babajani-Feremi, J-M Schoffelen, L Marzetti, F de Pasquale, F Di Pompeo, J Stout, M Woolrich, Q Luo, R Bucholz, P Fries, V Pizzella, G L Romani, M Corbetta, A Z Snyder; WU-Minn HCP Consortium

Affiliations

Adding dynamics to the Human Connectome Project with MEG

L J Larson-Prior et al. Neuroimage. 2013.

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.

Keywords: Connectome; Functional connectivity; Magnetoencephalography (MEG); Processing pipeline.

Copyright © 2013 Elsevier Inc. All rights reserved.

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Figures

Figure 1

Figure 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.

Figure 2

Figure 2

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

Figure 3

Figure 3

The motor task paradigm consists of a set of hand and foot movements (A) performed with both right (RH/RF) and left (LH/LF) limbs. A block design matching that used in task fMRI paces each movement (B). Each block begins with a 3 sec cue telling the subject which appendage to move in that trial.

Figure 4

Figure 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.

Figure 5

Figure 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.

Figure 6

Figure 6

Frequency specific source localization (A) and connectivity map based on Talairach atlas labeling scheme (Lancaster et al., 2000). (A) DICS source modeling in the beta band for LH movement in the motor task (−0.5-0 sec before movement onset). Beta band power is greatest in motor and more posterior regions. (B) Seed regions of interest were placed bilaterally in Brodmann’s area 4, and imaginary coherence calculated in the beta band −0.5-0 sec 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.

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