The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.
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"A full-brain segmentation was used as POSSUM's geometric object input. It was created with T1-and T2-weighted images from a single subject from the WU-Minn HCP dataset (Van Essen et al., 2012), using Fig. 1 "
[Show abstract][Hide abstract] ABSTRACT: In this paper we demonstrate a simulation framework that enables the direct and quantitative comparison of post-processing methods for diffusion weighted magnetic resonance (DW-MR) images. DW-MR datasets are employed in a range of techniques that enable estimates of local microstructure and global connectivity in the brain. These techniques require full alignment of images across the dataset, but this is rarely the case. Artefacts such as eddy-current (EC) distortion and motion lead to misalignment between images, which compromise the quality of the microstructural measures obtained from them. Numerous methods and software packages exist to correct these artefacts, some of which have become de-facto standards, but none have been subject to rigorous validation. In the literature, improved alignment is assessed using either qualitative visual measures or quantitative surrogate metrics. Here we introduce a simulation framework that allows for the direct, quantitative assessment of techniques, enabling objective comparisons of existing and future methods. DW-MR datasets are generated using a process that is based on the physics of MRI acquisition, which allows for the salient features of the images and their artefacts to be reproduced. We apply this framework in three ways. Firstly we assess the most commonly used method for artefact correction, FSL's eddy_correct, and compare it to a recently proposed alternative, eddy. We demonstrate quantitatively that using eddy_correct leads to significant errors in the corrected data, whilst eddy is able to provide much improved correction. Secondly we investigate the datasets required to achieve good correction with eddy, by looking at the minimum number of directions required and comparing the recommended full-sphere acquisitions to equivalent half-sphere protocols. Finally, we investigate the impact of correction quality by examining the fits from microstructure models to real and simulated data.
"We based our analysis on volumetric data from the preselected bundle of 100 unrelated human subjects from the S500 release of the Human Connectome Project , in which each subject went through four rs-fMRI sessions lasting 14min 33s resulting in 1200 volumes per session and n = 400 sessions in total. "
[Show abstract][Hide abstract] ABSTRACT: Investigating temporal variability of functional connectivity is an emerging
field in connectomics. Entering dynamic functional connectivity by applying
sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged
from this topic. We introduce frequency-resolved dynamic functional
connectivity (frdFC) by means of multivariate empirical mode decomposition
(MEMD) followed up by filter-bank investigations. We develop our method on the
most canonical form by applying a sliding window approach to the intrinsic mode
functions (IMFs) resulting from MEMD. We explore two modifications:
uniform-amplitude frequency scales by normalizing the IMFs by their
instantaneous amplitude and cumulative scales. By exploiting the well
established concept of scale-invariance in resting-state parameters, we compare
our frdFC approaches. In general, we find that MEMD is capable of generating
time courses to perform frdFC and we discover that the structure of
connectivity-states is robust over frequency scales and even becomes more
evident with decreasing frequency. This scale-stability varies with the number
of extracted clusters when applying k-means. We find a scale-stability drop-off
from k = 4 to k = 5 extracted connectivity-states, which is corroborated by
null-models, simulations, theoretical considerations, filter-banks, and
scale-adjusted windows. Our filter-bank studies show that filter design is more
delicate in the rs-fMRI than in the simulated case. Besides offering a baseline
for further frdFC research, we suggest and demonstrate the use of
scale-stability as a quality criterion for connectivity-state and model
selection. We present first evidence showing that scale-invariance plays an
important role in connectivity-state considerations. A data repository of our
frequency-resolved time-series is provided.
"Future studies with larger sample sizes should be carried out to validate the present results. In particular, the Human Connectome Project plans to release a large dataset of multimodal data 1 [Van Essen et al., 2012], which will provide a unique opportunity to test our results on the similarity between SC and resting-state fMRI-FC and MEG-FC, as well as to analyze whether these similarities change with different cognitive tasks . It would be interesting as well to test whether SC influences the test-retest reliability of fMRI-FC and MEG-FC estimates. "