Advances in diffusion MRI acquisition and processing in the Human Connectome Project

The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, whilst enabling scanning of a very large number of subjects. These advances result from 2 years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013

Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the Human Connectome Project

Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. Images of lower resolution have less spatial specificity but higher signal to noise ratio (SNR). At the same time higher angular contrast, important for resolving complex fibre patterns, also yields lower SNR. Considering these trade-offs, the Human Connectome Project (HCP) acquires high quality dMRI data for the same subjects at different field strengths (3T and 7T), which are publically released. Due to differences in the signal behavior and in the underlying scanner hardware, the HCP 3T and 7T data have complementary features in k- and q-space. The 3T dMRI has higher angular contrast and resolution, while the 7T dMRI has higher spatial resolution. Given the availability of these datasets, we explore the idea of fusing them together with the aim of combining their benefits. We extend a previously proposed data-fusion framework and apply it to integrate both datasets from the same subject into a single joint analysis. We use a generative model for performing parametric spherical deconvolution and estimate fibre orientations by simultaneously using data acquired under different protocols. We illustrate unique features from each dataset and how they are retained after fusion. We further show that this allows us to complement benefits and improve brain connectivity analysis compared to analyzing each of the datasets individually

Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data

The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h2 = 0.53–0.90, p < 10− 5), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests that the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application

Implementing Motor Unit Number Index (Munix) in a Large Clinical Trial: Real World Experience From 27 Centres

Objective: Motor Unit Number Index (MUNIX) is a quantitative neurophysiological method that reflects loss of motor neurons in Amyotrophic Lateral Sclerosis (ALS) in longitudinal studies. It has been utilized in one natural history ALS study and one drug trial (Biogen USA) after training and qualification of raters. Methods: Prior to testing patients, evaluators had to submit test-retest data of 4 healthy volunteers. Twenty-seven centres with 36 raters measured MUNIX in 4 sets of 6 different muscles twice. Coefficient of variation of all measurements had to be \u3c20% to pass the qualification process. MUNIX COV of the first attempt, number of repeated measurements and muscle specific COV were evaluated. Results: COV varied considerably between raters. Mean COV of all raters at the first measurements was 12.9% ± 13.5 (median 8.7%). Need of repetitions ranged from 0 to 43 (mean 10.7 ± 9.1, median 8). Biceps and first dorsal interosseus muscles showed highest repetition rates. MUNIX variability correlated considerably with variability of compound muscle action potential. Conclusion: MUNIX revealed generally good reliability, but was rater dependent and ongoing support for raters was needed. Significance: MUNIX can be implemented in large clinical trials as an outcome measure after training and a qualification process

Summary of TRACK-FA study procedures according to participant age.

The testing schedule varies according to age. All participants will also undergo a speech assessment, whilst all FRDA participants will undergo additional clinical testing.</p

TRACK-FA protocol used to obtain cervical spinal cord and brain MRI data.

TRACK-FA protocol used to obtain cervical spinal cord and brain MRI data.</p

The FARS Functional Staging for Ataxia scale describing the clinical presentation at each stage.

The FARS Functional Staging for Ataxia scale describing the clinical presentation at each stage.</p

Neurologic and functional measures completed by TRACK-FA participants.

Neurologic and functional measures completed by TRACK-FA participants.</p

The brain regions, modalities, and outcome measures for the TRACK-FA MRI protocol.

The brain regions, modalities, and outcome measures for the TRACK-FA MRI protocol.</p