The Human Connectome Project: A data acquisition perspective

Essen, David C. Van; Uğurbil, Kâmil; Auerbach, Edward J.; Barch, Deanna M.; Tej., Behrens; Bucholz, Richard D.; Chang, Andrew; Chen, L.; Corbetta, Maurizio; Curtiss, Sandra W.; Penna, Stefania Della; Feinberg, David; Glasser, Matt F.; Harel, Noam; Heath, Andrew C.; Larson‐Prior, Linda J.; Marcus, Daniel S.; Michalareas, Georgios; Moeller, Steen; Oostenveld, Robert; Petersen, Steven E.; Prior, Fred; Schlaggar, Bradley L.; Smith, Stephen M.; Snyder, Andrew; Xu, Junqian; Yacoub, Essa

doi:10.1016/j.neuroimage.2012.02.018

Cited by 2,302 publications

(2,069 citation statements)

References 62 publications

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“…Although continued development of prospective methods will improve the way that future studies use structural imaging to study anatomy [see Zaitsev et al, 2015], development of such innovations is not applicable to a number of extremely valuable legacy datasets and to many other on‐going large‐scale data collection initiatives [e.g., ABIDE, ADHD‐200, ADNI, Betula, DLBS, FCON1000, HCP, HABS, NIMH adolescents, PNC, SLS; ADHD‐200‐Consortium, 2012; Biswal et al, 2010; Chan et al, 2014; Dagley et al, 2015; Di Martino et al, 2014; Giedd et al, 1999; Jack et al, 2008; Nilsson et al, 1997, 2004; Park et al, 2012; Satterthwaite et al, 2014; Schaie and Willis, 2010; Van Essen et al, 2012b, 2013]. While many studies have led efforts to correct the motion‐related bias in EPI, less work has demonstrated a suitable technique for mitigating the motion‐related bias on T1w imaging.…”

Section: Discussionmentioning

confidence: 99%

Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion

Savalia

Agres

Chan

et al. 2016

Human Brain Mapping

174

189

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Motion‐contaminated T1‐weighted (T1w) magnetic resonance imaging (MRI) results in misestimates of brain structure. Because conventional T1w scans are not collected with direct measures of head motion, a practical alternative is needed to identify potential motion‐induced bias in measures of brain anatomy. Head movements during functional MRI (fMRI) scanning of 266 healthy adults (20–89 years) were analyzed to reveal stable features of in‐scanner head motion. The magnitude of head motion increased with age and exhibited within‐participant stability across different fMRI scans. fMRI head motion was then related to measurements of both quality control (QC) and brain anatomy derived from a T1w structural image from the same scan session. A procedure was adopted to “flag” individuals exhibiting excessive head movement during fMRI or poor T1w quality rating. The flagging procedure reliably reduced the influence of head motion on estimates of gray matter thickness across the cortical surface. Moreover, T1w images from flagged participants exhibited reduced estimates of gray matter thickness and volume in comparison to age‐ and gender‐matched samples, resulting in inflated effect sizes in the relationships between regional anatomical measures and age. Gray matter thickness differences were noted in numerous regions previously reported to undergo prominent atrophy with age. Recommendations are provided for mitigating this potential confound, and highlight how the procedure may lead to more accurate measurement and comparison of anatomical features. Hum Brain Mapp 38:472–492, 2017. © 2016 Wiley Periodicals, Inc.

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Section: Discussionmentioning

confidence: 99%

Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion

Savalia

Agres

Chan

et al. 2016

Human Brain Mapping

174

189

View full text Add to dashboard Cite

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“…The dataset was acquired on a Siemens 3 T Skyra MRI scanner equipped with a 32‐channel phased‐array head coil and a customized SC72 gradient insert featuring a maximum gradient strength of 100 mT/m 36. A spin‐echo Stejskal–Tanner sequence measured two b ‐shells of about 1000 and 2500 s/mm 2 with 76 and 75 gradient directions, respectively, which together with their antipodal points are uniformly distributed on the sphere 37.…”

Section: Methodsmentioning

confidence: 99%

Quantitative mapping of the per‐axon diffusion coefficients in brain white matter

Kaden

Kruggel

Alexander

2015

Magnetic Resonance in Med

209

296

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PurposeThis article presents a simple method for estimating the effective diffusion coefficients parallel and perpendicular to the axons unconfounded by the intravoxel fiber orientation distribution. We also call these parameters the per‐axon or microscopic diffusion coefficients.Theory and MethodsDiffusion MR imaging is used to probe the underlying tissue material. The key observation is that for a fixed b‐value the spherical mean of the diffusion signal over the gradient directions does not depend on the axon orientation distribution. By exploiting this invariance property, we propose a simple, fast, and robust estimator of the per‐axon diffusion coefficients, which we refer to as the spherical mean technique.ResultsWe demonstrate quantitative maps of the axon‐scale diffusion process, which has factored out the effects due to fiber dispersion and crossing, in human brain white matter. These microscopic diffusion coefficients are estimated in vivo using a widely available off‐the‐shelf pulse sequence featuring multiple b‐shells and high‐angular gradient resolution.ConclusionThe estimation of the per‐axon diffusion coefficients is essential for the accurate recovery of the fiber orientation distribution. In addition, the spherical mean technique enables us to discriminate microscopic tissue features from fiber dispersion, which potentially improves the sensitivity and/or specificity to various neurological conditions. Magn Reson Med, 2015. Magn Reson Med 75:1752–1763, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc.

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“…More recently, the Human Connectome Project (HCP) [4,5] is accumulating vast amounts of image data in order to accelerate our understanding of brain function. This and predecessor programs such as the Bioinformatics Research Network (BIRN) [6,7] have established medical imaging firmly in the realm of big-data-based science.…”

Section: Introductionmentioning

confidence: 99%

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

et al. 2013

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The Human Connectome Project: A data acquisition perspective

Cited by 2,302 publications

References 62 publications

Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion

Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion

Quantitative mapping of the per‐axon diffusion coefficients in brain white matter

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

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