The Human Connectome Project’s Neuroimaging Approach

By Matthew F. Glasser, Stephen M. Smith, Daniel S. Marcus, Jesper L. R. Andersson, E. Auerbach, Timothy Edward John Behrens, Timothy S. Coalson, M. Harms, Mark Jenkinson, Steen Moeller and 6 more. Nature Neuroscience, 2016

1,037 citations recorded by the source corpus.

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a6ee9fa4dc9366af18d8a0e861ca3b2fd2b2ce42

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Citations in the corpus

The UNC/UMN Baby Connectome Project (BCP): An Overview of the Study Design and Protocol Development (2018, neutral): “Indeed, the approach to acquisition and processing, and the quality of HCP and HCP-inspired data has advanced our knowledge of the structural and functional network architecture of the brain (Glasser et al., 2016b; Tavor et al., 2016).”
Graph Neural Networks in Network Neuroscience (2021, background): “ect (HCP) [18], the Baby Connectome Project (BCP) [19] and the Connectome Related to Human Disease (CRHD) [20].”
The impact of traditional neuroimaging methods on the spatial localization of cortical areas (2018, methodology): “(iv) Cross-subject alignment: The HCP-style approach gently initializes alignment of cortical areas on the surface using cortical folding patterns and then aligns areas across subjects using “areal features” (cortical myelin content, resting-state networks, and resting-state topographic maps) that a”
Neuroscience-Inspired Artificial Intelligence. (2017, methodology): “, 2013; Kriegeskorte and Kievit, 2013)—with promise for expediting connectomic analysis (Glasser et al., 2016), among other techniques.”
The Lifespan Human Connectome Project in Aging: An overview (2018, neutral): “Over 570 publications using HCP-YA data (as of July 2018) (Glasser et al., 2016; Marcus et al., 2013); (Barch et al.”
The challenge of mapping the human connectome based on diffusion tractography (2017, neutral): “Distortions caused by B 0 ﬁ eld inhomogeneities are introduced using an existing ﬁ eld map measured in a real acquisition and registered to the employed reference HCP data set (Fig.”
Population-Averaged Atlas of the Macroscale Human Structural Connectome and Its Network Topology (2018, neutral): “Improving the quality of resolved fiber pathways using diffusion MRI can be achieved by high-angular-resolution modalities (Glasser et al., 2016), a template averaged across a large number of subjects to facilitate fiber tracking (Yeh and Tseng, 2011), and neuroanatomical expertise to resolve errors”