The Human Connectome Project: A retrospective

Elam, Jennifer Stine; Glasser, Matthew F.; Harms, M.; Sotiropoulos, Stamatios N.; Andersson, Jesper; Burgess, Gregory C.; Curtiss, Sandra W.; Oostenveld, Robert; Larson-Prior, Linda J.; Schoffelen, Jan Mathijs; Hodge, Michael R.; Cler, Eileen A.; Marcus, Daniel; Deanna, M; Yacoub, Essa; Smith, Stephen M.; Uğurbil, Kâmil; Essen, David C. Van

doi:10.1016/j.neuroimage.2021.118543

Cited by 136 publications

(115 citation statements)

References 198 publications

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“…These disadvantages are partially offset by considering that normative templates are high-resolution, high-fidelity, and high-n aggregate imaging data from initiatives such as the Brain Genomics Superstruct Project [57][58][59]. This is in contrast with clinically acquired individual patient imaging, which is often limited by suboptimal acquisition parameters due to time constraints and outdated hardware [5,55].…”

Section: Limitations Of Normative Analysesmentioning

confidence: 99%

Untapped Neuroimaging Tools for Neuro-Oncology: Connectomics and Spatial Transcriptomics

Germann

Zadeh

Mansouri

et al. 2022

Cancers

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Neuro-oncology research is broad and includes several branches, one of which is neuroimaging. Magnetic resonance imaging (MRI) is instrumental for the diagnosis and treatment monitoring of patients with brain tumors. Most commonly, structural and perfusion MRI sequences are acquired to characterize tumors and understand their behaviors. Thanks to technological advances, structural brain MRI can now be transformed into a so-called average brain accounting for individual morphological differences, which enables retrospective group analysis. These normative analyses are uncommonly used in neuro-oncology research. Once the data have been normalized, voxel-wise analyses and spatial mapping can be performed. Additionally, investigations of underlying connectomics can be performed using functional and structural templates. Additionally, a recently available template of spatial transcriptomics has enabled the assessment of associated gene expression. The few published normative analyses have shown relationships between tumor characteristics and spatial localization, as well as insights into the circuitry associated with epileptogenic tumors and depression after cingulate tumor resection. The wide breadth of possibilities with normative analyses remain largely unexplored, specifically in terms of connectomics and imaging transcriptomics. We provide a framework for performing normative analyses in oncology while also highlighting their limitations. Normative analyses are an opportunity to address neuro-oncology questions from a different perspective.

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Section: Limitations Of Normative Analysesmentioning

confidence: 99%

Untapped Neuroimaging Tools for Neuro-Oncology: Connectomics and Spatial Transcriptomics

Germann

Zadeh

Mansouri

et al. 2022

Cancers

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“…Neuroimaging datasets included 628 typically developing participants aged 8 to 21 years (53.5% female; see Figure 1A) who were part of the Human Connectome Project in Development (HCP-D). The HCP-D is a large cross-sectional and longitudinal study aiming to characterize brain connectivity development in a sample approximating the demographics of youth in the United States with respect to race, ethnicity, and socioeconomic status (Elam et al, 2021;Harms et al, 2018;Somerville et al, 2018). Participants in this sample were recruited across four sites: Harvard University, University of California-Los Angeles, University of Minnesota, and Washington University in St. Louis.…”

Section: Participantsmentioning

confidence: 99%

“…Both versions (of the T1w) image were used for estimating the B1receive field to correct for effects of subject motion between the T1w and T2w images. Only the first two echos of the T1w image were used in processing due to artifacts in the later echos that affected surface reconstructions and T1w/T2w myelin maps (Elam et al, 2021).…”

Section: Image Acquisitionmentioning

confidence: 99%

“…Following cortical surface reconstruction, a single researcher with substantial experience vetting structural image data performed a 'SurfaceQC' review of the white and gray matter surface placement of each participant's data, informed by the T1w/T2w myelin maps (Elam et al, 2021;Glasser and Van Essen, 2011). Cases with significant surface placement errors requiring hand editing were flagged for future editing, and excluded from the cohort that has so far been publicly released and thus were not analyzed in this study.…”

Section: Image Processingmentioning

confidence: 99%

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Graded Variation In Cortical T1w/T2w Myelination During Adolescence

Baum

Flournoy

Glasser

et al. 2021

Preprint

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Myelination influences brain connectivity during sensitive periods of development by enhancing neural signaling speed and regulating synapse formation to reduce plasticity. However, in vivo studies characterizing the maturational timing of cortical myelination during human development remain scant. Here, we take advantage of recent advances in high-resolution cortical T1w/T2w myelin mapping methods, including principled correction of B1+ transmit field effects, using data from the Human Connectome Project in Development (N=628, ages 8-21) to characterize the maturational timing of myelination from childhood through early adulthood throughout the cerebral neocortex. We apply Bayesian spline models and functional latent clustering analysis to demonstrate graded variation in the rate of cortical T1w/T2w myelin growth in neocortical areas that is strongly correlated with the sensorimotor-association (S-A) axis of cortical organization reported by others. In sensorimotor areas T1w/T2w myelin starts at high levels at early ages, increases at a fast pace, and decelerates at later ages (18-21). In intermediate multimodal areas along the S-A axis, T1w/T2w myelin tends to start at intermediate levels and increase linearly at an intermediate pace. In transmodal and paralimbic association areas high along the S-A axis, T1w/T2w myelin tends to start at low levels and increase linearly at the slowest pace. These data provide evidence for graded variation along the S-A axis in the rate of cortical myelination during adolescence, which could reflect ongoing plasticity underlying the development of complex information processing and psychological functioning.

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“…Functional magnetic resonance imaging (fMRI) techniques have emerged as a powerful tool for the characterization of human brain connectivity and its relationship to health, behavior, and lifestyle [1]. The fMRI measurements comprise of an indirect and noninvasive measurement of brain activity based on the blood oxygen level dependent (BOLD) contrast [2].…”

Section: Introductionmentioning

confidence: 99%

Functional Connectivity Methods and Their Applications in fMRI Data

Shahhoseni

Miranda

2022

Entropy

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The availability of powerful non-invasive neuroimaging techniques has given rise to various studies that aim to map the human brain. These studies focus on not only finding brain activation signatures but also on understanding the overall organization of functional communication in the brain network. Based on the principle that distinct brain regions are functionally connected and continuously share information with each other, various approaches to finding these functional networks have been proposed in the literature. In this paper, we present an overview of the most common methods to estimate and characterize functional connectivity in fMRI data. We illustrate these methodologies with resting-state functional MRI data from the Human Connectome Project, providing details of their implementation and insights on the interpretations of the results. We aim to guide researchers that are new to the field of neuroimaging by providing the necessary tools to estimate and characterize brain circuitry.

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The Human Connectome Project: A retrospective

Cited by 136 publications

References 198 publications

Untapped Neuroimaging Tools for Neuro-Oncology: Connectomics and Spatial Transcriptomics

Untapped Neuroimaging Tools for Neuro-Oncology: Connectomics and Spatial Transcriptomics

Graded Variation In Cortical T1w/T2w Myelination During Adolescence

Functional Connectivity Methods and Their Applications in fMRI Data

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