The Human Connectome Project Turns to Mapping Brain Development, from Birth through Early Childhood

Fallik, Dawn

doi:10.1097/01.nt.0000503520.99794.44

Cited by 6 publications

(5 citation statements)

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“…Our dataset consisted of 13 subjects randomly selected from the Baby Connectome Project (BCP) [16,17]. We utilized 5 of them for training and the rest for testing.…”

Section: Datasetmentioning

confidence: 99%

“…Thanks to the graph representation, our method explicitly takes into account the q-space data structure and harnesses information from angular neighbors to improve the estimation accuracy of tissue microstructure. We evaluate our method using data from the Baby Connectome Project [16,17]. The results indicate that our method yields microstructural estimates with remarkably improved accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks

Chen

Hong

Zhang

et al. 2020

Lecture Notes in Computer Science

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Advanced diffusion models for tissue microstructure are widely employed to study brain disorders. However, these models usually require diffusion MRI (DMRI) data with densely sampled q-space, which is prohibitive in clinical settings. This problem can be resolved by using deep learning techniques, which learn the mapping between sparsely sampled q-space data and the high-quality diffusion microstructural indices estimated from densely sampled data. However, most existing methods simply view the input DMRI data as a vector without considering data structure in the qspace. In this paper, we propose to overcome this limitation by representing DMRI data using graphs and utilizing graph convolutional neural networks to estimate tissue microstructure. Our method makes full use of the q-space angular neighboring information to improve estimation accuracy. Experimental results based on data from the Baby Connectome Project demonstrate that our method outperforms state-of-the-art methods both qualitatively and quantitatively.

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“…Our dataset consisted of 13 subjects randomly selected from the Baby Connectome Project (BCP) [16,17]. We utilized 5 of them for training and the rest for testing.…”

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks

Chen

Hong

Zhang

et al. 2020

Lecture Notes in Computer Science

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“…To put this in perspective, in the Human Connectome Project (HCP) (Van Essen et al, 2012 ) each individual was allotted a DMRI scan time of about an hour. However, in the Baby Connectome Project (BCP) (Fallik, 2016 ; Cao et al, 2017 ; Howell et al, 2018 ), the tolerable scan time is well below 15 min. Infants are typically scanned without sedation while they are asleep.…”

Section: Introductionmentioning

confidence: 99%

Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space

Chen

Dong

Zhang

et al. 2018

Front. Neuroinform.

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Diffusion MRI requires sufficient coverage of the diffusion wavevector space, also known as the q-space, to adequately capture the pattern of water diffusion in various directions and scales. As a result, the acquisition time can be prohibitive for individuals who are unable to stay still in the scanner for an extensive period of time, such as infants. To address this problem, in this paper we harness non-local self-similar information in the x-q space of diffusion MRI data for q-space upsampling. Specifically, we first perform neighborhood matching to establish the relationships of signals in x-q space. The signal relationships are then used to regularize an ill-posed inverse problem related to the estimation of high angular resolution diffusion MRI data from its low-resolution counterpart. Our framework allows information from curved white matter structures to be used for effective regularization of the otherwise ill-posed problem. Extensive evaluations using synthetic and infant diffusion MRI data demonstrate the effectiveness of our method. Compared with the widely adopted interpolation methods using spherical radial basis functions and spherical harmonics, our method is able to produce high angular resolution diffusion MRI data with greater quality, both qualitatively and quantitatively.

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“…Brain connectomics marked a new era for brain function (using task-based and resting state fMRI) and structure (using diffusion tensor imaging) analysis using big neuroimaging data, such as the Human Connectome Project [1], the Baby Connectome Project [2], and UK Biobank [3]. However, while providing tools for quantifying connections between different neuroanatomical structures, conventional connectomics tools are not particularly designed to investigate the morphology (or shape) of the brain and its dynamic changes with time (e.g., cortical growth and cortical atrophy).…”

Section: Introductionmentioning

confidence: 99%

Estimation of shape and growth brain network atlases for connectomic brain mapping in developing infants

Rekik

Lin

et al. 2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

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In vivo brain connectomics have heavily relied on using functional and diffusion Magnetic Resonance Imaging (MRI) modalities to examine functional and structural relationships between pairs of anatomical regions in the brain. However, research work on brain morphological (i.e., shape-to-shape) connections, which can be derived from T1-w and T2-w MR images, in both typical and atypical development or ageing is very scarce. Furthermore, the brain cannot be only regarded as a static shape, since it is a dynamic complex system that changes at functional, structural and morphological levels. Hence, examining the ‘connection’ between brain shape and its changes with time (e.g., growth) may help advance our understanding of connectomic brain dynamics as well as disorders that may affect it. To address these limitations, we unprecedentedly introduce two population-based shape and growth connectivity analysis tools that further extend the field of connectomics to brain morphology and dynamics: the morphome and the kinectome. Specifically, for a population of anatomically labelled shapes, the morphome identifies a network of anatomical shape regions that are connected when morphologically similar at a single timepoint, whereas the kinectome identifies anatomical shape regions that elicit similar evolution dynamics across successive timepoints. These proposed generic tools can be easily invested to examine how a baseline shape influences its deformation trajectory at later timepoints using any longitudinal shape data. We evaluated these tools on 23 infants, with right and left cortical surfaces reconstructed at birth, 3, 6, 9 and 12 months of age. Investigating the relationship between the neonatal morphome and the postnatal kinectome (from birth to 1 year of age) gave insights into brain connectivity at birth and how it develops over time.

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The Human Connectome Project Turns to Mapping Brain Development, from Birth through Early Childhood

Cited by 6 publications

References 0 publications

Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks

Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks

Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space

Estimation of shape and growth brain network atlases for connectomic brain mapping in developing infants

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