Imaging human connectomes at the macroscale

By R. Craddock, Saâd Jbabdi, Chaogan Yan, Joshua T. Vogelstein, F. X. Castellanos, Adriana Di Martino, Clare Kelly, K. Heberlein, Stanley J. Colcombe, Michael P. Milham. Nature Methods, 2013

432 citations recorded by the source corpus.

At macroscopic scales, the human connectome comprises anatomically distinct brain areas, the structural pathways connecting them and their functional interactions. Annotation of phenotypic associations with variation in the connectome and cataloging of neurophenotypes promise to transform our understanding of the human brain. In this Review, we provide a survey of magnetic resonance imaging–based measurements of functional and structural connectivity. We highlight emerging areas of development and inquiry and emphasize the importance of integrating structural and functional perspectives on brain architecture.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e29c5a780db4315fcaadb8fdd55326d446458e03

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

EEG-Based Emotion Recognition Using Regularized Graph Neural Networks (2019, background): “Local inter-channel relations connect nearby groups of neurons and may reveal anatomical connectivity at macroscale [18], [21].”
The WU-Minn Human Connectome Project: An Overview (2013, background): “The HCP is one of many large-scale imaging projects currently underway around the world (see Craddock et al., 2013), but it is distinctive if not unique in several important respects.”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, background): “Modern neuroimaging parcellations typically range from tens to several hundreds of regions (Craddock et al., 2013).”
CLASSIFICATION OF SCHIZOPHRENIA AND BIPOLAR PATIENTS USING STATIC AND DYNAMIC RESTING-STATE FMRI BRAIN CONNECTIVITY (2016, background): “A commonly used measure is functional magnetic resonance imaging (fMRI), which measures synchronized brain activity via blood oxygenation and infers functional interactions among different brain regions (Craddock et al., 2013).”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “There is currently no consensus regarding an optimal set of criteria for defining an areal parcellation (Craddock et al., 2013; Glasser et al., 2016a; Gordon et al., 2016), and even less known about the stability of these parcellations at younger ages.”
Cognitive Network Neuroscience (2015, background): “, 2009); methodological approaches to examining brain networks (Craddock et al., 2013); and relevance of network approaches to cognitive neuroscience (Sporns, 2014).”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Studies on modeling the human brain as a complex system have grown remarkably as neuroscientists seek to understand the comprehensive information underlying cognition, behavior, and perception (Bassett and Bullmore, 2006; Reijneveld et al., 2007; Bullmore and Sporns, 2009, 2012; He and Evans, 2010; ”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, background): “Resting-state (intrinsic) functional connectivity (rsfc-MRI) has evolved to become one of the most common brain imaging modalities (Craddock et al., 2013; Fox and Raichle, 2007; Power et al., 2014b; Smith et al., 2013; Van Dijk et al., 2010), and has been critical for understanding fundamental…”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, methodology): “This approach is often referred to as “bag of edges” (Craddock et al., 2013) and has been widely used when the underlying parcellation is the same among all subjects.”
Deriving reproducible biomarkers from multi‐site resting‐state data: An Autism‐based example (2016, methodology): “We speciﬁcally used the data processed with the Conﬁgurable Pipeline for the Analysis of Connectomes [34] (C-PAC).”
The hubs of the human connectome are generally implicated in the anatomy of brain disorders (2014, background): “Although DTI provides the most direct access to the structural network of the living human brain, it also has limitations (Morris et al., 2008; Jones and Cercignani, 2010; Craddock et al., 2013; Jones et al., 2013).”
Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, neutral): “For a review on macroconnectomics see Craddock et al. (2013) and Fornito et al. (2013); and for parcellation schemes see Caspers et al. (2013).”