Functional connectomics from resting-state fMRI.

By Stephen M. Smith, Diego Vidaurre, Christian F. Beckmann, Matthew F. Glasser, Mark Jenkinson, Karla L. Miller, Thomas E. Nichols, Emma Claire Robinson, Gholamreza Salimi-Khorshidi, Mark William Woolrich and 3 more. Trends in Cognitive Sciences, 2013

984 citations recorded by the source corpus.

Spontaneous fluctuations in activity in different parts of the brain can be used to study functional brain networks. We review the use of resting-state functional MRI (rfMRI) for the purpose of mapping the macroscopic functional connectome. After describing MRI acquisition and image-processing methods commonly used to generate data in a form amenable to connectomics network analysis, we discuss different approaches for estimating network structure from that data. Finally, we describe new possibilities resulting from the high-quality rfMRI data being generated by the Human Connectome Project and highlight some upcoming challenges in functional connectomics.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c8812e9136cba9ff05045d5d65844af68e7ceddb

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Citations in the corpus

Activity flow over resting-state networks shapes cognitive task activations (2016, result): “Overall these results support the conclusion that the choice of whether or not to use GSR does not substantially affect the outcome of activity flow mapping.”
Network-Level Structure-Function Relationships in Human Neocortex (2016, positive): “…Skyra scanner, with the following parameters: time to repetition = 720 ms, time to echo = 33.1 ms, flip angle = 52o, bandwidth = 2290 Hz/pixel, in-plane field of view = 208 × 180 mm, 72 slices, 2 mm isotropic voxels, and multiband acceleration factor = 8 (Ugurbil et al. 2013; Smith et al. 2013).”
Canonical Genetic Signatures of the Adult Human Brain (2015, positive): “Each of four 15-min resting state runs for each subject in the group was cleaned (denoised) using 24-parameter motion regression followed by ICA+FIX denoising 30, 48 .”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, background): “…(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 properties of brain organization (Damoiseaux et al.,…”
A multi-modal parcellation of human cerebral cortex (2016, neutral): “Parcellated analyses are comparably useful when characterizing structural or functional connectivity, as previously recognized23,28.”
Brain network dynamics are hierarchically organized in time (2017, background): “(18, 39), and thus will be described only briefly here.”
How reliable are MEG resting-state connectivity metrics? (2016, background): “(Although in high-quality fMRI recordings, partial methods are found to be more discriminative than marginal approaches (Smith et al., 2011, 2013).)”
Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture (2014, background): “Recorded under similar conditions over an extended period of time, the human brain possesses a stable set of functionally coupled networks that echo many known features of anatomical organization [1–13].”
Multimodal population brain imaging in the UK Biobank prospective epidemiological study (2016, neutral): “Nonlinear methods (e.g. penalized regression or data-driven feature selection)22 could enable use of much larger number of predictor variables.”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, background): “There have been a variety of methods proposed to measure functional connectivity (FC) among brain regions using fMRI data (Van Den Heuvel and Hulshoff Pol, 2010; Smith et al., 2013; Calhoun and De Lacy, 2017).”
Intrinsic and task-evoked network architectures of the human brain. (2014, neutral): “Resting-state data collection details for this data set can be found elsewhere (Smith et al., 2013), as can task data details (Barch et al., 2013).”
Parcellations and Connectivity Patterns in Human and Macaque Cerebral Cortex (2016, background): “…Validation in the Macaque It is widely appreciated that functional connectivity is an inherently indirect surrogate for direct anatomical connectivity, because correlated activity can arise from common inputs and/or indirect anatomical pathways as well as direct connections (Smith et al. 2013).”