Opportunities and limitations of intrinsic functional connectivity MRI

By Randy L. Buckner, Fenna M. Krienen, Thomas Yeo. Nature Neuroscience, 2013

897 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4a17584bdf16685c382c006762ff80b44bf21928

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Citations in the corpus

The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. (2013, background): “The precise physiological origin of the slow fluctuations is presently unclear but several lines of evidence suggest that, while there are multiple determinants of the spontaneous activity fluctuations, regions that show monosynaptic or polysynaptic connections tend to fluctuate together (Leopold an”
Intrinsic and task-evoked network architectures of the human brain. (2014, neutral): “More recently, studies examining many regions have emphasized differences between resting-state FC and FC during a small number of tasks (Buckner et al., 2013; Hasson et al., 2009; Mennes et al., 2013).”
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, background): “2011), RSFC is sufficiently constrained by anatomy to provide insights into large-scale circuit organization (Fox and Raichle 2007; Buckner et al. 2013) and are strongly associated with task-evoked networks (Smith et al.”
Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior (2019, background): “Resting-state functional connectivity (RSFC) is a powerful tool for measuring the synchronization of fMRI signals between brain regions, while participants are lying at rest without any “extrinsic” task (Biswal et al., 1995; Fox and Raichle, 2007; Buckner et al., 2013).”
Dynamic functional connectivity of the default mode network tracks daydreaming (2014, background): “The study of brain activity in an awake, task-free (“resting”) state, in which daydreaming is a predominant activity, has recently emerged as an approach to understanding functional networks (Buckner et al., 2013).”
Automated long-term recording and analysis of neural activity in behaving animals (2016, background): “…in the firing rate of neurons (Bair et al., 2001; Freeman et al., 2014; Kato et al., 2015) or between compound neural activity in different brain regions (Biswal et al., 1995; Buckner et al., 2013) have been shown to occur over longer time scales (<0.1 Hz) also in other systems, including humans.”
Contributions and challenges for network models in cognitive neuroscience (2014, background): “The latter approach is driven by methodological developments in neuroimaging that have enabled the noninvasive mapping of structural and functional brain connec tivity 6,7 .”
Inscapes: a movie paradigm to improve compliance in functional magnetic resonance imaging (2015, background): “…et al. write that a portion of the patterns observed under any acquisition state (including rest and task) arise from “invariant constraints” that include anatomic connectivity, while the other portion arises from “dynamic properties” evoked by the task elements of the state (Buckner et al., 2013).”
Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture (2014, background): “But it does shift emphasis from the resting-state being a ‘privileged state’ for revealing functional networks, towards it being only one of many behavioural states that each reveal a consistent central tendency as well as idiosyncratic aspects of coupling [67].”
Optimization of multifocal transcranial current stimulation for weighted cortical pattern targeting from realistic modeling of electric fields (2013, neutral): “…traditional task-based fMRI, resting state fcMRI examines correlations in spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal in the absence of any explicit input or output, while subjects simply rest in the scanner (see, e.g., Buckner et al. (2013), and references therein).”
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion (2017, background): “Population-average networks have provided important insights into the large-scale functional organization of the human brain (Buckner et al., 2013; Wig et al., 2017).”
The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition (2016, background): “These results underscore the human brain’s ability to reconfigure network organization selectively and adaptively when confronted with changing cognitive demands to achieve an optimal balance between segregation and integration. mands (Smith, 2012; Buckner et al., 2013).”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, background): “R-fMRI has become increasingly popular (Buckner et al., 2013; Fox and Raichle, 2007; Kelly et al., 2012) because of its substantial reliability (Shehzad et al., 2009; Zuo and Xing, 2014), sensitivity (Castellanos et al., 2013; Fox and Greicius, 2010; Greicius, 2008) and comparability across labs…”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, background): “Such changes in structural and functional connectivity could potentially lead to novel biomarkers for neuropsychiatric disorders (Castellanos et al., 2013) but, as mentioned above, there are many obstacles to progress.”