Resting state fMRI: A review of methods and clinical applications

By Megan H Lee, Christopher D. Smyser, Joshua S. Shimony. American Journal of Neuroradiology, 2012

1,022 citations recorded by the source corpus.

SUMMARY: Resting-state fMRI measures spontaneous low-frequency fluctuations in the BOLD signal to investigate the functional architecture of the brain. Application of this technique has allowed the identification of various RSNs, or spatially distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest. Various methods exist for analyzing resting-state data, including seed-based approaches, independent component analysis, graph methods, clustering algorithms, neural networks, and pattern classifiers. Clinical applications of resting-state fMRI are at an early stage of development. However, its use in presurgical planning for patients with brain tumor and epilepsy demonstrates early promise, and the technique may have a future role in providing diagnostic and prognostic information for neurologic and psychiatric diseases.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d1ea37260aca3638fc55672028cc77ed09030e65

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Citations in the corpus

The salience network is responsible for switching between the default mode network and the central executive network: Replication from DCM (2014, methodology): “All rights reserved. has also been applied to resting state data (Lee et al., 2012) and has shown differences in functional network connectivity with age (Stevens et al., 2009) and in patients with schizophrenia (Yu et al., 2011).”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “During the last two decades, there has been an explosion of fMRI studies mapping neural functions to distinct parts of the brain at rest or during task performance (Greicius et al., 2003), however, more attention has been directed toward resting-state fMRI (rs-fMRI) data (Lee et al., 2013).”
Unraveling inner experiences during resting state (2013, background): “For several disease populations, group differences in resting state measures compared to healthy controls have been detected, which may help to understand the pathophysiological bases of these diseases (e.g., Lustig et al., 2003; Lee et al., 2012; Jukuri et al., 2013).”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, background): “Brain functional neuroimaging techniques including functional magnetic resonance imaging (fMRI) (Lee et al., 2013; Power et al., 2014b), positron emission tomography (PET), and electroencephalography (EEG) have become important tools in investigating brain disease (Abi-Dargham and Horga, 2016).”