Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data

By David M. Cole, Stephen M. Smith, Christian F. Beckmann. Frontiers in Systems Neuroscience, 2010

1,023 citations recorded by the source corpus.

The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state functional magnetic resonance imaging signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/3e488f0bd5ebdf0340e1d70d72e220ce1c94d51b

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Citations in the corpus

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses (2017, background): “Although there are some methodological challenges to ICA (Cole et al., 2010) there is now a consensus that spatial ICA is a powerful analytical tool for investigating functional connectivity across multiple functional domains, such as sensory, motor, cognitive, and limbic systems, simultaneously.”
Schizophrenia, neuroimaging and connectomics (2012, background): “…mentioned, positive and negative associations between connectivity levels and various symptom dimensions of schizophrenia have been reported (Cole et al., 2010; Salvador et al., 2010a; Vercammen et al., 2010; Whitfield-Gabrieli et al., 2009) suggesting thefindingsmay, to some extent, be…”
Neuroplasticity subserving motor skill learning (2011, positive): “…in the BOLD signal, in the absence of any overt input or behavior, have been widely reported in the past 15 years (for a review, see Fox and Raichle, 2007; Cole et al., 2010) and can be recorded at different times before or after training without affecting subsequent behavioral testing.”
Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques (2013, background): “The role of anticorrelations within RSNs remains unclear and controversial [Cole et al., 2010; Fox et al., 2005; Murphy et al., 2009], though it has been suggested that they subserve a ‘‘differentiating role,’’ segregating neuronal processes that have competing representations [Fox et al.”
Health, USA Reviewed by: (2010, neutral): “It is therefore necessary for each study to be clear about the hypotheses being tested and the biophysical interpretation of connectivity parameters (Buchel and Friston, 2000; Penny et al., 2004b; Ramnani et al., 2004; Lee et al., 2006; Friston, 2009; Roebroeck et al., 2009a,b; Cole et al., 2010).”
The Nuisance of Nuisance Regression: Spectral Misspecification in a Common Approach to Resting-State fMRI Preprocessing Reintroduces Noise and Obscures Functional Connectivity (2013, methodology): “Resting-state functional connectivity magnetic resonance imaging (RS-fcMRI) is a dominant method for characterizing the human functional connectome (Cole et al., 2010; Van Essen and Ugurbil, 2012).”
Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty (2011, neutral): “…to characterize functional brain networks and connectivity (Beckmann and Smith, 2004; Calhoun et al., 2001, 2003); (Bullmore and Sporns, 2009; Cole et al., 2010; Deco et al., 2011; Friston et al., 1997; Greicius et al., 2003; McIntosh, 2000; Rubinov and Sporns, 2010; Sun et al., 2004;…”
Handbook of Functional MRI Data Analysis: Index (2011, background): “Some work has indicated that treating the global signal as a nuisance in resting-state fMRI data analysis may induce false negative correlations (Murphy et al., 2009; Weissenbacher et al., 2009; Cole et al., 2010), but there is still a debate on whether these negative correlations are mathematical e”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, neutral): “The univariate and multivariate approaches typically applied to resting-state data (for review, see Cole et al., 2010) assume that the strength of interactions between regions is constant over time.”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, background): “Concerns about the many sources of nuisance variation impacting on R-fMRI measures continue to grow (Cole et al., 2010; Kelly et al., 2012).”
Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks (2017, background): “It describes the temporal and spatial characteristics of the underlying hidden components or networks.(20,28) Even though it is advantageous that prior knowledge is not required for the processing of the source signal, one should be able to identify the ICA network components from those of noise by ”
Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes (2013, neutral): “As researchers increase their efforts to characterize variations in the functional connectome observed across studies and individuals, concerns about the many known and unknown sources of nuisance variation present and their impact on resting state fMRI (R-fMRI) measures continue to grow (Cole et al”