ICA-based artefact and accelerated fMRI acquisition for improved Resting State Network imaging

By Ludovica Griffanti, Gholamreza Salimi Khorshidi, Christian F. Beckmann, E. Auerbach, Gwenaëlle Douaud, Claire E. Sexton, Eniko Zsoldos, Nicola Filippini, Clare E. Mackay, Steen Moeller and 6 more. NeuroImage, 2014

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The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB’s ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures were assessed using timeseries (amplitude and spectra), network matrix and spatial map analyses. For timeseries and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/feec8b37445c5e814fdef9cdcd0a5881d442632f

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In Cognitive Neuroscience

Citations in the corpus

Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, methodology): “The main differences between the HCP and GSP data were that the HCP data were collected on a custom-made Siemens 3T Skyra scanner using a multiband sequence, and denoised using ICA-FIX (Griffanti et al. 2014) and Wishart soft thresholding with minimal (2mm) smoothing (HCP S900 manual; Glasser et al.”
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion (2017, methodology): “In the case of the HCP data, we utilized the MSMAll ICA-FIX data on fs_LR32K surface space (HCP S900 manual; Van Essen et al. 2012a; Van Essen et al. 2012b; Glasser et al. 2013; Smith et al. 2013; Griffanti et al., 2014; Salimi-Khorshidi et al. 2014) with additional nuisance regression, censoring (B”
Brain network dynamics are hierarchically organized in time (2017, methodology): “After structured artifact removal using ICA followed by FMRIB’s ICA-based X-noisefier (FIX) from the FMRIB Software Library (FSL) (41), which removed more than 99% of the artifactual ICA components in the dataset, we used group spatial ICA to obtain a “parcellation” of 50 components that covers both”
Simultaneous multislice (SMS) imaging techniques (2015, methodology): “In addition, the increase in the (temporal) degrees-offreedom allows the application of advanced analysis strategies such as temporal independent component analysis or ICA (94,95), as well as a denser temporal sampling of physiological fluctuations in the fMRI signal (96), which helps in their ident”
Recent progress and outstanding issues in motion correction in resting state fMRI (2014, neutral): “It is not yet known whether higher-dimensional ICA (e.g. (Griffanti et al., 2014; Salimi-Khorshidi et al., 2014)) in high quality datasets (such as the Human Connectome Project data) will be able to adequately separate such variance; such investigations are underway.”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, background): “ICA-FIXincreasedRSNreproducibilityandhasbeendemonstratedto have the potential for accurate component classi ﬁ cation and denoising of fMRI data (Salimi-Khorshidi et al., 2014; Griffanti et al., 2014).”
Discovering dynamic brain networks from big data in rest and task (2017, methodology): “We then use ICA (Beckmann and Smith, 2004) followed by automated classification and removal of the artefact components (Griffanti et al., 2014).”
Hand classification of fMRI ICA noise components (2017, neutral): “…70%), as shown in previous works: – standard sequences at 3T: around 70% in (Rummel et al., 2013) and 88% in (Griffanti et al., 2014); – multiband sequences at 3T: 90% in the HCP (Smith et al., 2013) and 88% in (Griffanti et al., 2014); – standard sequence at 1.5T, 82% in (Griffanti et al., 2015).”
ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data (2015, positive): “For more radical removal of noise, (Griffanti et al., 2014) proposed to incorporate 24 RPs within a non-aggressive denoising procedure in ICA-FIX (Salimi-Khorshidi et al.”
Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior (2019, methodology): “Recent studies have suggested that ICA-FIX does not fully eliminate global motion-related and respiratory-related artifacts (Burgess et al., 2016; Siegel et al., 2017), so further censoring was performed.”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “…(ICA; Beckmann et al., 2005) of single-subject time series has increasingly been used for denoising, with noise components selected either by a trained classifier (ICA-FIX; Griffanti et al., 2014; Salimi-Khorshidi et al., 2014) or using a priori heuristics (ICA-AROMA; Pruim et al., 2015a, 2015b).”
The Human Connectome Project’s Neuroimaging Approach (2016, methodology): “The seven tenets of the HCP-style paradigm span the domains of acquisition, analysis, and sharing of MRI-based data: (1) acquiring large amounts of high quality, multi-modal data on as many subjects as feasible to measure architectural, functional, connectional, and topographical information4 (2) ac”