ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging

Abstract

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 was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series 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.

Keywords: Artefact removal; Functional connectivity; Functional magnetic resonance imaging (fMRI); Multiband acceleration; Resting-state.

Fig. 1

Graphical illustration of overall evaluation.

Fig. 2

Temporal SNR estimation for various cleaning procedures and acquisition protocols. The boxplots show the distribution across 53 subjects. From the raw SNR results (2.A), it is clear that the cleaning procedure increases the SNR, while the reduced voxel volume and EPI acceleration decreases it. However, taking into account the increased number of timepoints (2.B), the statistical power for simple analyses applied to MB6 data is seen to be comparable to those from the standard acquisition. This is of great value because it means that the increase in statistical power due to the acceleration counters the loss in SNR caused by the increase in spatial resolution.

Fig. 3

Timeseries amplitudes. This measure was obtained by scaling (the standard deviation of the) single-subject timeseries associated with each group-level map by the standard deviation of the corresponding uncleaned timeseries. The boxplots show the distribution of amplitudes across components (each component is first averaged across subjects). All cleanup approaches decrease the amplitude; the amplitude is higher with MB6 sequence than with Standard. STD=Standard sequence; uncl=uncleaned; soft=FIX soft cleaning; agg=FIX aggressive cleaning; nets=Nets cleaning.

Fig. 4

Temporal power spectra (4.A) for different cleaning approaches, obtained from scaled timeseries (i.e., each normalised by the amplitude of the corresponding uncleaned timeseries), averaging the spectra across subjects and then calculating median spectra across components. Uncleaned data have the highest power both at low and high frequency; however, after normalising for power at the highest frequencies (last 0.8% of the spectrum width, where the content of thermal noise is higher than the content in signal) (4. B), it is clear that with soft cleanup we obtained the highest contrast-to-noise ratio. Results are shown for MB6 data, at d=100 (y axis in logarithmic scale).

Fig. 5

Temporal power spectra of the different protocols for each cleaning option. The spectra were obtained by averaging the single subjects’ spectra (from timeseries scaled using the amplitude of uncleaned data) and calculating median spectra across components, without normalisation for high frequency power. Y axis in logarithmic scale.

Fig. 6

Networks’ similarity across subjects. The boxplots show the correlation coefficients (full correlation, partial correlation, and regularised ICOV) between network matrices (unwrapped into a vector of network matrix edges) for all pairs of subjects, with different cleaning steps and for different protocols. ICOV= L1-regularised partial correlation; STD=Standard sequence; uncl=uncleaned; soft=FIX soft cleaning; agg=FIX aggressive cleaning; nets=Nets cleaning.

Fig. 7

Group-level z-statistic maps of two RSNs (sensory-motor network and visual areas), derived from Standard (d=30) and MB6 (d=30 and d=100) datasets using the corresponding training data templates, without and with soft or aggressive FIX cleanup. Individual subjects’ z-statistic maps were mixture model corrected and combined using fixed-effects averaging. Group maps are thresholded at abs(z)>3 (red-yellow colour coding for positive z values, blue-light blue for negative ones). The effect of the cleaning is quite strong in terms of noise removal and more focal signal (as highlighted with the ring around the right sensory-motor network). With high dimensionality the RSNs are split into multiple components, allowing a more detailed analysis of network connectivity.

Fig. 8

Spatial correlations. The boxplots show the distributions across components of the correlation coefficients between the group maps (obtained with ME and FE statistics) and the corresponding templates, for different cleaning approaches and for different acquisition protocols. STD=Standard sequence; uncl=uncleaned; soft=FIX soft cleaning; agg=FIX aggressive cleaning.