A Comparative Evaluation of the Efficacy of Single-Echo and Multi-Echo fMRI Denoising Strategies
Poster No:
1573
Submission Type:
Abstract Submission
Authors:
Toby Constable1, Alex Fornito2, Kane Pavlovich3, Arshiya Sangchooli4, Priscila Levi1, Jeggan Tiego1
Institutions:
1Monash University, Melbourne, VIC, 2Monash University, Clayton, Victoria, 3Monash University, Melbourne, Victoria, 4Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria
First Author:
Co-Author(s):
Arshiya Sangchooli
Melbourne School of Psychological Sciences, The University of Melbourne
Melbourne, Victoria
Melbourne School of Psychological Sciences, The University of Melbourne
Melbourne, Victoria
Introduction:
Resting-state (rs) fMRI-derived measures such as inter-regional functional coupling (FC) networks have been used extensively to explore the potential neural underpinnings of behaviour (Li et al., 2019). However, rs-fMRI signals are vulnerable to noise contaminants such as head motion, which can compromise measurement fidelity (Hillman, 2014; Liu, 2016; Ogawa et al., 1990) and complicate attempts to obtain reliable effect sizes in brain-behaviour associations (Marek et al., 2022). Recent advances such as time-lagged regression models that address spatiotemporally heterogeneous noise (e.g., Regressor Interpolation at Progressive Time Delays [RIPTiDe]) offer promising approaches for mitigating contaminants while also addressing more recently identified artefacts such as functional connectivity inflation (FCI) – whereby average FC spuriously inflates with time spent in the scanner (Korponay et al., 2024). RIPTiDe may also provide a more nuanced alternative to global (or grey-matter) signal regression, which has been criticised for introducing spurious anti-correlations in FC (Murphy et al., 2009). However, the potential advantages of RIPTiDe in behavioural prediction over the more commonly practised global (or grey-matter) signal regression remains unknown. Investigations concerning the potential benefits of multi-echo (ME) over single-echo (SE) data acquisitions also remain largely neglected – especially in behaviour prediction. In ME-rs-fMRI data acquisitions, multiple images are collected per radio-frequency pulse. Associated images can be combined to maximise signal-to-noise ratios ('optimum combination'), and voxel clusters unlikely to be neural can also be detected by investigating signal intensity changes between images. The influence of these clusters can then be removed from the data using ME- independent components analysis (ICA).
Methods:
In this study (n = 358), we compared the denoising efficacy of 60 ME and 30 SE preprocessing pipelines using six robust quality control metrics – variance explained by the first principal component of the parcellated time-series (VE1), signal-to-noise ratio (TSNR), delta variation signal (DVARS), quality-control functional-connectivity (QC-FC), QC-FC-Distance Dependence, and FCI. We then evaluated relative pipeline behavioural prediction (personality and cognition) through FC-based kernel ridge regression (KRR). Preprocessing strategies assessed included white-matter, cerebrospinal fluid and grey-matter time-series regression, RIPTiDe, Automatic Removal of Motion Artifacts ICA (ICA-AROMA), FMRIB's ICA-based X-noiseifier (FSL-FIX), and ME-ICA.
Results:
Quality-control metric results generally favoured ICA-FIX and the application of RIPTiDe without also including white-matter and cerebrospinal fluid-associated regressors for both SE and ME data types (after ME optimum combination) (e.g. Figure 1). Furthermore, ME pipelines generally outperformed SE pipelines in these metrics. However, ICA-AROMA and including only the twenty-four head motion parameters as additional regressors generally performed best for behaviour-prediction (Figure 2). As such, no single pipeline was associated with both superior denoising efficacy and superior behaviour prediction – consistent with recent prior literature (Pavlovich et al., 2024). Preliminary results indicate that combining ICA-AROMA and RIPTiDe - without also applying white-matter and cerebrospinal fluid regression - generally balances denoising efficacy and brain-behaviour predictions for both ME and SE data. Of these suggested preprocessing strategies, the ME variant appeared superior relative to that involving SE data, emphasising the potential gains associated with optimally combining ME data.
Conclusions:
RIPTiDe can be effectively integrated with ICA-AROMA for SE and ME data preprocessing – but the collection of ME data appears associated with superior data quality and behaviour-prediction accuracy relative to SE data.
Modeling and Analysis Methods:
Classification and Predictive Modeling
Methods Development 1
Motion Correction and Preprocessing
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Data analysis
Workflows
1|2Indicates the priority used for review
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Provide references using APA citation style.
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Pavlovich, K., Pang, J., Holmes, A., Constable, T., & Fornito, A. (2024). The efficacy of resting-state fMRI denoising pipelines for motion correction and behavioural prediction (p. 2024.12.01.626250). bioRxiv. https://doi.org/10.1101/2024.12.01.626250