Cleaning up the fMRI time series: Mitigating noise with advanced acquisition and correction strategies
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Cited by (12)
Hemodynamic timing in resting-state and breathing-task BOLD fMRI
2023, NeuroImageEvaluating the efficacy of multi-echo ICA denoising on model-based fMRI
2022, NeuroImageCitation Excerpt :Functional MRI data is powerful tool for investigating neural activity in the human brain, providing a window into brain organization (Bassett and Sporns, 2017; Buckner et al., 2011; Bullmore and Sporns, 2012; Busch et al., 2022; Feilong et al., 2021; Gomez et al., 2019a; Gordon et al., 2017; Gratton et al., 2018; Huntenburg et al., 2018; Kanwisher et al., 1997; Margulies et al., 2016; Murphy et al., 2018; Thomas Yeo et al., 2011) and neural computations (Baldassano et al., 2017; Caucheteux and King, 2022; Constantinescu et al., 2016; Doeller et al., 2010; Güçlü and van Gerven, 2015; Hasson et al., 2008; Huth et al., 2016; Kriegeskorte et al., 2008; Lescroart and Gallant, 2019; Popham et al., 2021; Sha et al., 2015). However, the contribution of non-neuronal noise, such as motion, heart rate, respiration, and hardware-related artifacts, severely impacts the quality of fMRI data (Bright and Murphy, 2017; Caballero-Gaudes and Reynolds, 2017; Friston et al., 1996; Liu, 2016). As such, optimizing data acquisition and preprocessing/denoising is critically important for ensuring accurate and reproducible results in all fMRI studies.
Reliability and stability challenges in ABCD task fMRI data
2022, NeuroImageCitation Excerpt :In that regard, we expected ROIs to have modestly higher values than regions with lesser task relevance and by extension less consistent incidence of activation in the literature. We expected within-session reliability to increase with age, as movement decreases with age in developmental samples (Engelhardt et al., 2017) and movement is a considerable source of additional variance in imaging research (Bright and Murphy, 2017; Diedrichsen and Shadmehr, 2005). Consistent with our previous findings in an adult sample (Korucuoglu et al., 2020, 2021), we expected more active regions to be modestly more reliable/stable.
Feasibility of spiral fMRI based on an LTI gradient model
2021, NeuroImageCitation Excerpt :We observed that the tSNR was largest for reconstructions using concurrent monitoring. This is expected, as we are reducing the temporal variance in the image time series by correcting for both system-related field variations and physiological field fluctuations (Bollmann et al., 2017; Bright and Murphy, 2017). These are not captured by the GIRF where we use the same trajectory for each volume.
New acquisition techniques and their prospects for the achievable resolution of fMRI
2021, Progress in NeurobiologyCitation Excerpt :While not the focus of this review, it is important to note that not only the data acquisition, but also the data analysis greatly impacts the spatial specificity. The data acquired in fMRI studies are subject to complex analysis pipelines, in which they are interpolated (Glasser et al., 2013), projected (Operto et al., 2008), pooled (Polimeni et al., 2018), cleaned (Murphy and Bright, 2017) and smoothed (Blazejewska et al., 2019). This can drastically reduce the effective resolution of the data after the analysis.
A practical modification to a resting state fMRI protocol for improved characterization of cerebrovascular function
2021, NeuroImageCitation Excerpt :Based on the mechanism of neurovascular coupling, the BOLD contrast is widely used as a surrogate measure for neural activity (Glover, 2011). However, there are many non-neural factors that can affect the BOLD signal, with multiple strategies and algorithms to mitigate and remove these sources of noise, (Wise et al., 2004, Murphy et al., 2013, Caballero-Gaudes and Reynolds, 2017, Greve et al., 2013, Bright and Murphy, 2017, Liu, 2013, Chu et al., 2018, Kim and Ogawa, 2012). We considered how resting-state fMRI scans are commonly deployed in neuroimaging research, and designed our protocol accordingly.