FMRI

Motion-related artefacts in fMRI

Participant in-scanner motion is one of the prominent sources of artefacts in fMRI data. This was recognized as an issue in fMRI long ago (Hajnal et al., 1994; Friston et al., 1996). Motion has complex effects on the signal - including increases, decreases or complex waveforms, depending on factors such as the timing, duration and trajectory of motion (Power et al., 2015, Byrge and Kennedy 2018). Crucially, motion can substantially affect estimates of functional connectivity (Power et al., 2012; Satterthwaite et al., 2012). Initial motion-correction approaches involved regression of motion parameters and their derivatives from voxel-wise BOLD time series (Friston et al., 1996), or regression of the average or "global" signal (Aguirre et al., 1998). Later solutions included censoring of motion-affected frames (Power et al., 2012), removal of non-BOLD (artefactual) signal using independent component analysis applied to multi-echo fMRI data, based on the dependence of the BOLD signal on echo time (Kundu et al., 2012, 2013), or "despiking" of motion-related non-stationary events from a wavelet decomposition of the signal (Patel et al., 2014). The latter method was subsequently extended to provide revised estimates of voxel-wise effective degrees of freedom (df) of the BOLD time series, which due to denoising are lower than the nominal N(df ) = N(time-points), and which affect estimates of edge probability when incorporated into network analysis (Patel and Bullmore, 2016).

Other prominent artifacts, particularly in fMRI, relate to respiration, vasculature and arousal (e.g.: Murphy et al., 2013; Caballero-Gaudes and Reynolds, 2017).

Diagnostic measures

Multiple diagnostic measures can be investigated during quality control of fMRI data, to ensure that the processed data are maximally free of motion-related (or other) artefacts.

One key quantity used for diagnostic purposed is the framewise displacement (FD), a subject-specific time-series indexing an overall estimate of movement over time. During processing, re-alignment of scans is used to estimate 6 motion parameters for each participant (3 translation parameters and 3 rotation parameters). Subsequently, these were used to calculate an overall estimate of motion - the framewise displacement (FD), defined as the sum of the absolute temporal derivatives of the six motion parameters, following conversion of rotational parameters to distances by computing the arc length displacement on the surface of a sphere with radius 50 mm (as in Power et al. (2012) and Patel et al. (2014)): FD(t) = ∑|d(t−1)−d(t)| +50 · (π/180) ·∑|r(t−1)−r(t)| where d denotes translation distances {x,y,z}, and r denotes rotation angles {α,β,γ}. For each participant, a single (scalar) estimate of overall motion, the mea FD, can be calculated by averaging the FD time series.

ME-ICA processing for NSPN and BioDep

The following pipeline describes processing steps for multi-echo ICA (ME-ICA) data, as agreed by Manfred, Frantisek and others in May 2018. This is the pipeline applied to NSPN and BioDep projects.

Briefly, the processing pipeline includes the following steps:

...

The specific ME-ICA script to be run is located on the BCNI server at: "/home/rr480/Code/MATLAB/fMRI/MEICA/meica_manfred_2018_05_10/meica.py". This includes the options listed below (some of which are specific to NSPN data). For a detailed description of all processing steps, see MEICA.

 -d 'echo[1,2,3].nii'  
 -a 'brainmask.nii' 
 -e 13.0,30.55,48.1 
 --TR=2.43 
 --tpattern=seq-z 
 -b 15s 
 --fres=2.5 
 --mask_mode='anat' 
 --no_skullstrip 
 --OVERWRITE