Bandpass Filter, BPF
Bandpass filtering is essential to most standard resting-state (rs-fMRI) pipelines. When Biswal (1995) wrote the first rs-fMRI study, low-pass filtering with a 0.08 Hz cutoff was applied to the dataset. Biswal took a seed or ROI in the cortical motor area and conduct a whole-brain temporal correlation. He also observed that the power spectrum of the spontaneous fluctuation resides mainly < 0.1 Hz. Other earlier works, e.g. Lowe et al., 1998 and Cordes et al., 2000 follow the same footsteps.
A study focused on this question showed that only frequencies below 0.1 Hz contribute to regionally specific BOLD correlations, with faster frequencies relating to cardiac or respiratory factors. Based on this finding, the majority of spontaneous BOLD studies low-pass filter data at a cut-off of 0.08 or 0.1 Hz. [Fox & Raichle, 2007]What is a bandpass filter?
A bandpass filter only allows certain frequency range to pass through while attenuating the frequency components outside that range. There are two cutoff frequencies associated with a bandpass filter, low and high cutoff. Roughly speaking, it can be achieved by a combination of low-pass and high-pass filtering. Some studies prefer to use the word 'low-pass' filtering on the resting-state data. This is valid because high-pass filter has already been applied to remove drift during preprocessing steps. The use of Butterworth filter is common. In Matlab, it is best to use forward and reverse algorithms to prevent phase shift and distortion. Other filter includes Gaussian filter (FSL, e.g. in Damoiseaux, et al., 2006).
Why and when should we filter?
First reason is the definition of the rs-fMRI as the low-frequency spontaneous signal fluctuation. Second reason is to avoid the influence of physiological noise, the primary non-neuronal signals that corrupt our dataset. Seed-based and ICA-based methods are the most common methods in rs-fMRI analysis. In the ICA-based method, the higher frequency artefacts will be identified as individual components. Unlike ICA, the seed-based or ROI method requires us to remove unwanted artifacts manually. There is one warning. Bandpass filtering works best only at very fast acquisition or very low TR. For a TR = 0.50 msec, for example, the highest frequency range is up to 2 Hz. In other words, aliasing is prevented up to ~ 1 Hz. This is high enough to capture both respiratory and cardiac components. For higher TR, a combination of filtering and multiple regression is used to clean up the data. For more review, see: DP Auer (2008)
What is the cutoff frequency?
The lower cutoff frequency is normally 0.01 Hz, a usual nominal value to remove drift. The earliest and most common high cutoff value is either 0.08 or 0.1 Hz. Another popular value of 0.15 Hz is used by other studies (e.g. Greicius, et al., 2003 and Ellen. et al., 2008) and it is recommended by some (e.g. Urs Braun, et al., 2012). It seems that the high cutoff value is more ambiguous, but in order to be comfortable in choosing that value, we should study the frequency content of the resting brain. I like one of the earliest studies about this topic by Cordes et al., 2001. Using a fast TR (high speed acquisition to reduce aliasing), frequency components of 0 - 0.1 Hz are found to dominate three different areas in the brain: motor, auditory, and visual. More recently, a higher value of 0.25 Hz is popular with a much faster acquisition (e.g. Boubela, et al., 2013 and Kalcher, et al., 2014).
How do we extract frequency spectrum in rs-fMRI dataset? DYI = One can use Matlab to get the time series of a voxel then use FFT to get the frequency spectrum. Alternatively, one can use ICA to separate the components and identify the components of interest, say, associated with the primary motor cortex.
Identifying Nuisance Components by ICA
Spatial ICA, which separates rs-fMRI data into spatially independent patterns of activity, has been popular as an exploratory fMRI data analysis. What does ICA give you? The spatial and temporal components, plus the frequency content of that component (McKeown et al., 2003). This can be extremely useful to check whether a certain IC is resting-state component or just noise.
Single-subject ICA Approach
There are two ways you can use ICA with your resting-state data. First, by Group ICA (gICA) to perform model-free multivariate exploratory analysis on the multiple subject dataset. This can be done through temporal concatenation. Second, to a single-subject dataset. Usually, the noise components identified here will be used in the subsequent pipeline in the GLM, that is, to regress them out from the dataset. This can be achieved in FSL by using the fsl_regfilt command after performing MELODIC.
Often, the number of calculated # IC is also known as model order or ICA dimensionality. It can be freely selected up to n − 1, where n is the number of time points or volumes. It is common to set 20 - 30 using a standard pulse sequence with long TR, ~150 volumes (e.g. Calhoun, et al., 2001; Greicius, et al., 2007) or 60 (e.g., van de Ven, et al. 2007). Using a probabilistic approach, FSL-MELODIC sets the automatic dimensionality option by default so I leave this setting as it is. With this option, different participant dataset yields to a different number of components, (e.g. de Luca, et al., 2005). Under-estimating the model order may prevent us from capturing the full spectra of noise in the data. This results in a less effective method to clean the data. Also, the ICs may contain both signals and noise, making the judgment difficult. Overestimating the # IC, according to Li YO. et al., 2007, reduces the stability of the IC estimates and degrades the estimation of task-related brain activations. Another nice discussion on this topic is in a study by Abou-Elseoud et al., 2010.
How can we identify noise/nuisance ICs?
Manual visual inspection is the most direct approach. A nice publication with figures by Kelly RE Jr., et al., 2010 tells us generic rules to identify nuisance components from the ICA results. Some important points to label the components as noise:
More than 75% of the frequency bands are > 0.1 Hz. More useful by using faster TR.
Activation around the perimeter is highly likely due to movements.
Activation around the ventricles, especially the lateral and fourth ventricles are usually obvious.
Sporadic little activation blobs in the white matter.
Activation around the major arteries or sinuses.
Sudden spike (movement) or signal drops (artifacts).
The separation is often not that clear-cut. If doubtful, do not remove that component.
Once these components have been identified, we can place them in GLM as regressors. Efforts are made to create an automated software to classify signals and noise and to denoise resting-state data, e.g. SOCK and FSL-FIX.
How many # ICs should I remove?
Again there is no consensus but it assumes that the more noise you throw, the cleaner your data will be. Unfortunately, to some datasets, ICA does not do the job well. This is particularly true with high TR, causing aliasing in the temporal dataset. In other words, some components contain both signal and noise, or spatially it looks noisy but temporally its frequency spectrum is around 0 - 0.15 Hz. Based on my personal experience with regular and multi-band accelerated pulse sequences (MB = 3), I often found that almost 50% of the components are noise. The challenge would be to carefully select the noise while at the same time avoiding a false positive.
Once these components have been identified, we can place them in GLM as regressors. Efforts are made to create an automated software to classify signals and noise and to denoise resting-state data, e.g. SOCK and FSL-FIX.
How many # ICs should I remove?
Again there is no consensus but it assumes that the more noise you throw, the cleaner your data will be. Unfortunately, to some datasets, ICA does not do the job well. This is particularly true with high TR, causing aliasing in the temporal dataset. In other words, some components contain both signal and noise, or spatially it looks noisy but temporally its frequency spectrum is around 0 - 0.15 Hz. Based on my personal experience with regular and multi-band accelerated pulse sequences (MB = 3), I often found that almost 50% of the components are noise. The challenge would be to carefully select the noise while at the same time avoiding a false positive.
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