In addition, the noise over the spatial dimension across fNIRS channels (source–detector pairs) is also typically nonuniform. Specific source–detector pairs or measurements at a particular wavelength may vary considerably in the level of noise compared with the other measurements. This noise is largely determined by how well the sensors are placed on the head, contact with the skin, the presence of hair under the fNIRS probe, and so on. In practice, it is not uncommon to have an order of magnitude difference or more in the signal-to-noise ratio between channels with good and poor contract on the head. Thus, the noise across fNIRS channels should not be assumed to be normal and exhibits heteroscedasticity. Due to the small number of fNIRS measurement channels compared with voxels in an fMRI scan, the central limit theorem generally does not apply in the fNIRS spatial domain. In the analysis methods used in the program NIRS-SPM,14 noise covariance estimates are performed by pooling the data across channels with the iterative restricted maximal likelihood (ReML) method. The ReML method is used to estimate both the noise covariance and the autoregressive (AR) coefficients within the covariance structure used in the prewhitening procedure (discussed in Sec. 4 of this work). Because this method obtains only one set of hyperparameters for oxy- and deoxy-hemoglobin that define the covariance structure applied to the whole channel set (e.g., across channels with varied qualities of optode-head contact), this estimate can be skewed by having one or a few channels that have very different noise from the other channels, as demonstrated in Sec. 6 of this work. As a result, the use of ReML in the NIRS-SPM software requires an additional preprocessing step, described in the user manual, to remove “bad” channels prior to computing the ReML covariance models. This step is not generally required in fMRI analysis, since the spatial noise of fMRI is more uniform and can otherwise be spatially smoothed (spatial precoloring). However, in fNIRS data, the noise per optical source–detector pair can vary significantly, thus the same noise model is often not sufficient for analysis (we will return to this concept in Sec. 6 of this paper). In addition, spatial heteroscedastic noise across the fNIRS measurements is of concern in group-level analyses, where corresponding channels are analyzed across subjects, and in analysis pooling data from multiple channels on the probe such as region-of-interest or image reconstruction methods.