Independent component analysis of functional MRI: what is signal and what is noise?

Abstract

Many sources of fluctuation contribute to the functional magnetic resonance imaging (fMRI) signal, complicating attempts to infer those changes that are truly related to brain activation. Unlike methods of analysis of fMRI data that test the time course of each voxel against a hypothesized waveform, data-driven methods, such as independent component analysis and clustering, attempt to find common features within the data. This exploratory approach can be revealing when the brain activation is difficult to predict beforehand, such as with complex stimuli and internal shifts of activation that are not time-locked to an easily specified sensory or motor event. These methods can be further improved by incorporating prior knowledge regarding the temporal and spatial extent of brain activation.

Figure 1

Results of applying temporal ICA to single-slice fMRI data. The subject was shown a flashing (8 Hz) annular checkerboard pattern interleaved with periods of fixation. There were five runs of 30 scans of fixation (10.0 s), 31 scans of stimulation (10.3 s), and 60 scans of post-stimulus fixation (20.0 s). The power spectrum is estimated in the range 0–1.5 Hz (Nyquist frequency). The slice is aligned with the calcarine sulcus and contains a portion of the primary visual areas. The six independent components shown are represented by the spatial map (the 2.5% highest and lowest values are shown as white and black pixels on a background formed by the average of the dataset providing anatomical references). The components are sorted according to variance contribution. (a) The first IC loads heavily in primary visual areas (V) (left column), and its time course (middle column, thin line) closely follows the stimuli time course (middle column, thick line). The power spectrum (right column) of the time course and stimulus time course are closely matched. (b) The second component contains pulsations related to the heartbeat as demonstrated in the time course and power spectrum. (c,d) The third and fourth components appear related to slower breathing-related periodic confounds. (e) Component five is a white noise (broad band) component with a more spiky character, and the component image is dominated by the (negative) boundary area (B), suggesting that this is mostly related to motion artifact. (f) The sixth element is a low-frequency component with a period of about 10–15 s unrelated to the stimulus sequence and possibly represents an artifact related to vasomotor oscillations [72].

Figure 2

Effects of motion correction on ICA components. (a) The predictability of the data, estimated by the diagonals of the Hat matrix, H = X (X′X)⁻¹ X′, where the columns of X represent the largest 1/3 of the eigenvectors of the covariance matrix, is plotted. The horizontal line is a heuristic used in regression to imply high leverage points. Note that common motion correction schemes (AIR and SPM) do not measurably affect predictability. MCICA = motion corrected ICA. (b) Even after standard motion correction, ICA components indicative of movement can still be isolated.