Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.
Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink‐related brain activity.
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.
Many functional neuroimaging studies of biological motion have used as stimuli point-light displays of walking figures and compared the resulting activations with those evoked by the same display elements moving in a random or noncoherent manner. Although these studies have established that biological motion activates the superior temporal sulcus (STS), the use of random motion controls has left open the possibility that coordinated and meaningful nonbiological motion might activate these same brain regions and thus call into question their specificity for processing biological motion. Here we used functional magnetic resonance imaging and an anatomical region-of-interest approach to test a hierarchy of three questions regarding activity within the STS. First, by comparing responses in the STS with animations of human and robot walking figures, we determined (1) that the STS is sensitive to biological motion itself, not merely to the superficial characteristics of the stimulus. Then we determined that the STS responds more strongly to biological motion (as conveyed by the walking robot) than to (2) a nonmeaningful but complex nonbiological motion (a disjointed mechanical figure) and (3) a complex and meaningful nonbiological motion (the movements of a grandfather clock). In subsequent whole-brain voxel-based analyses, we confirmed robust STS activity that was strongly right lateralized. In addition, we observed significant deactivations in the STS that differentiated biological and nonbiological motion. These voxel-based analyses also revealed regions of motion-related positive activity in other brain regions, including MT or V5, fusiform gyri, right premotor cortex, and the intraparietal sulci.
Independent component analysis (ICA), which separates fMRI data into spatially independent patterns of activity, has recently been shown to be a suitable method for exploratory fMRI analysis. The validity of the assumptions of ICA, mainly that the underlying components are spatially independent and add linearly, was explored with a representative fMRI data set by calculating the log-likelihood of observing each voxel's time course conditioned on the ICA model. The probability of observing the time courses from white-matter voxels was higher compared to other observed brain regions. Regions containing blood vessels had the lowest probabilities. The statistical distribution of probabilities over all voxels did not resemble that expected for a small number of independent components mixed with Gaussian noise. These results suggest the ICA model may more accurately represent the data in specific regions of the brain, and that both the activity-dependent sources of blood flow and noise are non-Gaussian.
Background The objective of this study was to examine the effects of aerobic exercise on evoked dopamine release and activity of the ventral striatum using positron emission tomography and functional magnetic resonance imaging in Parkinson's disease (PD). Methods Thirty‐five participants were randomly allocated to a 36‐session aerobic exercise or control intervention. Each participant underwent an functional magnetic resonance imaging scan while playing a reward task before and after the intervention to determine the effect of exercise on the activity of the ventral striatum in anticipation of reward. A subset of participants (n = 25) completed [11C] raclopride positron emission tomography scans to determine the effect of aerobic exercise on repetitive transcranial magnetic stimulation‐evoked release of endogenous dopamine in the dorsal striatum. All participants completed motor (MDS‐UPDRS part III, finger tapping, Timed‐up‐and‐go) and nonmotor assessments (Starkstein Apathy Scale, Beck Depression Inventory, reaction time, Positive and Negative Affect Schedule, Trail Making Test [A and B], and Montreal Cognitive Assessment) before and after the interventions. Results The aerobic group exhibited increased activity in the ventral striatum during functional magnetic resonance imaging in anticipation of 75% probability of reward (P = 0.01). The aerobic group also demonstrated increased repetitive transcranial magnetic stimulation‐evoked dopamine release in the caudate nucleus (P = 0.04) and increased baseline nondisplaceable binding potential in the posterior putamen of the less affected repetitive transcranial magnetic stimulation‐stimulated hemisphere measured by position emission tomography (P = 0.03). Conclusions Aerobic exercise alters the responsivity of the ventral striatum, likely related to changes to the mesolimbic dopaminergic pathway, and increases evoked dopamine release in the caudate nucleus. This suggests that the therapeutic benefits of exercise are in part related to corticostriatal plasticity and enhanced dopamine release. © 2019 International Parkinson and Movement Disorder Society
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