NeuroImage
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Nonlinear effects in fMRI BOLD data may substantially influence estimates of task-related activations, particularly in rapid event-related designs. If the BOLD response to each stimulus is assumed to be independent of the stimulation history, nonlinear interactions create a prediction error that may reduce sensitivity. When stimulus density differs among conditions, nonlinear effects can cause artifactual differences in activation. ⋯ Our estimates of nonlinearity appear relatively consistent throughout the brain, and these estimates can be used to form adjusted linear predictors for future rapid event-related fMRI studies. Adjusting the linear model for these known nonlinear effects results in a substantially better model fit. The biggest advantages to using predictors adjusted for known nonlinear effects are (1) higher sensitivity at the individual subject level of analysis, (2) better control of confounds related to nonlinear effects, and (3) more accurate estimates of design efficiency in experimental fMRI design.
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Using functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) source dipole analysis in 10 normal subjects, two electrical source dipoles in the contralateral fronto-parietal operculum were identified during repetitive painful subepidermal stimulation of the right index finger. The anterior source dipole peaking at 79 +/- 8 ms (mean +/- SD) was located in the frontal operculum, and oriented tangentially toward the cortical surface. The posterior source dipole peaking at 118 +/- 12 ms was located in the upper bank of the Sylvian fissure corresponding to the second somatosensory cortex (S2). ⋯ However, due to low signal-to-noise ratio of ipsilateral EEG sources in individual recordings, separation of sources into anterior and posterior clusters was not performed. Combined fMRI and source dipole EEG analysis of individual data suggests the presence of two distinct electrical sources in the fronto-parietal operculum participating in processing of somatosensory stimuli. The anterior region of the fronto-parietal operculum shows earlier peak activation than the posterior region.
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Anatomical and functional MRI images were acquired in a group of healthy elderly subjects (n = 11) and a group of patients diagnosed with probable Alzheimer's disease, from mild to moderate severity (n = 8). During functional sessions, verbal episodic Encoding and Recognition tasks were presented to subjects. Both groups were compared in terms of gray matter volume and cerebral activation. ⋯ This additional activity elicited by episodic memory processes was not found to correlate with the degree of medial temporal atrophy in our group of patients. Our study shows that function in brain regions critical to episodic memory is altered in AD. During episodic Recognition, these functional changes may closely correlate with the progressive structural changes observed in the hippocampal region.
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Independent component analysis (ICA) is a valuable technique for the multivariate data-driven analysis of functional magnetic resonance imaging (fMRI) data sets. Applications of ICA have been developed mainly for single subject studies, although different solutions for group studies have been proposed. These approaches combine data sets from multiple subjects into a single aggregate data set before ICA estimation and, thus, require some additional assumptions about the separability across subjects of group independent components. ⋯ We present real visual activation fMRI data from two experiments, with different spatiotemporal structures, and demonstrate the validity of this framework for a blind extraction and selection of meaningful activity and functional connectivity group patterns. Our approach is either alternative or complementary to the group ICA of aggregated data sets in that it exploits commonalities across multiple subject-specific patterns, while addressing as much as possible of the intersubject variability of the measured responses. This property is particularly of interest for a blind group and subgroup pattern extraction and selection.