Traditionally, emotional stimuli have been thought to be automatically processed via a bottom-up automatic “capture of attention” mechanism. Recently, this view has been challenged by evidence that emotion processing depends on the availability of attentional resources. Although these two views are not mutually exclusive, direct evidence reconciling them is lacking. One limitation of previous investigations supporting the traditional or competing views is that they have not systematically investigated the impact of emotional charge of task-irrelevant distraction in conjunction with manipulations of attentional demands. Using event-related fMRI, we investigated the nature of emotion-cognition interactions in a perceptual discrimination task with emotional distraction, by manipulating both the emotional charge of the distracting information and the demands of the main task. Findings suggest that emotion processing is both automatic and modulated by attention, but emotion and attention were only found to interact when finer assessments of emotional charge (comparison of most vs. least emotional conditions) were considered along with an effective manipulation of processing load (high vs. low). The study also identified brain regions reflecting the detrimental impact of emotional distraction on performance as well as regions involved in helping with such distraction. Activity in the dorsomedial prefrontal cortex (PFC) and ventrolateral PFC was linked to a detrimental impact of emotional distraction, whereas the dorsal anterior cingulate cortex and lateral occiptal cortex were involved in helping with emotional distraction. These findings demonstrate that task-irrelevant emotion processing is subjective to both the emotional content of distraction and the level of attentional demand.

Our study confirms the link between sleep disordered breathing and enuresis. All pediatric health care providers should be aware of this risk. The risk may be magnified in patients with concomitant daytime incontinence.

Independent component analysis (ICA) and statistical parametric mapping (SPM) are two commonly used methods of analyzing fMRI measurements. Typically, these methods are applied separately to the measurements to produce brain maps indicating active brain regions in response to a stimulus or a performed task. However, ICA can also be used to develop a hemodynamic response model that can be used as a regressor in SPM of fMRI measurements. This may lead to a more accurate method of localizing brain activity that corresponds to performing a task or to various pathologies. In this study, BOLD fMRI data were acquired from a subject performing a finger flexion task in a block design paradigm. Both spatial and temporal ICA was performed on the subject's BOLD fMRI measurements. Two hemodynamic response model signals were generated from ICA results to use as regressors in SPM of the subject data. IC maps and SPM-generated brain maps of the subject data using the canonical hemodynamic response model and the ICA-derived models were compared. In all cases, there was significant overlap in voxel activations.

1 tpenney@ualberta.ca, 2 goodyear@ucalgary.ca, 3 dpittman@ucalgary.ca, 4 pfederic@ucalgary.ca, 5 z.koles@ualberta.ca Abstract-Independent component analysis (ICA) and statistical parametric mapping (SPM) are two commonly used methods of analyzing fMRI measurements taken from a patient's brain. Typically, these methods are applied separately to the measurements to produce brain maps indicating where the patient's brain was active while he or she performed a task or experienced a stimulus. ICA can also be used to develop a hemodynamic response model that can be used as a regressor in SPM of fMRI measurements, a statistical analysis procedure that uses the general linear model (GLM). This may lead to a more accurate method of localizing patient brain activity because the hemodynamic response model is tailored to the patient's own measurements. In this study, BOLD fMRI data and EEG data were acquired from a continuous EEG-fMRI study done on a patient suffering from refractory right temporal lobe epilepsy. Spatial ICA was performed on the subject's BOLD fMRI measurements. One hemodynamic response model signal derived from the spatial ICA (sICA) results was generated to use as a regressor in SPM of the subject data. SPM-generated brain maps of the subject data using the canonical hemodynamic response model and the sICA-derived model were compared.

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