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Functional neuroimaging studies have implicated the fusiform gyri (FG) in structural encoding of faces, while event-related potential (ERP) and magnetoencephalography studies have shown that such encoding occurs approximately 170 ms poststimulus. Behavioral and functional neuroimaging studies suggest that processes involved in face recognition may be strongly modulated by socially relevant information conveyed by faces. To test the hypothesis that affective information indeed modulates early stages of face processing, ERPs were recorded to individually assessed liked, neutral, and disliked faces and checkerboard-reversal stimuli. At the N170 latency, the cortical three-dimensional distribution of current density was computed in stereotactic space using a tomographic source localization technique. Mean activity was extracted from the FG, defined by structure-probability maps, and a meta-cluster delineated by the coordinates of the voxel with the strongest face-sensitive response from five published functional magnetic resonance imaging studies. In the FG, approximately 160 ms poststimulus, liked faces elicited stronger activation than disliked and neutral faces and checkerboard-reversal stimuli. Further, confirming recent results, affect-modulated brain electrical activity started very early in the human brain (approximately 112 ms). These findings suggest that affective features conveyed by faces modulate structural face encoding. Behavioral results from an independent study revealed that the stimuli were not biased toward particular facial expressions and confirmed that liked faces were rated as more attractive. Increased FG activation for liked faces may thus be interpreted as reflecting enhanced attention due to their saliency.
BACKGROUND: The frontal lobe has been crucially involved in the neurobiology of major depression, but inconsistencies among studies exist, in part due to a failure of considering modulatory variables such as symptom severity, comorbidity with anxiety, and distinct subtypes, as codeterminants for patterns of brain activation in depression. METHODS: Resting electroencephalogram was recorded in 38 unmedicated subjects with major depressive disorder and 18 normal comparison subjects, and analyzed with a tomographic source localization method that computes the cortical three-dimensional distribution of current density for standard electroencephalogram frequency bands. Symptom severity and anxiety were measured via self-report and melancholic features via clinical interview. RESULTS: Depressed subjects showed more excitatory (beta3, 21.5-30.0 Hz) activity in the right superior and inferior frontal lobe (Brodmann's area 9/10/11) than comparison subjects. In melancholic subjects, this effect was particularly pronounced for severe depression, and right frontal activity correlated positively with anxiety. Depressed subjects showed posterior cingulate and precuneus hypoactivity. CONCLUSIONS: While confirming prior results implicating right frontal and posterior cingulate regions, this study highlights the importance of depression severity, anxiety, and melancholic features in patterns of brain activity accompanying depression.
The relationships between brain electrical and metabolic activity are being uncovered currently in animal models using invasive methods; however, in the human brain this relationship remains not well understood. In particular, the relationship between noninvasive measurements of electrical activity and metabolism remains largely undefined. To understand better these relations, cerebral activity was measured simultaneously with electroencephalography (EEG) and positron emission tomography using [(18)f]-fluoro-2-deoxy-D-glucose (PET-FDG) in 12 normal human subjects during rest. Intracerebral distributions of current density were estimated, yielding tomographic maps for seven standard EEG frequency bands. The PET and EEG data were registered to the same space and voxel dimensions, and correlational maps were created on a voxel-by-voxel basis across all subjects. For each band, significant positive and negative correlations were found that are generally consistent with extant understanding of EEG band power function. With increasing EEG frequency, there was an increase in the number of positively correlated voxels, whereas the lower alpha band (8.5-10.0 Hz) was associated with the highest number of negative correlations. This work presents a method for comparing EEG signals with other more traditionally tomographic functional imaging data on a 3-D basis. This method will be useful in the future when it is applied to functional imaging methods with faster time resolution, such as short half-life PET blood flow tracers and functional magnetic resonance imaging.