Affective experience has been described in terms of two primary dimensions: intensity and valence. In the human brain, it is intrinsically difficult to dissociate the neural coding of these affective dimensions for visual and auditory stimuli, but such dissociation is more readily achieved in olfaction, where intensity and valence can be manipulated independently. Using event-related functional magnetic resonance imaging (fMRI), we found amygdala activation to be associated with intensity, and not valence, of odors. Activity in regions of orbitofrontal cortex, in contrast, were associated with valence independent of intensity. These findings show that distinct olfactory regions subserve the analysis of the degree and quality of olfactory stimulation, suggesting that the affective representations of intensity and valence draw upon dissociable neural substrates.

Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phaselocked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higherorder dependencies. Applied to detected-and undetectedtarget ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.Although the locations of the brain areas generating eventrelated potentials (ERPs) cannot be uniquely determined by scalp recordings from any number of channels (1), several methods have been proposed for decomposing evoked responses into activations of distinct neural sources. Most of these also attempt to locate the active areas, by assuming either that they have a known or simple spatial configuration (2) or that generators are restricted to a small subset of possible locations and orientations (3). Other methods based on rotations of principal components use optimization criteria not directly related to brain anatomy and physiology. These methods may assume that each response component has the same time course of activation in every experimental condition (4). All these methods use second-order spatiotemporal correlations to perform the decomposition.Here we report a statistical method for decomposing one or more event-related brain responses into a sum of components with spatially fixed scalp distributions and maximally independent (though possibly overlapping) time courses. Independence requires the absence of higher-order as well as secondorder correlations between the time courses. Independence, therefore, is a stronger condition than decorrelation and, in particular, is not satisfied by decomposition into principal components by principal component analysis (PCA).Although the ...

High-level visual cortex in humans includes functionally defined regions that preferentially respond to objects, faces and places. It is unknown how these regions develop and whether their development relates to recognition memory. We used functional magnetic resonance imaging to examine the development of several functionally defined regions including object (lateral occipital complex, LOC)-, face ('fusiform face area', FFA; superior temporal sulcus, STS)- and place ('parahippocampal place area', PPA)-selective cortices in children (ages 7-11), adolescents (12-16) and adults. Right FFA and left PPA volumes were substantially larger in adults than in children. This development occurred by expansion of FFA and PPA into surrounding cortex and was correlated with improved recognition memory for faces and places, respectively. In contrast, LOC and STS volumes and object-recognition memory remained constant across ages. Thus, the ventral stream undergoes a prolonged maturation that varies temporally across functional regions, is determined by brain region rather than stimulus category, and is correlated with the development of category-specific recognition memory.

The social motivation hypothesis of autism posits that infants with autism do not experience social stimuli as rewarding, thereby leading to a cascade of potentially negative consequences for later development. While possible downstream effects of this hypothesis such as altered face and voice processing have been examined, there has not been a direct investigation of social reward processing in autism. Here we use functional magnetic resonance imaging to examine social and monetary rewarded implicit learning in children with and without autism spectrum disorders (ASD). Sixteen males with ASD and sixteen age- and IQ-matched typically developing (TD) males were scanned while performing two versions of a rewarded implicit learning task. In addition to examining responses to reward, we investigated the neural circuitry supporting rewarded learning and the relationship between these factors and social development. We found diminished neural responses to both social and monetary rewards in ASD, with a pronounced reduction in response to social rewards (SR). Children with ASD also demonstrated a further deficit in frontostriatal response during social, but not monetary, rewarded learning. Moreover, we show a relationship between ventral striatum activity and social reciprocity in TD children. Together, these data support the hypothesis that children with ASD have diminished neural responses to SR, and that this deficit relates to social learning impairments.

Children who carry one variant of a brain protein associated with autism exhibit fewer long-range connections between the prefrontal cortex and more posterior brain regions.

Impulsive behavior is thought to reflect a trait-like characteristic that can have broad consequences for an individual’s success and well-being, but its neurobiological basis remains elusive. Although striatal dopamine D2-like receptors have been linked with impulsive behavior and behavioral inhibition in rodents, a role for D2-like receptor function in frontostriatal circuits mediating inhibitory control in humans has not been shown. We investigated this role in a study of healthy research participants who underwent positron emission tomography with the D2/D3 dopamine-receptor ligand [18F]fallypride, and blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) while they performed the Stop-signal Task, a test of response inhibition. Striatal dopamine D2/D3-receptor availability was negatively correlated with speed of response inhibition (stop-signal reaction time), and positively correlated with inhibition-related fMRI activation in frontostriatal neural circuitry. Correlations involving D2/D3 receptor availability were strongest in the dorsal regions (caudate and putamen) of the striatum, consistent with findings of animal studies relating dopamine receptors and response inhibition. The results suggest that striatal D2-like receptor function in humans plays a major role in the neural circuitry that mediates behavioral control, an ability that is essential for adaptive responding and is compromised in a variety of common neuropsychiatric disorders.

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The ability to flexibly respond to changes in the environment is critical for adaptive behavior. Reversal learning (RL) procedures test adaptive response updating when contingencies are altered. We used functional magnetic resonance imaging to examine brain areas that support specific RL components. We compared neural responses to RL and initial learning (acquisition) to isolate reversal-related brain activation independent of cognitive control processes invoked during initial feedback-based learning. Lateral orbitofrontal cortex (OFC) was more activated during reversal than acquisition, suggesting its relevance for reformation of established stimulus-response associations. In addition, the dorsal anterior cingulate (dACC) and right inferior frontal gyrus (rIFG) correlated with change in postreversal accuracy. Because optimal RL likely requires suppression of a prior learned response, we hypothesized that similar regions serve both response inhibition (RI) and inhibition of learned associations during reversal. However, reversal-specific responding and stopping (requiring RI and assessed via the stop-signal task) revealed distinct frontal regions. Although RI-related regions do not appear to support inhibition of prepotent learned associations, a subset of these regions, dACC and rIFG, guide actions consistent with current reward contingencies. These regions and lateral OFC represent distinct neural components that support behavioral flexibility important for adaptive learning.