Selective attention to color or motion enhances activity in specialized areas of extrastriate cortex, but mechanisms of attentional modulation remain unclear. By dissociating modulation of visually evoked transient activity from the baseline for a particular attentional set, human functional neuroimaging was used to investigate the physiological basis of such effects. Baseline activity in motion- and color-sensitive areas of extrastriate cortex was enhanced by selective attention to these attributes, even without moving or colored stimuli. Further, visually evoked responses increased along with baseline activity. These results are consistent with the hypothesis that attention modulates sensitivity of neuronal populations to inputs by changing background activity.

In the past decade the importance of synchronized dynamics in the brain has emerged from both empirical and theoretical perspectives. Fast dynamic synchronous interactions of an oscillatory or nonoscillatory nature may constitute a form of temporal coding that underlies feature binding and perceptual synthesis. The relationship between synchronization among neuronal populations and the population ring rates addresses two important issues: the distinction between rate coding and synchronization coding models of neuronal interactions and the degree to which empirical measurements of population activity, such as those employed by neuroimaging, are sensitive to changes in synchronization. We examined the relationship between mean population activity and synchronization using biologically plausible simulations. In this article, we focus on continuous stationary dynam ics. (In a companion article, Chawla (forthcoming), we address the same issue using stimulus-evoked transients.) By manipulating parameters such as extrinsic input, intrinsic noise, synaptic ef cacy, density of extrinsic connections, the voltage-sensitive nature of postsynaptic mechanisms, the number of neurons, and the laminar structure within the populations, we were able to introduce variations in both mean activity and synchronization under a variety of simulated neuronal architectures. Analyses of the simulated spike trains and local eld potentials showed that in nearly every domain of the model's parameter space, mean activity and synchronization were tightly coupled. This coupling appears to be mediated by an increase in synchronous gain when effective membrane time constants are lowered by increased activity. These observations show that under the assumptions implicit in our models, rate coding and synchrony coding in neural systems with reciprocal interconnections are two perspectives on the same underlying dynamic. This suggests that in the absence of speci c mechanisms decoupling changes in synchronization from ring levels, indexes of brain activity that are based purely on synaptic activity (e.g., functional magnetic resonance imaging) may also be sensitive to changes in synchronous coupling.

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This paper presents a nonlinear principal component analysis (PCA) that identi¢es underlying sources causing the expression of spatial modes or patterns of activity in neuroimaging time-series. The critical aspect of this technique is that, in relation to conventional PCA, the sources can interact to produce (second-order) spatial modes that represent the modulation of one (¢rst-order) spatial mode by another. This nonlinear PCA uses a simple neural network architecture that embodies a speci¢c form for the nonlinear mixing of sources that cause observed data. This form is motivated by a second-order approximation to any general nonlinear mixing and emphasizes interactions among pairs of sources. By introducing these nonlinearities principal components obtain with a unique rotation and scaling that does not depend on the biologically implausible constraints adopted by conventional PCA.The technique is illustrated by application to functional (positron emission tomography and functional magnetic resonance imaging) imaging data where the ensuing ¢rst-and second-order modes can be interpreted in terms of distributed brain systems. The interactions among sources render the expression of any one mode context-sensitive, where that context is established by the expression of other modes. The examples considered include interactions between cognitive states and time (i.e. adaptation or plasticity in PET data) and among functionally specialized brain systems (using a fMRI study of colour and motion processing).