Abstract
Event Abstract Back to Event Spatial attention modulates steady state VEPs in retinotopic human visual cortex Introduction In primates, sustained allocation of spatial attention causes significant increases in single-unit firing rates in higher visual areas such as V4 but similar increases are hard to measure in V1. Paradoxically, fMRI measurements of spatial attentional show strong responses in all visual areas, including V1. Recently, two groups have suggested that these fMRI changes are either purely additive (Buracas and Boynton 2007) or a mixture of additive and multiplicative mechanisms (Li et al, 2008). Buracas and Boynton suggested that spatial attention acts to marginally increase the baseline firing rates or presynaptic activity of all neurons in the attended region of cortex irrespective of their tuning. Such a change might be undetectable at the level of a single unit, yet could generate a large DC increase in metabolic demand that would influence the fMRI BOLD signal. To test this hypothesis, this we measured the effect of spatial attention on steady state visually-evoked potentials (SSVEPs) in four retinotopically-defined visual areas using source-imaged EEG. MethodsWe studied visual spatial attention in 7 subjects. SSVEP stimuli consisted of two randomly-oriented 3cpd, 3 degree Gabor gratings at 50% contrast located 3 degrees to the left and right of a fixation point. There were three attentional conditions. In condition 1 subjects were cued to detect small contrast modulations in the grating on the left. In condition 2 subjects performed the same task on the right. In condition 3, subjects ignored both gratings and performed a demanding letter discrimination task at fixation. Performance was approximately 75% correct on all conditions. EEG data were collected with a whole-head, 128-channel EGI Netstation system, and the locations of all electrodes were recorded using a 3D digitizer. Minimum-norm inverses were computed using anatomically-correct headmodels, and the timecourse of the mean cortical current density was extracted from fMRI-defined visual areas V1, V3a, V4 and MT+. Spectral analysis was used to separate the responses to the two stimulus gratings in all four visual areas. ResultsIn conditions 1 and 2 we found that attending to a target increases the amplitude of the frequency component associated with that target in all the studied areas, including V1. ConclusionSteady state VEPs are modulated by attention in all stages of cortical visual processing. The modulations that we measure have no DC components and are best modeled by a multiplicative, rather than an additive gain function. Interestingly, signal levels of the “ignored” gratings in conditions 1 and 2 were lower than those in condition 3 suggesting that attentional selection is more effective for well-separated targets or, perhaps, more necessary when those targets share common spatial features such as shape and spatial frequency.
Highlights
Given the complexity of our visual environment, the ability to selectively attend to certain locations, while ignoring others, is crucial for reducing the amount of visual information to manageable levels and for optimizing performance
We found that dynamic neural responses were modulated by attention in all the visual areas we examined, including V1
In conditions 1 and 2 we found that the allocation of spatial attention to a target increases the amplitude of the frequency component corresponding to that target in all the studied areas, including V1 (Figure 2)
Summary
Given the complexity of our visual environment, the ability to selectively attend to certain locations, while ignoring others, is crucial for reducing the amount of visual information to manageable levels and for optimizing performance. While fMRI literature on humans show robust attentional modulation in lower visual areas, V1, this is practically absent in the primate electrophysiological literature. One explanation for this could be the difference in methods. Buracas and Boynton [1] showed that attentional effects on the BOLD signal can be modeled by a purely additive mechanism They hypothesized that their data could be explained by non-spike-related activity or by a DC increase in the firing rate of whole populations of neurons. Either of these mechanisms might be too small to be detected in single cell electrophysiological measurements, but could, generate significant changes in metabolic activity
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