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Neural substrates for dividing and focusing attention between simultaneous auditory and visual events.

NeuroImage | 2006

With information constantly bombarding the human sensory systems, how is it that we attend to behaviorally relevant information? The present study examined the behavioral and neural bases of attending to one sense while ignoring another sense (bimodal selective attention) contrasted with attending simultaneously to two senses (bimodal divided attention). In a functional magnetic resonance imaging (fMRI) study, subjects simultaneously heard novel melodies and viewed geometric shapes. They were instructed to actively attend to only one or to both senses (selective or divided attention) or as a baseline condition, passively observe both (bimodal passive). Memory tests for both attended and unattended stimuli validated that subjects were following the attention instructions. Selective attention led to increased activity in relevant sensory cortices while simultaneously leading to decreased activity in irrelevant sensory cortices. The divided attention instruction did not lead to a global increase in sensory cortex activity compared to the bimodal passive baseline condition. However, divided attention did recruit heteromodal areas in the left dorsolateral prefrontal cortex, while selective attention did not recruit any frontal areas. We propose that sustained selective and divided bimodal attention were achieved via distinct neural processes. Selective attention was achieved primarily via modulation of the sensory cortices. Divided attention was achieved for most individuals via recruitment of the middle-dorsolateral prefrontal cortex. However, there was also a trade-off between activity in posterior-dorsolateral prefrontal cortex and sensory regions, such that individuals who demonstrated the best performance during divided attention also showed the greatest recruitment of sensory cortices.

Pubmed ID: 16616520 RIS Download

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Analysis of Functional NeuroImages (tool)

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Set of (mostly) C programs that run on X11+Unix-based platforms (Linux, Mac OS X, Solaris, etc.) for processing, analyzing, and displaying functional MRI (FMRI) data defined over 3D volumes and over 2D cortical surface meshes. AFNI is freely distributed as source code plus some precompiled binaries.

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