Several previous studies have compared the blood oxygen level-dependent (BOLD) hemodynamic response (HDR) in healthy elderly subjects to the HDR in young subjects. Some studies have found a relative decreased amplitude in the elderly in the visual cortex, whereas other studies have found the elderly HDR amplitude in the visual cortex to be nearly identical to that in young subjects. A possible explanation for the different findings is that the peak voxel HDR is similar between the groups, but that the HDR in the group-averaged region-of-interest (ROI) is “washed out” by the inclusion of less significant voxels (due to a smaller extent of activation in the elderly) or by the inclusion of negative-peaking voxels. We tested this hypothesis using event-related functional magnetic resonance imaging (fMRI). While undergoing fMRI, subjects performed a simple visual and motor task, pressing with their index fingers in response to visual presentation of the word tap. Data from 18 subjects, 8 young and 10 elderly, were analyzed. For each subject, a visual and a motor ROI was selected by choosing the most significant positive voxels within the anatomically defined ROI. This individual subject approach excluded both low-significance and negative-peaking voxels. Similar peaks were found for the elderly and the young subjects in both motor and visual regions and a more sustained BOLD response was found for the elderly in both regions. Additionally, as predicted, a greater percentage of voxels with a negative HDR was found for the elderly in the visual region; this finding was also replicated in our reanalysis of an independent fMRI and aging study from the fMRI Data Center. Functional neuroimaging observations of negative HDRs in visual areas have been interpreted as the effect of unconstrained processing during rest. Our results suggest that the elderly may have more unconstrained visual processing during the rest condition in the scanner. The observation that the group differences in the BOLD response are sensitive to voxel selection (e.g., inclusion of low-significance and/or negative voxels) underscores the importance of ROI selection criteria in the interpretation of fMRI studies using elderly populations.

This research tests a model of the computational role of three cortical regions in tasks like algebra equation solving. The model assumes that there is a left parietal region-of-interest (ROI) where the problem expression is represented and transformed, a left prefrontal ROI where information for solving the task is retrieved, and a motor ROI where hand movements to produce the answer are programmed. A functional magnetic resonance imaging (fMRI) study of an abstract symbolmanipulation task was performed to articulate the roles of these three regions. Participants learned to associate words with instructions for transforming strings of letters. The study manipulated the need to retrieve these instructions, the need to transform the strings, and whether there was a delay between calculation of the answer and the output of the answer. As predicted, the left parietal ROI mainly reflected the need for a transformation and the left prefrontal ROI the need for retrieval. Homologous right ROIs showed similar but weaker responses. Neither the prefrontal nor the parietal ROIs responded to delay, but the motor ROI did respond to delay, implying motor rehearsal over the delay. Except for the motor ROI, these patterns of activity did not vary with response hand. In an ACT-R model, it was shown that the activity of an imaginal buffer predicted the blood oxygen level-dependent (BOLD) response of the parietal ROI, the activity of a retrieval buffer predicted the response of the prefrontal ROI, and the activity of a manual buffer predicted the response of the motor ROI.

Event-related fMRI was used to dissociate the neural systems involved in category learning with and without awareness. Ten subjects performed a speeded response category learning task. Functional MR images were acquired during both explicit and implicit learning conditions. Behavioral data showed evidence of learning in both conditions. Functional imaging data showed different activation patterns in implicit and explicit trials. Decreased activation in extrastriate region V3 was found with implicit learning, and increased activation in V3, the medial temporal lobe, and frontal regions were found with explicit learning. These results support the theory that implicit and explicit learning utilize dissociable neural systems. Moreover, in both the implicit and explicit conditions a similar pattern of decreased activation was found in parietal regions. This commonality suggests that these dissociable systems also operate in parallel.