Teaching professionalism to medical students.
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Biomedical subjects
Publications and source records attributed to B Cutillo.
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Nine subjects performed a cued S1-S2 matching task in which two sequentially presented visual stimuli (either letter strings or non-verbal graphical patterns) were compared according to verbal (phonemic, semantic, syntactic) or non-verbal (graphic identity) criteria. The Laplacian derivation was used to spatially enhance the topography of averaged evoked potentials (EPs) recorded from 59 scalp electrodes. Several effects distinguished the non-verbal from the verbal conditions. For example, following S1 a P250 EP that reached maximum amplitude over the occipital area was larger for the non-verbal patterns, whereas word and word-like letter strings (but not unfamiliar characters) elicited an N470 in the left temporal region. In anticipation of S2, a CNV-like slow potential was enhanced over posterior regions for the non-verbal stimuli. During the matching interval following S2, a P475 peak was observed to be larger for non-verbal patterns than for letter strings over right frontal and temporal regions. Other effects distinguished the verbal conditions from one another. In particular, following S1 a left frontal P445 potential was enhanced to closed class versus open class words, and following S2 a P620 potential in the left temporal region was enhanced for phonological matching relative to semantic matching. These results suggest that processing of verbal and non-verbal stimuli depends on a network of subprocessors that are regionalized to functionally specialized cortical areas and that operate both sequentially and in parallel in order to extract and synthesize multiple forms of attribute-specific information. In contrast to neuropsychological approaches to the study of pattern recognition and reading, the fine-grain temporal resolution of EP measurements, in combination with the improved spatial resolution obtained through computation of Laplacian derivation wave forms from a large number of electrodes, permits characterization of both the regionalization of subprocesses and the subsecond dynamics of their engagement.
Previous studies suggest that evidence for the sub-second activation of distributed neural networks can be obtained by computing the covariance between segments of the scalp-recorded evoked potential. However, the cortical representation of such potentials is not known. Here we report a case study where the evoked potential covariance (EPC) measure was applied to data recorded from a 58-channel subdural grid implanted in an epilepsy patient. Recordings were made while the patient performed a task that required judging the somatosensory intensities of electrical stimuli and executing precise finger flexion responses in response to a subset of those stimuli. Post-stimulus EPC patterns involved covariances between somatosensory, motor, and temporal regions. Pre-stimulus EPC patterns involved these same regions, but only when it could be anticipated that the upcoming stimulus would likely require a response. The majority of the observed EPCs occurred with non-zero time-lags, and these EPCs often involved non-adjacent electrode pairs. Thus, the observed EPCs were unlikely to arise solely from volume conduction. Rather, they appeared to reflect the transient integration of activity across distinct cortical processing nodes.
Working memory (WM), the ability to momentarily maintain information in an active state, is central to higher cognitive functions. The processes involved in WM operate on a sub-second timescale, and thus evoked potential measures have an appropriate temporal resolution for studying them. In the experiment reported here, evoked potential covariances (EPC) between scalp recording sites were computed for a task requiring maintenance of numeric information in WM; these EPCs were compared to those observed in a control task which had the same stimuli and responses but less of a WM requirement. EPC patterns differed between conditions prior to the stimulus, and in an interval spanning the P300 peak in the match detection trials which required response inhibition. The pattern of prestimulus EPCs was more complex and left-sided in the WM task, when memory codes were being maintained and responses contingent on those codes were being prepared. P300 peak latency was 140 msec shorter in the WM task, and the P300 EPC pattern was more anterior and left-sided. In contrast, EPC patterns did not differ during early stages of stimulus processing or during response execution. These results suggest that distinct EPC patterns associated with WM only occur during intervals in which the information in an active state is being utilized for task performance.