Andreas Roepstorff

Reduced effective connectivity between right parietal and inferior frontal cortex during audiospatial perception in neglect patients with a right-hemisphere lesion

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A lesion to the right hemisphere of the brain in humans commonly leads to perceptual neglect of the left side of the sensorium. The clinical observation that lesions to disparate cortical and subcortical areas converge upon similar behavioural symptoms points to neglect as a dysconnection syndrome that may result from the disruption of a distributed network, rather than aberrant computations in any particular brain region. To test this hypothesis, we used Bayesian analysis of effective connectivity based on electroencephalographic recordings in ten patients (6 male, 4 female; age range 41–68) with left-sided neglect following a right-hemisphere lesion. In line with previous research, age-matched healthy controls showed a contralateral increase in connection strength between parietal and frontal cortex with respect to the laterality of audiospatial oddball stimuli. Neglect patients, however, showed a dysconnection between parietal and frontal cortex in the right hemisphere when oddballs appeared on their left side, but preserved connectivity in the left hemisphere when stimuli appeared on their right. This preserved fronto-parietal connectivity was associated with lower neglect severity. Moreover, we saw ipsilateral fronto-temporal connectivity increases for oddballs appearing on the neglected side, which might be a compensatory mechanism for residual left side awareness. No group differences were found in intrinsic (within-region) connectivity. While further validation is required in a bigger sample, our findings are in keeping with the idea that neglect results from the disruption of a distributed network, rather than a lesion to any single brain region.
Original languageEnglish
Article number108052
JournalHearing Research
ISSN0378-5955
DOIs
Publication statusE-pub ahead of print - Jul 2020

    Research areas

  • Neglect Syndrome, EEG/MEG, DCM, Bayesian model selection

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