Dataset Information

An fMRI study of visual hemifield integration and cerebral lateralization.

ABSTRACT: The human brain integrates hemifield-split visual information via interhemispheric transfer. The degree to which neural circuits involved in this process behave differently during word recognition as compared to object recognition is not known. Evidence from neuroimaging (fMRI) suggests that interhemispheric transfer during word viewing converges in the left hemisphere, in two distinct brain areas, an "occipital word form area" (OWFA) and a more anterior occipitotemporal "visual word form area" (VWFA). We used a novel fMRI half-field repetition technique to test whether or not these areas also integrate nonverbal hemifield-split string stimuli of similar visual complexity. We found that the fMRI responses of both the OWFA and VWFA while viewing nonverbal stimuli were strikingly different than those measured during word viewing, especially with respect to half-stimulus changes restricted to a single hemifield. We conclude that normal reading relies on left-lateralized neural mechanisms, which integrate hemifield-split visual information for words but not for nonverbal stimuli.

SUBMITTER: Strother L

PROVIDER: S-EPMC5484576 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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An fMRI study of visual hemifield integration and cerebral lateralization.

Strother Lars L Zhou Zhiheng Z Coros Alexandra K AK Vilis Tutis T

Neuropsychologia 20170407

The human brain integrates hemifield-split visual information via interhemispheric transfer. The degree to which neural circuits involved in this process behave differently during word recognition as compared to object recognition is not known. Evidence from neuroimaging (fMRI) suggests that interhemispheric transfer during word viewing converges in the left hemisphere, in two distinct brain areas, an "occipital word form area" (OWFA) and a more anterior occipitotemporal "visual word form area" ...[more]

PMID: 28396097

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Project description:PURPOSE:To develop a visual field (VF) feature model to predict the reversal of glaucoma hemifield test (GHT) results to within normal limits (WNL) after 2 consecutive outside normal limits (ONL) results. DESIGN:Retrospective cohort study. PARTICIPANTS:Visual fields of 44?503 eyes from 26?130 participants. METHODS:Eyes with 3 or more consecutive reliable VFs measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm standard 24-2) were included. Eyes with ONL GHT results for the 2 baseline VFs were selected. We extracted 3 categories of VF features from the baseline tests: (1) VF global indices (mean deviation [MD] and pattern standard deviation), (2) mismatch between baseline VFs, and (3) VF loss patterns (archetypes). Logistic regression was applied to predict the GHT results reversal. Cross-validation was applied to evaluate the model on testing data by the area under the receiver operating characteristic curve (AUC). We ascertained clinical glaucoma status on a patient subset (n = 97) to determine the usefulness of our model. MAIN OUTCOME MEASURES:Predictive models for GHT results reversal using VF features. RESULTS:For the 16?604 eyes with 2 initial ONL results, the prevalence of a subsequent WNL result increased from 0.1% for MD < -12 dB to 13.8% for MD ?-3 dB. Compared with models with VF global indices, the AUC of predictive models increased from 0.669 (MD ?-3 dB) and 0.697 (-6 dB ? MD < -3 dB) to 0.770 and 0.820, respectively, by adding VF mismatch features and computationally derived VF archetypes (P < 0.001 for both). The GHT results reversal was associated with a large mismatch between baseline VFs. Moreover, the GHT results reversal was associated more with VF archetypes of nonglaucomatous loss, severe widespread loss, and lens rim artifacts. For a subset of 97 eyes, using our model to predict absence of glaucoma based on clinical evidence after 2 ONL results yielded significantly better prediction accuracy (87.7%; P < 0.001) than predicting GHT results reversal (68.8%) with a prescribed specificity 67.7%. CONCLUSIONS:Using VF features may predict the GHT results reversal to WNL after 2 consecutive ONL results.

Dataset Information

An fMRI study of visual hemifield integration and cerebral lateralization.

Publications

An fMRI study of visual hemifield integration and cerebral lateralization.

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