Validation of the brain injury associated visual impairment - impact questionnaire (BIVI-IQ).

PURPOSE: The Brain Injury associated Visual Impairment - Impact Questionnaire (BIVI-IQ) was developed to assess the impact of post-stroke visual impairment. The development of the questionnaire used robust methods involving stroke survivors and clinicians. The aim of this study was to assess the validity of the BIVI-IQ in a stroke population. METHODS: Stroke survivors with visual impairment were recruited from stroke units, outpatient clinics and non-healthcare settings. Participants were asked to complete questionnaire sets on three separate occasions; the BIVI-IQ at each visit with additional questionnaires at baseline and visit 2. Vision assessment and anchor questions from participants and clinicians were collected. The analysis included assessment of missing data, acceptability, Rasch model analysis, test-retest reliability, construct validity (NEI VFQ-25, EQ-5D-5L) and responsiveness to change. RESULTS: 316 stroke survivors completed at least one questionnaire of the 326 recruited. Mean age was 67 years and 64% were male. Adequate fit statistics to the Rasch model were reached (χ2 = 73.12, p = 0.02) with two items removed and thresholds of two adjusted, indicating validity and unidimensionality. Excellent test-retest reliability was demonstrated (ICC = 0.905) with a 3-month interval. Construct validity was demonstrated with a strong significant correlation to the NEI VFQ-25 (r = 0.837, p < 0.01). The BIVI-IQ also demonstrated responsiveness to change with significant differences identified between groups based on participant and clinician anchor questions (X2 = 23.29, p < 0.001; X2 = 24.56, p < 0.001). CONCLUSION: The BIVI-IQ has been shown to be valid and practical for 'everyday' use by clinicians and researchers to monitor vision-related quality of life in stroke survivors with visual impairment.

Feature-space clustering for fMRI meta-analysis.

Clustering functional magnetic resonance imaging (fMRI) time series has emerged in recent years as a possible alternative to parametric modeling approaches. Most of the work so far has been concerned with clustering raw time series. In this contribution we investigate the applicability of a clustering method applied to features extracted from the data. This approach is extremely versatile and encompasses previously published results [Goutte et al., 1999] as special cases. A typical application is in data reduction: as the increase in temporal resolution of fMRI experiments routinely yields fMRI sequences containing several hundreds of images, it is sometimes necessary to invoke feature extraction to reduce the dimensionality of the data space. A second interesting application is in the meta-analysis of fMRI experiment, where features are obtained from a possibly large number of single-voxel analyses. In particular this allows the checking of the differences and agreements between different methods of analysis. Both approaches are illustrated on a fMRI data set involving visual stimulation, and we show that the feature space clustering approach yields nontrivial results and, in particular, shows interesting differences between individual voxel analysis performed with traditional methods.