Purpose: To identify the most common beliefs concerning the negative consequences of falling and determine whether these motivate avoidance of activity. Design and Methods: A questionnaire assessing feared consequences of falling was completed by 224 community-living people aged older than 75. Beliefs about the consequences of falling were related to demographic characteristics, falling history, and avoidance of activity. The questionnaires were completed again by 166 participants 6 months later. Results: Commonly feared consequences of falling were loss of functional independence and damage to identity. These fears were correlated with avoidance of activity (after adjusting for age, sex, and recent falling history) and predicted avoidance in activity 6 months later (after adjusting for baseline levels of avoidance). Implications:Concerns about damage to social identity, as well as functional incapacity, are common and may motivate avoidance of activity.

This paper presents a new approach to characterize subtle diffuse changes in multiple sclerosis (MS) using histograms derived from magnetization transfer ratio (MTR) images. Two major parts dominate our histogram analysis; (1) Classification of MTR histograms into control and MS subgroups; (2) Correlation with current disability, as measured by the EDSS scale (a measure of disease severity). Two data reduction schemes are used to reduce the complexity of the analysis: linear discriminant analysis (LDA) and principal component analysis (PCA). LDA is better for the classification of MTR histograms as it takes into account the between-class variation. By using LDA, the space of MTR histograms is transformed to the optimal discriminant space for a nearest mean classifier. In contrast, PCA is useful for correlation with current disability as it takes into account the variation within each subgroup in its process. A multiple regression analysis is used to evaluate the multiple correlation of those principal components with the degree of disability in MS. This is the first application of such classification and correlation techniques to magnetic resonance imaging histogram data. Our MTR histogram analysis approach give improved classification success and improved correlation compared with methods that use traditional histogram features such as peak height and peak location

A two-compartment exchange model for perfusion quantification using arterial spin labeling (ASL) is presented, which corrects for the assumption that the capillary wall has infinite permeability to water. The model incorporates an extravascular and a blood compartment with the permeability surface area product (PS) of the capillary wall characterizing the passage of water between the compartments. The new model predicts that labeled spins spend longer in the blood compartment before exchange. This makes an accurate blood T(1) measurement crucial for perfusion quantification; conversely, the tissue T(1) measurement is less important and may be unnecessary for pulsed ASL experiments. The model gives up to 62% reduction in perfusion estimate for human imaging at 1.5T compared to the single compartment model. For typical human perfusion rates at 1.5T it can be assumed that the venous outflow signal is negligible. This simplifies the solution, introducing only one more parameter than the single compartment model, PS/v(bw), where v(bw) is the fractional blood water volume per unit volume of tissue. The simplified model produces an improved fit to continuous ASL data collected at varying delay time. The fitting yields reasonable values for perfusion and PS/v(bw).

PURPOSE: To explore the diagnostic potential of magnetization transfer ratio (MTR) histogram analysis in patients with neuropsychiatric systemic lupus erythematosus (SLE) by using multivariate discriminant analysis (MDA). MATERIALS AND METHODS: Volumetric magnetization transfer imaging was performed in nine patients with active non-thromboembolic, neuropsychiatric SLE, 10 patients with SLE who had had neuropsychiatric SLE previously, 10 patients with SLE but no history of neuropsychiatric SLE, 10 patients with inactive multiple sclerosis, and 10 healthy control subjects. For each subject, an MTR histogram of the whole brain was generated, and an MDA score was produced for each histogram. Each patient was assigned to a clinical subgroup on the basis of these MDA scores. For assignment, binary comparisons between subgroups were made. The accuracy of this classification method was assessed and compared with that of conventional MTR histogram analysis.
RESULTS: With MDA, the success rate of binary classification was 60%-100%, depending on which two groups were compared. When the different clinical subgroups were separated, MDA parameters were always better than conventional MTR histogram parameters, with P values ranging from.05 to less than 1 x 10(-6) of those attained with the best conventional parameter.
CONCLUSION: With MDA, MTR histograms of brain tissue may provide diagnostic information for individual patients in the clinical context of SLE.