Toward the development of a training program that promotes a "social model of disability" for physical therapists: a discussion on experiential learning surrounding the use of "acceptance of disability" by physical therapists.

[Purpose] This study aimed to extract knowledge for the development of a training program for creating a social model of disability for physical therapists, focusing on the experiential learning of those physical therapists who did not use acceptance of disability according to their subjective judgment. [Participants and Methods] The study included 11 physical therapists who were interviewed about their use of acceptance of disability and the circumstances leading to its non-use. [Results] The study identified the past and current use of acceptance of disability, as well as cases and reasons for its discontinuation, along with changes in clinical content. [Conclusion] The study extracted knowledge for the development of training programs in line with the components of the experiential learning model.

Prediction of Alzheimer's Disease in Amnestic Mild Cognitive Impairment Subtypes: Stratification Based on Imaging Biomarkers.

BACKGROUND: Prediction of progression to Alzheimer's disease (AD) in amnestic mild cognitive impairment (MCI) is challenging because of its heterogeneity. OBJECTIVE: To evaluate a stratification method on different cohorts and to investigate whether stratification in amnestic MCI could improve prediction accuracy. METHODS: We identified 80 and 79 patients with amnestic MCI from different cohorts, respectively. They underwent baseline magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scans. We performed hierarchical clustering with three imaging biomarkers: Brain volume on MRI, left hippocampus grey matter loss on MRI, and left inferior temporal gyrus glucose hypometabolism on FDG-PET. Regions-of-interest for biomarkers were defined by the Automated Anatomical Labeling atlas. We performed voxel-wise statistical parametric mapping to explore differences between clusters in patterns of grey matter loss and glucose hypometabolism. We compared time to progression using an interval-censored parametric model. We evaluated predictive performance using logistic regression. RESULTS: Similar clusters were found in different cohorts. MCI1 had the healthiest biomarker profile of cognitive performance and imaging biomarkers. MCI2 had cognitive performance and MRI measures intermediate between those of nonconverters and converters. MCI3 showed the severest reduction in brain volume and left hippocampal atrophy. MCI4 showed remarkable glucose hypometabolism in the left inferior temporal gyrus, and also demonstrated significant decreases in most cognitive scores, including non-memory functions. MCI4 showed the highest risk for progression. The prediction of progression of MCI2 especially benefited from the stratification. CONCLUSION: Stratification with imaging biomarkers in amnestic MCI can be a good approach for improving predictive performance.

Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease.

BACKGROUND: The choice of biomarkers for early detection of Alzheimer's disease (AD) is important for improving the accuracy of imaging-based prediction of conversion from mild cognitive impairment (MCI) to AD. The primary goal of this study was to assess the effects of imaging modalities and brain atlases on prediction. We also investigated the influence of support vector machine recursive feature elimination (SVM-RFE) on predictive performance. METHODS: Eighty individuals with amnestic MCI [40 developed AD within 3 years] underwent structural magnetic resonance imaging (MRI) and (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans at baseline. Using Automated Anatomical Labeling (AAL) and LONI Probabilistic Brain Atlas (LPBA40), we extracted features representing gray matter density and relative cerebral metabolic rate for glucose in each region of interest from the baseline MRI and FDG-PET data, respectively. We used linear SVM ensemble with bagging and computed the area under the receiver operating characteristic curve (AUC) as a measure of classification performance. We performed multiple SVM-RFE to compute feature ranking. We performed analysis of variance on the mean AUCs for eight feature sets. RESULTS: The interactions between atlas and modality choices were significant. The main effect of SVM-RFE was significant, but the interactions with the other factors were not significant. COMPARISON WITH EXISTING METHOD: Multimodal features were found to be better than unimodal features to predict AD. FDG-PET was found to be better than MRI. CONCLUSIONS: Imaging modalities and brain atlases interact with each other and affect prediction. SVM-RFE can improve the predictive accuracy when using atlas-based features.

A comparison of three brain atlases for MCI prediction.

BACKGROUND: Although previous voxel-based studies using features extracted by atlas-based parcellation produced relatively poor performances on the prediction of Alzheimer's disease (AD) in subjects with mild cognitive impairment (MCI), classification performance usually depends on features extracted from the original images by atlas-based parcellation. To establish whether classification performance differs depending on the choice of atlases, support vector machine (SVM)-based classification using different brain atlases was performed. NEW METHOD: Seventy-seven three-dimensional T1-weighted MRI data sets of subjects with amnestic MCI, including 39 subjects who developed AD (MCI-C) within three years and 38 who did not (MCI-NC), were used for voxel-based morphometry (VBM) analyses and analyzed using SVM-based pattern recognition methods combined with a feature selection method based on the SVM recursive feature elimination (RFE) method. Three brain atlases were used for the feature selections: the Automated Anatomical Labeling (AAL) Atlas, Brodmann's Areas (BA), and the LONI Probabilistic Brain Atlas (LPBA40). RESULTS: The VBM analysis showed a significant cluster of gray matter density reduction, located at the left hippocampal region, in MCI-C compared to MCI-NC. The SVM analyses with the SVM-RFE algorithm revealed that the best classification performance was achieved by LPBA40 with 37 selected features, giving an accuracy of 77.9%. The overall performance in LPBA40 was better than that of AAL and BA regardless of the number of selected features. CONCLUSIONS: These results suggest that feature selection is crucial to improve the classification performance in atlas-based analysis and that the choice of atlases is also important.

Anisotropic conduction properties in canine atria analyzed by high-resolution optical mapping: preferential direction of conduction block changes from longitudinal to transverse with increasing age.

BACKGROUND: Anisotropic conduction properties may provide a substrate for reentrant arrhythmias. We investigated the age-dependent changes of structural and functional anisotropy in isolated right atria from infant (1 to 2 months), young (6 to 12 months), and old (6 to 10 years) dogs. METHODS AND RESULTS: The histology of the mapped atrial tissues (a small subepicardial area, 2.8x4.2 mm) was characterized by an age-dependent increase of myofiber width and fat cell infiltration between myofibers. Cx43 was distributed homogeneously over the entire cell surface in infant dogs, whereas it progressively polarized to the cell termini with increasing age. The activation sequences were analyzed by high-resolution optical mapping using a voltage-sensitive dye. Activation fronts from the pacing site proceeded more rapidly along fiber orientation (longitudinal) than across it (transverse). Infant dogs showed "elliptical" isochrones with a smooth transition between longitudinal and transverse propagation, whereas old dogs had a "square" pattern with a sharp transition. Conduction block occurred predominantly during longitudinal propagation in infant dogs but during transverse propagation in old dogs. The shape of the wave front and the degree of lateral uncoupling seemed to decide the preferential direction of block. A zigzag activation causing an extremely slow transverse conduction was observed only in old dogs. CONCLUSIONS: Along with the age-dependent structural anisotropy, the preferential direction of block changed from longitudinal to transverse in association with a change in the wave front configuration. A zigzag propagation based on lateral uncoupling would predispose the elderly to multiple reentry and a higher incidence of atrial fibrillation.