When a parent suffers ABI: Investigation of emotional distress in children.

PRIMARY OBJECTIVE: To investigate the type of emotional and behavioural impact that having a parent with a severe acquired brain injury (ABI) has on children during the first period of adjustment. METHODS AND PROCEDURE: The study involved 25 couples in which one of the spouses was affected by ABI, and their 35 children (3-14 years). The children attended three sessions with a psychologist aimed at identifying their spontaneous playing and relational behaviour by means of a grid created on the basis of ICD-10 criteria. Both members of each parental couple attended a session with the psychologist, and were administered the Dyadic Adjustment Scale, the 36-item Health Survey and the Caregiver Burden Inventory. RESULTS: 63% of the children showed signs of emotional suffering, the presence of which was underestimated by their parents on the basis of the psychologist's assessments. The variables that correlated most closely with the children's psychological condition were related to the quality of their parents' relationship. CONCLUSIONS: Our findings confirm the need for early interventions aimed at both parents and their children in order to investigate the children's emotional-affective situation, and favour an understanding of their discomfort by their parents.

Multiple RF classifier for the hippocampus segmentation: Method and validation on EADC-ADNI Harmonized Hippocampal Protocol.

The hippocampus has a key role in a number of neurodegenerative diseases, such as Alzheimer's Disease. Here we present a novel method for the automated segmentation of the hippocampus from structural magnetic resonance images (MRI), based on a combination of multiple classifiers. The method is validated on a cohort of 50 T1 MRI scans, comprehending healthy control, mild cognitive impairment, and Alzheimer's Disease subjects. The preliminary release of the EADC-ADNI Harmonized Protocol training labels is used as gold standard. The fully automated pipeline consists of a registration using an affine transformation, the extraction of a local bounding box, and the classification of each voxel in two classes (background and hippocampus). The classification is performed slice-by-slice along each of the three orthogonal directions of the 3D-MRI using a Random Forest (RF) classifier, followed by a fusion of the three full segmentations. Dice coefficients obtained by multiple RF (0.87 ± 0.03) are larger than those obtained by a single monolithic RF applied to the entire bounding box, and are comparable to state-of-the-art. A test on an external cohort of 50 T1 MRI scans shows that the presented method is robust and reliable. Additionally, a comparison of local changes in the morphology of the hippocampi between the three subject groups is performed. Our work showed that a multiple classification approach can be implemented for the segmentation for the measurement of volume and shape changes of the hippocampus with diagnostic purposes.

Automated voxel-by-voxel tissue classification for hippocampal segmentation: methods and validation.

The hippocampus is an important structural biomarker for Alzheimer's disease (AD) and has a primary role in the pathogenesis of other neurological and psychiatric diseases. This study presents a fully automated pattern recognition system for an accurate and reproducible segmentation of the hippocampus in structural Magnetic Resonance Imaging (MRI). The method was validated on a mixed cohort of 56 T1-weighted structural brain images, and consists of three processing levels: (a) Linear registration: all brain images were registered to a standard template and an automated method was applied to capture the global shape of the hippocampus. (b) Feature extraction: all voxels included in the previously selected volume were characterized by 315 features computed from local information. (c) Voxel classification: a Random Forest algorithm was used to classify voxels as belonging or not belonging to the hippocampus. In order to improve the classification performance, an adaptive learning method based on the use of the Pearson's correlation coefficient was developed. The segmentation results (Dice similarity index = 0.81 ± 0.03) compare well with other state-of-the art approaches. A validation study was conducted on an independent dataset of 100 T1-weighted brain images, achieving significantly better results than those obtained with FreeSurfer.

Blood level of cimetidine in relation to age.

The blood level versus time curve for unchanged cimetidine after a 200 mg oral dose has been determined in 20 apparently healthy subjects, ranging from 22 to 84 years of age. A significant relationship between the area under the curve (AUC) and age was found (r = 0.81; P less than 0.001). The peak concentrations of cimetidine were significantly inversely related to body weight (r = -0.71; P less than 0.001). The age-related increase in bioavailability of oral cimetidine, as measured by AUC, was probably due to decreased total clearance of the drug, which resulted from the opposed changes (by themselves not significant) of distribution volume towards a decrease and of half-life towards an increase with age. Reduction in the standard oral dose of cimetidine by one third to one half should be feasible in the elderly without loss of efficacy, and it may be advisable in order to obviate extreme individual responses that may occur in this population.