[Show abstract][Hide abstract] ABSTRACT: The purpose of this study is to develop a software for the extraction of the hippocampus and surrounding medial temporal lobe (MTL) regions from T1-weighted magnetic resonance (MR) images with no interactive input from the user, to introduce a novel statistical indicator, computed on the intensities in the automatically extracted MTL regions, which measures atrophy, and to evaluate the accuracy of the newly developed intensity-based measure of MTL atrophy to (a) distinguish between patients with Alzheimer disease (AD), patients with amnestic mild cognitive impairment (aMCI), and elderly controls by using established criteria for patients with AD and aMCI as the reference standard and (b) infer about the clinical outcome of aMCI patients. For the development of the software, the study included 61 patients with mild AD (17 men, 44 women; mean age +/- standard deviation (SD), 75.8 years +/- 7.8; Mini Mental State Examination (MMSE) score, 24.1 +/- 3.1), 42 patients with aMCI (11 men, 31 women; mean age +/- SD, 75.2 years +/- 4.9; MMSE score, 27.9 +/- 1.9), and 30 elderly healthy controls (10 men, 20 women; mean age +/- SD, 74.7 years +/- 5.2; MMSE score, 29.1 +/- 0.8). For the evaluation of the statistical indicator, 150 patients with mild AD (62 men, 88 women; mean age +/- SD, 76.3 years +/- 5.8; MMSE score, 23.2 +/- 4.1), 247 patients with aMCI (143 men, 104 women; mean age +/- SD, 75.3 years +/- 6.7; MMSE score, 27.0 +/- 1.8), and 135 elderly healthy controls (61 men, 74 women; mean age +/- SD, 76.4 years +/- 6.1). Fifty aMCI patients were evaluated every 6 months over a 3 year period to assess conversion to AD. For each participant, two subimages of the MTL regions were automatically extracted from T1-weighted MR images with high spatial resolution. An intensity-based MTL atrophy measure was found to separate control, MCI, and AD cohorts. Group differences were assessed by using two-sample t test. Individual classification was analyzed by using receiver operating characteristic (ROC) curves. Compared to controls, significant differences in the intensity-based MTL atrophy measure were detected in both groups of patients (AD vs controls, 0.28 +/- 0.03 vs 0.34 +/- 0.03, P < 0.001; aMCI vs controls, 0.31 +/- 0.03 vs 0.34 +/- 0.03, P < 0.001). Moreover, the subgroup of aMCI converters was significantly different from controls (0.27 +/- 0.034 vs 0.34 +/- 0.03, P < 0.001). Regarding the ROC curve for intergroup discrimination, the area under the curve was 0.863 for AD patients vs controls, 0.746 for all aMCI patients vs controls, and 0.880 for aMCI converters vs controls. With specificity set at 85%, the sensitivity was 74% for AD vs controls, 45% for aMCI vs controls, and 83% for aMCI converters vs controls. The automated analysis of MTL atrophy in the segmented volume is applied to the early assessment of AD, leading to the discrimination of aMCI converters with an average 3 year follow-up. This procedure can provide additional useful information in the early diagnosis of AD.
Medical Physics 08/2009; 36(8):3737-47. · 2.91 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Night work can be dangerous because both circadian sleep propensity (process C) and sleep pressure due to the prolonged wakefulness (process S) contribute to the reduction of vigilance levels. As naps are a countermeasure to sleepiness, this study evaluates the role they play in preventing sleep-related accidents in Italian shift-working police drivers.
The study concerns highway car accidents that occurred to Italian shift-working police drivers; it was performed in 2 steps: a retrospective analysis of the overall number of accidents that occurred during the years 1993--1997 (n, 1195), followed by a validation analysis of a smaller cohort of accidents prospectively collected during 2003 (n, 84).
RETROSPECTIVE ANALYSIS: The influence of process S, process C, driver characteristics, and context conditions on accident risk, estimated by means of Cox hazard regression, revealed that nighttime accident risk was mainly influenced by process S levels. Consequently, an experimental mathematical model linking the hourly observed number of accidents to process S levels was designed. Its generalization to the theoretical case of drivers omitting naps showed an increase of about 38% of accidents. PROSPECTIVE ANALYSIS: In order to validate our results, we compared retrospective and prospective sleep patterns: no statistical difference was found. Again, the hourly number of accidents increased with homeostatic sleep pressure; the theoretical efficacy of napping was quantified in 48% accidents decrease.
Our data seem to confirm that napping before working a night shift is an effective countermeasure to alertness and performance deterioration associated with night work. Moreover, this self-initiated behavior could have a prophylactic efficacy in reducing the number of car accidents.
[Show abstract][Hide abstract] ABSTRACT: evaluation of shift-work effect on sleepiness, sleep disorders, and sleep-related accidents in a population of police officers.
Aquestionnaire-based survey was used to gather information on age and physical characteristics, working conditions, sleep problems, and accidents. Sleepiness was measured by the Epworth Sleepiness Scale (ESS) while the presence of sleep disorders was evaluated by a score (SDS) drawn from indicators of insomnia, breathing disorders, periodic limb movements and restless legs syndrome, and hypersomnia. The effects of age, gender, body mass index, working conditions, and seniority on ESS, SD score, and accidents were analyzed by linear and logistic regression.
The self-administered questionnaires were filled in by police officers in the district of Genoa (Italy).
1,280 police officers: 611 shift workers (SW) and 669 non-shift workers (NSW).
The ESS score was not higher in SW than in NSW, while the SDS was significantly influenced by shift-work conditions and seniority in shift work. The occurrence of sleep-ascribed accidents was significantly increased in the SW group and related to the presence of indicators of sleep disorders. There was evidence for sleep disorders in 35.7% of SW and in 26.3% of NSW.
Shift-work conditions and seniority may enhance sleep disorders and may favor sleep-related accidents, but they do not influence ESS score. Stressful conditions could cause sleepiness to be underestimated, or else they might overcome sleepiness. However, our data should alert occupational health physicians for the diagnosis and prevention of possible undetected intrinsic sleep disorders, which could possibly worsen shift workers' health and increase the risk of accidents.
[Show abstract][Hide abstract] ABSTRACT: The aim of this study was to evaluate the effects of shiftwork on sleepiness, sleep disorders and sleep related accidents in a population of policemen. Data concerning age and physical characteristics, working conditions, sleep problems and accidents were collected by a questionnaire. Sleepiness was evaluated by the Epworth Sleepiness Scale (ESS) while the presence of sleep disorders was evaluated by a score (SD-score) drawn from indicators of insomnia, breathing disorders, periodic limb movements-restless leg syndrome and hypersomnia. The effects of age, gender, body mass index, working condition and seniority on ESS, SD-score and accidents were analysed by linear and logistic regression. Participants were 1280 policemen: 611 shiftworkers and 669 non-shiftworkers. The ESS scores were not higher in shiftworkers than in non-shiftworkers, but the SD-score was found to be significantly influenced by shiftwork condition and seniority. The occurrence of sleep-related accidents was found to have been significantly increased for shiftworkers and related to the presence of indicators of sleep disorders. The sleepiness could be underestimated or even overcome by the influence of stressing conditions. However our data should alert occupational health physicians for the diagnosis and prevention of possible lurking intrinsic sleep disorders likely to influence health problems and risk of accidents in shiftworkers.
[Show abstract][Hide abstract] ABSTRACT: Research in Alzheimer's disease (AD) has seen a tremendous growth of candidate biomarkers in the last decade. The role of such established or putative biomarkers is to allow an accurate diagnosis of AD, to infer about its prognosis, to monitor disease progression and evaluate changes induced by disease-modifying drugs. An ideal biomarker should detect a specific pathophysiological feature of AD, not present in the healthy condition, in other primary dementias, or in confounding conditions. Besides being reliable, a biomarker should be detectable by means of procedures which must be relatively non-invasive, simple to perform, widely available and not too expensive. At present, no candidate meets these requirements representing the high standards aimed at by researchers. Among others, various morphological brain measures performed by means of magnetic resonance imaging (MRI), ranging from the total brain volume to some restricted regions such as the hippocampal volume, have been proposed. Nowadays the efforts are directed toward finding an automated, unsupervised method of evaluating atrophy in some specific brain region, such as the medial temporal lobe (MTL). In this work we provide an extensive review of the state of the art on the automatic and semi-automatic image processing techniques for the early assessment of patients at risk of developing AD. Our main focus is the relevance of the morphological analysis of MTL, and in particular of the hippocampal formation, in making the diagnosis of AD and in distinguishing it from other dementias.