Effects of a diet integration with an oily emulsion of DHA-phospholipids containing melatonin and tryptophan in elderly patients suffering from mild cognitive impairment.

Age-related changes in nutritional status can play an important role in brain functioning. Specific nutrient deficiencies in the elderly may exacerbate pathological processes in the brain. Consequently, the potential of nutritional intervention to prevent or delay cognitive impairment and the development of dementia is an important topic. A randomized, double-blind, placebo-controlled trial has been performed in 25 elderly subjects (86 ± 6 years, 20 females, 5 males) with mild cognitive impairment (MCI). These subjects were randomly assigned to supplement their diet with either an oily emulsion of docosahexaenoic acid (DHA)-phospholipids containing melatonin and tryptophan (11 subjects) or a placebo (14-matched subjects) for 12 weeks. The main aim of this study was to evaluate the efficacy of the dietary supplement on cognition, by the assessment at the start and after 12 weeks of: (1) Orientation and other cognitive functions: Mini-Mental State Examination (MMSE); (2) Short-term memory: digit, verbal, and spatial span (digit span; verbal span; Corsi's test); (3) Long-term memory: Rey's auditory-verbal learning test; 'short story' test; Rey-Osterrieth complex figure (recall); (4) Attentional abilities: attentive matrices; (5) Executive functions: Weigl's sorting test; phonological fluency 'FAS'; (6) Visuo-constructional and visuo-spatial abilities: copy of simple drawings; Rey-Osterrieth complex figure (copy); (7) Language: semantic fluency; (8) Mood: Geriatric Depression Scale (GDS). Moreover, Sniffin' Sticks olfaction test and Mini Nutritional Assessment (MNA) have been performed. After 12 weeks, a significant treatment effect for the MMSE (P < 0.001) and a positive trend for the semantic verbal fluency was found in the supplement group (P < 0.06). A significant treatment effect was found out for the olfactory sensitivity assessment (P < 0.009). As regards the nutrition evaluation, after 12 weeks of treatment the supplemented group showed an improvement in the MNA score with a significant difference relative to placebo (P < 0.005). Older adults with MCI had significant improvements in several measures of cognitive function when supplemented with an oily emulsion of DHA-phospholipids containing melatonin and tryptophan for 12 weeks, compared with the placebo.

Functional disability in early Alzheimer's disease - a validation study of the Italian version of the disability assessment for dementia scale.

AIM: To determine the applicability and psychometric properties of the Italian version of the Disability Assessment of Dementia scale (DAD-I) in a community-residing population with early-stage Alzheimer's disease (AD). METHODS: The DAD-I was administered to the primary caregivers of 159 patients (mean age +/- SD 77.1 +/- 5.2) with mild AD (mean Mini Mental State Examination, MMSE, +/- SD 23.1 +/- 2.2). RESULTS: The DAD-I showed excellent internal consistency reliability (Cronbach's alpha = 0.92) and good construct validity. The DAD-I score was not significantly associated with gender, education and presumed duration of the illness, and had a low negative correlation with age. The DAD-I score correlated moderately with the traditional Instrumental Activities of Daily Living and Activities of Daily Living scales, respectively, with r = 0.53 and r = 0.54 (p < 0.0001). Relatively low, but statistically significant correlations (r ranging between 0.21 and 0.31) with the MMSE were also found. CONCLUSION: The DAD-I was found to be a reliable and valid instrument to assess functional disability in community-dwelling Italian subjects with early-stage AD.

Visuospatial planning and problem solving in Alzheimer's disease patients: a study with the Tower of London Test.

BACKGROUND: Executive dysfunction in Alzheimer's disease (AD) has been recently recognized as an early and prominent clinical sign. The Tower of London (ToL), a task specifically devised to test executive functions of visuospatial planning and problem solving, has frequently been used in neuropsychological experiments, but rarely in the clinical ground. METHODS: One hundred and sixty-one AD patients and 212 nondemented healthy controls were administered a simplified ToL version. RESULTS: AD patients were significantly impaired (p < 0.0001) in all ToL scores and in the total execution time. The 'accuracy' score of ToL at a cut off of

Two different Alzheimer diseases in men and women: clues from advanced neural networks and artificial intelligence.

BACKGROUND: Studies of the gender-related differences in the clinical presentation of Alzheimer's disease (AD) have focused on specific aspects of the disease (eg, circulating metabolites, cognitive capacity, or epidemiologic trends). OBJECTIVE: This study accounts for several descriptors of the disease simultaneously, providing a multidimensional analysis of a cohort of patients with AD. METHODS: Our analysis was conducted using self-organizing maps (SOMs). The high number (60) of independent variables (clinical, demographic, biochemical, and neuropsychological) observed in the study patients defines a complex and high-dimensional input space that can be processed by SOMs. Without supervision, SOMs examine nonlinear relations among the variables and cluster observations so that topologic relationships between variables correspond to the similarity of their distribution. Through such nonlinear autoclustering, subsets of observations (ie, clusters of subjects) can be identified in which essential information is concentrated. Each subject is identified by particular values of the variables (the record), and a specific set of variable values (the codebook) defines a distinct class. RESULTS: The study sample included 211 patients with mild to moderate AD (143 women, 68 men; mean [SD] age, 71.9 [7.2] years). All patients were assigned to 3 macroclasses-called A, B, and C-on the basis of matrix codebook neighborhoods. In terms of vectorial distance between codebooks, class A and B were quite similar, whereas the separation between class C and classes A and B was evident. The SOM distribution of values of variables across the output matrix did not show any specific pattern for most of the considered characteristics. However, we found only male patients in class C. This class distinction was not substantially changed when sex was removed from the database. Male and female patients were comparable with respect to dementia severity, demographic characteristics, psychiatric and behavioral symptoms, indicators of physical disability, and general health status. CONCLUSIONS: SOMs indicate nonlinear multifactorial interactions among the descriptors of the features of AD that seem to be linked to sex and would have been missed by traditional statistical analysis. This finding may offer a novel epidemiologic rationale for research into different pathogenic mechanisms in men and women with AD.

Artificial neural networks and artificial organisms can predict Alzheimer pathology in individual patients only on the basis of cognitive and functional status.

Data from several studies have pointed out the existence of a strong correlation between Alzheimer's disease (AD) neuropathology and cognitive state. However, because of their highly complex and nonlinear relationship, it has been difficult to develop a predictive model for individual patient classification through traditional statistical approaches. When exposed to complex data sets, artificial neural networks (ANNs) can recognize patterns, learn the relationship of different variables, and address classification tasks. To predict the results of postmortem brain examinations, we applied ANNs to the Nun Study data set, a longitudinal epidemiological study, which includes annual cognitive and functional evaluation. One hundred seventeen subjects from the study participated in this analysis. We determined how demographic data and the cognitive and functional variables of each subject during the last year of her life could predict the presence of brain pathology expressed as Braak stages, neurofibrillary tangles (NFTs) and neuritic plaques (NPs) count in the neocortex and hippocampus, and brain atrophy. The result of this analysis was then compared with traditional statistical models. ANNs proved to be better predictors than Linear Discriminant Analysis in all experimentations (+ approximately 10% in overall accuracy), especially when assembled in Artificial Organisms (+ approximately 20% in overall accuracy). Demographic, cognitive, and clinical variables were better predictors of tangles count in the neocortex and in the hippocampus when compared to NPs count. These findings strengthen the hypothesis that neurofibrillary pathology may represent the major anatomic substrate of the cognitive impairment found in AD.

Use of artificial networks in clinical trials: a pilot study to predict responsiveness to donepezil in Alzheimer's disease.

OBJECTIVES: To evaluate the accuracy of artificial neural networks compared with discriminant analysis in classifying positive and negative response to the cholinesterase inhibitor donepezil in a group of Alzheimer's disease (AD) patients. DESIGN: Convenience sample. SETTING: Patients with mild to moderate AD consecutively admitted to a geriatric day hospital and treated with donepezil 5 mg/day. PARTICIPANTS: Sixty-one older patients of both sexes with AD. MEASUREMENTS: Accuracy in detecting subjects sensitive (responders) or not (nonresponders) to 3-month therapy with ANNs. The criterion standard for evaluation of efficacy was the scores of Alzheimer's Disease Assessment Scale-Cognitive portion and Clinician's Interview Based Impression of Change-plus scales. RESULTS: ANNs were more effective in discriminating between responders and nonresponders than other advanced statistical methods, particularly linear discriminant analysis. The total accuracy in predicting the outcome was 92.59%. CONCLUSIONS: ANNs appear to be a useful tool in detecting patient responsiveness to pharmacological treatment in AD.

Tower of London test: a comparison between conventional statistic approach and modelling based on artificial neural network in differentiating fronto-temporal dementia from Alzheimer's disease.

The early differentiation of Alzheimer's disease (AD) from frontotemporal dementia (FTD) may be difficult. The Tower of London (ToL), thought to assess executive functions such as planning and visuo-spatial working memory, could help in this purpose. Twentytwo Dementia Centers consecutively recruited patients with early FTD or AD. ToL performances of these groups were analyzed using both the conventional statistical approaches and the Artificial Neural Networks (ANNs) modelling. Ninety-four non aphasic FTD and 160 AD patients were recruited. ToL Accuracy Score (AS) significantly (p < 0.05) differentiated FTD from AD patients. However, the discriminant validity of AS checked by ROC curve analysis, yielded no significant results in terms of sensitivity and specificity (AUC 0.63). The performances of the 12 Success Subscores (SS) together with age, gender and schooling years were entered into advanced ANNs developed by Semeion Institute. The best ANNs were selected and submitted to ROC curves. The non-linear model was able to discriminate FTD from AD with an average AUC for 7 independent trials of 0.82. The use of hidden information contained in the different items of ToL and the non linear processing of the data through ANNs allows a high discrimination between FTD and AD in individual patients.

The traveling salesman problem as a new screening test in early Alzheimer's disease: an exploratory study. Visual problem-solving in AD.

BACKGROUND AND AIMS: In the clinical setting, brief general mental status tests tend to detect early-stage Alzheimer's disease (AD) less well than more specific cognitive tests. Some preliminary information was collected on the diagnostic accuracy of the Traveling Salesman Problem (TSP) compared with the Mini-Mental State Examination (MMSE) in recognizing early AD from normal aging. METHODS: Fifteen AD outpatients (mean +/- SD MMSE: 24.45 +/- 2.61) and 30 age- and education-matched controls were submitted in a single blind protocol to a paper-and-pencil visually-presented version of the TSP, containing a random array of 30 points (TSP30). The task consisted of drawing the shortest continuous path, passing through each point once and only once, and returning to the starting point. Path lengths for subjects' solutions were computed and compared with the optimal solution given by a specific evolutionary algorithm called GenD. RESULTS: TP30 discriminated significantly better between AD subjects and controls (ROC curve AUC = 0.976; 95% CI 0.94-1.01) compared with the MMSE corrected for age and education (ROC curve AUC = 0.877; 95% CI 0.74-1.005). A path length of 478.2354, taken as "cut-off point", classified correctly subjects with a sensitivity of 93.3% and a specificity of 99.3%, whereas a score corrected for age and education of 25.85 on the MMSE had a sensitivity of 73.3% and a specificity of 96.7%. CONCLUSIONS: The TSP seems to be particularly sensitive to early AD and independent of patient's age and educational level. The high diagnostic ability, simplicity, and independence of age and education make the TSP promising as a screening test for early AD.