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Background. Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people. The presence of deviant patterns in simple resting state EEG recordings in autism, consistent with the atypical organization of the cerebral cortex present, prompted us in applying this potent analytical systems in search of a EEG signature of the disease. Aim of the study. The aim of the study is to assess how effectively this methodology distinguishes subjects with autism from typically developing ones. Methods. Fifteen definite ASD subjects (13 males; 2 females; age range 7–14; mean value = 10.4) and ten typically developing subjects (4 males; 6 females; age range 7–12; mean value 9.2) were included in the study. Patients received Autism diagnoses according to DSM-V criteria, subsequently confirmed by the ADOS scale. A segment of artefact-free EEG lasting 60 seconds was used to compute input values for subsequent analyses. MS-ROM/I-FAST coupled with a well-documented evolutionary system able to select predictive features (TWIST) created an invariant features vector input of EEG on which supervised machine learning systems acted as blind classifiers. Results. The overall predictive capability of machine learning system in sorting out autistic cases from normal control amounted consistently to 100% with all kind of systems employed using training-testing protocol and to 84% - 92.8% using Leave One Out protocol. The similarities among the ANN weight matrixes measured with apposite algorithms were not affected by the age of the subjects. This suggests that the ANNs do not read age-related EEG patterns, but rather invariant features related to the brain's underlying disconnection signature. Conclusion. This pilot study seems to open up new avenues for the development of non-invasive diagnostic testing for the early detection of ASD. [ABSTRACT FROM AUTHOR]

Background: There is increasing interest in DNA methylation studies in Alzheimer's disease (AD), but little is still known concerning the relationship between gene-promoter methylation and circulating biomarkers of one-carbon metabolism in patients.Objective: To detect the connections among circulating folate, homocysteine (hcy) and vitamin B12 levels and promoter methylation levels of PSEN1, BACE1, DNMT1, DNMT3A, DNMT3B, and MTHFR genes in blood DNA.Methods: We applied a data mining system called Auto Contractive Map to an existing database of 100 AD and 100 control individuals.Results: Low vitamin B12 was linked to the AD condition, to low folates, and to high hcy. Low PSEN1 methylation was linked to low folate levels as well as to low promoter methylation of BACE1 and DNMTs genes. Low hcy was linked to controls, to high folates and vitamin B12, as well as to high methylation levels of most of the studied genes.Conclusions: The present pilot study suggests that promoter methylation levels of the studied genes are linked to circulating levels of folates, hcy, and vitamin B12. [ABSTRACT FROM AUTHOR]

Background: The pharmacological treatment of Alzheimer's disease (AD) is based largely on cholinesterase inhibitors (ChEI).Objective: To investigate whether or not some non-pharmacological and contextual factors measured prior to starting treatment such as past occupation, lifestyles, marital status, degree of autonomy and cognitive impairment, living alone or with others, and the degree of brain atrophy are associated with a better response to ChEI treatment.Methods: Eighty-four AD and six AD with cerebrovascular disease (AD + CVD) outpatients of Treviso Dementia (TREDEM) Registry, with an average cholinesterase inhibitors treatment length of four years, were considered. The outpatients had undergone a complete evaluation and some non-pharmacological and contextual factors were collected. We defined responder a patient with a delta score T0 - T1 equal or inferior to 2.0 points per year of MMSE and a non-responder a patient with a delta score T0 - T1 superior to 2.0 points per year. In order to identify hidden relationships between variables related to response and non-response, we use a special kind of artificial neural network called Auto-CM, able to create a semantic connectivity map of the variables considered in the study.Results: A higher cognitive profile, a previous intellectual occupation, healthier lifestyles, being married and not living alone, a higher degree of autonomy, and lower degree of brain atrophy at baseline resulted in affecting the response to long-term ChEI therapy.Conclusion: Non-pharmacological and contextual factors appear to influence the effectiveness of treatment with ChEI in the long term. [ABSTRACT FROM AUTHOR]

Visual-constructional apraxia is a prominent feature of dementia with Lewy bodies (DLB) that might help to clinically distinguish it from Alzheimer's disease (AD). The main goal of this study was to assess performance on the copy intersecting-pentagon item of the Mini-Mental State Examination with the new Qualitative Scoring method for the Pentagon copy Test (QSPT). In order to determine which aspects of the drawings might differentiate DLB from AD, pentagon drawings of autopsy-verified DLB (n = 16) and AD (n = 15) patients were assessed using the QSPT. The qualitative scoring encompasses the assessment of different parameters of the drawing, such as number of angles, distance/intersection, closure/opening, rotation, and closing-in. The QSPT scores were compared between groups using linear analyses and artificial neural network analyses at four different time points. Linear analyses showed that during the first evaluation, number of angles was the only parameter that showed a significant difference between DLB and AD patients. A gradual decline in other parameters and total pentagon score occurred in both groups during subsequent years, with greater decline for the DLB group. The artificial neural network analyses using auto-contractive maps showed that, with disease progression, DLB became related to relatively lower qualitative pentagon scores, whereas AD became related to relatively higher qualitative scores. These findings suggest that the QSPT might be a sensitive measure of visuo-constructive abilities able to differentiate DLB from AD at disease onset and as the diseases progress, but further studies on larger population are necessary in order to establish its clinical relevance. [ABSTRACT FROM AUTHOR]

Background: The role of folate and homocysteine in brain atrophy associated with Alzheimer's disease is not completely understood. Objective: The aim of this study was to investigate the relationships between serum folate and homocysteine levels and the degree of cortical-subcortical and hippocampal atrophy in a first relatively preliminary sample of the Treviso Dementia (TREDEM) study using a potent data mining method. Methods: Physiological data, biochemical parameters, clinical assessment data, brain atrophy severity assessed with CT scans, and neuropsycological and disability data were assessed in a group of 232 outpatients (93 men and 139 women, aged 40.2-100 years) enrolled in the TREDEM study carried out in Treviso (Italy). A semantic connectivity map obtained through the Auto-CM system, a fourth generation artificial neural network (ANN), was used to offer some insight regarding the complex biological connections between the studied variables and the degree of brain atrophy. Results: Close associations between low serum folate levels and severe cortical-subcortical atrophy along with severe hippocampal atrophy measured by the width of the temporal horns of lateral ventricles were found. We also showed an association between high homocysteine levels and severe cortical-subcortical and hippocampal atrophy. Conclusion: The role of folate, which is inversely associated with the severity of brain atrophy, was confirmed. Our results also confirm the association between high homocysteine levels and severe cortical-subcortical and hippocampal atrophy. Auto-CM ANN is able to highlight associations sometimes visible only in longitudinal studies through intelligent data mining of a cross-sectional study. [ABSTRACT FROM AUTHOR]

The differential diagnosis across different variants of degenerative diseases is sometimes controversial. This study aimed to validate a qualitative scoring method for the pentagons copy test (QSPT) of Mini-Mental State Examination (MMSE) based on the assessment of different parameters of the pentagons drawing, such as number of angles, distance/intersection, closure/opening, rotation, closing-in, and to verify its efficacy to differentiate dementia with Lewy Body (DLB) from Alzheimer's disease (AD). We established the reliability of the qualitative scoring method through the inter-raters and intra-subjects analysis. QSPT was then applied to forty-six AD and forty-six DLB patients, using two phases statistical approach, standard and artificial neural network respectively. DLB patients had significant lower total score in the copy of pentagons and number of angles, distance/intersection, closure/opening, rotation compared to AD. However the logistic regression did not allow to establish any suitable modeling, whereas using Auto-Contractive Map (Auto-CM) the DLB was more strongly associated with low scores in some qualitative parameters of pentagon copying, i.e. number of angles and opening/closure and, for the remaining subitems of the MMSE, in naming, repetition and written comprehension, and for demographic variables of gender (male) and education (6-13 years). Twist system modeling showed that the QSPT had a good sensitivity (70.29%) and specificity (78.67%) (ROC-AUC 0.74). The proposed qualitative method of assessment of pentagons copying used in combination with non-linear analysis, showed to be consistent and effective in the differential diagnosis between Lewy Body and Alzheimer's dementia. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The search for markers that are able to predict the conversion of amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) is crucial for early mechanistic therapies. Using artificial neural networks (ANNs), 22 variables that are known risk factors of AD were analyzed in 80 patients with aMCI, for a period spanning at least 2 years. The cases were chosen from 195 aMCI subjects recruited by four Italian Alzheimer's disease units. The parameters of glucose metabolism disorder, female gender, and apolipoprotein E ℇ3/ℇ4 genotype were found to be the biological variables with high relevance for predicting the conversion of aMCI. The scores of attention and short term memory tests also were predictors. Surprisingly, the plasma concentration of amyloid-β _{42} had a low predictive value. The results support the utility of ANN analysis as a new tool in the interpretation of data from heterogeneous and distinct sources. [ABSTRACT FROM AUTHOR]

Background: The population longitudinal study named "The Conselice Study" has been the focus of the present investigation. 65 years old or older participants of this population study on brain aging were followed up for 5 years: 937 subjects completed the follow-up. Relationships of 46 genetic, phenotypic, clinical and nutritional factors on incident cognitive decline and incident dementia cases were investigated. Results: A new statistical approach, called the Auto Contractive Map (AutoCM) was applied to find relationship between variables and a possible hierarchy in the relevance of each variable with incident dementia. This method, based on an artificial adaptive system, was able to define the association strength of each variable with all the others. Moreover, few variables resulted to be aggregation points in the variable connectivity map related to cognitive decline and dementia. Gene variants and cognate phenotypic variables showed differential degrees of relevance to brain aging and dementia. A risk map for age associated cognitive decline and dementia has been constructed and will be presented and discussed. Conclusion: This map of variables may be use to identify subjects with increased risk of developing cognitive decline end/or dementia and provide pivotal information for early intervention protocols for prevention of dementia. [ABSTRACT FROM AUTHOR]

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 α = 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. Copyright © 2008 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

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 ≤29/36 yielded a sensitivity of 71.2% and a specificity of 76.4% (AUC 0.79) for the diagnosis of AD versus controls. Conclusions: Visuospatial planning and problem solving are significantly impaired in early dementia of the Alzheimer’s type. A successful sensitivity/specificity ratio, the independence of education and the simplicity of this version of ToL make it a useful executive functioning screening test for early AD. Copyright © 2007 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Objectives Validation of an Italian version of the Telephone Interview for Cognitive Status (I-TICS). Methods Telephone administration of the I-TICS within 6 weeks of face-to-face testing with the Mini Mental State Examination (MMSE), in Probable Alzheimer's disease (AD) patients and healthy controls. Two hundred and seven consecutive outpatients with cognitive impairment were recruited from Dementia Clinic of University Campus BioMedico. Of these, 45 probable AD patients with complete data were analyzed. Other dementias, Mild Cognitive Impairment (MCI), and patients with incomplete data were excluded. The control sample consisted of 64 age- and sex-matched healthy subjects. For diagnosis, an extensive clinical evaluation, laboratory testing, brain imaging, EEG, neuropsychological battery and a depression scale were used. For I-TICS validation, telephone I-TICS and face-to-face MMSE were administered. Results The I-TICS correlated highly and linearly with the MMSE (Pearson's r = 0.904). Conversion equations are provided. Sensitivity and specificity were similar between tests (area under curve = 0.894 for the I-TICS; 0.966 for the MMSE). I-TICS sensitivity was 84% and specificity 86% at a cut-off score of 28. No significant difference in accuracy with the MMSE was present. Total agreement between I-TICS and MMSE was ‘substantial’ at 86% (Cohen's K = 0.717). Repeated testing in a subset of patients showed a disease progression related decrease of 4.2 points/year (t = 2.664; p = 0.018) in I-TICS scores. Conclusion The I-TICS is a valid instrument in clinical and research screening and monitoring of AD. Potential applications in other dementias and MCI are worth further studies. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: Previous studies have shown that in platelets of mild Alzheimer Disease (AD) patients there are alterations of specific APP forms, paralleled by alteration in expression level of both ADAM 10 and BACE when compared to control subjects. Due to the poor linear relation among each key-element of beta-amyloid cascade and the target diagnosis, the use of systems able to afford non linear tasks, like artificial neural networks (ANNs), should allow a better discriminating capacity in comparison with classical statistics. Objective: To evaluate the accuracy of ANNs in AD diagnosis. Methods: 37 mild-AD patients and 25 control subjects were enrolled, and APP, ADM10 and BACE measures were performed. Fifteen different models of feed-forward and complex-recurrent ANNs (provided by Semeion Research Centre), based on different learning laws (back propagation, sinenet, bi-modal) were compared with the linear discriminant analysis (LDA). Results: The best ANN model correctly identified mild AD patients in the 94% of cases and the control subjects in the 92%. The corresponding diagnostic performance obtained with LDA was 90% and 73%. Conclusion: This preliminary study suggests that the processing of biochemical tests related to beta-amyloid cascade with ANNs allows a very good discrimination of AD in early stages, higher than that obtainable with classical statistics methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Folate metabolism, also known as one-carbon metabolism, is required for several cellular processes including DNA synthesis, repair and methylation. Impairments of this pathway have been often linked to Alzheimer’s disease (AD). In addition, increasing evidence from large scale case-control studies, genome-wide association studies, and meta-analyses of the literature suggest that polymorphisms of genes involved in one-carbon metabolism influence the levels of folate, homocysteine and vitamin B12, and might be among AD risk factors. We analyzed a dataset of 30 genetic and biochemical variables (folate, homocysteine, vitamin B12, and 27 genotypes generated by nine common biallelic polymorphisms of genes involved in folate metabolism) obtained from 40 late-onset AD patients and 40 matched controls to assess the predictive capacity of Artificial Neural Networks (ANNs) in distinguish consistently these two different conditions and to identify the variables expressing the maximal amount of relevant information to the condition of being affected by dementia of Alzheimer’s type. Moreover, we constructed a semantic connectivity map to offer some insight regarding the complex biological connections among the studied variables and the two conditions (being AD or control). TWIST system, an evolutionary algorithm able to remove redundant and noisy information from complex data sets, selected 16 variables that allowed specialized ANNs to discriminate between AD and control subjects with over 90% accuracy. The semantic connectivity map provided important information on the complex biological connections among one-carbon metabolic variables highlighting those most closely linked to the AD condition. [ABSTRACT FROM AUTHOR]

Objective This paper proposes a new, complex algorithm for the blind classification of the original electroencephalogram (EEG) tracing of each subject, without any preliminary pre-processing. The medical need in this field is to reach an early differential diagnosis between subjects affected by mild cognitive impairment (MCI), early Alzheimer's disease (AD) and the healthy elderly (CTR) using only the recording and the analysis of few minutes of their EEG. Methods and material This study analyzed the EEGs of 272 subjects, recorded at Rome's Neurology Unit of the Policlinico Campus Bio-Medico. The EEG recordings were performed using 19 electrodes, in a 0.3–70 Hz bandpass, positioned according to the International 10–20 System. Many powerful learning machines and algorithms have been proposed during the last 20 years to effectively resolve this complex problem, resulting in different and interesting outcomes. Among these algorithms, a new artificial adaptive system, named implicit function as squashing time (I-FAST), is able to diagnose, with high accuracy, a few minutes of the subject's EEG track; whether it manifests an AD, MCI or CTR condition. An updating of this system, carried out by adding a new algorithm, named multi scale ranked organizing maps (MS-ROM), to the I-FAST system, is presented, in order to classify with greater accuracy the unprocessed EEG's of AD, MCI and control subjects. Results The proposed system has been measured on three independent pattern recognition tasks from unprocessed EEG tracks of a sample of AD subjects, MCI subjects and CTR: (a) AD compared with CTR; (b) AD compared with MCI; (c) CTR compared with MCI. While the values of accuracy of the previous system in distinguishing between AD and MCI were around 92%, the new proposed system reaches values between 94% and 98%. Similarly, the overall accuracy with best artificial neural networks (ANNs) is 98.25% for the distinguishing between CTR and MCI. Conclusions This new version of I-FAST makes different steps forward: (a) avoidance of pre-processing phase and filtering procedure of EEG data, being the algorithm able to directly process an unprocessed EEG; (b) noise elimination, through the use of a training variant with input selection and testing system, based on naïve Bayes classifier; (c) a more robust classification phase, showing the stability of results on nine well known learning machine algorithms; (d) extraction of spatial invariants of an EEG signal using, in addition to the unsupervised ANN, the principal component analysis and the multi scale entropy, together with the MS-ROM; a more accurate performance in this specific task. [ABSTRACT FROM AUTHOR]

Summary: Objective: This paper presents the results obtained with the innovative use of special types of artificial neural networks (ANNs) assembled in a novel methodology named IFAST (implicit function as squashing time) capable of compressing the temporal sequence of electroencephalographic (EEG) data into spatial invariants. The aim of this study is to assess the potential of this parallel and nonlinear EEG analysis technique in distinguishing between subjects with mild cognitive impairment (MCI) and Alzheimer''s disease (AD) patients with a high degree of accuracy in comparison with standard and advanced nonlinear techniques. The principal aim of the study was testing the hypothesis that automatic classification of MCI and AD subjects can be reasonably correct when the spatial content of the EEG voltage is properly extracted by ANNs. Methods and material: Resting eyes-closed EEG data were recorded in 180 AD patients and in 115 MCI subjects. The spatial content of the EEG voltage was extracted by IFAST step-wise procedure using ANNs. The data input for the classification operated by ANNs were not the EEG data, but the connections weights of a nonlinear auto-associative ANN trained to reproduce the recorded EEG tracks. These weights represented a good model of the peculiar spatial features of the EEG patterns at scalp surface. The classification based on these parameters was binary (MCI versus AD) and was performed by a supervised ANN. Half of the EEG database was used for the ANN training and the remaining half was utilised for the automatic classification phase (testing). Results: The best results distinguishing between AD and MCI reached to 92.33%. The comparative results obtained with the best method so far described in the literature, based on blind source separation and Wavelet pre-processing, were 80.43% (p <0.001). Conclusion: The results confirmed the working hypothesis that a correct automatic classification of MCI and AD subjects can be obtained extracting spatial information content of the resting EEG voltage by ANNs and represent the basis for research aimed at integrating spatial and temporal information content of the EEG. [Copyright &y& Elsevier]