The implicit function as squashing time model: a novel parallel nonlinear EEG analysis technique distinguishing mild cognitive impairment and Alzheimer's disease subjects with high degree of accuracy

Quantitative EEG Spectral and Connectivity Analysis for Cognitive Decline in Amnestic Mild Cognitive Impairment

BACKGROUND

Mild cognitive impairment (MCI) is considered to be the borderline of cognitive changes associated with aging and very early dementia. Cognitive functions in MCI can improve, remain stable or progress to clinically probable AD. Quantitative electroencephalography (qEEG) can become a useful tool for using the analytical techniques to quantify EEG patterns indicating cognitive impairment.

OBJECTIVE

The aim of our study was to assess spectral and connectivity analysis of the EEG resting state activity in amnestic MCI (aMCI) patients in comparison with healthy control group (CogN).

METHODS

30 aMCI patients and 23 CogN group, matched by age and education, underwent equal neuropsychological assessment and EEG recording, according to the same protocol.

RESULTS

qEEG spectral analysis revealed decrease of global relative beta band power and increase of global relative theta and delta power in aMCI patients. Whereas, decreased coherence in centroparietal right area considered to be an early qEEG biomarker of functional disconnection of the brain network in aMCI patients. In conclusion, the demonstrated changes in qEEG, especially, the coherence patterns are specific biomarkers of cognitive impairment in aMCI.

CONCLUSIONS

Therefore, qEEG measurements appears to be a useful tool that complements neuropsychological diagnostics, assessing the risk of progression and provides a basis for possible interventions designed to improve cognitive functions or even inhibit the progression of the disease.

Alzheimer's Disease with Epileptiform EEG Activity: Abnormal Cortical Sources of Resting State Delta Rhythms in Patients with Amnesic Mild Cognitive Impairment

BACKGROUND

Patients with amnesic mild cognitive impairment due to Alzheimer's disease (ADMCI) typically show a "slowing" of cortical resting-state eyes-closed electroencephalographic (rsEEG) rhythms. Some of them also show subclinical, non-convulsive, and epileptiform EEG activity (EEA) with an unclear relationship with that "slowing."

OBJECTIVE

Here we tested the hypothesis that the "slowing" of rsEEG rhythms is related to EEA in ADMCI patients.

METHODS

Clinical and instrumental datasets in 62 ADMCI patients and 38 normal elderly (Nold) subjects were available in a national archive. No participant had received a clinical diagnosis of epilepsy. The eLORETA freeware estimated rsEEG cortical sources. The area under the receiver operating characteristic curve (AUROCC) indexed the accuracy of eLORETA solutions in the classification between ADMCI-EEA and ADMCI-noEEA individuals.

RESULTS

EEA was observed in 15% (N = 8) of the ADMCI patients. The ADMCI-EEA group showed: 1) more abnormal Aβ 42 levels in the cerebrospinal fluid as compared to the ADMCI-noEEA group and 2) higher temporal and occipital delta (<4 Hz) rsEEG source activities as compared to the ADMCI-noEEA and Nold groups. Those source activities showed moderate accuracy (AUROCC = 0.70-0.75) in the discrimination between ADMCI-noEEA versus ADMCI-EEA individuals.

CONCLUSION

It can be speculated that in ADMCI-EEA patients, AD-related amyloid neuropathology may be related to an over-excitation in neurophysiological low-frequency (delta) oscillatory mechanisms underpinning cortical arousal and quiet vigilance.

Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential

Objective: To examine whether prefrontal electroencephalography (EEG) can be used for screening dementia.

Methods: We estimated the global cognitive decline using the results of Mini-Mental Status Examination (MMSE), measurements of brain activity from resting-state EEG, responses elicited by auditory stimulation [sensory event-related potential (ERP)], and selective attention tasks (selective-attention ERP) from 122 elderly participants (dementia, 35; control, 87). We investigated that the association between MMSE and each EEG/ERP variable by using Pearson’s correlation coefficient and performing univariate linear regression analysis. Kernel density estimation was used to examine the distribution of each EEG/ERP variable in the dementia and non-dementia groups. Both Univariate and multiple logistic regression analyses with the estimated odds ratios were conducted to assess the associations between the EEG/ERP variables and dementia prevalence. To develop the predictive models, five-fold cross-validation was applied to multiple classification algorithms.

Results: Most prefrontal EEG/ERP variables, previously known to be associated with cognitive decline, show correlations with the MMSE score (strongest correlation has |r| = 0.68). Although variables such as the frontal asymmetry of the resting-state EEG are not well correlated with the MMSE score, they indicate risk factors for dementia. The selective-attention ERP and resting-state EEG variables outperform the MMSE scores in dementia prediction (areas under the receiver operating characteristic curve of 0.891, 0.824, and 0.803, respectively). In addition, combining EEG/ERP variables and MMSE scores improves the model predictive performance, whereas adding demographic risk factors do not improve the prediction accuracy.

Conclusion: Prefrontal EEG markers outperform MMSE scores in predicting dementia, and additional prediction accuracy is expected when combining them with MMSE scores.

Significance: Prefrontal EEG is effective for screening dementia when used independently or in combination with MMSE.

Bone strain index as a predictor of further vertebral fracture in osteoporotic women: An artificial intelligence-based analysis

BACKGROUND

Osteoporosis is an asymptomatic disease of high prevalence and incidence, leading to bone fractures burdened by high mortality and disability, mainly when several subsequent fractures occur. A fragility fracture predictive model, Artificial Intelligence-based, to identify dual X-ray absorptiometry (DXA) variables able to characterise those patients who are prone to further fractures called Bone Strain Index, was evaluated in this study.

METHODS

In a prospective, longitudinal, multicentric study 172 female outpatients with at least one vertebral fracture at the first observation were enrolled. They performed a spine X-ray to calculate spine deformity index (SDI) and a lumbar and femoral DXA scan to assess bone mineral density (BMD) and bone strain index (BSI) at baseline and after a follow-up period of 3 years in average. At the end of the follow-up, 93 women developed a further vertebral fracture. The further vertebral fracture was considered as one unit increase of SDI. We assessed the predictive capacity of supervised Artificial Neural Networks (ANNs) to distinguish women who developed a further fracture from those without it, and to detect those variables providing the maximal amount of relevant information to discriminate the two groups. ANNs choose appropriate input data automatically (TWIST-system, Training With Input Selection and Testing). Moreover, we built a semantic connectivity map usingthe Auto Contractive Map to provide further insights about the convoluted connections between the osteoporotic variables under consideration and the two scenarios (further fracture vs no further fracture).

RESULTS

TWIST system selected 5 out of 13 available variables: age, menopause age, BMI, FTot BMC, FTot BSI. With training testing procedure, ANNs reached predictive accuracy of 79.36%, with a sensitivity of 75% and a specificity of 83.72%. The semantic connectivity map highlighted the role of BSI in predicting the risk of a further fracture.

CONCLUSIONS

Artificial Intelligence is a useful method to analyse a complex system like that regarding osteoporosis, able to identify patients prone to a further fragility fracture. BSI appears to be a useful DXA index in identifying those patients who are at risk of further vertebral fractures.

Abnormalities of functional cortical source connectivity of resting-state electroencephalographic alpha rhythms are similar in patients with mild cognitive impairment due to Alzheimer's and Lewy body diseases

Previous evidence has shown different resting-state eyes-closed electroencephalographic delta (<4 Hz) and alpha (8-10.5 Hz) source connectivity in subjects with dementia due to Alzheimer's (ADD) and Lewy body (DLB) diseases. The present study tested if the same differences may be observed in the prodromal stages of mild cognitive impairment (MCI). Here, clinical and resting-state eyes-closed electroencephalographic data in age-, gender-, and education-matched 30 ADMCI, 23 DLBMCI, and 30 healthy elderly (Nold) subjects were available in our international archive. Mini-Mental State Evaluation (MMSE) score was matched in the ADMCI and DLBMCI groups. The eLORETA freeware estimated delta and alpha source connectivity by the tool called lagged linear connectivity (LLC). Area under receiver operating characteristic curve (AUROCC) indexed the classification accuracy among individuals. Results showed that widespread interhemispheric and intrahemispheric LLC solutions in alpha sources were abnormally lower in both MCI groups compared with the Nold group, but with no differences were found between the 2 MCI groups. AUROCCs of LLC solutions in alpha sources exhibited significant accuracies (0.72-0.75) in the discrimination of Nold versus ADMCI-DLBMCI individuals, but not between the 2 MCI groups. These findings disclose similar abnormalities in ADMCI and DLBMCI patients as revealed by alpha source connectivity. It can be speculated that source connectivity mostly reflects common cholinergic impairment in prodromal state of both AD and DLB, before a substantial dopaminergic derangement in the dementia stage of DLB.

Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer's Disease and Mild Cognitive Impairment

Brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) are difficult to demarcate to assess the progression of AD. This study presents a statistical framework on the basis of MRI volumes and neuropsychological scores. A feature selection technique using backward stepwise linear regression together with linear discriminant analysis is designed to classify cognitive normal (CN) subjects, early MCI (EMCI), late MCI (LMCI), and AD subjects in an exhaustive two-group classification process. Results show a dominance of the neuropsychological parameters like MMSE and RAVLT. Cortical volumetric measures of the temporal, parietal, and cingulate regions are found to be significant classification factors. Moreover, an asymmetrical distribution of the volumetric measures across hemispheres is seen for CN versus EMCI and EMCI versus AD, showing dominance of the right hemisphere; whereas CN versus LMCI and EMCI versus LMCI show dominance of the left hemisphere. A 2-fold cross-validation showed an average accuracy of 93.9%, 90.8%, and 94.5%, for the CN versus AD, CN versus LMCI, and EMCI versus AD, respectively. The accuracy for groups that are difficult to differentiate like EMCI versus LMCI was 73.6%. With the inclusion of the neuropsychological scores, a significant improvement (24.59%) was obtained over using MRI measures alone.

An improved I-FAST system for the diagnosis of Alzheimer's disease from unprocessed electroencephalograms by using robust invariant features

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 0.3-70Hz 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.

Role of XPC, XPD, XRCC1, GSTP genetic polymorphisms and Barrett's esophagus in a cohort of Italian subjects. A neural network analysis

BACKGROUND

Barrett's esophagus (BE), a metaplastic premalignant disorder, represents the primary risk factor for the development of esophageal adenocarcinoma. Chronic gastroesophageal reflux disease and central obesity have been associated with BE and esophageal adenocarcinoma, but relatively little is known about the specific genes that confer susceptibility to BE carcinogenesis.

METHODS

A total of 74 patients with BE and 67 controls coming from six gastrointestinal Italian units were evaluated for six polymorphisms in four genes: XPC, XPD nucleotide excision repair (NER) genes, XRCC1 (BER gene), and glutathione S-transferase P1. Smoking status was analyzed together with the genetic data. Statistical analysis was performed through Artificial Neural Networks.

RESULTS

Distributions of sex, smoking history, and polymorphisms among BE cases and controls did not show statistically significant differences. The r-value from linear correlation allowed us to identify possible protective factors as well as possible risk factors. The application of advanced intelligent systems allowed for the selection of a subgroup of nine variables. Artificial Neural Networks applied on the final data set reached mean global accuracy of 60%, reaching as high as 65.88%.

CONCLUSION

We report here results from an exploratory study. Results from this study failed to find an association among the tested single nucleotide polymorphisms and BE phenotype through classical statistical methods. On the contrary, advanced intelligent systems are really able to handle the disease complexity, not treating the data with reductionist approaches unable to detect multiple genes of smaller effect in predisposing to the disease.

IMPACT

To detect multiple genes of smaller effects in predisposing individuals to Barrett's esophagus.

Recognition of morphometric vertebral fractures by artificial neural networks: analysis from GISMO Lombardia Database

BACKGROUND

It is known that bone mineral density (BMD) predicts the fracture's risk only partially and the severity and number of vertebral fractures are predictive of subsequent osteoporotic fractures (OF). Spinal deformity index (SDI) integrates the severity and number of morphometric vertebral fractures. Nowadays, there is interest in developing algorithms that use traditional statistics for predicting OF. Some studies suggest their poor sensitivity. Artificial Neural Networks (ANNs) could represent an alternative. So far, no study investigated ANNs ability in predicting OF and SDI. The aim of the present study is to compare ANNs and Logistic Regression (LR) in recognising, on the basis of osteoporotic risk-factors and other clinical information, patients with SDI≥1 and SDI≥5 from those with SDI = 0.

METHODOLOGY

We compared ANNs prognostic performance with that of LR in identifying SDI≥1/SDI≥5 in 372 women with postmenopausal-osteoporosis (SDI≥1, n = 176; SDI = 0, n = 196; SDI≥5, n = 51), using 45 variables (44 clinical parameters plus BMD). ANNs were allowed to choose relevant input data automatically (TWIST-system-Semeion). Among 45 variables, 17 and 25 were selected by TWIST-system-Semeion, in SDI≥1 vs SDI = 0 (first) and SDI≥5 vs SDI = 0 (second) analysis. In the first analysis sensitivity of LR and ANNs was 35.8% and 72.5%, specificity 76.5% and 78.5% and accuracy 56.2% and 75.5%, respectively. In the second analysis, sensitivity of LR and ANNs was 37.3% and 74.8%, specificity 90.3% and 87.8%, and accuracy 63.8% and 81.3%, respectively.

CONCLUSIONS

ANNs showed a better performance in identifying both SDI≥1 and SDI≥5, with a higher sensitivity, suggesting its promising role in the development of algorithm for predicting OF.

Is it possible to clinically differentiate erosive from nonerosive reflux disease patients? A study using an artificial neural networks-assisted algorithm

BACKGROUND

The use of either symptom questionnaires or artificial neural networks (ANNs) has proven to improve the accuracy in diagnosing gastroesophageal reflux disease (GERD). However, the differentiation between the erosive and nonerosive reflux disease based upon symptoms at presentation still remains inconclusive.

AIM

To assess the capability of a combined approach, that is, the use of a novel GERD questionnaire - the QUestionario Italiano Diagnostico (QUID) questionnaire - and of an ANNs-assisted algorithm, to discriminate between nonerosive gastroesophageal reflux disease (NERD) and erosive esophagitis (EE) patients.

METHODS

Five hundred and fifty-seven adult outpatients with typical GERD symptoms and 94 asymptomatic adult patients, were submitted to the QUID questionnaire. GERD patients were then submitted to upper gastrointestinal endoscopy to differentiate them between EE and NERD patients.

RESULTS

The QUID score resulted significantly (P<0.001) higher in GERD patients versus controls, but it was not statistically significantly different between EE and NERD patients. ANNs assisted diagnosis had greater specificity, sensitivity and accuracy compared with the linear discriminant analysis only to differentiate GERD patients from controls. However, no single technique was able to satisfactorily discriminate between EE and NERD patients.

CONCLUSION

Our study suggests that the combination between QUID questionnaire and an ANNs-assisted algorithm is useful only to differentiate GERD patients from healthy individuals but fails to further discriminate erosive from nonerosive patients.

Polymorphisms in folate-metabolizing genes, chromosome damage, and risk of Down syndrome in Italian women: identification of key factors using artificial neural networks

BACKGROUND

Studies in mothers of Down syndrome individuals (MDS) point to a role for polymorphisms in folate metabolic genes in increasing chromosome damage and maternal risk for a Down syndrome (DS) pregnancy, suggesting complex gene-gene interactions. This study aimed to analyze a dataset of genetic and cytogenetic data in an Italian group of MDS and mothers of healthy children (control mothers) to assess the predictive capacity of artificial neural networks assembled in TWIST system in distinguish consistently these two different conditions and to identify the variables expressing the maximal amount of relevant information to the condition of being mother of a DS child.The dataset consisted of the following variables: the frequency of chromosome damage in peripheral lymphocytes (BNMN frequency) and the genotype for 7 common polymorphisms in folate metabolic genes (MTHFR 677C>T and 1298A>C, MTRR 66A>G, MTR 2756A>G, RFC1 80G>A and TYMS 28bp repeats and 1494 6bp deletion). Data were analysed using TWIST system in combination with supervised artificial neural networks, and a semantic connectivity map.

RESULTS

TWIST system selected 6 variables (BNMN frequency, MTHFR 677TT, RFC1 80AA, TYMS 1494 6bp +/+, TYMS 28bp 3R/3R and MTR 2756AA genotypes) that were subsequently used to discriminate between MDS and control mothers with 90% accuracy. The semantic connectivity map provided important information on the complex biological connections between the studied variables and the two conditions (being MDS or control mother).

CONCLUSIONS

Overall, the study suggests a link between polymorphisms in folate metabolic genes and DS risk in Italian women.

Evidence-based evaluation of diagnostic accuracy of resting EEG in dementia and mild cognitive impairment

Cognitive impairment is the most frequent chronic condition in the elderly, and dementia is the most disabling form of cognitive impairment in elderly. In the absence of specific and reliable markers of etiologically different dementia syndromes and their preclinical stages, diagnosis in living patients is probabilistic and based on standardized clinical diagnostic criteria. There is still not enough information on the validity of the EEG method in dementia work-up, and an updated evidence-based consensus on appropriateness of this method in the initial evaluation of patients with suspected cognitive disorder and dementia is missing. Using an evidence-based technique we searched for articles on diagnostic accuracy of spontaneous EEG in dementia disorders published from 1980 until June 2008. Inclusion criteria were: original article published in English with 10 or more subjects per diagnostic group, diagnosed according to the established consensus clinical diagnostic criteria used as a "gold standard." In addition, it should have been possible to calculate from the reported results indexes of diagnostic test accuracy: sensitivity, specificity, likelihood ratios and diagnostic odds ratios. Forty-six articles were retrieved that satisfied eligibility criteria. Thirty-four (74%) studies employed case-control design where study population was recruited from consecutive patients at specialist clinic settings, 12 (26%) were prospective in terms of reported clinical followup of study population. Four (9%) studies used population-based samples and 5 (11%) studies stated in methods the recruitment procedures for patients and healthy subjects. Number of patients included in diagnostic groups and healthy subjects varied in included studies between 10 and 180 and 10 and 171, respectively. Figures on sensitivity and specificity across the studies varied widely. Positive likelihood ratio in studies reporting classification accuracies between Alzheimer's disease and controls ranged between 2.3 and 38.5, and diagnostic odds ratios consequently showed large variations between 7 and 219. In conclusion, despite the wealth of published research and reported high indexes of diagnostic accuracy of EEG, and qEEG in particular, in individual studies, evidence of diagnostic utility of resting EEG in dementia and mild cognitive impairment (MCI) is still not sufficient to establish this method for the initial evaluation of subjects with cognitive impairment in the routine clinical practice. Joint effort of preferably multicenter studies using uniform standards should develop optimized methods, investigate added diagnostic value of EEG in clinically established dementia diagnosis and predictive utility of EEG in MCI and questionable dementia.

Artificial Neural Networks for Early Prediction of Mortality in Patients with Non Variceal Upper GI Bleeding (UGIB)

BACKGROUND

Mortality for non variceal upper gastrointestinal bleeding (UGIB) is clinically relevant in the first 12-24 hours of the onset of haemorrhage and therefore identification of clinical factors predictive of the risk of death before endoscopic examination may allow for early corrective therapeutic intervention.

AIM

1) Identify simple and early clinical variables predictive of the risk of death in patients with non variceal UGIB; 2) assess previsional gain of a predictive model developed with conventional statistics vs. that developed with artificial neural networks (ANNs).

METHODS AND RESULTS

Analysis was performed on 807 patients with nonvariceal UGIB (527 males, 280 females), as a part of a multicentre Italian study. The mortality was considered "bleeding-related" if occurred within 30 days from the index bleeding episode. A total of 50 independent variables were analysed, 49 of which clinico-anamnestic, all collected prior to endoscopic examination plus the haemoglobin value measured on admission in the emergency department. Death occurred in 42 (5.2%). Conventional statistical techniques (linear discriminant analysis) were compared with ANNs (Twist® system-Semeion) adopting the same result validation protocol with random allocation of the sample in training and testing subsets and subsequent cross-over. ANNs resulted to be significantly more accurate than LDA with an overall accuracy rate near to 90%.

CONCLUSION

Artificial neural networks technology is highly promising in the development of accurate diagnostic tools designed to recognize patients at high risk of death for UGIB.