Complex tensor factorisation with PARAFAC2 for the estimation of brain connectivity from the EEG

Objective: The coupling between neuronal populations and its magnitude have been shown to be informative for various clinical applications. One method to estimate functional brain connectivity is with electroencephalography (EEG) from which the cross-spectrum between different sensor locations is derived. We wish to test the efficacy of tensor factorisation in the estimation of brain connectivity. Methods: An EEG model in the complex domain is derived that shows the suitability of the PARAFAC2 model. Complex tensor factorisation based on PARAFAC2 is used to decompose the EEG into scalp components described by the spatial, spectral, and complex trial profiles. A connectivity metric is also derived on the complex trial profiles of the extracted components. Results: Results on a benchmark EEG dataset confirmed that PARAFAC2 can estimate connectivity better than traditional tensor analysis such as PARAFAC within a range of signal-tonoise ratios. MVAR-ICA outperformed PARAFAC2 for very low signal-to-noise ratios while being inferior in most of the range, and in contrast to our method MVAR-ICA does not allow the estimation of trial to trial information. The analysis of EEG from patients with mild cognitive impairment or Alzheimer’s disease showed that PARAFAC2 identifies loss of brain connectivity agreeing with prior pathological knowledge. Conclusion: The complex PARAFAC2 algorithm is suitable for EEG connectivity estimation since it allows to extract meaningful coupled sources and provides better estimates than complex PARAFAC and MVAR-ICA. Significance: A new paradigm that employs complex tensor factorisation has demonstrated to be successful in identifying brain connectivity and the location of couples sources for both a benchmark and a real-world EEG dataset. This can enable future applications and has the potential to solve some the issues that deteriorate the performance of traditional connectivity metrics

Graph-Based Permutation Patterns for the Analysis of Task-Related fMRI Signals on DTI Networks in Mild Cognitive Impairment

Permutation Entropy ($PE$) is a powerful nonlinear analysis technique for univariate time series. Very recently, Permutation Entropy for Graph signals ($PE_G$) has been proposed to extend $PE$ to data residing on irregular domains. However, $PE_G$ is limited as it provides a single value to characterise a whole graph signal. Here, we introduce a novel approach to evaluate graph signals at the vertex level: graph-based permutation patterns. Synthetic datasets show the efficacy of our method. We reveal that dynamics in graph signals, undetectable with $PE_G$, can be discerned using our graph-based permutation patterns. These are then validated in the analysis of DTI and fMRI data acquired during a working memory task in mild cognitive impairment, where we explore functional brain signals on structural white matter networks. Our findings suggest that graph-based permutation patterns change in individual brain regions as the disease progresses. Thus, graph-based permutation patterns offer promise by enabling the granular scale analysis of graph signals.Comment: 5 pages, 5 figures, 1 tabl

Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition

The aim of the present study was to discriminate the electroencephalogram (EEG) of 5 patients with vascular dementia (VaD), 15 patients with stroke-related mild cognitive impairment (MCI), and 15 control normal subjects during a working memory (WM) task. We used independent component analysis (ICA) and wavelet transform (WT) as a hybrid preprocessing approach for EEG artifact removal. Three different features were extracted from the cleaned EEG signals: spectral entropy (SpecEn), permutation entropy (PerEn) and Tsallis entropy (TsEn). Two classification schemes were applied - support vector machine (SVM) and k-nearest neighbors (kNN) - with fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) as a dimensionality reduction technique. The FNPAQR dimensionality reduction technique increased the SVM classification accuracy from 82.22% to 90.37% and from 82.6% to 86.67% for kNN. These results suggest that FNPAQR consistently improves the discrimination of VaD, MCI patients and control normal subjects and it could be a useful feature selection to help the identification of patients with VaD and MCI

Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing

The aim of the present study was to reveal markers from the electroencephalography (EEG) using approximation entropy (ApEn) and permutation entropy (PerEn). EEGs' of 15 stroke-related patients with mild cognitive impairment (MCI) and 15 control healthy subjects during a working memory (WM) task have EEG artifacts were removed using a wavelet (WT) based method. A t-test (p <; 0.05) was used to test the hypothesis that the irregularity (ApEn and PerEn) in MCIs was reduced in comparison with control subjects. ApEn and PerEn showed reduced irregularity in the EEGs of MCI patients. Therefore, ApEn and PerEn could be used as markers associated with MCI detection and identification and the EEG could be a valuable tool for inspecting the background activity in the identification of patients with MCI

EEG markers for early detection and characterization of vascular dementia during working memory tasks

The aim of the this study was to reveal markers using spectral entropy (SpecEn), sample entropy (SampEn) and Hurst Exponent (H) from the electroencephalography (EEG) background activity of 5 vascular dementia (VaD) patients, 15 stroke-related patients with mild cognitive impairment (MCI) and 15 control healthy subjects during a working memory (WM) task. EEG artifacts were removed using independent component analysis technique and wavelet technique. With ANOVA (p < 0.05), SpecEn was used to test the hypothesis of slowing the EEG signal down in both VaD and MCI compared to control subjects, whereas the SampEn and H features were used to test the hypothesis that the irregularity and complexity in both VaD and MCI were reduced in comparison with control subjects. SampEn and H results in reducing the complexity in VaD and MCI patients. Therefore, SampEn could be the EEG marker that associated with VaD detection whereas H could be the marker for stroke-related MCI identification. EEG could be as a valuable marker for inspecting the background activity in the identification of patients with VaD and stroke-related MCI

Developing a new test to identify consolidation-related memory markers of normal ageing and Alzheimer's Disease

Background: For newly encoded memories to be remembered, they must be consolidated. Research suggests that severe memory problems in AD are due, at least in part, to a fault in awake consolidation, which becomes increasingly vulnerable to interference from post-encoding sensory input. Importantly, post-encoding awake quiescence (quiet rest) strikingly reduces forgetting in AD because it provides conditions that are conducive to consolidation. However, it remains poorly understood how awake consolidation changes ‘normally’ with increasing age and how this differs from AD-related changes. The aim of this study was to develop a new test that can measure normal age-related and pathological AD-related changes in awake consolidation. Method: A new memory discrimination test was applied in healthy younger (N=40) and older (N=40) adults. Participants completed an incidental encoding task, where they were presented photos of everyday items, before experiencing a 10-minute delay condition of either (i) awake quiescence or (ii) ongoing sensory input. They then completed a visual memory discrimination test for the earlier presented photos. Older adults also completed a battery of neuropsychological measures. Result: Performance in the memory discrimination test was significantly poorer in older than younger adults. There was a significant main effect of delay condition because both younger and older adults who rested outperformed those who experienced ongoing sensory input. No significant interaction between age group and delay condition was observed. Conclusion: Despite age-related declines in memory performance, the magnitude of the consolidation interference effect was comparable in younger and older adults. This indicates that a stark increase in consolidation interference is unlikely to be accounted for by normal ageing and there is potential for consolidation interference to be used as a cognitive marker of AD

Graph-based permutation patterns for the analysis of task-related fMRI signals on DTI networks in mild cognitive impairment

Permutation Entropy (PE) is a powerful nonlinear analysis technique for univariate time series. Very recently, Permutation Entropy for Graph signals (PEG) has been proposed to extend PE to data residing on irregular domains. However, PEG is limited as it provides a single value to characterise a whole graph signal. Here, we introduce a novel approach to evaluate graph signals at the vertex level: graph-based permutation patterns. Synthetic datasets show the efficacy of our method. We reveal that dynamics in graph signals, undetectable with PEG, can be discerned using our graph-based permutation patterns. These are then validated in the analysis of DTI and fMRI data acquired during a working memory task in mild cognitive impairment, where we explore functional brain signals on structural white matter networks. Our findings suggest that graph-based permutation patterns change in individual brain regions as the disease progresses. Thus, graph-based permutation patterns offer promise by enabling the granular scale analysis of graph signals

Automated Extraction Improves Multiplex Molecular Detection of Infection in Septic Patients

Sepsis is one of the leading causes of morbidity and mortality in hospitalized patients worldwide. Molecular technologies for rapid detection of microorganisms in patients with sepsis have only recently become available. LightCycler SeptiFast test Mgrade (Roche Diagnostics GmbH) is a multiplex PCR analysis able to detect DNA of the 25 most frequent pathogens in bloodstream infections. The time and labor saved while avoiding excessive laboratory manipulation is the rationale for selecting the automated MagNA Pure compact nucleic acid isolation kit-I (Roche Applied Science, GmbH) as an alternative to conventional SeptiFast extraction. For the purposes of this study, we evaluate extraction in order to demonstrate the feasibility of automation. Finally, a prospective observational study was done using 106 clinical samples obtained from 76 patients in our ICU. Both extraction methods were used in parallel to test the samples. When molecular detection test results using both manual and automated extraction were compared with the data from blood cultures obtained at the same time, the results show that SeptiFast with the alternative MagNA Pure compact extraction not only shortens the complete workflow to 3.57 hrs., but also increases sensitivity of the molecular assay for detecting infection as defined by positive blood culture confirmation

Phenotyping Ex-Combatants From EEG Scalp Connectivity

Being involved in war experiences may have severe consequences in mental health. This exposure has been associated in Colombian ex-combatants with risk of proactive aggression modulating emotional processing. However, the extent of the cognitive processes underlying aggressive behavior is still an open issue. In this work, we propose a SVM-based processing pipeline to identify different cognitive phenotypes associated with atypical emotional processing, based on canonical correlation analysis of EEG network features, and cognitive and behavioral evaluations. Results show the existence of cognitive phenotypes associated with differences in the mean value of leaf fraction and diameter of EEG networks across groups. The ability of identifying phenotypes in these otherwise healthy subjects opens up the possibility to aid in the development of specific interventions aimed to reduce expression of proactive aggression in ex-combatants and assessing the efficacy of such interventions

LDL particle size and composition and incident cardiovascular disease in a South-European population: The Hortega-Liposcale Follow-up Study.

The association of low-density lipoprotein (LDL) particle composition with cardiovascular risk has not been explored before. The aim was to evaluate the relationship between baseline LDL particle size and composition (proportions of large, medium and small LDL particles over their sum expressed as small-LDL %, medium-LDL % and large-LDL %) and incident cardiovascular disease in a population-based study. Methods: Direct measurement of LDL particles was performed using a two-dimensional NMR-technique (Liposcale®). LDL cholesterol was assessed using both standard photometrical methods and the Liposcale® technique in a representative sample of 1162 adult men and women from Spain. Results: The geometric mean of total LDL particle concentration in the study sample was 827.2 mg/dL (95% CI 814.7, 839.8). During a mean follow-up of 12.4 ± 3.3 years, a total of 159 events occurred. Medium LDL particles were positively associated with all cardiovascular disease, coronary heart disease (CHD) and stroke after adjustment for traditional risk factors and treatment. Regarding LDL particle composition, the multivariable adjusted hazard ratios for CHD for a 5% increase in medium and small LDL % by a corresponding decrease of large LDL % were 1.93 (1.55, 2.39) and 1.41 (1.14, 1.74), respectively. Conclusions: Medium LDL particles were associated with incident cardiovascular disease. LDL particles showed the strongest association with cardiovascular events when the particle composition, rather than the total concentration, was investigated. A change in baseline composition of LDL particles from large to medium and small LDL particles was associated with an increased cardiovascular risk, especially for CHD