On the variability of functional connectivity and network measures in source-reconstructed eeg time-series

The idea of estimating the statistical interdependence among (interacting) brain regions has motivated numerous researchers to investigate how the resulting connectivity patterns and networks may organize themselves under any conceivable scenario. Even though this idea has developed beyond its initial stages, its practical application is still far away from being widespread. One concurrent cause may be related to the proliferation of different approaches that aim to catch the underlying statistical interdependence among the (interacting) units. This issue has probably con-tributed to hindering comparisons among different studies. Not only do all these approaches go under the same name (functional connectivity), but they have often been tested and validated using different methods, therefore, making it difficult to understand to what extent they are similar or not. In this study, we aim to compare a set of different approaches commonly used to estimate the functional connectivity on a public EEG dataset representing a possible realistic scenario. As expected, our results show that source-level EEG connectivity estimates and the derived network measures, even though pointing to the same direction, may display substantial dependency on the (often ar-bitrary) choice of the selected connectivity metric and thresholding approach. In our opinion, the observed variability reflects the ambiguity and concern that should always be discussed when re-porting findings based on any connectivity metric

Eeg fingerprints under naturalistic viewing using a portable device

The electroencephalogram (EEG) has been proven to be a promising technique for personal identification and verification. Recently, the aperiodic component of the power spectrum was shown to outperform other commonly used EEG features. Beyond that, EEG characteristics may capture relevant features related to emotional states. In this work, we aim to understand if the aperiodic component of the power spectrum, as shown for resting-state experimental paradigms, is able to capture EEG-based subject-specific features in a naturalistic stimuli scenario. In order to answer this question, we performed an analysis using two freely available datasets containing EEG recordings from participants during viewing of film clips that aim to trigger different emotional states. Our study confirms that the aperiodic components of the power spectrum, as evaluated in terms of offset and exponent parameters, are able to detect subject-specific features extracted from the scalp EEG. In particular, our results show that the performance of the system was significantly higher for the film clip scenario if compared with resting-state, thus suggesting that under naturalistic stimuli it is even easier to identify a subject. As a consequence, we suggest a paradigm shift, from task-based or resting-state to naturalistic stimuli, when assessing the performance of EEG-based biometric systems

(1)H-NMR analysis provides a metabolomic profile of patients with multiple sclerosis

OBJECTIVE: To investigate the metabolomic profiles of patients with multiple sclerosis (MS) and to define the metabolic pathways potentially related to MS pathogenesis. METHODS: Plasma samples from 73 patients with MS (therapy-free for at least 90 days) and 88 healthy controls (HC) were analyzed by (1)H-NMR spectroscopy. Data analysis was conducted with principal components analysis followed by a supervised analysis (orthogonal partial least squares discriminant analysis [OPLS-DA]). The metabolites were identified and quantified using Chenomx software, and the receiver operating characteristic (ROC) curves were calculated. RESULTS: The model obtained with the OPLS-DA identified predictive metabolic differences between the patients with MS and HC (R2X = 0.615, R2Y = 0.619, Q2 = 0.476; p < 0.001). The differential metabolites included glucose, 5-OH-tryptophan, and tryptophan, which were lower in the MS group, and 3-OH-butyrate, acetoacetate, acetone, alanine, and choline, which were higher in the MS group. The suitability of the model was evaluated using an external set of samples. The values returned by the model were used to build the corresponding ROC curve (area under the curve of 0.98). CONCLUSION: NMR metabolomic analysis was able to discriminate different metabolic profiles in patients with MS compared with HC. With the exception of choline, the main metabolic changes could be connected to 2 different metabolic pathways: tryptophan metabolism and energy metabolism. Metabolomics appears to represent a promising noninvasive approach for the study of M

Urinary metabolomics (GC-MS) reveals that low and high birth weight infants share elevated inositol concentrations at birth

Objective: Metabolomics is a new ‘‘omics’’ platform aimed at high-throughput identification, quantification and characterization of small molecule metabolites. The metabolomics approach has been successfully applied to the classification different physiological states and identification of perturbed biochemical pathways. The purpose of the current investigation is the application of metabolomics to explore biological mechanisms which may lead to the onset of metabolic syndrome in adulthood. Methods: We evaluated differences in metabolites in the urine collected within 12 hours from 23 infants with IUGR (IntraUterine Growth Restriction), or LGA (Large for Gestational Age), compared to control infants (10 patients defined AGA: Appropriate for Gestational Age). Urinary metabolites were quantified by GC-MS and used to highlight similarities between the two metabolic diseases and identify metabolic markers for their predisposition. Quantified metabolites were analyzed using a multivariate statistics coupled with receiver operator characteristic curve (ROC) analysis of identified biomarkers. Results: Urinary myo-inositol was the most important discriminant between LGA + IUGR and control infants, and displayed an area under the ROC curve¼1. Conclusion: We postulate that the increase in plasma and consequently urinary inositol may constitute a marker of altered glucose metabolism during fetal development in both IUGR and LGA newborns

The metabolomic profile of lymphoma subtypes: A pilot study

Lymphoma defines a group of different diseases. This study examined pre-treatment plasma samples from 66 adult patients (aged 20-74) newly diagnosed with any lymphoma subtype, and 96 frequency matched population controls. We used gas chromatography-mass spectrometry (GC-MS) to compare the metabolic profile by case/control status and across the major lymphoma subtypes. We conducted univariate and multivariate analyses, and partial least square discriminant analysis (PLS-DA). When compared to the controls, statistically validated models were obtained for diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), multiple myeloma (MM), and Hodgkin lymphoma (HL), but not follicular lymphoma (FL). The metabolomic analysis highlighted interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects: Important metabolites, such as hypoxanthine and elaidic acid, were more abundant in all lymphoma subtypes. The small sample size of the individual lymphoma subtypes prevented obtaining PLS-DA validated models, although specific peculiar features of each subtype were observed; for instance, fatty acids were most represented in MM and HL patients, while 2-aminoadipic acid, 2-aminoheptanedioic acid, erythritol, and threitol characterized DLBCL and CLL. Metabolomic analysis was able to highlight interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects. Further studies are warranted to understand whether the peculiar metabolic patterns observed might serve as early biomarkers of lymphoma

Metabolomics As a Tool for the Characterization of Drug-Resistant Epilepsy.

PURPOSE: Drug resistance is a critical issue in the treatment of epilepsy, contributing to clinical emergencies and increasing both serious social and economic burdens on the health system. The wide variety of potential drug combinations followed by often failed consecutive attempts to match drugs to an individual patient may mean that this treatment stage may last for years with suboptimal benefit to the patient. Given these challenges, it is valuable to explore the availability of new methodologies able to shorten the period of determining a rationale pharmacologic treatment. Metabolomics could provide such a tool to investigate possible markers of drug resistance in subjects with epilepsy. METHODS: Blood samples were collected from (1) controls (C) (n = 35), (2) patients with epilepsy "responder" (R) (n = 18), and (3) patients with epilepsy "non-responder" (NR) (n = 17) to the drug therapy. The samples were analyzed using nuclear magnetic resonance spectroscopy, followed by multivariate statistical analysis. KEY FINDINGS: A different metabolic profile based on metabolomics analysis of the serum was observed between C and patients with epilepsy and also between R and NR patients. It was possible to identify the discriminant metabolites for the three classes under investigation. Serum from patients with epilepsy were characterized by increased levels of 3-OH-butyrate, 2-OH-valerate, 2-OH-butyrate, acetoacetate, acetone, acetate, choline, alanine, glutamate, scyllo-inositol (C  R > NR). SIGNIFICANCE: In conclusion, metabolomics may represent an important tool for discovery of differences between subjects affected by epilepsy responding or resistant to therapies and for the study of its pathophysiology, optimizing the therapeutic resources and the quality of life of patients

Urinary metabolomic analysis to identify preterm neonates exposed to histological chorioamnionitis : A pilot study

Objective Chorioamnionitis is a leading cause of preterm birth worldwide, with higher incidence at lower gestational ages. An early and reliable diagnosis of histological chorioamnionitis (HCA) in preterm infants may be helpful in guiding postnatal management, especially the administration of prophylactic antibiotics to prevent early-onset sepsis. The main aim of this study was to investigate metabolomic analysis of urines collected in the first 24 hours of life as diagnostic tool of HCA. Methods Gestational age-, birth weight-, delivery mode- and sex- matched (1:2) preterm neonates (< 35 weeks\u2019 gestation) born to mothers with or without HCA were enrolled from an observational study. Gas chromatography-mass spectrometry (GC-MS)-based metabolomic analysis was performed on urine samples non-invasively collected in the first 24 hours of life. Univariate analysis, partial least square discriminant analysis (PLS-DA) and its associated variable importance in projection (VIP) score were performed. The most affected metabolic pathways were examined by Metabolite Sets Enrichment Analysis (MSEA). Results Fifteen cases (mean GA 30.2 \ub1 3.8 weeks, mean BW 1415 \ub1 471.9 grams) and 30 controls (mean GA 30.2 \ub1 2.9 weeks, mean BW 1426 \ub1 569.8 grams) were enrolled. Following univariate analysis, 29 metabolites had a significantly different concentration between cases and controls. The supervised PLS-DA model confirmed a separation between the two groups. Only gluconic acid, an oxidation product of glucose, was higher in cases than in controls. All other VIP metabolites were more abundant in the control group. Glutamate metabolism, mitochondrial electron transport chain, citric acid cycle, galactose metabolism, and fructose and mannose degradation metabolism were the most significantly altered pathways (P < 0.01). Conclusions For the first time, urinary metabolomics was able to discriminate neonates born to mothers with and without HCA. The identification of specifically altered metabolic pathways may be helpful in understanding metabolic derangement following chorioamnionitis