Statistical Analysis of Methotrexate Degradation by UV-C Photolysis and UV-C/TiO2 Photocatalysis.

Methotrexate (MTX) is a folic acid analog and has been used to treat a wide variety of malignant and non-malignant diseases. The wide use of these substances has led to the continuous discharge of the parent compound and its metabolites in wastewater. In conventional wastewater treatment plants, the removal or degradation of drugs is not complete. In order to study the MTX degradation by photolysis and photocatalysis processes, two reactors were used with TiO2 as a catalyst and UV-C lamps as a radiation source. H2O2 addition was also studied (absence and 3 mM/L), and different initial pHs (3.5, 7, and 9.5) were tested to define the best degradation parameters. Results were analyzed by means of ANOVA and the Tukey test. Results show that photolysis in acidic conditions with 3 mM of H2O2 added is the best condition for MTX degradation in these reactors, with a kinetic constant of 0.028 min-1. According to the ANOVA test, all considered factors (process, pH, H2O2 addition, and experimentation time) caused statistically significant differences in the MTX degradation results.

Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity.

Human brain activity generates scalp potentials (electroencephalography - EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography - MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community.

Chronic Oral Anticoagulation Therapy and Prognosis of Patients Admitted to Hospital for COVID-19: Insights from the HOPE COVID-19 Registry.

Background: Most evidence regarding anticoagulation and COVID-19 refers to the hospitalization setting, but the role of oral anticoagulation (OAC) before hospital admission has not been well explored. We compared clinical outcomes and short-term prognosis between patients with and without prior OAC therapy who were hospitalized for COVID-19. Methods: Analysis of the whole cohort of the HOPE COVID-19 Registry which included patients discharged (deceased or alive) after hospital admission for COVID-19 in 9 countries. All-cause mortality was the primary endpoint. Study outcomes were compared after adjusting variables using propensity score matching (PSM) analyses. Results: 7698 patients were suitable for the present analysis (675 (8.8%) on OAC at admission: 427 (5.6%) on VKAs and 248 (3.2%) on DOACs). After PSM, 1276 patients were analyzed (638 with OAC; 638 without OAC), without significant differences regarding the risk of thromboembolic events (OR 1.11, 95% CI 0.59-2.08). The risk of clinically relevant bleeding (OR 3.04, 95% CI 1.92-4.83), as well as the risk of mortality (HR 1.22, 95% CI 1.01-1.47; log-rank p value = 0.041), was significantly increased in previous OAC users. Amongst patients on prior OAC only, there were no differences in the risk of clinically relevant bleeding, thromboembolic events, or mortality when comparing previous VKA or DOAC users, after PSM. Conclusion: Hospitalized COVID-19 patients on prior OAC therapy had a higher risk of mortality and worse clinical outcomes compared to patients without prior OAC therapy, even after adjusting for comorbidities using a PSM. There were no differences in clinical outcomes in patients previously taking VKAs or DOACs. This trial is registered with NCT04334291/EUPAS34399.

Aspirin Therapy on Prophylactic Anticoagulation for Patients Hospitalized With COVID-19: A Propensity Score-Matched Cohort Analysis of the HOPE-COVID-19 Registry.

Background COVID-19 is an infectious illness, featured by an increased risk of thromboembolism. However, no standard antithrombotic therapy is currently recommended for patients hospitalized with COVID-19. The aim of this study was to evaluate safety and efficacy of additional therapy with aspirin over prophylactic anticoagulation (PAC) in patients hospitalized with COVID-19 and its impact on survival. Methods and Results A total of 8168 patients hospitalized for COVID-19 were enrolled in a multicenter-international prospective registry (HOPE COVID-19). Clinical data and in-hospital complications, including mortality, were recorded. Study population included patients treated with PAC or with PAC and aspirin. A comparison of clinical outcomes between patients treated with PAC versus PAC and aspirin was performed using an adjusted analysis with propensity score matching. Of 7824 patients with complete data, 360 (4.6%) received PAC and aspirin and 2949 (37.6%) PAC. Propensity-score matching yielded 298 patients from each group. In the propensity score-matched population, cumulative incidence of in-hospital mortality was lower in patients treated with PAC and aspirin versus PAC (15% versus 21%, Log Rank P=0.01). At multivariable analysis in propensity matched population of patients with COVID-19, including age, sex, hypertension, diabetes, kidney failure, and invasive ventilation, aspirin treatment was associated with lower risk of in-hospital mortality (hazard ratio [HR], 0.62; [95% CI 0.42-0.92], P=0.018). Conclusions Combination PAC and aspirin was associated with lower mortality risk among patients hospitalized with COVID-19 in a propensity score matched population compared to PAC alone.

Study of coliforms and Clostridium bacteria inactivation in wastewaters by a pilot photolysis process and by the maturation lagoons of a low-cost nature-based WWTP.

The inactivation processes of coliform bacteria (total and fecal) and sulphito-reducing Clostridium bacteria (vegetative species and spores) in water maturation lagoon of a low-cost nature-based wastewater treatment plant using constructed wetlands and through processes of photolysis in a pilot photoreactor have been comparatively studied. The different inactivation mechanisms by photolysis of these bacteria have been studied following the criteria of different statistical and kinetic models. Clostridium disinfection treatments fit models in which two types of bacteria populations coexist, one sensitive (vegetative species) and the other (spores) resistant to the treatment, the sensitive one (94%) with an inactivation rate of k = 0.24 ± 0.07 min-1 and the resistant one (6%) with k = 0.11 ± 0.05 min-1. Total coliform photolytic disinfection also shows two populations with different physiological state. The time required to reduce the first logarithmic decimal cycle of the different types of bacteria (physiological states) are δ1 = 4.2 ± 0.9 and δ2 = 8.3 ± 1.1 min, respectively. For fecal coliform photolytic disinfection, only bacteria population, with k = 1.15 ± 0.19 min-1, is found. The results obtained confirm the photolytic disinfection processes and maturation lagoon are effective systems for Clostridia bacteria removal after water treatment by nature-based systems. Total removal of coliform bacteria is not achieved by maturation lagoons, but their reduction is significant using low doses of cumulative radiation.

Antiplatelet therapy and outcome in COVID-19: the Health Outcome Predictive Evaluation Registry.

BACKGROUND: Standard therapy for COVID-19 is continuously evolving. Autopsy studies showed high prevalence of platelet-fibrin-rich microthrombi in several organs. The aim of the study was therefore to evaluate the safety and efficacy of antiplatelet therapy (APT) in hospitalised patients with COVID-19 and its impact on survival. METHODS: 7824 consecutive patients with COVID-19 were enrolled in a multicentre international prospective registry (Health Outcome Predictive Evaluation-COVID-19 Registry). Clinical data and in-hospital complications were recorded. Data on APT, including aspirin and other antiplatelet drugs, were obtained for each patient. RESULTS: During hospitalisation, 730 (9%) patients received single APT (93%, n=680) or dual APT (7%, n=50). Patients treated with APT were older (74±12 years vs 63±17 years, p<0.01), more frequently male (68% vs 57%, p<0.01) and had higher prevalence of diabetes (39% vs 16%, p<0.01). Patients treated with APT showed no differences in terms of in-hospital mortality (18% vs 19%, p=0.64), need for invasive ventilation (8.7% vs 8.5%, p=0.88), embolic events (2.9% vs 2.5% p=0.34) and bleeding (2.1% vs 2.4%, p=0.43), but had shorter duration of mechanical ventilation (8±5 days vs 11±7 days, p=0.01); however, when comparing patients with APT versus no APT and no anticoagulation therapy, APT was associated with lower mortality rates (log-rank p<0.01, relative risk 0.79, 95% CI 0.70 to 0.94). On multivariable analysis, in-hospital APT was associated with lower mortality risk (relative risk 0.39, 95% CI 0.32 to 0.48, p<0.01). CONCLUSIONS: APT during hospitalisation for COVID-19 could be associated with lower mortality risk and shorter duration of mechanical ventilation, without increased risk of bleeding. TRIAL REGISTRATION NUMBER: NCT04334291.

Prevalence and 30-Day Mortality in Hospitalized Patients With Covid-19 and Prior Lung Diseases.

INTRODUCTION: Patients with pre-existing respiratory diseases in the setting of COVID-19 may have a greater risk of severe complications and even death. METHODS: A retrospective, multicenter, cohort study with 5847 COVID-19 patients admitted to hospitals. Patients were separated in two groups, with/without previous lung disease. Evaluation of factors associated with survival and secondary composite end-point such as ICU admission and respiratory support, were explored. RESULTS: 1,271 patients (22%) had a previous lung disease, mostly COPD. All-cause mortality occurred in 376 patients with lung disease (29.5%) and in 819 patients without (17.9%) (p < 0.001). Kaplan-Meier curves showed that patients with lung diseases had a worse 30-day survival (HR = 1.78; 95%C.I. 1.58-2.01; p < 0.001) and COPD had almost 40% mortality. Multivariable Cox regression showed that prior lung disease remained a risk factor for mortality (HR, 1.21; 95%C.I. 1.02-1.44; p = 0.02). Variables independently associated with all-cause mortality risk in patients with lung diseases were oxygen saturation less than 92% on admission (HR, 4.35; 95% CI 3.08-6.15) and elevated D-dimer (HR, 1.84; 95% CI 1.27-2.67). Age younger than 60 years (HR 0.37; 95% CI 0.21-0.65) was associated with decreased risk of death. CONCLUSIONS: Previous lung disease is a risk factor for mortality in patients with COVID-19. Older age, male gender, home oxygen therapy, and respiratory failure on admission were associated with an increased mortality. Efforts must be done to identify respiratory patients to set measures to improve their clinical outcomes.

INTRODUCCIÓN: Los pacientes con enfermedades respiratorias preexistentes pueden tener en el contexto de la covid-19 un mayor riesgo de complicaciones graves e incluso de muerte. MÉTODOS: Estudio de cohortes multicéntrico y retrospectivo de 5.847 pacientes con covid-19 ingresados en hospitales. Los pacientes se separaron en 2 grupos, sin y con enfermedad pulmonar previa. Se evaluaron factores asociados con la supervivencia y criterios combinados de valoración secundarios, como el ingreso en la UCI y la necesidad de asistencia respiratoria. RESULTADOS: Mil doscientos setenta y un (1.271) pacientes (22%) tenían una enfermedad pulmonar previa, principalmente EPOC. La mortalidad por todas las causas ocurrió en 376 pacientes con enfermedad pulmonar (29,5%) y en 819 pacientes sin enfermedad pulmonar (17,9%; p < 0,001). Las curvas de Kaplan-Meier mostraron que los pacientes con enfermedades pulmonares tenían una peor supervivencia a los 30 días (HR: 1,78; IC del 95%: 1,58-2,01; p < 0,001) y la EPOC tenía una mortalidad de casi el 40%. La regresión de Cox multivariante mostró que la enfermedad pulmonar previa seguía siendo un factor de riesgo de mortalidad (HR: 1,21; IC del 95%: 1,02-1,44; p = 0,02). Las variables asociadas de forma independiente con el riesgo de muerte por todas las causas en pacientes con enfermedades pulmonares fueron la saturación de oxígeno inferior al 92% al ingreso (HR: 4,35; IC del 95%: 3,08-6,15) y el dímero D elevado (HR: 1,84; IC del 95%: 1,27-2,67). La edad menor de 60 años (HR: 0,37; IC del 95%: 0,21-0,65) se asoció con una disminución del riesgo de muerte. CONCLUSIONES: La enfermedad pulmonar previa es un factor de riesgo de muerte en pacientes con covid-19. La edad avanzada, el sexo masculino, la oxigenoterapia domiciliaria y la insuficiencia respiratoria al ingreso se asociaron con un aumento de la mortalidad. Se deben realizar esfuerzos para identificar a los pacientes respiratorios y establecer medidas para mejorar sus resultados clínicos.

Impact of smoking on COVID-19 outcomes: a HOPE Registry subanalysis.

BACKGROUND: Smoking has been associated with poorer outcomes in relation to COVID-19. Smokers have higher risk of mortality and have a more severe clinical course. There is paucity of data available on this issue, and a definitive link between smoking and COVID-19 prognosis has yet to be established. METHODS: We included 5224 patients with COVID-19 with an available smoking history in a multicentre international registry Health Outcome Predictive Evaluation for COVID-19 (NCT04334291). Patients were included following an in-hospital admission with a COVID-19 diagnosis. We analysed the outcomes of patients with a current or prior history of smoking compared with the non-smoking group. The primary endpoint was all-cause in-hospital death. RESULTS: Finally, 5224 patients with COVID-19 with available smoking status were analysed. A total of 3983 (67.9%) patients were non-smokers, 934 (15.9%) were former smokers and 307 (5.2%) were active smokers. The median age was 66 years (IQR 52.0-77.0) and 58.6% were male. The most frequent comorbidities were hypertension (48.5%) and dyslipidaemia (33.0%). A relevant lung disease was present in 19.4%. In-hospital complications such sepsis (23.6%) and embolic events (4.3%) occurred more frequently in the smoker group (p<0.001 for both). All cause-death was higher among smokers (active or former smokers) compared with non-smokers (27.6 vs 18.4%, p<0.001). Following a multivariate analysis, current smoking was considered as an independent predictor of mortality (OR 1.77, 95% CI 1.11 to 2.82, p=0.017) and a combined endpoint of severe disease (OR 1.68, 95% CI 1.16 to 2.43, p=0.006). CONCLUSION: Smoking has a negative prognostic impact on patients hospitalised with COVID-19.

Renin-angiotensin system inhibitors effect before and during hospitalization in COVID-19 outcomes: Final analysis of the international HOPE COVID-19 (Health Outcome Predictive Evaluation for COVID-19) registry.

BACKGROUND: The use of Renin-Angiotensin system inhibitors (RASi) in patients with coronavirus disease 2019 (COVID-19) has been questioned because both share a target receptor site. METHODS: HOPE-COVID-19 (NCT04334291) is an international investigator-initiated registry. Patients are eligible when discharged after an in-hospital stay with COVID-19, dead or alive. Here, we analyze the impact of previous and continued in-hospital treatment with RASi in all-cause mortality and the development of in-stay complications. RESULTS: We included 6503 patients, over 18 years, from Spain and Italy with data on their RASi status. Of those, 36.8% were receiving any RASi before admission. RASi patients were older, more frequently male, with more comorbidities and frailer. Their probability of death and ICU admission was higher. However, after adjustment, these differences disappeared. Regarding RASi in-hospital use, those who continued the treatment were younger, with balanced comorbidities but with less severe COVID19. Raw mortality and secondary events were less frequent in RASi. After adjustment, patients receiving RASi still presented significantly better outcomes, with less mortality, ICU admissions, respiratory insufficiency, need for mechanical ventilation or prone, sepsis, SIRS and renal failure (p<0.05 for all). However, we did not find differences regarding the hospital use of RASi and the development of heart failure. CONCLUSION: RASi historic use, at admission, is not related to an adjusted worse prognosis in hospitalized COVID-19 patients, although it points out a high-risk population. In this setting, the in-hospital prescription of RASi is associated with improved survival and fewer short-term complications.

Prevalence and 30-Day Mortality in Hospitalized Patients With Covid-19 and Prior Lung Diseases / Prevalencia y mortalidad a 30 días en pacientes hospitalizados por COVID-19 y enfermedades pulmonares previas

INTRODUCTION: Patients with pre-existing respiratory diseases in the setting of COVID-19 may have a greater risk of severe complications and even death. METHODS: A retrospective, multicenter, cohort study with 5847 COVID-19 patients admitted to hospitals. Patients were separated in two groups, with/without previous lung disease. Evaluation of factors associated with survival and secondary composite end-point such as ICU admission and respiratory support, were explored. RESULTS: 1,271 patients (22%) had a previous lung disease, mostly COPD. All-cause mortality occurred in 376 patients with lung disease (29.5%) and in 819 patients without (17.9%) (p < 0.001). Kaplan-Meier curves showed that patients with lung diseases had a worse 30-day survival (HR = 1.78; 95%C.I. 1.58-2.01; p < 0.001) and COPD had almost 40% mortality. Multivariable Cox regression showed that prior lung disease remained a risk factor for mortality (HR, 1.21; 95%C.I. 1.02-1.44; p = 0.02). Variables independently associated with all-cause mortality risk in patients with lung diseases were oxygen saturation less than 92% on admission (HR, 4.35; 95% CI 3.08-6.15) and elevated D-dimer (HR, 1.84; 95% CI 1.27-2.67). Age younger than 60 years (HR 0.37; 95% CI 0.21-0.65) was associated with decreased risk of death. CONCLUSIONS: Previous lung disease is a risk factor for mortality in patients with COVID-19. Older age, male gender, home oxygen therapy, and respiratory failure on admission were associated with an increased mortality. Efforts must be done to identify respiratory patients to set measures to improve their clinical outcomes

INTRODUCCIÓN: Los pacientes con enfermedades respiratorias preexistentes pueden tener en el contexto de la covid-19 un mayor riesgo de complicaciones graves e incluso de muerte. MÉTODOS: Estudio de cohortes multicéntrico y retrospectivo de 5.847 pacientes con covid-19 ingresados en hospitales. Los pacientes se separaron en 2 grupos, sin y con enfermedad pulmonar previa. Se evaluaron factores asociados con la supervivencia y criterios combinados de valoración secundarios, como el ingreso en la UCI y la necesidad de asistencia respiratoria. RESULTADOS: Mil doscientos setenta y un (1.271) pacientes (22%) tenían una enfermedad pulmonar previa, principalmente EPOC. La mortalidad por todas las causas ocurrió en 376 pacientes con enfermedad pulmonar (29,5%) y en 819 pacientes sin enfermedad pulmonar (17,9%; p < 0,001). Las curvas de Kaplan-Meier mostraron que los pacientes con enfermedades pulmonares tenían una peor supervivencia a los 30 días (HR: 1,78; IC del 95%: 1,58-2,01; p < 0,001) y la EPOC tenía una mortalidad de casi el 40%. La regresión de Cox multivariante mostró que la enfermedad pulmonar previa seguía siendo un factor de riesgo de mortalidad (HR: 1,21; IC del 95%: 1,02-1,44; p = 0,02). Las variables asociadas de forma independiente con el riesgo de muerte por todas las causas en pacientes con enfermedades pulmonares fueron la saturación de oxígeno inferior al 92% al ingreso (HR: 4,35; IC del 95%: 3,08-6,15) y el dímero D elevado (HR: 1,84; IC del 95%: 1,27-2,67). La edad menor de 60 años (HR: 0,37; IC del 95%: 0,21-0,65) se asoció con una disminución del riesgo de muerte. CONCLUSIONES: La enfermedad pulmonar previa es un factor de riesgo de muerte en pacientes con covid-19. La edad avanzada, el sexo masculino, la oxigenoterapia domiciliaria y la insuficiencia respiratoria al ingreso se asociaron con un aumento de la mortalidad. Se deben realizar esfuerzos para identificar a los pacientes respiratorios y establecer medidas para mejorar sus resultados clínicos

Artificial neural network detects human uncertainty.

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

Efficient Computation of Functional Brain Networks: toward Real-Time Functional Connectivity.

Functional Connectivity has demonstrated to be a key concept for unraveling how the brain balances functional segregation and integration properties while processing information. This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures. PLV, PLI, ImC, and wPLI as Phase Synchronization measures, Mutual Information as an information theory based measure, and Generalized Synchronization indices are computed much more efficiently than prior open-source available implementations. Furthermore, network theory related measures like Strength, Shortest Path Length, Clustering Coefficient, and Betweenness Centrality are also implemented showing computational times up to thousands of times faster than most well-known implementations. Altogether, this work significantly expands what can be computed in feasible times, even enabling whole-head real-time network analysis of brain function.

Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model.

Identifying early-onset schizophrenia spectrum disorders (SSD) at a very early stage remains challenging. To assess the diagnostic predictive value of multiple types of data at the emergence of early-onset first-episode psychosis (FEP), various support vector machine (SVM) classifiers were developed. The data were from a 2-year, prospective, longitudinal study of 81 patients (age 9-17 years) with early-onset FEP and a stable diagnosis during follow-up and 42 age- and sex-matched healthy controls (HC). The input was different combinations of baseline clinical, neuropsychological, magnetic resonance imaging brain volumetric and biochemical data, and the output was the diagnosis at follow-up (SSD vs. non-SSD, SSD vs. HC, and non-SSD vs. HC). Enhanced recursive feature elimination was performed for the SSD vs. non-SSD classifier to select and rank the input variables with the highest predictive value for a diagnostic outcome of SSD. After validation with a test set and considering all baseline variables together, the SSD vs. non-SSD, SSD vs. HC and non-SSD vs. HC classifiers achieved an accuracy of 0.81, 0.99 and 0.99, respectively. Regarding the SSD vs. non-SSD classifier, a combination of baseline clinical variables (severity of negative, disorganized symptoms and hallucinations or poor insight) and neuropsychological variables (impaired attention, motor coordination, and global cognition) showed the highest predictive value for a diagnostic outcome of SSD. Neuroimaging and biochemical variables at baseline did not add to the predictive value. Thus, comprehensive clinical/cognitive assessment remains the most reliable approach for differential diagnosis during early-onset FEP. SVMs may constitute promising multivariate tools in the search for predictors of diagnostic outcome in FEP.

Dynamics of brain networks in the aesthetic appreciation.

Neuroimage experiments have been essential for identifying active brain networks. During cognitive tasks as in, e.g., aesthetic appreciation, such networks include regions that belong to the default mode network (DMN). Theoretically, DMN activity should be interrupted during cognitive tasks demanding attention, as is the case for aesthetic appreciation. Analyzing the functional connectivity dynamics along three temporal windows and two conditions, beautiful and not beautiful stimuli, here we report experimental support for the hypothesis that aesthetic appreciation relies on the activation of two different networks, an initial aesthetic network and a delayed aesthetic network, engaged within distinct time frames. Activation of the DMN might correspond mainly to the delayed aesthetic network. We discuss adaptive and evolutionary explanations for the relationships existing between the DMN and aesthetic networks and offer unique inputs to debates on the mind/brain interaction.

Optimizing functional network representation of multivariate time series.

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

Differential patterns of connectivity in progressive mild cognitive impairment.

It is now widely accepted that Alzheimer's disease is characterized by a functional disconnection between brain regions. The disease appears to begin up to decades prior to clinical diagnosis. Therefore, in the present study, we combined magnetoencephalography, a memory task, and functional connectivity analysis in mild cognitive impairment subjects in order to identify functional connectivity patterns that could characterize subjects who would eventually go on to develop the disease. We monitored 19 subjects and finally 5 of them developed Alzheimer's disease. These progressive patients showed a differential profile of functional connectivity values compared with those patients who remained stable over time. Specifically there were higher synchronization values over the parieto-occipital region in α and ß frequency bands. The involvement of this brain region in amyloid-ß accumulation and its possible association with hyper-synchronization are also discussed.

Alteration and reorganization of functional networks: a new perspective in brain injury study.

Plasticity is the mechanism underlying the brain's potential capability to compensate injury. Recently several studies have shown how functional connections among the brain areas are severely altered by brain injury and plasticity leading to a reorganization of the networks. This new approach studies the impact of brain injury by means of alteration of functional interactions. The concept of functional connectivity refers to the statistical interdependencies between physiological time series simultaneously recorded in various areas of the brain and it could be an essential tool for brain functional studies, being its deviation from healthy reference an indicator for damage. In this article, we review studies investigating functional connectivity changes after brain injury and subsequent recovery, providing an accessible introduction to common mathematical methods to infer functional connectivity, exploring their capabilities, future perspectives, and clinical uses in brain injury studies.