Functional cortical source connectivity of resting state electroencephalographic alpha rhythms shows similar abnormalities in patients with mild cognitive impairment due to Alzheimer's and Parkinson's diseases

Objective: This study tested the hypothesis that markers of functional cortical source connectivity of resting state eyes-closed electroencephalographic (rsEEG) rhythms may be abnormal in subjects with mild cognitive impairment due to Alzheimer's (ADMCI) and Parkinson's (PDMCI) diseases compared to healthy elderly subjects (Nold). Methods: rsEEG data had been collected in ADMCI, PDMCI, and Nold subjects (N = 75 for any group). eLORETA freeware estimated functional lagged linear connectivity (LLC) from rsEEG cortical sources. Area under receiver operating characteristic (AUROC) curve indexed the accuracy in the classification of Nold and MCI individuals. Results: Posterior interhemispheric and widespread intrahemispheric alpha LLC solutions were abnormally lower in both MCI groups compared to the Nold group. At the individual level, AUROC curves of LLC solutions in posterior alpha sources exhibited moderate accuracies (0.70-0.72) in the discrimination of Nold vs. ADMCI-PDMCI individuals. No differences in the LLC solutions were found between the two MCI groups. Conclusions: These findings unveil similar abnormalities in functional cortical connectivity estimated in widespread alpha sources in ADMCI and PDMCI. This was true at both group and individual levels. Significance: The similar abnormality of alpha source connectivity in ADMCI and PDMCI subjects might reflect common cholinergic impairment. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved

Annali storici di Principato Citra, A. 7, n. 1.1 (2009)

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A Distributed multi-purpose IP flow monitor

Traffic monitoring is a research field whose results can be exploited for several purposes, such as network resource management, security and accounting. An effective monitor needs to be capable of analyzing the traffic flowing through the monitored network by losing as few packets as possible since packet loss may result in a non accurate measurement of the required metrics. Such a monitor captures the packets from the network, associates each packet to a flow by evaluating its characteristics, performs some flow measurements, and exports the results of data analysis. In high speed networks such tasks might be hard to accomplish in an efficient way, as the number of analyzed flows is very high. For this reason, we decided to design and implement a distributed monitoring system comprising several components each responsible for a different task. Such a distributed approach helps overcome the problem of an overloaded monitoring system. Furthermore, distributed systems need an appropriate protocol, that defines the kind as well as the sequence of messages exchanged between system components. In this paper we present both the monitoring architecture and the corresponding management protocol. Finally, in order for the monitoring system to support different kinds of applications, we developed an open framework allowing a user to define a customized set of metrics

Biliopancreatic Limb Length in One Anastomosis Gastric Bypass: Which Is the Best?

The use of one anastomosis gastric bypass (OAGB) is rapidly spreading. Concerns about biliary reflux and malabsorption with consequent nutritional deficits exist, so studies on biliopancreatic limb (BPL) adequate length in OAGB are required to balance excess weight loss in percentage (% EWL), resolution of comorbidities, and nutritional deficit. The purpose was to evaluate, at 2 years after OAGB, the effects of BPL length on weight loss, resolution of comorbidity, and nutritional deficiencies in patients

Does antrum size matter in sleeve gastrectomy? A prospective randomized study

Laparoscopic sleeve gastrectomy (LSG) is a safe and effective bariatric procedure in terms of excess weight loss. Nevertheless, controversies still exist on several technical and operative aspects of LSG. The aim of this study is to evaluate variations in anthropometric features in subjects with a LSG gastric resection starting from 2 cm or 6 cm from the pylorus. Secondary aim was the evaluation of differences in morbidity, food tolerance, and GERD incidence studied with upper endoscopy (UE) and GERD Health-Related Quality-of-Life score

One Anastomosis Gastric Bypass after Sleeve Gastrectomy Failure: Does a Single Procedure Fit for all?

Introduction Considering the multitude of bariatric procedures performed all over the world, the necessity of revisional surgery increased accordingly. Several authors argued that with the great diffusion of sleeve gastrectomy (SG), the number of patients who experienced a weight regain at long follow-up was congruous and physiologic, even if not negligible. Recent studies showed that one anastomosis gastric bypass (OAGB) was an effective and safe option also as redo surgery. The aim of the study was to evaluate effectiveness of OAGB as redo surgery after SG in term of anthropometric features and remission of comorbidities. Methods Bariatric patients experiencing weight regain and insufficient weight loss after SG and undergoing OAGB as redo surgery were retrospectively analyzed. During post-OAGB outpatient visit weight, %EWL, BMI, comorbidities, and vitamin deficiencies were evaluated. A further visit was scheduled for the evaluation of postoperative esophagitis/gastritis at upper endoscopy. Results Fifty-nine (Reviewer #3-1) patients underwent OAGB as redo-surgery consequently to the worsening of the bariatric outcomes at 29.42 +/- 7.29 months from SG (mean weight, BMI and %EWL were 120.89 +/- 16.79 kg, 43 +/- 4.39 and 19.84 +/- 30.29, respectively). Conversely, at a mean follow-up of 34.32 +/- 1.71 months (Reviewer #3-3) after OAGB, no weight regain or insufficient weight loss cases were recorded (mean weight 71.25 +/- 10.22 kg, mean BMI 24.46 +/- 2.06 kg/m2, mean %EWL 69.49 +/- 14.4, p < 0.0001) (Reviewer #3-2). Conclusion OAGB is a safe and effective bariatric procedure in terms of morbidity, mortality, and %EWL also as revision surgery after SG. Further larger studies are needed to address this issue

Abnormalities of resting-state functional cortical connectivity in patients with dementia due to alzheimer's and lewy body diseases: An EEG study

WOS: 000428233700003PubMed ID: 29407464Previous evidence showed abnormal posterior sources of resting-state delta (0.7 were considered). Interhemispheric and intrahemispheric LLCs in widespread delta sources were abnormally higher in the ADD group and, unexpectedly, normal in DLB and PDD groups. Intrahemispheric LLC was reduced in widespread alpha sources dramatically in ADD, markedly in DLB, and moderately in PDD group. Furthermore, the interhemispheric LLC in widespread alpha sources showed lower values in ADD and DLB than PDD groups. At the individual level, AUROC curves of LLC in alpha sources exhibited better classification accuracies for the discrimination of ADD versus Nold individuals (0.84) than for DLB versus Nold participants (0.78) and PDD versus Nold participants (0.75). Functional cortical connectivity markers in delta and alpha sources suggest a more compromised neurophysiological reserve in ADD than DLB, at both group and individual levels.H2020 Marie S. Curie ITN-ETN projectThe present study was developed based on the data of the informal European Consortium PDWAVES and European Consortium of Dementia with Lewy Body. The members and institutional affiliations of the consortia are reported in the cover page of this manuscript. The research activities of the Unit of University of Rome "La Sapienza" were partially supported by the H2020 Marie S. Curie ITN-ETN project with the short title "BBDiag" (http://bddiag-itn-etn.eu). We thank Mrs. Jessica Janson and Mrs. Marina Selivanova for their support to those activities in the framework of the BBDiag project

Classification of patients with alzheimer's disease and dementia with lewy bodies using resting EEG selected features at sensor and source levels: A proof-of-concept study

Background: Early differentiation between Alzheimer's disease (AD) and Dementia with Lewy Bodies (DLB) is important for accurate prognosis, as DLB patients typically show faster disease progression. Cortical neural networks, necessary for human cognitive function, may be disrupted differently in DLB and AD patients, allowing diagnostic differentiation between AD and DLB. Objective: This proof-of-concept study assessed whether the application of machine learning techniques to data derived from resting-state electroencephalographic (rsEEG) rhythms (discriminant sensor power, 19 electrodes) and source connectivity (between five cortical regions of interest) allowed differentiation between DLB and AD. Methods: Clinical, demographic, and rsEEG datasets from DLB patients (N=30), AD patients (N=30), and control seniors (NOld, N=30), matched for age, sex, and education, were taken from our international database. Individual (delta, theta, alpha) and fixed (beta) rsEEG frequency bands were included. The rsEEG features for the classification task were computed at both sensor and source levels. The source level was based on eLORETA freeware toolboxes for estimating cortical source activity and linear lagged connectivity. Fluctuations of rsEEG recordings (band-pass waveform envelopes of each EEG rhythm) were also computed at both sensor and source levels. After blind feature reduction, rsEEG features served as input to support vector machine (SVM) classifiers. Discrimination of individuals from the three groups was measured with standard performance metrics (accuracy, sensitivity, and specificity). Results: The trained SVM two-class classifiers showed classification accuracies of 97.6% for NOld vs. AD, 99.7% for NOld vs. DLB, and 97.8% for AD vs. DLB. Three-class classifiers (AD vs. DLB vs. NOld) showed classification accuracy of 94.79%. Conclusion: These promising preliminary results should encourage future prospective and longitudinal cross-validation studies using higher resolution EEG techniques and harmonized clinical procedures to enable the clinical application of these machine learning techniques

QEEG abnormalities in cognitively unimpaired patients with delirium

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