Your search for: 'author:("Rossini, Paolo Maria ")' has returned 21 results. (0.015s) Save search

Authors: Landi, Doriana | Rossini, Paolo Maria

Article Type: Research Article

Abstract: Brain plasticity can be considered the main result of brain communication with the 'external' and 'internal' environment. Learning new skills as well as endogenous brain function recovery following a lesion are based on neural plasticity, a dynamic phenomenon occurring in response to modification of conscious and pre- or sub-conscious experiences as they progressively stabilize at the synaptic and neural networks level. In spite of previously accepted theory, brain plasticity occurs throughout lifespan being an inner property of the system. Different models of brain plasticity are examined in relation with different modifications of the CNS: healthy brain ageing, neurodegenerative disorders, ischemic …stroke and multiple sclerosis. A clarification of advantageous as well as of aberrant brain plasticity mechanisms in pathological conditions may help to improve the development of rehabilitation methods to better address and facilitate such processes. Show more

Keywords: Restorative plasticity, fMRI, EEG, ageing, Alzheimer's disease, ischemic stroke, multiple sclerosis

DOI: 10.3233/RNN-2010-0538

Citation: Restorative Neurology and Neuroscience, vol. 28, no. 3, pp. 349-366, 2010

Authors: Vecchio, Fabrizio | Miraglia, Francesca | Bramanti, Placido | Rossini, Paolo Maria

Article Type: Research Article

Abstract: Modern analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity. To test the hypothesis that aging processes modulate the brain connectivity network, EEG recording was conducted on 113 healthy volunteers. They were divided into three groups in accordance with their ages: 36 Young (15–45 years), 46 Adult (50–70 years), and 31 Elderly (>70 years). To evaluate the stability of the investigated parameters, a subgroup of 10 subjects underwent a second EEG recording two weeks later. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by eLORETA on cortical sources. …EEG frequency bands of interest were: delta (2–4 Hz), theta (4–8 Hz), alpha1 (8–10.5 Hz), alpha2 (10.5–13 Hz), beta1 (13–20 Hz), beta2 (20–30 Hz), and gamma (30–40 Hz). The spectral connectivity analysis of cortical sources showed that the normalized Characteristic Path Length (λ) presented the pattern Young > Adult>Elderly in the higher alpha band. Elderly also showed a greater increase in delta and theta bands than Young. The correlation between age and λ showed that higher ages corresponded to higher λ in delta and theta and lower in the alpha2 band; this pattern reflects the age-related modulation of higher (alpha) and decreased (delta) connectivity. The Normalized Clustering coefficient (γ) and small-world network modeling (σ) showed non-significant age-modulation. Evidence from the present study suggests that graph theory can aid in the analysis of connectivity patterns estimated from EEG and can facilitate the study of the physiological and pathological brain aging features of functional connectivity networks. Show more

Keywords: Delta and alpha bands, EEG, eLORETA, functional connectivity, graph theory, small-world networks

DOI: 10.3233/JAD-140090

Citation: Journal of Alzheimer's Disease, vol. 41, no. 4, pp. 1239-1249, 2014

Authors: Vecchio, Fabrizio | Miraglia, Francesca | Marra, Camillo | Quaranta, Davide | Vita, Maria Gabriella | Bramanti, Placido | Rossini, Paolo Maria

Article Type: Research Article

Abstract: The aim of this study was to investigate the neuronal network characteristics in physiological and pathological brain aging. A database of 378 participants divided in three groups was analyzed: Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal elderly (Nold) subjects. Through EEG recordings, cortical sources were evaluated by sLORETA software, while graph theory parameters (Characteristic Path Length λ, Clustering coefficient γ, and small-world network σ) were computed to the undirected and weighted networks, obtained by the lagged linear coherence evaluated by eLORETA software. EEG cortical sources from spectral analysis showed significant differences in delta, theta, and alpha 1 bands. …Furthermore, the analysis of eLORETA cortical connectivity suggested that for the normalized Characteristic Path Length (λ) the pattern differences between normal cognition and dementia were observed in the theta band (MCI subjects are find similar to healthy subjects), while for the normalized Clustering coefficient (γ) a significant increment was found for AD group in delta, theta, and alpha 1 bands; finally, the small world (σ) parameter presented a significant interaction between AD and MCI groups showing a theta increase in MCI. The fact that AD patients respect the MCI subjects were significantly impaired in theta but not in alpha bands connectivity are in line with the hypothesis of an intermediate status of MCI between normal condition and overt dementia. Show more

Keywords: Alzheimer's disease, delta and alpha bands, EEG, functional connectivity, graph theory, mild cognitive impairment, sLORETA/eLORETA

DOI: 10.3233/JAD-132087

Citation: Journal of Alzheimer's Disease, vol. 41, no. 1, pp. 113-127, 2014

Authors: Rossini, Paolo Maria | Di Iorio, Riccardo | Granata, Giuseppe | Miraglia, Francesca | Vecchio, Fabrizio

Article Type: Article Commentary

Abstract: In a recent study, analyzing the modulation of γ-band oscillations, Naro and colleagues demonstrated that transcranial alternating current stimulation could drive the gamma rhythms in the human EEG in cognitive healthy elderly subjects but not in mild cognitive impairment (MCI) prodromal to Alzheimer’s disease (AD) and in early AD patients. Therefore, this method is proposed to intercept early in the disease course those MCI subjects who are in a pre-symptomatic stage of an already established AD. This prediction index may help the clinician to adopt a better prevention/follow-up strategy. In this direction, the novel advances in EEG analysis for the …evaluation of brain reactivity and connectivity-namely via innovative mathematical approach, i.e., graph theory-represent a promising tool for a non-invasive and easy-to-perform neurophysiological marker that could be used for the pre-symptomatic diagnosis of AD and to predict MCI progression to dementia. Show more

Keywords: Alzheimer’s disease, effective connectivity, electroencephalography, functional connectivity, graph theory, mild cognitive impairment, non-invasive brain stimulation

DOI: 10.3233/JAD-160482

Citation: Journal of Alzheimer's Disease, vol. 53, no. 4, pp. 1389-1393, 2016

Authors: Vecchio, Fabrizio | Miraglia, Francesca | Quaranta, Davide | Lacidogna, Giordano | Marra, Camillo | Rossini, Paolo Maria

Article Type: Research Article

Abstract: Electroencephalographic (EEG) rhythms are linked to any kind of learning and cognitive performance including motor tasks. The brain is a complex network consisting of spatially distributed networks dedicated to different functions including cognitive domains where dynamic interactions of several brain areas play a pivotal role. Brain connectome could be a useful approach not only to mechanisms underlying brain cognitive functions, but also to those supporting different mental states. This goal was approached via a learning task providing the possibility to predict performance and learning along physiological and pathological brain aging. Eighty-six subjects (22 healthy, 47 amnesic mild cognitive impairment, 17 …Alzheimer’s disease) were recruited reflecting the whole spectrum of normal and abnormal brain connectivity scenarios. EEG recordings were performed at rest, with closed eyes, both before and after the task (Sensory Motor Learning task consisting of a visual rotation paradigm). Brain network properties were described by Small World index (SW), representing a combination of segregation and integration properties. Correlation analyses showed that alpha 2 SW in pre-task significantly predict learning (r  =  –0.2592, p  < 0.0342): lower alpha 2 SW (higher possibility to increase during task and better the learning of this task), higher the learning as measured by the number of reached targets. These results suggest that, by means of an innovative analysis applied to a low-cost and widely available techniques (SW applied to EEG), the functional connectome approach as well as conventional biomarkers would be effective methods for monitoring learning progress during training both in normal and abnormal conditions. Show more

Keywords: Alpha band, Alzheimer’s disease, EEG, eLORETA, functional brain connectivity, graph theory, learning, mild cognitive impairment, precision medicine

DOI: 10.3233/JAD-180342

Citation: Journal of Alzheimer's Disease, vol. 66, no. 2, pp. 471-481, 2018

Authors: Vecchio, Fabrizio | Miraglia, Francesca | Alù, Francesca | Menna, Matteo | Judica, Elda | Cotelli, Maria | Rossini, Paolo Maria

Article Type: Research Article

Abstract: Background: Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer’s disease (AD) in particular with the aim to classify, at the individual level, AD patients and healthy controls cooperating with neuropsychological tests for an early diagnosis. Advanced network analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity and could be used in classification processes. If successfully reached, this goal would add a low-cost, easily accessible, and non-invasive technique with neuropsychological tests. Objective: To investigate the possibility to automatically classify physiological versus pathological aging from cortical sources’ connectivity based on a …support vector machine (SVM) applied to EEG small-world parameter. Methods: A total of 295 subjects were recruited: 120 healthy volunteers and 175 AD. Graph theory functions were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA. A machine-learning classifier (SVM) was applied. EEG frequency bands were: delta (2–4 Hz), theta (4–8 Hz), alpha1 (8–10.5 Hz), alpha2 (10.5–13 Hz), beta1 (13–20 Hz), beta2 (20–30 Hz), and gamma (30–40 Hz). Results: The receiver operating characteristic curve showed AUC of 0.97±0.03 (indicating very high classification accuracy). The classifier showed 95% ±5% sensitivity, 96% ±3% specificity, and 95% ±3% accuracy for the classification. Conclusion: EEG connectivity analysis via a combination of source/connectivity biomarkers, highly correlating with neuropsychological AD diagnosis, could represent a promising tool in identification of AD patients. This approach represents a low-cost and non-invasive method, one that utilizes widely available techniques which, when combined, reach high sensitivity/specificity and optimal classification accuracy on an individual basis (0.97 of AUC). Show more

Keywords: Alzheimer’s disease, delta and alpha bands, EEG, functional connectivity, graph theory, LORETA, machine learning classifier, small-world, support vector machine

DOI: 10.3233/JAD-200171

Citation: Journal of Alzheimer's Disease, vol. 75, no. 4, pp. 1253-1261, 2020

Authors: Vecchio, Fabrizio | Miraglia, Francesca | Alú, Francesca | Orticoni, Alessandro | Judica, Elda | Cotelli, Maria | Rossini, Paolo Maria

Article Type: Research Article

Abstract: Background: Most common progressive brain diseases in the elderly are Alzheimer’s disease (AD) and vascular dementia (VaD). They present with relatively similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms are different. Objective: The aim is to explore the brain connectivity differences between AD and VaD patients compared to mild cognitive impairment (MCI) and normal elderly (Nold) subjects applying graph theory, in particular the Small World (SW) analysis. Methods: 274 resting state EEGs were analyzed in 100 AD, 80 MCI, 40 VaD, and 54 Nold subjects. Graph theory analyses were applied to undirected and weighted networks obtained by …lagged linear coherence evaluated by eLORETA tool. Results: VaD and AD patients presented more ordered low frequency structure (lower value of SW) than Nold and MCI subjects, and more random organization (higher value of SW) in low and high frequency alpha rhythms. Differences between patients have been found in high frequency alpha rhythms in VaD (higher value of SW) with respect to AD, and in theta band with a trend which is more similar to MCI and Nold than to AD. MCI subjects presented a network organization which is intermediate, in low frequency bands, between Nold and patients. Conclusion: Graph theory applied to EEG data has proved very useful in identifying differences in brain network patterns in subjects with dementia, proving to be a valid tool for differential diagnosis. Future studies will aim to validate this method to diagnose especially in the early stages of the disease and at single subject level. Show more

Keywords: Brain networks, EEG, functional coupling, LORETA, Small World

DOI: 10.3233/JAD-210394

Citation: Journal of Alzheimer's Disease, vol. 82, no. 2, pp. 871-879, 2021

Authors: Rossini, Paolo Maria | Cappa, Stefano F. | Lattanzio, Fabrizia | Perani, Daniela | Spadin, Patrizia | Tagliavini, Fabrizio | Vanacore, Nicola

Article Type: Research Article

Abstract: Alzheimer’s disease is the most common age-related neurodegenerative disorder and its burden on patients, families, and society grows significantly with lifespan. Early modifications of risk-enhancing lifestyles and treatment initiation expand personal autonomy and reduce management costs. Many clinical trials with potentially disease-modifying drugs are devoted to mild cognitive impairment (MCI) prodromal-to-Alzheimer’s disease. The identification of biomarkers for early diagnosis may thus be crucial for early intervention and identification of high-risk subjects, the most appropriate target of new drugs as soon as they will be discovered. INTERCEPTOR is a strategic project by the Italian Ministry of Health and the Italian Medicines …Agency (AIFA), aiming to validate the best combination (highly accurate, non-invasive, available on the whole national territory and financially sustainable) of biomarkers and organizational model for early diagnosis. 500 MCI subjects will be enrolled at baseline and followed-up for 3 years for at least 400 of them in order to define a “hub & spoke” nationwide model with recruiting (spokes) centers for MCI identification and expert (hubs) centers for risk diagnosis. Show more

Keywords: Alzheimer’s disease, biomarkers, early diagnosis, healthcare organizational models, mild cognitive impairment, prodromal Alzheimer’s disease, public health

DOI: 10.3233/JAD-190670

Citation: Journal of Alzheimer's Disease, vol. 72, no. 2, pp. 373-388, 2019

Authors: Bucossi, Serena | Ventriglia, Mariacarla | Panetta, Valentina | Salustri, Carlo | Pasqualetti, Patrizio | Mariani, Stefania | Siotto, Mariacristina | Rossini, Paolo Maria | Squitti, Rosanna

Article Type: Research Article

Abstract: There is an ongoing debate on the involvement of systemic copper (Cu) dysfunctions in Alzheimer's disease (AD), and clinical studies comparing Cu levels in serum, plasma, and cerebrospinal fluid (CSF) of AD patients with those of healthy controls have delivered non-univocal and often conflicting results. In an attempt to evaluate whether Cu should be considered a potential marker of AD, we applied meta-analysis to a selection of 26 studies published in the literature. Meta-analysis is a quantitative method that combines the results of independent reports to distinguish between small effects and no effects, random variations, variations in sample used, or …in different analytical approaches. The subjects' sample obtained by merging studies was a pooled total of 761 AD subjects and 664 controls for serum Cu studies, 205 AD subjects and 167 controls for plasma Cu, and of 116 AD subjects and 129 controls for CSF Cu. Our meta-analysis of serum data showed that AD patients have higher levels of serum Cu than healthy controls. Plasma data did not allow conclusions, due to their high heterogeneity, but the meta-analysis of the combined serum and plasma studies confirmed higher Cu levels in AD. The analysis of CSF data, instead, revealed no difference between AD patients and controls. Show more

Keywords: Alzheimer's disease, cerebrospinal fluid, copper, meta-analysis, plasma, serum

DOI: 10.3233/JAD-2010-101473

Citation: Journal of Alzheimer's Disease, vol. 24, no. 1, pp. 175-185, 2011

Authors: Moretti, Davide Vito | Frisoni, Giovanni Battista | Pievani, Michela | Rosini, Sandra | Geroldi, Cristina | Binetti, Giuliano | Rossini, Paolo Maria

Article Type: Research Article

Abstract: The working hypothesis of paper is that the functional coupling of brain areas is combined with different neuroradiological substrates and has different clinical manifestations. 31 normal old subjects and 85 subjects with mild cognitive impairment (MCI) underwent EEG recordings and magnetic resonance imaging (MRI). Intrahemispheric and interhemispheric linear EEG coherences were computed. At first, all normal old and MCI subjects were compared. Subsequently, three subgroups of MCI were obtained based on neuroradiological substrate (subcortical cerebrovascular damage, MCI-CVD; cholinergic pathways vascular damage MCI-CHOL; and hippocampal atrophy, MCI-HIPP) and compared with a normal old sample matched for age, education and Mini-Mental State …Examination score. The group of MCI subjects compared to normal old subjects shows: 1) decrease of intrahemispheric coherence in fronto-parietal regions (both right and left hemisphere); 2) increase of interhemispheric coherence on frontal regions in delta frequency; and 3) increase of interhemispheric coherence on temporal regions (from delta to alpha3 frequency bands). In the MCI subgroups, hippocampal atrophy is linked to an increase of interhemispheric coherence seen on frontal and temporal regions whereas subcortical CVD is linked to the largest decrease of coherence in fronto-parietal regions. MCI-CVD patients performed worst on Trail Making Test battery whereas MCI-HIPP patients were impaired on Rey word list delayed recall and Rey figure recall. Show more

Keywords: Brain rhythms, cognitive tests, electroencephalography, linear coherence, mild cognitive impairment

DOI: 10.3233/JAD-2008-14303

Citation: Journal of Alzheimer's Disease, vol. 14, no. 3, pp. 285-299, 2008