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Article: A generalized deep learning network for fractional anisotropy reconstruction: Application to epilepsy and multiple sclerosis.

Gaviraghi, Marta / Ricciardi, Antonio / Palesi, Fulvia / Brownlee, Wallace / Vitali, Paolo / Prados, Ferran / Kanber, Baris / Gandini Wheeler-Kingshott, Claudia A M

Frontiers in neuroinformatics

2022 Volume 16, Page(s) 891234

Abstract: Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of ... ...

Abstract	Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissues
Language	English
Publishing date	2022-08-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452979-5
ISSN	1662-5196
ISSN	1662-5196
DOI	10.3389/fninf.2022.891234
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Article ; Online: What contributes to disability in progressive MS? A brain and cervical cord-matched quantitative MRI study.

Tur, Carmen / Battiston, Marco / Yiannakas, Marios C / Collorone, Sara / Calvi, Alberto / Prados, Ferran / Kanber, Baris / Grussu, Francesco / Ricciardi, Antonio / Pajak, Patrizia / Martinelli, Daniele / Schneider, Torben / Ciccarelli, Olga / Samson, Rebecca S / Wheeler-Kingshott, Claudia Am Gandini

Multiple sclerosis (Houndmills, Basingstoke, England)

2024 Volume 30, Issue 4-5, Page(s) 516–534

Abstract: Background: We assessed the ability of a brain-and-cord-matched quantitative magnetic resonance imaging (qMRI) protocol to differentiate patients with progressive multiple sclerosis (PMS) from controls, in terms of normal-appearing (NA) tissue ... ...

Abstract	Background: We assessed the ability of a brain-and-cord-matched quantitative magnetic resonance imaging (qMRI) protocol to differentiate patients with progressive multiple sclerosis (PMS) from controls, in terms of normal-appearing (NA) tissue abnormalities, and explain disability. Methods: A total of 27 patients and 16 controls were assessed on the Expanded Disability Status Scale (EDSS), 25-foot timed walk (TWT), 9-hole peg (9HPT) and symbol digit modalities (SDMT) tests. All underwent 3T brain and (C2-C3) cord structural imaging and qMRI (relaxometry, quantitative magnetisation transfer, multi-shell diffusion-weighted imaging), using a fast brain-and-cord-matched protocol with brain-and-cord-unified imaging readouts. Lesion and NA-tissue volumes and qMRI metrics reflecting demyelination and axonal loss were obtained. Random forest analyses identified the most relevant volumetric/qMRI measures to clinical outcomes. Confounder-adjusted linear regression estimated the actual MRI-clinical associations. Results: Several qMRI/volumetric differences between patients and controls were observed ( Conclusion: Fast brain-and-cord-matched qMRI protocols are feasible and identify demyelination - combined with other mechanisms - as key for disability accumulation in PMS.
MeSH term(s)	Humans ; Cervical Cord/pathology ; Multiple Sclerosis/pathology ; Brain/pathology ; Magnetic Resonance Imaging/methods ; Multiple Sclerosis, Chronic Progressive/pathology ; Gray Matter/pathology
Language	English
Publishing date	2024-02-19
Publishing country	England
Document type	Journal Article
ZDB-ID	1290669-4
ISSN	1477-0970 ; 1352-4585
ISSN (online)	1477-0970
ISSN	1352-4585
DOI	10.1177/13524585241229969
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Article: Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset.

Ricciardi, Antonio / Grussu, Francesco / Kanber, Baris / Prados, Ferran / Yiannakas, Marios C / Solanky, Bhavana S / Riemer, Frank / Golay, Xavier / Brownlee, Wallace / Ciccarelli, Olga / Alexander, Daniel C / Gandini Wheeler-Kingshott, Claudia A M

Frontiers in neuroinformatics

2023 Volume 17, Page(s) 1060511

Abstract: Introduction: Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap ...

Abstract	Introduction: Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns. Methods: In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself. Results and discussion: Average classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks.
Language	English
Publishing date	2023-03-23
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452979-5
ISSN	1662-5196
ISSN	1662-5196
DOI	10.3389/fninf.2023.1060511
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Book: Il distretto della pesca di Mazara del Vallo

Fazio, Vincenzo / Ricciardi, Antonio

una buona pratica di cooperazione tra aziende internazionali

(Economia : Ricerche ; 688)

2008

Author's details	a cura di Vincenzo Fazio, Antonio Ricciardi
Series title	Economia : Ricerche ; 688
Keywords	Fischwirtschaft ; Regionales Cluster ; Internationale Unternehmenskooperation ; Fischereipolitik ; Mittelmeerraum ; Italien ; Sizilien
Language	Italian
Size	169 p., ill., 23 cm
Publisher	F. Angeli
Publishing place	Milano
Document type	Book
Note	Enth. 5 Beitr. ; Half title: Unione europea; Regione siciliana, Assessorato ; Includes bibliographical references ; Mazara del Vallo, near Trapani (Sicily)
ISBN	9788856813593 ; 8856813599
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Book: Relazioni di cooperazione e reti di imprese

Izzo, Francesco / Ricciardi, Antonio

il caso della Campania

(Economia : Sez. 5 ; 448)

2006

Author's details	a cura di Francesco Izzo; Antonio Ricciardi; introduzione di Vincenzo Maggioni
Series title	Economia : Sez. 5 ; 448
Keywords	Industriestruktur ; Unternehmenskooperation ; Unternehmensnetzwerk ; Regionales Cluster ; Kampanien ; 60#39
Language	Italian
Size	XIV, 374 S., graph. Darst., 23 cm
Publisher	F. Angeli
Publishing place	Milano
Document type	Book
Note	Contains bibliography, bibliographical references and notes ; Enth. 6 Beitr. ; On cover: IPE, Istituto per ricerche e attività educative ; On t.p.: SUN, Seconda Università degli studi di Napoli
ISBN	8846476360 ; 9788846476364
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Article ; Online: Integrating hazard, exposure, vulnerability and resilience for risk and emergency management in a volcanic context: the ADVISE model.

Bonadonna, Costanza / Frischknecht, Corine / Menoni, Scira / Romerio, Franco / Gregg, Chris E / Rosi, Mauro / Biass, Sebastien / Asgary, Ali / Pistolesi, Marco / Guobadia, Dehrick / Gattuso, Alessandro / Ricciardi, Antonio / Cristiani, Chiara

Journal of applied volcanology

2021 Volume 10, Issue 1, Page(s) 7

Abstract: Risk assessments in volcanic contexts are complicated by the multi-hazard nature of both unrest and eruption phases, which frequently occur over a wide range of spatial and temporal scales. As an attempt to capture the multi-dimensional and dynamic ... ...

Abstract	Risk assessments in volcanic contexts are complicated by the multi-hazard nature of both unrest and eruption phases, which frequently occur over a wide range of spatial and temporal scales. As an attempt to capture the multi-dimensional and dynamic nature of volcanic risk, we developed an Supplementary information: The online version contains supplementary material available at 10.1186/s13617-021-00108-5.
Language	English
Publishing date	2021-11-08
Publishing country	Germany
Document type	Journal Article
ISSN	2191-5040
ISSN (online)	2191-5040
DOI	10.1186/s13617-021-00108-5
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Article: Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis.

Johnson, Daniel / Ricciardi, Antonio / Brownlee, Wallace / Kanber, Baris / Prados, Ferran / Collorone, Sara / Kaden, Enrico / Toosy, Ahmed / Alexander, Daniel C / Gandini Wheeler-Kingshott, Claudia A M / Ciccarelli, Olga / Grussu, Francesco

Frontiers in neurology

2021 Volume 12, Page(s) 662855

Abstract: Background: ...

Abstract	Background:
Language	English
Publishing date	2021-06-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564214-5
ISSN	1664-2295
ISSN	1664-2295
DOI	10.3389/fneur.2021.662855
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Article ; Online: Cardiovascular risk factors in secondary progressive multiple sclerosis: A cross-sectional analysis from the MS-STAT2 randomized controlled trial.

European journal of neurology

2023 Volume 30, Issue 9, Page(s) 2769–2780

Abstract: Background and purpose: There is increasing evidence that cardiovascular risk (CVR) contributes to disability progression in multiple sclerosis (MS). CVR is particularly prevalent in secondary progressive MS (SPMS) and can be quantified through ... ...

Abstract	Background and purpose: There is increasing evidence that cardiovascular risk (CVR) contributes to disability progression in multiple sclerosis (MS). CVR is particularly prevalent in secondary progressive MS (SPMS) and can be quantified through validated composite CVR scores. The aim was to examine the cross-sectional relationships between excess modifiable CVR, whole and regional brain atrophy on magnetic resonance imaging, and disability in patients with SPMS. Methods: Participants had SPMS, and data were collected at enrolment into the MS-STAT2 trial. Composite CVR scores were calculated using the QRISK3 software. Prematurely achieved CVR due to modifiable risk factors was expressed as QRISK3 premature CVR, derived through reference to the normative QRISK3 dataset and expressed in years. Associations were determined with multiple linear regressions. Results: For the 218 participants, mean age was 54 years and median Expanded Disability Status Scale was 6.0. Each additional year of prematurely achieved CVR was associated with a 2.7 mL (beta coefficient; 95% confidence interval 0.8-4.7; p = 0.006) smaller normalized whole brain volume. The strongest relationship was seen for the cortical grey matter (beta coefficient 1.6 mL per year; 95% confidence interval 0.5-2.7; p = 0.003), and associations were also found with poorer verbal working memory performance. Body mass index demonstrated the strongest relationships with normalized brain volumes, whilst serum lipid ratios demonstrated strong relationships with verbal and visuospatial working memory performance. Conclusions: Prematurely achieved CVR is associated with lower normalized brain volumes in SPMS. Future longitudinal analyses of this clinical trial dataset will be important to determine whether CVR predicts future disease worsening.
MeSH term(s)	Humans ; Middle Aged ; Multiple Sclerosis/pathology ; Multiple Sclerosis, Chronic Progressive/diagnostic imaging ; Multiple Sclerosis, Chronic Progressive/pathology ; Cardiovascular Diseases/diagnostic imaging ; Cardiovascular Diseases/epidemiology ; Cardiovascular Diseases/etiology ; Cross-Sectional Studies ; Risk Factors ; Brain/diagnostic imaging ; Brain/pathology ; Magnetic Resonance Imaging/methods ; Memory, Short-Term ; Heart Disease Risk Factors ; Atrophy/pathology ; Disability Evaluation ; Disease Progression ; STAT2 Transcription Factor
Chemical Substances	STAT2 protein, human ; STAT2 Transcription Factor
Language	English
Publishing date	2023-06-23
Publishing country	England
Document type	Randomized Controlled Trial ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1280785-0
ISSN	1468-1331 ; 1351-5101 ; 1471-0552
ISSN (online)	1468-1331
ISSN	1351-5101 ; 1471-0552
DOI	10.1111/ene.15924
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Zs.A 4738: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MO 592: Show issues

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Book ; Online: Assessing the effectiveness and the economic impact of evacuation

Bonadonna, Costanza / Asgary, Ali / Romerio, Franco / Zulemyan, Tais / Frischknecht, Corine / Cristiani, Chiara / Rosi, Mauro / Gregg, Chris E. / Biass, Sebastien / Pistolesi, Marco / Menoni, Scira / Ricciardi, Antonio

eISSN: 1684-9981

the case of Vulcano Island, Italy

2021

Abstract: Evacuation planning and management represents a key aspect of volcanic crises because it can increase people protection as well as minimize the potential impact on the economy, properties, and infrastructure of the affected area. Assessment of evacuation ...

Abstract	Evacuation planning and management represents a key aspect of volcanic crises because it can increase people protection as well as minimize the potential impact on the economy, properties, and infrastructure of the affected area. Assessment of evacuation scenarios that consider human and economic impact is best done in a pre-disaster context as it helps authorities develop evacuation plans and make informed decisions outside the highly stressful time period that characterizes crises. We present an agent-based simulation tool that assesses the effectiveness of different evacuation scenarios using Vulcano island (Italy) as a case study. Simulation results show that the overall time needed to evacuate people should be analysed together with the percentage of people evacuated as a function of time and that a simultaneous evacuation on Vulcano is more efficient than a staged evacuation. We also present a model to assess the economic impact of evacuation as a function of evacuation duration and starting period that reveals that an evacuation of Vulcano would cause significant economic impact to the tourism industry if lasting more than 3 months (in case it was initiated at the beginning of the visitor season) to 1 year (in case it was initiated at the end of the visitor season).
Subject code	910
Language	English
Publishing date	2021-10-15
Publishing country	de
Document type	Book ; Online
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Article: A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features.

Castellazzi, Gloria / Cuzzoni, Maria Giovanna / Cotta Ramusino, Matteo / Martinelli, Daniele / Denaro, Federica / Ricciardi, Antonio / Vitali, Paolo / Anzalone, Nicoletta / Bernini, Sara / Palesi, Fulvia / Sinforiani, Elena / Costa, Alfredo / Micieli, Giuseppe / D'Angelo, Egidio / Magenes, Giovanni / Gandini Wheeler-Kingshott, Claudia A M

Frontiers in neuroinformatics

2020 Volume 14, Page(s) 25

Abstract: Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. ... ...

Abstract	Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD-AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a "mixed VD-AD dementia" (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations.
Language	English
Publishing date	2020-06-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452979-5
ISSN	1662-5196
ISSN	1662-5196
DOI	10.3389/fninf.2020.00025
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