Paolo BoscoIRCCS Stella Maris Foundation, Pisa, Italy · FiRMLAB
Paolo Bosco
PhD
About
45
Publications
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1,191
Citations
Citations since 2017
Introduction
Additional affiliations
March 2013 - August 2016
January 2010 - February 2013
Publications
Publications (45)
Childhood apraxia of speech (CAS) is a subtype of motor speech disorder usually co-occurring with language impairment. A supramodal processing difficulty, involving executive functions (EFs), might contribute to the cognitive endophenotypes and behavioral manifestations. The present study aimed to profile the EFs in CAS, investigating the relations...
Quantitative Susceptibility Mapping (QSM) is an MRI-based technique allowing the non-invasive quantification of iron content and myelination in the brain. The RIN – Neuroimaging Network established an optimized and harmonized protocol for QSM across ten sites with 3T MRI systems from three different vendors to enable multicentric studies. The asses...
Quantitative Susceptibility Mapping (QSM) can measure iron concentration increase in the primary motor cortex (M1) of patients with Amyotrophic Lateral Sclerosis (ALS). However, such alteration is confined to only specific regions interested by upper motor neuron pathology; therefore, mean QSM values in the entire M1 have limited diagnostic accurac...
Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherent...
Introduction
Cognitive difficulties and neuropsychological alterations in Duchenne and Becker muscular dystrophy (DMD, BMD) boys are not yet sufficiently explored, although this topic could have a relevant impact, finding novel biomarkers of disease both at genetics and neuroimaging point of view. The current study aims to: 1) analyze the neuropsyc...
Objective
Automated voxel-based analysis methods are used to detect cortical hypometabolism typical of Alzheimer’s Disease (AD) on FDG-PET brain scans. We compared the accuracy of two clinically validated tools for their ability to identify those MCI subjects progressing to AD at follow- up, to evaluate the impact of the analysis method on FDG-PET...
Background:
In clinical practice, the amy-PET is globally inspected to provide a binary outcome, but the role of a regional assessment has not been fully investigated yet.
Objective:
To deepen the role of regional amyloid burden and its implication on clinical-neuropsychological features.
Materials:
Amy-PET and a complete neuropsychological as...
Autism Spectrum Disorder (ASD) and Childhood Apraxia of Speech (CAS) are developmental disorders with distinct diagnostic criteria and different epidemiology. However, a common genetic background as well as overlapping clinical features between ASD and CAS have been recently reported. To date, brain structural language-related abnormalities have be...
Machine Learning (ML) approaches have been widely applied to medical data in order to find reliable classifiers to improve diagnosis and detect candidate biomarkers of a disease. However, as a powerful, multivariate, data-driven approach, ML can be mislead by biases and outliers in the training set, finding sample-dependent classification patterns....
No univocal and reliable brain-based biomarkers have been detected to date in Autism Spectrum Disorders (ASD). Neuroimaging studies have consistently revealed alterations in brain structure and function of individuals with ASD; however, it remains difficult to ascertain the extent and localization of affected brain networks. In this context, the ap...
Purpose:
The lack of inter-method agreement can produce inconsistent results in neuroimaging studies. We evaluated the intra-method repeatability and the inter-method reproducibility of two widely-used automatic segmentation methods for brain MRI: the FreeSurfer (FS) and the Statistical Parametric Mapping (SPM) software packages.
Methods:
We seg...
The intermethod agreement between automated algorithms for brainstem segmentation is investigated, focusing on the potential involvement of this structure in Autism Spectrum Disorders (ASD). Inconsistencies highlighted in previous studies on brainstem in the population with ASD may in part be a result of poor agreement in the extraction of structur...
To quantify the "segmentation noise" of several widely used fully automatic methods for measuring longitudinal hippocampal atrophy in Alzheimer's disease and compare the results to the segmentation noise of manual segmentation over both 1 and 3 years. The segmentation noise of 5 longitudinal hippocampal atrophy measurement methods was quantified, i...
The complexity and heterogeneity of Autism Spectrum Disorders (ASD) require the implementation of dedicated analysis techniques to obtain the maximum from the interrelationship among many variables that describe affected individuals, spanning from clinical phenotypic characterization and genetic profile to structural and functional brain images. Th...
Introduction:
Hippocampal volume is a core biomarker of Alzheimer's disease (AD). However, its contribution over the standard diagnostic workup is unclear.
Methods:
Three hundred fifty-six patients, under clinical evaluation for cognitive impairment, with suspected AD and Mini-Mental State Examination ≥20, were recruited across 17 European memor...
Background: NIA-AA and IWG diagnostic criteria for Alzheimer's Disease (AD) include core structural, functional, and CSF biomarkers. The impact of core biomarkers in clinical settings is still unclear. This study aimed at measuring the impact of core biomarkers on the diagnostic confidence of uncertain AD cases in a routine memory clinic setting. /...
The purpose of this study is to assess the reproducibility of hippocampal atrophy rate measurements of commonly used fully-automated algorithms in Alzheimer disease (AD). The reproducibility of hippocampal atrophy rate for FSL/FIRST, AdaBoost, FreeSurfer, MAPS independently and MAPS combined with the boundary shift integral (MAPS-HBSI) were calcula...
Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer’s disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and nor...
Background. Structural MRI measures for monitoring Alzheimer’s Disease (AD) progression are becoming instrumental in the clinical practice, and more so in the context of longitudinal studies. This investigation addresses the impact of four image analysis approaches on the longitudinal performance of the hippocampal volume.
Methods. We present an hi...
The hippocampus segmentation in Magnetic Resonance (MRI) scans is a relevant issue for the diagnosis of many pathologies. The present work describes a fully automated method for the hippocampal segmentation and discusses the results obtained on three datasets provided by different institutions and referring to different pathologies that involve hip...
BACKGROUND: An international Delphi panel has defined a harmonized protocol (HarP) for the manual segmentation of the hippocampus on MR. The aim of this study is to study the concurrent validity of the HarP toward local protocols, and its major sources of variance.
METHODS: Fourteen tracers segmented 10 Alzheimer's Disease Neuroimaging Initiative (...
Decision-making systems trained on structural magnetic resonance imaging data of subjects affected by the Alzheimer's disease (AD) and healthy controls (CTRL) are becoming widespread prognostic tools for subjects with mild cognitive impairment (MCI). This study compares the performances of three classification methods based on support vector machin...
The brain of a patient with Alzheimer's disease (AD) undergoes changes starting many years before the development of the first clinical symptoms. The recent availability of large prospective datasets makes it possible to create sophisticated brain models of healthy subjects and patients with AD, showing pathophysiological changes occurring over tim...
Background:
In the framework of the clinical validation of research tools, this investigation presents a validation study of an automatic medial temporal lobe atrophy measure that is applied to a naturalistic population sampled from memory clinic patients across Europe.
Methods:
The procedure was developed on 1.5-T magnetic resonance images from...
Despite the widespread use of neuroimaging tools (morphological and functional) in the routine diagnostic of cerebral diseases, the information available by the end user -the clinician- remains largely limited to qualitative visual analysis. This restriction greatly reduces the diagnostic impact of neuroimaging in routine clinical practice and incr...
Background / Purpose:
We show how to use the inter-individual variability to extract common and correlated characteristics within a homogeneous group of subjects. We then define coherence patterns computed on groups. The pattern is the expression of the coherent regions whose intensity and pattern factorize all the inter-individual variability wi...
The aim of this work is to evaluate the potential of combining different computer-aided detection (CADe) methods to increase the actual support for radiologists of automated systems in the identification of pulmonary nodules in CT scans.
The outputs of three different CADe systems developed by researchers of the Italian MAGIC-5 collaboration were c...
Medial temporal lobe (MTL) atrophy is one of the key biomarkers to detect early neurodegenerative changes in the course of Alzheimer's disease (AD). There is active research aimed at identifying automated methodologies able to extract accurate classification indexes from T1-weighted magnetic resonance images (MRI). Such indexes should be fit for id...
We present the development of a web-based interface (proAD) for the automatic analysis of structural T1-weighted magnetic resonance images (MRI). This web-based tool is meant to be a high level interface to a sophisticated analysis environment aimed at the early diagnosis of Alzheimer's disease patients. The analysis procedure relies on clinical di...
In this work we present a method for the combined analysis of MR and PET images for the early assessment of Alzheimer's disease. This method is fully automatic and can get local structural and functional information from brain scans. By combining functional information with those provided by structural MRI modality, it is possible to improve the ab...
Multiple biomarkers have proved sensitive to AD and MCI, a potential prodromal stage of AD. These include patterns of regional cerebral atrophy and hypometabolism detected by MR imaging and FDG-PET1 and quantification of specific proteins in the CSF. There is active research aimed at identifying automated methodologies able to extract accurate clas...
The Channeler Ant Model (CAM) is an algorithm based on virtual ant colonies, conceived for the segmentation of complex structures with different shapes and intensity in a 3D environment. It exploits the natural capabilities of virtual ant colonies to modify the environment and communicate with each other by pheromone deposition. When applied to lun...
In this paper the Channeler Ant Model (CAM) and some results of its applications to the analysis of medical images are described. The CAM is an algorithm able to segment 3D structures with different shapes, intensity and background. It makes use of virtual ant colonies and exploits their natural capabilities to modify the environment and communicat...
In this paper we present the development of a software for the extraction of the hippocampus and surrounding medial-temporal-lobe (MTL) regions from T1-weighted magnetic resonance (MR) and from Positron Emission Tomography (PET) images with no interactive input from the user. With this software we introduce a novel statistical index computed on the...
Breast cancer has become a serious public health problem around the world. However, this pathology can be treated if it is detected in early stages. This task is achieved by a radiologist, who should read a large amount of mammograms per day, either for a screening or diagnostic purpose in mammography. However human factors could affect the diagnos...
Projects
Project (1)
ARIANNA project aims to develop an IT platform (https://arianna.pi.infn.it) to support an interdisciplinary research network dedicated to the study of Autism Spectrum Disorders (ASD). Indeed the complexity and heterogeneity of ASD requires dedicated multivariate analysis techniques to get the most from the interrelationship among the many variables that describe affected individuals. The ARIANNA research team develops and validates new effective methods to analyse neuroimaging data acquired in multiple sites.
