Christian Salvatore

Prof. Christian Salvatore

orcid

Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2021 in Physica Medica
Verified
Journal Article
Closed access
ICIsabella Castiglioni
Isabella Castiglioni
Leonardo RundoLeonardo Rundo
Leonardo Rundo
MCMarina Codari
Marina Codari
GLGiovanni Di Leo
Giovanni Di Leo
CSChristian Salvatore
Christian Salvatore
FSFrancesco Sardanelli
Francesco Sardanelli

1 January 2014 in Journal of Neuroscience Methods
Verified
Journal Article
Closed access
CSChristian Salvatore
Christian Salvatore
ACAntonio Cerasa
Antonio Cerasa
ICI. Castiglioni
I. Castiglioni
FGFrancesca Gallivanone
Francesca Gallivanone
AAAntonio Augimeri
Antonio Augimeri
AQAldo Quattrone
Aldo Quattrone

1 September 2015 in Frontiers in Neuroscience
Verified
Journal Article
Open access
PDF file
CSChristian Salvatore
Christian Salvatore
ACAntonio Cerasa
Antonio Cerasa
PBPetronilla Battista
Petronilla Battista
MGMaria C Gilardi
Maria C Gilardi
AQAldo Quattrone
Aldo Quattrone
AIAlzheimer'S Disease Neuroimaging Initiative
Alzheimer'S Disease Neuroimaging Initiative
Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, The vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc and 162 healthy controls (CN) selected from the Alzheimer’s disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.

5 February 2015 in Journal of Autism and Developmental Disorders
Verified
Journal Article
Closed access
PDF file
ACAlessandro Crippa
Alessandro Crippa
CSChristian Salvatore
Christian Salvatore
PPPaolo Perego
Paolo Perego
SFSara Forti
Sara Forti
MNMaria Nobile
Maria Nobile
ICIsabella Castiglioni
Isabella Castiglioni

1 January 2017 in Behavioural Neurology
Verified
Journal Article
Open access
PDF file
PBPetronilla Battista
Petronilla Battista
CSChristian Salvatore
Christian Salvatore
ICIsabella Castiglioni
Isabella Castiglioni
Subjects with Alzheimer’s disease (AD) show loss of cognitive functions and change in behavioral and functional state affecting the quality of their daily life and that of their families and caregivers. A neuropsychological assessment plays a crucial role in detecting such changes from normal conditions. However, despite the existence of clinical measures that are used to classify and diagnose AD, a large amount of subjectivity continues to exist. Our aim was to assess the potential of machine learning in quantifying this process and optimizing or even reducing the amount of neuropsychological tests used to classify AD patients, also at an early stage of impairment. We investigated the role of twelve state-of-the-art neuropsychological tests in the automatic classification of subjects with none, mild, or severe impairment as measured by the clinical dementia rating (CDR). Data were obtained from the ADNI database. In the groups of measures used as features, we included measures of both cognitive domains and subdomains. Our findings show that some tests are more frequently best predictors for the automatic classification, namely, LM, ADAS-Cog, AVLT, and FAQ, with a major role of the ADAS-Cog measures of delayed and immediate memory and the FAQ measure of financial competency.

1 July 2020 in Neuroscience & Biobehavioral Reviews
Verified
Journal Article
Closed access
PBPetronilla Battista
Petronilla Battista
CSChristian Salvatore
Christian Salvatore
MBManuela Berlingeri
Manuela Berlingeri
ACAntonio Cerasa
Antonio Cerasa
ICIsabella Castiglioni
Isabella Castiglioni