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Article ; Online: Editorial for the Special Issue on “Machine Learning in Healthcare and Biomedical Application”

Alessia Sarica

Algorithms, Vol 15, Iss 97, p

2022 Volume 97

Abstract: In the last decade, Machine Learning (ML) has indisputably had a pervasive application in healthcare and biomedical applications [.] ...

Abstract	In the last decade, Machine Learning (ML) has indisputably had a pervasive application in healthcare and biomedical applications [.]
Keywords	n/a ; Industrial engineering. Management engineering ; T55.4-60.8 ; Electronic computers. Computer science ; QA75.5-76.95
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Editorial: Brain hemispheric specialization and its pathological change revealed by neuroimaging and neuropsychology.

Sarica, Alessia / Quattrone, Andrea / Jehna, Margit / Vaccaro, Maria Grazia

Frontiers in neurology

2022 Volume 13, Page(s) 1071148

Language	English
Publishing date	2022-11-04
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2564214-5
ISSN	1664-2295
ISSN	1664-2295
DOI	10.3389/fneur.2022.1071148
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Article ; Online: Explainable machine learning with pairwise interactions for the classification of Parkinson's disease and SWEDD from clinical and imaging features.

Sarica, Alessia / Quattrone, Andrea / Quattrone, Aldo

Brain imaging and behavior

2022 Volume 16, Issue 5, Page(s) 2188–2198

Abstract: Scans without evidence of dopaminergic deficit (SWEDD) refers to patients who mimics motor and non-motor symptoms of Parkinson's disease (PD) but showing integrity of dopaminergic system. For this reason, the differential diagnosis between SWEDD and PD ... ...

Abstract	Scans without evidence of dopaminergic deficit (SWEDD) refers to patients who mimics motor and non-motor symptoms of Parkinson's disease (PD) but showing integrity of dopaminergic system. For this reason, the differential diagnosis between SWEDD and PD patients is often not possible in absence of dopamine imaging. Machine Learning (ML) showed optimal performance in automatically distinguishing these two diseases from clinical and imaging data. However, the most common applied ML algorithms provide high accuracy at expense of findings intelligibility. In this work, a novel ML glass-box model, the Explainable Boosting Machine (EBM), based on Generalized Additive Models plus interactions (GA
MeSH term(s)	Humans ; Parkinson Disease/diagnostic imaging ; Parkinson Disease/metabolism ; Dopamine/metabolism ; Magnetic Resonance Imaging ; Machine Learning ; Corpus Striatum/metabolism
Chemical Substances	Dopamine (VTD58H1Z2X)
Language	English
Publishing date	2022-05-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	2377165-3
ISSN	1931-7565 ; 1931-7557
ISSN (online)	1931-7565
ISSN	1931-7557
DOI	10.1007/s11682-022-00688-9
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Article: Sex Differences in Conversion Risk from Mild Cognitive Impairment to Alzheimer's Disease: An Explainable Machine Learning Study with Random Survival Forests and SHAP.

Sarica, Alessia / Pelagi, Assunta / Aracri, Federica / Arcuri, Fulvia / Quattrone, Aldo / Quattrone, Andrea / For The Alzheimer's Disease Neuroimaging Initiative

Brain sciences

2024 Volume 14, Issue 3

Abstract: Alzheimer's disease (AD) exhibits sex-linked variations, with women having a higher prevalence, and little is known about the sexual dimorphism in progressing from Mild Cognitive Impairment (MCI) to AD. The main aim of our study was to shed light on the ... ...

Abstract	Alzheimer's disease (AD) exhibits sex-linked variations, with women having a higher prevalence, and little is known about the sexual dimorphism in progressing from Mild Cognitive Impairment (MCI) to AD. The main aim of our study was to shed light on the sex-specific conversion-to-AD risk factors using Random Survival Forests (RSF), a Machine Learning survival approach, and Shapley Additive Explanations (SHAP) on dementia biomarkers in stable (sMCI) and progressive (pMCI) patients. With this purpose, we built two separate models for male (M-RSF) and female (F-RSF) cohorts to assess whether global explanations differ between the sexes. Similarly, SHAP local explanations were obtained to investigate changes across sexes in feature contributions to individual risk predictions. The M-RSF achieved higher performance on the test set (0.87) than the F-RSF (0.79), and global explanations of male and female models had limited similarity (<71.1%). Common influential variables across the sexes included brain glucose metabolism and CSF biomarkers. Conversely, the M-RSF had a notable contribution from hippocampus, which had a lower impact on the F-RSF, while verbal memory and executive function were key contributors only in F-RSF. Our findings confirmed that females had a higher risk of progressing to dementia; moreover, we highlighted distinct sex-driven patterns of variable importance, uncovering different feature contribution risks across sexes that decrease/increase the conversion-to-AD risk.
Language	English
Publishing date	2024-02-22
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2651993-8
ISSN	2076-3425
ISSN	2076-3425
DOI	10.3390/brainsci14030201
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Article: Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor.

Calomino, Camilla / Quattrone, Andrea / Bianco, Maria Giovanna / Nisticò, Rita / Buonocore, Jolanda / Crasà, Marianna / Vaccaro, Maria Grazia / Sarica, Alessia / Quattrone, Aldo

Frontiers in neurology

2024 Volume 15, Page(s) 1372262

Abstract: Objective: To investigate the performance of structural MRI cortical and subcortical morphometric data combined with blink-reflex recovery cycle (BRrc) values using machine learning (ML) models in distinguishing between essential tremor (ET) with ... ...

Abstract	Objective: To investigate the performance of structural MRI cortical and subcortical morphometric data combined with blink-reflex recovery cycle (BRrc) values using machine learning (ML) models in distinguishing between essential tremor (ET) with resting tremor (rET) and classic ET. Methods: We enrolled 47 ET, 43 rET patients and 45 healthy controls (HC). All participants underwent brain 3 T-MRI and BRrc examination at different interstimulus intervals (ISIs, 100-300 msec). MRI data (cortical thickness, volumes, surface area, roughness, mean curvature and subcortical volumes) were extracted using Freesurfer on T1-weighted images. We employed two decision tree-based ML classification algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) combining MRI data and BRrc values to differentiate between rET and ET patients. Results: ML models based exclusively on MRI features reached acceptable performance (AUC: 0.85-0.86) in differentiating rET from ET patients and from HC. Similar performances were obtained by ML models based on BRrc data (AUC: 0.81-0.82 in rET vs. ET and AUC: 0.88-0.89 in rET vs. HC). ML models combining imaging data (cortical thickness, surface, roughness, and mean curvature) together with BRrc values showed the highest classification performance in distinguishing between rET and ET patients, reaching AUC of 0.94 ± 0.05. The improvement in classification performances when BRrc data were added to imaging features was confirmed by both ML algorithms. Conclusion: This study highlights the usefulness of adding a simple electrophysiological assessment such as BRrc to MRI cortical morphometric features for accurately distinguishing rET from ET patients, paving the way for a better classification of these ET syndromes.
Language	English
Publishing date	2024-02-23
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564214-5
ISSN	1664-2295
ISSN	1664-2295
DOI	10.3389/fneur.2024.1372262
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Article: Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer's disease.

Sarica, Alessia / Aracri, Federica / Bianco, Maria Giovanna / Arcuri, Fulvia / Quattrone, Andrea / Quattrone, Aldo

Brain informatics

2023 Volume 10, Issue 1, Page(s) 31

Abstract: Random Survival Forests (RSF) has recently showed better performance than statistical survival methods as Cox proportional hazard (CPH) in predicting conversion risk from mild cognitive impairment (MCI) to Alzheimer's disease (AD). However, RSF ... ...

Abstract	Random Survival Forests (RSF) has recently showed better performance than statistical survival methods as Cox proportional hazard (CPH) in predicting conversion risk from mild cognitive impairment (MCI) to Alzheimer's disease (AD). However, RSF application in real-world clinical setting is still limited due to its black-box nature.For this reason, we aimed at providing a comprehensive study of RSF explainability with SHapley Additive exPlanations (SHAP) on biomarkers of stable and progressive patients (sMCI and pMCI) from Alzheimer's Disease Neuroimaging Initiative. We evaluated three global explanations-RSF feature importance, permutation importance and SHAP importance-and we quantitatively compared them with Rank-Biased Overlap (RBO). Moreover, we assessed whether multicollinearity among variables may perturb SHAP outcome. Lastly, we stratified pMCI test patients in high, medium and low risk grade, to investigate individual SHAP explanation of one pMCI patient per risk group.We confirmed that RSF had higher accuracy (0.890) than CPH (0.819), and its stability and robustness was demonstrated by high overlap (RBO > 90%) between feature rankings within first eight features. SHAP local explanations with and without correlated variables had no substantial difference, showing that multicollinearity did not alter the model. FDG, ABETA42 and HCI were the first important features in global explanations, with the highest contribution also in local explanation. FAQ, mPACCdigit, mPACCtrailsB and RAVLT immediate had the highest influence among all clinical and neuropsychological assessments in increasing progression risk, as particularly evident in pMCI patients' individual explanation. In conclusion, our findings suggest that RSF represents a useful tool to support clinicians in estimating conversion-to-AD risk and that SHAP explainer boosts its clinical utility with intelligible and interpretable individual outcomes that highlights key features associated with AD prognosis.
Language	English
Publishing date	2023-11-18
Publishing country	Germany
Document type	Journal Article
ISSN	2198-4018
ISSN	2198-4018
DOI	10.1186/s40708-023-00211-w
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Article: Functional connectivity of the cortico-subcortical sensorimotor loop is modulated by the severity of nigrostriatal dopaminergic denervation in Parkinson's Disease.

Quarantelli, Mario / Quattrone, Andrea / Sarica, Alessia / Cicone, Francesco / Cascini, Giuseppe Lucio / Quattrone, Aldo

NPJ Parkinson's disease

2022 Volume 8, Issue 1, Page(s) 122

Abstract: To assess if the severity of nigrostriatal innervation loss affects the functional connectivity (FC) of the sensorimotor cortico-striato-thalamic-cortical loop (CSTCL) in Parkinson's Disease (PD), Resting-State functional MRI ... ...

Abstract	To assess if the severity of nigrostriatal innervation loss affects the functional connectivity (FC) of the sensorimotor cortico-striato-thalamic-cortical loop (CSTCL) in Parkinson's Disease (PD), Resting-State functional MRI and
Language	English
Publishing date	2022-09-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	2819218-7
ISSN	2373-8057
ISSN	2373-8057
DOI	10.1038/s41531-022-00385-w
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Article ; Online: Structural connectivity alterations in the motor network of patients with scans without evidence of dopaminergic deficit (SWEDD).

Caligiuri, Maria Eugenia / Quattrone, Andrea / Bianco, Maria Giovanna / Sarica, Alessia / Quattrone, Aldo

Journal of neurology

2022 Volume 269, Issue 11, Page(s) 5926–5933

Abstract: Background: Approximatively, 10% of patients initially diagnosed with Parkinson's disease (PD) show preserved presynaptic dopaminergic function in the nigrostriatal pathway on DAT-SPECT imaging. This syndrome is not compatible with PD diagnosis, and is ... ...

Abstract	Background: Approximatively, 10% of patients initially diagnosed with Parkinson's disease (PD) show preserved presynaptic dopaminergic function in the nigrostriatal pathway on DAT-SPECT imaging. This syndrome is not compatible with PD diagnosis, and is known as scans without evidence of dopaminergic deficit (SWEDD). Objective: To investigate structural connectivity of cerebello-subcortico-cortical networks, including the nigrostriatal pathway, in an international cohort of subjects with SWEDD compared to normal controls using probabilistic tractography. Methods: Twenty-eight patients with SWEDD and 21 age- and sex-matched healthy controls (HC) were selected from the Parkinson's Progression Markers Initiative (PPMI) database. All participants underwent whole-brain 3D T1-weighted and diffusion-weighted MRI, as well as DAT-SPECT. Probabilistic tractography was performed in network-mode between regions of the cerebello-thalamo-basal ganglia-cortical circuits, to extract the connectivity strength between pairs of nodes of the circuit, as well as volumetric and diffusion measures of each reconstructed tract. Analysis of covariance with age and sex as covariates of non-interest was performed to assess group differences. Statistical significance was set at p < 0.05 after false-discovery-rate correction for multiple comparisons. Results: Compared to HC, patients with SWEDD showed increased fractional anisotropy in bilateral thalamo-putamen-precentral, left nigro-putaminal and left thalamo-pallidal pathways. Furthermore, we found decreased mean streamline length in bilateral thalamo-nigro-cerebellar pathways and in the left nigro-caudate connection. Conclusions: Clinical heterogeneity of SWEDD syndrome may account for involvement of different brain circuits, such as the cerebello-thalamo-cortical and the nigrostriatal pathways, characteristic of different tremulous disorders.
MeSH term(s)	Basal Ganglia ; Dopamine/metabolism ; Humans ; Parkinson Disease ; Tomography, Emission-Computed, Single-Photon ; Tremor
Chemical Substances	Dopamine (VTD58H1Z2X)
Language	English
Publishing date	2022-07-06
Publishing country	Germany
Document type	Journal Article
ZDB-ID	187050-6
ISSN	1432-1459 ; 0340-5354 ; 0012-1037 ; 0939-1517 ; 1619-800X
ISSN (online)	1432-1459
ISSN	0340-5354 ; 0012-1037 ; 0939-1517 ; 1619-800X
DOI	10.1007/s00415-022-11259-9
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Article: Cluster Analysis Method Reveals Gender Attitudes in Sociosexual Orientation of a Southern Italy Population During the COVID-19 Lockdown.

Vaccaro, Maria Grazia / Izzo, Giulia / Sarica, Alessia / La Vignera, Sandro / Aversa, Antonio

Sexuality research & social policy : journal of NSRC : SR & SP

2022 , Page(s) 1–14

Abstract: Introduction: The COVID-19 epidemic and its lockdown dramatically impacted the general well-being of the population and affected sociosexual experiences, thus modifying sexual behavior, desire, and well-being. Clustering analysis has not yet been ... ...

Abstract	Introduction: The COVID-19 epidemic and its lockdown dramatically impacted the general well-being of the population and affected sociosexual experiences, thus modifying sexual behavior, desire, and well-being. Clustering analysis has not yet been applied to research and data investigating sociosexuality. The cluster analysis method could be a valid support for clinicians in investigating the condition of a population with respect to problems related to sociosexuality. The aim of the present study was to analyze the different perceptions of the sociosexual experiences in southern population during the COVID-19 pandemic. Methods: We enrolled 734 (450 female) participants with a carried out anonymous web-based survey from the 16th of April 2020 to the 3rd June of 2020. The revised Sociosexual Orientation Inventory (SOI-R) is a self-report test assessing three theoretically meaningful facets of sociosexual orientation (behavior, attitude, and desire). Results: We found eleven clusters, and the findings showed, for the first time, an intra- and inter-diagnostic heterogeneity in the sexual profile of participants. Theoretically, we identified subtype clusters whose sexual attitude was to avoid sexual promiscuity with significant gender differences. Women show a greater propensity for attitude and desire facet than men. Conclusions: Our new method of unsupervised learning could represent a reliable tool to support socio-cultural analysis studies on issues influenced by cultural mechanisms in a quick and explanatory way, as in the case of sexual orientation and attitude differences between men and women. Social and policy implications: Understanding these gaps is fundamental for policy makers, managers of social networks, those who deal with engaged couples and families, and sexuality starting from the very youngest adolescents. We claim to devise a strategy to measure how much a sexist culture implicitly and explicitly limits the freedom of sexual expression and how this can affect psycho-sexual well-being in a society. Supplementary information: The online version contains supplementary material available at 10.1007/s13178-022-00771-2.
Language	English
Publishing date	2022-10-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	2136442-4
ISSN	1553-6610 ; 1868-9884
ISSN (online)	1553-6610
ISSN	1868-9884
DOI	10.1007/s13178-022-00771-2
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Article ; Online: Combined blood Neurofilament light chain and third ventricle width to differentiate Progressive Supranuclear Palsy from Parkinson's Disease: A machine learning study.

Bianco, Maria Giovanna / Cristiani, Costanza Maria / Scaramuzzino, Luana / Sarica, Alessia / Augimeri, Antonio / Chimento, Ilaria / Buonocore, Jolanda / Parrotta, Elvira Immacolata / Quattrone, Andrea / Cuda, Gianni / Quattrone, Aldo

Parkinsonism & related disorders

2024 Volume 123, Page(s) 106978

Abstract: Introduction: Differentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may be clinically challenging. In this study, we explored the performance of machine learning models based on MR imaging and blood molecular biomarkers in ... ...

Abstract	Introduction: Differentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may be clinically challenging. In this study, we explored the performance of machine learning models based on MR imaging and blood molecular biomarkers in distinguishing between these two neurodegenerative diseases. Methods: Twenty-eight PSP patients, 46 PD patients and 60 control subjects (HC) were consecutively enrolled in the study. Serum concentration of neurofilament light chain protein (Nf-L) was assessed by single molecule array (SIMOA), while an automatic segmentation algorithm was employed for T1-weighted measurements of third ventricle width/intracranial diameter ratio (3 Results: PSP patients showed higher serum Nf-L levels and larger 3 Conclusion: Our findings highlight the usefulness of combining blood and simple linear MRI biomarkers to accurately distinguish between PSP and PD patients. This multimodal approach may play a pivotal role in patient management and clinical decision-making, paving the way for more effective and timely interventions in these neurodegenerative diseases.
Language	English
Publishing date	2024-04-24
Publishing country	England
Document type	Journal Article
ZDB-ID	1311489-x
ISSN	1873-5126 ; 1353-8020
ISSN (online)	1873-5126
ISSN	1353-8020
DOI	10.1016/j.parkreldis.2024.106978
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