Article
Clinical Neurology
Femke Dijkstra, Ilse de Volder, Mineke Viaene, Patrick Cras, David Crosiers
Summary: This study suggests that impaired bed mobility can be a clinical symptom for screening prodromal PD and predicting motor complications in early PD.
PARKINSONISM & RELATED DISORDERS
(2022)
Article
Geriatrics & Gerontology
Meng-Xi Zhou, Qin Wang, Yin Lin, Qian Xu, Li Wu, Ya-Jing Chen, Yu-Han Jiang, Qing He, Lei Zhao, You-Rong Dong, Jian-Ren Liu, Wei Chen
Summary: This study found that ocular movements are impaired in newly diagnosed, drug-naive PD patients, and these changes could be indicators for disease progression in PD.
FRONTIERS IN AGING NEUROSCIENCE
(2022)
Article
Clinical Neurology
Talene A. Yacoubian, Yu-Hua Dean Fang, Adam Gerstenecker, Amy Amara, Natividad Stover, Lauren Ruffrage, Christopher Collette, Richard Kennedy, Yue Zhang, Huixian Hong, Hongwei Qin, Jonathan McConathy, Etty N. Benveniste, David G. Standaert
Summary: This study aimed to evaluate the presence of brain and systemic inflammation in newly diagnosed Parkinson's disease (PD) patients. The results showed increased central inflammation in de novo PD patients and its correlation with cognitive decline. These findings suggest that inflammation may play a significant role in the pathophysiology of PD.
MOVEMENT DISORDERS
(2023)
Article
Clinical Neurology
Talene A. Yacoubian, Yu-Hua Dean Fang, Adam Gerstenecker, Amy Amara, Natividad Stover, Lauren Ruffrage, Christopher Collette, Richard Kennedy, Yue Zhang, Huixian Hong, Hongwei Qin, Jonathan McConathy, Etty N. Benveniste, David G. Standaert
Summary: This study aimed to assess the presence of brain and systemic inflammation in subjects newly diagnosed with Parkinson's disease (PD). The results showed increased central inflammation in de novo PD subjects compared to controls, and the presence of inflammation may predict cognitive decline.
MOVEMENT DISORDERS
(2023)
Article
Neurosciences
Jan Rusz, Radim Krupicka, Slavka Viteckova, Tereza Tykalova, Michal Novotny, Jan Novak, Petr Dusek, Evzen Ruzicka
Summary: This study investigated the presence and relationship of temporal speech and gait parameters in patients with different motor subtypes of Parkinson's disease (PD). The findings suggest that speech and gait rhythm disorders are specific to the postural instability/gait disorder (PIGD) subtype, with PIGD patients showing abnormalities in consonant timing, stride length, velocity, and cadence.
CNS NEUROSCIENCE & THERAPEUTICS
(2023)
Article
Neurosciences
Jingru Ren, Lei Yan, Hao Zhou, Chenxi Pan, Chen Xue, Jun Wu, Weiguo Liu
Summary: This study aimed to investigate the relationship between GBA-related Parkinson's disease (GBA-PD) and specific neurotransmitter deficits, as well as their associations with cognitive impairment. The results showed widespread gray matter atrophy in GBA-PD patients compared to healthy controls, which was correlated with the spatial distribution of serotonergic, dopaminergic, and acetylcholinergic pathways. Executive function and language in cognitive domains were also associated with the strength of these neurotransmitter circuits' colocalization.
NEUROBIOLOGY OF DISEASE
(2023)
Article
Neurosciences
Jianxia Xu, Yubing Chen, Hui Wang, Yuqian Li, Lanting Li, Jingru Ren, Yu Sun, Weiguo Liu
Summary: Early-stage Parkinson's disease may be associated with abnormal attention bias and particularly auditory attention processing. Altered neural network connectivity is expected to be a potential neuroimaging biomarker for predicting depression in PD.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Medicine, General & Internal
Jae-Myung Kim, Seong-Min Choi, Soo Hyun Cho, Byeong C. Kim
Summary: Restless legs syndrome (RLS) affects clinical factors, particularly sleep quality and non-motor symptoms, in patients with Parkinson's disease (PD). It is important to note that RLS is more prevalent in de novo PD patients and mainly impacts sleep disturbances.
Article
Clinical Neurology
Kyung Ah Woo, Joo Hong Joun, Eun Jin Yoon, Chan Young Lee, Beomseok Jeon, Yu Kyeong Kim, Jee-Young Lee
Summary: This study investigated the relationship between monoaminergic degeneration and ocular motor abnormalities in newly diagnosed Parkinson's disease (PD) patients. The results showed that saccadic accuracy was related to motor severity, while latency was related to cognitive function. Degeneration in the anterior and posterior putamen was associated with reduced saccadic accuracy, while degeneration in the dorsal raphe was associated with decreased smooth pursuit gain.
MOVEMENT DISORDERS
(2023)
Article
Geriatrics & Gerontology
Javier Oltra, Carme Uribe, Anna Campabadal, Anna Inguanzo, Gemma C. Monte-Rubio, Maria J. Marti, Yaroslau Compta, Francesc Valldeoriola, Carme Junque, Barbara Segura
Summary: A study found that there are differences in symptoms, brain structure and cognitive function between male and female PD patients. Compared to females, male patients had more severe symptoms, greater brain atrophy, and worse cognitive function.
FRONTIERS IN AGING NEUROSCIENCE
(2022)
Article
Neurosciences
Cecilia Boccalini, Giulia Carli, Andrea Pilotto, Alessandro Padovani, Daniela Perani
Summary: This study investigated the impact of gender differences on clinical features, dopaminergic dysfunction, and connectivity in patients with Parkinson's disease across different clinical subtypes. Results showed that males and females with idiopathic PD exhibit distinct vulnerabilities and disease expressions, particularly in cognitive function, anxiety symptoms, and dopaminergic binding patterns in different brain regions.
NEUROBIOLOGY OF DISEASE
(2022)
Article
Clinical Neurology
Matteo Conti, Roberta Bovenzi, Elena Garasto, Tommaso Schirinzi, Fabio Placidi, Nicola B. Mercuri, Rocco Cerroni, Mariangela Pierantozzi, Alessandro Stefani
Summary: In early stages of Parkinson's disease, alterations in EEG functional connectivity were observed, characterized by reduced connectivity in α-β frequency bands, increased connectivity in the γ band, and differences in assortativity coefficient. Network measures analysis helps to understand the complexity of Parkinson's disease physiopathology.
FRONTIERS IN NEUROLOGY
(2022)
Review
Clinical Neurology
Kaitlyn M. L. Cramb, Dayne Beccano-Kelly, Stephanie J. Cragg, Richard Wade-Martins
Summary: Cramb et al. provide a review of evidence suggesting dopamine release deficits occur prior to neurodegeneration in Parkinson's disease. They also highlight the need for further investigation in understanding the mechanisms behind these deficits.
Article
Clinical Neurology
Sygrid van der Zee, Prabesh Kanel, Marleen J. J. Gerritsen, Jeffrey M. Boertien, Anne C. Slomp, Martijn L. T. M. Muller, Nicolaas Bohnen, Jacoba M. Spikman, Teus van Laar
Summary: This study assessed the cholinergic innervation status in early-stage Parkinson's disease (PD) patients and found that cholinergic innervation changes were associated with cognitive impairment. Regardless of cognitive status, patients showed cholinergic denervation in the posterior cortical regions. Cognitively intact patients exhibited higher cholinergic activity in the cerebellar, frontal, and subcortical regions, suggesting compensatory cholinergic upregulation in early-stage PD. Limited or failing cholinergic upregulation may play an important role in early cognitive impairment in PD.
MOVEMENT DISORDERS
(2022)
Article
Clinical Neurology
Daniele Urso, Valentina Leta, Lucia Batzu, Tayyabah Yousaf, Chloe Farrell, Daniel J. van Wamelen, K. Ray Chaudhuri
Summary: While PIGD subtype classification did not predict cognitive decline in de novo PD patients, postural instability was shown to be an independent predictor of cognitive impairment.
JOURNAL OF NEUROLOGY
(2022)
Article
Engineering, Biomedical
Vincenzo Catrambone, Gaetano Valenza
Summary: This study proposes an analysis framework to estimate the directional information flow between whole-brain and heartbeat dynamics. The experimental results demonstrate that there is a bidirectional increase in brain-heart interplay during cognitive workload and an increase in descending interplay during an autonomic maneuver. These changes cannot be detected by isolated cortical and heartbeat dynamics.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Article
Neurosciences
Allegra Conti, Constantina Andrada Treaba, Ambica Mehndiratta, Valeria Teresa Barletta, Caterina Mainero, Nicola Toschi
Summary: The relationship between central hallmarks of multiple sclerosis (MS), such as white matter (WM)/cortical demyelinated lesions and cortical gray matter atrophy, remains unclear. A machine learning model was employed to predict mean cortical thinning in different brain regions using demographic and lesion-related characteristics. The study found that volume and rimless WM lesions, patient age, and volume of intracortical lesions have the most predictive power.
Letter
Cardiac & Cardiovascular Systems
Gaetano Valenza
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
(2023)
Article
Biophysics
Mimma Nardelli, Luca Citi, Riccardo Barbieri, Gaetano Valenza
Summary: Assessment of heartbeat dynamics can be used for non-invasive monitoring of cardiovascular and autonomic states. However, the non-specificity of such measurements limits their applicability in naturalistic conditions. In this study, we investigate the irregularity and complexity of cardiac sympathetic and vagal activity series in different populations. Our results show significant differences between pathological/old subjects and young subjects, providing new insights into physiology and improving the specificity of heartbeat-derived biomarkers.
PHYSIOLOGICAL MEASUREMENT
(2023)
Article
Behavioral Sciences
Maria Giovanna Bianco, Andrea Duggento, Salvatore Nigro, Allegra Conti, Nicola Toschi, Luca Passamonti
Summary: This study investigated the heritability of directed functional connections in the brain using the state-space formulation of Granger causality (GC) method. The results showed that the directed connectivity of certain cortico-subcortical circuits is strongly influenced by genetic factors, while other connections are less affected.
BRAIN AND BEHAVIOR
(2023)
Article
Health Care Sciences & Services
Marianna Inglese, Matteo Ferrante, Tommaso Boccato, Allegra Conti, Chiara A. Pistolese, Oreste C. Buonomo, Rolando M. D'Angelillo, Nicola Toschi
Summary: Traditional imaging techniques such as X-rays and MRI have limitations in breast cancer diagnosis and prediction, leading to the emergence of PET as a more effective tool. This study used dynamic PET scans to extract radiomic features and trained a model for classification and prognosis prediction. The results showed superior performance of the dynamic radiomics approach, outperforming standard PET imaging in accuracy. This study demonstrates the enhanced clinical utility of dynomics in improving breast cancer diagnosis and prognosis.
JOURNAL OF PERSONALIZED MEDICINE
(2023)
Article
Business
Elena Parra Vargas, Jestine Philip, Lucia A. Carrasco-Ribelles, Irene Alice Chicchi Giglioli, Gaetano Valenza, Javier Marin-Morales, Mariano Alcaniz Raya
Summary: This research used EEG and GSR techniques to analyze relationship-oriented leadership (ROL) and task-oriented leadership (TOL), and found that EEG measures were better at recognizing brain activity associated with ROL. The study also developed a machine learning model to predict ROL with 81% accuracy.
MANAGEMENT DECISION
(2023)
Article
Medicine, General & Internal
Francesco Alfano, Francesca Cesari, Anna Maria Gori, Martina Berteotti, Emilia Salvadori, Betti Giusti, Alessia Bertelli, Ada Kura, Carmen Barbato, Benedetta Formelli, Francesca Pescini, Enrico Fainardi, Stefano Chiti, Chiara Marzi, Stefano Diciotti, Rossella Marcucci, Anna Poggesi
Summary: This study aims to evaluate the predictive capability of biomarkers for vascular damage and brain tissue injury in anticoagulated atrial fibrillation (AF) patients. The results show that circulating biomarkers MMP-2 and TIMP are associated with cerebral microbleeds, white matter hyperintensity, and small vessel disease. IL-8 and TIMP are associated with enlarged perivascular spaces.
JOURNAL OF CLINICAL MEDICINE
(2023)
Review
Medicine, General & Internal
Mario Mascalchi, Giulia Picozzi, Donella Puliti, Stefano Diciotti, Annalisa Deliperi, Chiara Romei, Fabio Falaschi, Francesco Pistelli, Michela Grazzini, Letizia Vannucchi, Simonetta Bisanzi, Marco Zappa, Giuseppe Gorini, Francesca Maria Carozzi, Laura Carrozzi, Eugenio Paci
Summary: The ITALUNG trial, conducted from 2004, compared the mortality rates of lung cancer and other causes in smokers and ex-smokers aged 55-69 who were randomly assigned to receive low-dose CT (LDCT) or usual care. After 13 years of follow-up, the ITALUNG trial demonstrated a lower mortality rate for lung cancer and cardiovascular diseases in the screened subjects, particularly in women. In addition, the trial generated multiple ancillary studies on various aspects of lung cancer screening, including software development, assessment of lung nodules and calcifications, and biomarker assays.
Article
Psychology, Multidisciplinary
Claudio Paolucci, Federica Giorgini, Riccardo Scheda, Flavio Valerio Alessi, Stefano Diciotti
Summary: This study proposes an AI pre-screening tool to identify potentially alarming signs of Autism Spectrum Disorder (ASD) in pre-verbal interactions. The effectiveness of these features in classifying individuals with ASD vs. controls is evaluated using explainable artificial intelligence, with a focus on body-related sensorimotor features. The results highlight the significance of early detection in ASD diagnosis.
COMPUTERS IN HUMAN BEHAVIOR
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Marianna Inglese, Matteo Ferrante, Andrea Duggento, Tommaso Boccato, Nicola Toschi
Summary: Positron emission tomography (PET) is a noninvasive imaging technology used to assess tissue metabolism and function. Dynamic PET acquisitions provide information about tracer delivery, target interaction, and physiological washout, which can be analyzed using time activity curves (TACs). Conventional PET analysis requires invasive arterial blood sampling, but this study demonstrates that deep learning models can accurately discriminate breast cancer lesions using TACs without arterial blood sampling, outperforming traditional SUV analysis.
IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES
(2023)
Article
Oncology
Nuhamin Gebrewold Petros, Jesper Alvarsson-Hjort, Gergoe Hadlaczky, Danuta Wasserman, Manuel Ottaviano, Sergio Gonzalez-Martinez, Sara Carletto, Enzo Pasquale Scilingo, Gaetano Valenza, Vladimir Carli
Summary: This study aimed to explore the factors influencing the use of the NEVERMIND eHealth system among patients with breast and prostate cancer. The results showed that perceived usefulness was the strongest predictor of system use, and early engagement had an influence on sustained use.
Proceedings Paper
Computer Science, Artificial Intelligence
Guido Gagliardi, Antonio Luca Alfeo, Vincenzo Catrambone, Mario G. C. A. Cimino, Maarten De Vos, Gaetano Valenza
Summary: This study proposes a novel explainable artificial intelligence architecture for emotion recognition using electroencephalographic and electrocardiographic signals. The proposed approach combines synchronous brain and heart information to derive vectorial features, which are then classified using a convolutional neural network. The results outperform the current state of the art, especially in terms of recognition accuracy.
2023 11TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, NER
(2023)
Article
Engineering, Biomedical
Vincenzo Catrambone, Gaetano Valenza
Summary: This study investigates the functional brain-heart interplay (BHI) during mental and physical stress conditions. The results show that the brain mainly influences the heart through efferent signals, with vagal oscillations affected during mental stress and sympathovagal oscillations affected during physical stress.
IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE
(2023)
Article
Computer Science, Information Systems
Guido Gagliardi, Antonio Luca Alfeo, Vincenzo Catrambone, Diego Candia-Rivera, Mario G. C. A. Cimino, Gaetano Valenza
Summary: EEG-based emotion recognition has potential applications in various scientific fields. A novel input representation that rearranges EEG features as an image is proposed, enabling emotion recognition through image-based machine learning methods. The proposed approach achieves high accuracy in emotion classification tasks.