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Marusic U, Peskar M, Šömen MM, Kalc M, Holobar A, Gramann K, Wollesen B, Wunderlich A, Michel C, Miladinović A, Catalan M, Buoite Stella A, Ajcevic M, Manganotti P. Neuromuscular assessment of force development, postural, and gait performance under cognitive-motor dual-tasking in healthy older adults and people with early Parkinson's disease: Study protocol for a cross-sectional Mobile Brain/Body Imaging (MoBI) study. Open Res Eur 2023; 3:58. [PMID: 38009088 PMCID: PMC10674089 DOI: 10.12688/openreseurope.15781.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/23/2023] [Indexed: 11/28/2023]
Abstract
Background Neuromuscular dysfunction is common in older adults and more pronounced in neurodegenerative diseases. In Parkinson's disease (PD), a complex set of factors often prevents the effective performance of activities of daily living that require intact and simultaneous performance of the motor and cognitive tasks. Methods The cross-sectional study includes a multifactorial mixed-measure design. Between-subject factor grouping the sample will be Parkinson's Disease (early PD vs. healthy). The within-subject factors will be the task complexity (single- vs. dual-task) in each motor activity, i.e., overground walking, semi-tandem stance, and isometric knee extension, and a walking condition (wide vs. narrow lane) will be implemented for the overground walking activity only. To study dual-task (DT) effects, in each motor activity participants will be given a secondary cognitive task, i.e., a visual discrimination task for the overground walking, an attention task for the semi-tandem, and mental arithmetic for the isometric extension. Analyses of DT effects and underlying neuronal correlates will focus on both gait and cognitive performance where applicable. Based on an a priori sample size calculation, a total N = 42 older adults (55-75 years) will be recruited. Disease-specific changes such as laterality in motor unit behavior and cortical control of movement will be studied with high-density surface electromyography and electroencephalography during static and dynamic motor activities, together with whole-body kinematics. Discussion This study will be one of the first to holistically address early PD neurophysiological and neuromuscular patterns in an ecologically valid environment under cognitive-motor DT conditions of different complexities. The outcomes of the study aim to identify the biomarker for early PD either at the electrophysiological, muscular or kinematic level or in the communication between these systems. Clinical Trial Registration ClinicalTrials.Gov, NCT05477654. This study was approved by the Medical Ethical Committee (106/2021).
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Affiliation(s)
- Uros Marusic
- Science and Research Centre Koper, Institute for Kinesiology Research, Koper, Slovenia
- Department of Health Sciences, Alma Mater Europaea Evropski Center Maribor, Maribor, Slovenia
| | - Manca Peskar
- Science and Research Centre Koper, Institute for Kinesiology Research, Koper, Slovenia
- Department of Psychology and Ergonomics, Faculty V: Mechanical Engineering and Transport Systems, Technische Universitat Berlin, Berlin, Berlin, Germany
| | - Maja Maša Šömen
- Science and Research Centre Koper, Institute for Kinesiology Research, Koper, Slovenia
- Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Miloš Kalc
- Science and Research Centre Koper, Institute for Kinesiology Research, Koper, Slovenia
- Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Ales Holobar
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Klaus Gramann
- Department of Psychology and Ergonomics, Faculty V: Mechanical Engineering and Transport Systems, Technische Universitat Berlin, Berlin, Berlin, Germany
| | - Bettina Wollesen
- Department of Psychology and Ergonomics, Faculty V: Mechanical Engineering and Transport Systems, Technische Universitat Berlin, Berlin, Berlin, Germany
- Institute of Human Movement Science, Faculty of Psychology and Human Movement, University Hamburg, Hamburg, Germany
| | - Anna Wunderlich
- Department of Psychology and Ergonomics, Faculty V: Mechanical Engineering and Transport Systems, Technische Universitat Berlin, Berlin, Berlin, Germany
| | - Christoph Michel
- Functional Brain Mapping Lab, Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
| | | | - Mauro Catalan
- Clinical Unit of Neurology, Department of Medicine, Surgery, and Health Sciences, University of Trieste, Trieste, Italy
| | - Alex Buoite Stella
- Clinical Unit of Neurology, Department of Medicine, Surgery, and Health Sciences, University of Trieste, Trieste, Italy
| | - Milos Ajcevic
- Clinical Unit of Neurology, Department of Medicine, Surgery, and Health Sciences, University of Trieste, Trieste, Italy
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Paolo Manganotti
- Clinical Unit of Neurology, Department of Medicine, Surgery, and Health Sciences, University of Trieste, Trieste, Italy
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Boscolo Galazzo I, Tonin L, Miladinović A, Storti SF. Editorial: Brain-connectivity-based computer interfaces. Front Hum Neurosci 2023; 17:1281446. [PMID: 37736145 PMCID: PMC10509283 DOI: 10.3389/fnhum.2023.1281446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 08/28/2023] [Indexed: 09/23/2023] Open
Affiliation(s)
| | - Luca Tonin
- Department of Information Engineering, University of Padua, Padua, Italy
- Padova Neuroscience Center, University of Padua, Padua, Italy
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Zanus C, Miladinović A, De Dea F, Skabar A, Stecca M, Ajčević M, Accardo A, Carrozzi M. Sleep Spindle-Related EEG Connectivity in Children with Attention-Deficit/Hyperactivity Disorder: An Exploratory Study. Entropy (Basel) 2023; 25:1244. [PMID: 37761543 PMCID: PMC10530036 DOI: 10.3390/e25091244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/20/2023] [Accepted: 08/16/2023] [Indexed: 09/29/2023]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a neurobehavioral disorder with known brain abnormalities but no biomarkers to support clinical diagnosis. Recently, EEG analysis methods such as functional connectivity have rekindled interest in using EEG for ADHD diagnosis. Most studies have focused on resting-state EEG, while connectivity during sleep and spindle activity has been underexplored. Here we present the results of a preliminary study exploring spindle-related connectivity as a possible biomarker for ADHD. We compared sensor-space connectivity parameters in eight children with ADHD and nine age/sex-matched healthy controls during sleep, before, during, and after spindle activity in various frequency bands. All connectivity parameters were significantly different between the two groups in the delta and gamma bands, and Principal Component Analysis (PCA) in the gamma band distinguished ADHD from healthy subjects. Cluster coefficient and path length values in the sigma band were also significantly different between epochs, indicating different spindle-related brain activity in ADHD.
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Affiliation(s)
- Caterina Zanus
- Institute for Maternal and Child Health—IRCCS “Burlo Garofolo”, 34137 Trieste, Italy; (C.Z.); (M.C.)
| | - Aleksandar Miladinović
- Institute for Maternal and Child Health—IRCCS “Burlo Garofolo”, 34137 Trieste, Italy; (C.Z.); (M.C.)
| | - Federica De Dea
- Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy (M.A.); (A.A.)
- Department of Life Science, University of Trieste, 34127 Trieste, Italy
| | - Aldo Skabar
- Institute for Maternal and Child Health—IRCCS “Burlo Garofolo”, 34137 Trieste, Italy; (C.Z.); (M.C.)
| | - Matteo Stecca
- Institute for Maternal and Child Health—IRCCS “Burlo Garofolo”, 34137 Trieste, Italy; (C.Z.); (M.C.)
| | - Miloš Ajčević
- Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy (M.A.); (A.A.)
| | - Agostino Accardo
- Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy (M.A.); (A.A.)
| | - Marco Carrozzi
- Institute for Maternal and Child Health—IRCCS “Burlo Garofolo”, 34137 Trieste, Italy; (C.Z.); (M.C.)
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Ajčević M, Iscra K, Furlanis G, Michelutti M, Miladinović A, Buoite Stella A, Ukmar M, Cova MA, Accardo A, Manganotti P. Cerebral hypoperfusion in post-COVID-19 cognitively impaired subjects revealed by arterial spin labeling MRI. Sci Rep 2023; 13:5808. [PMID: 37037833 PMCID: PMC10086005 DOI: 10.1038/s41598-023-32275-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/24/2023] [Indexed: 04/12/2023] Open
Abstract
Cognitive impairment is one of the most prevalent symptoms of post Severe Acute Respiratory Syndrome COronaVirus 2 (SARS-CoV-2) state, which is known as Long COVID. Advanced neuroimaging techniques may contribute to a better understanding of the pathophysiological brain changes and the underlying mechanisms in post-COVID-19 subjects. We aimed at investigating regional cerebral perfusion alterations in post-COVID-19 subjects who reported a subjective cognitive impairment after a mild SARS-CoV-2 infection, using a non-invasive Arterial Spin Labeling (ASL) MRI technique and analysis. Using MRI-ASL image processing, we investigated the brain perfusion alterations in 24 patients (53.0 ± 14.5 years, 15F/9M) with persistent cognitive complaints in the post COVID-19 period. Voxelwise and region-of-interest analyses were performed to identify statistically significant differences in cerebral blood flow (CBF) maps between post-COVID-19 patients, and age and sex matched healthy controls (54.8 ± 9.1 years, 13F/9M). The results showed a significant hypoperfusion in a widespread cerebral network in the post-COVID-19 group, predominantly affecting the frontal cortex, as well as the parietal and temporal cortex, as identified by a non-parametric permutation testing (p < 0.05, FWE-corrected with TFCE). The hypoperfusion areas identified in the right hemisphere regions were more extensive. These findings support the hypothesis of a large network dysfunction in post-COVID subjects with cognitive complaints. The non-invasive nature of the ASL-MRI method may play an important role in the monitoring and prognosis of post-COVID-19 subjects.
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Affiliation(s)
- Miloš Ajčević
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Katerina Iscra
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Giovanni Furlanis
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, Trieste University Hospital-ASUGI, University of Trieste, Trieste, Italy
| | - Marco Michelutti
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, Trieste University Hospital-ASUGI, University of Trieste, Trieste, Italy
| | | | - Alex Buoite Stella
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, Trieste University Hospital-ASUGI, University of Trieste, Trieste, Italy
| | - Maja Ukmar
- Radiology Unit, Department of Medicine, Surgery and Health Sciences, Trieste University Hospital-ASUGI, University of Trieste, Trieste, Italy
| | - Maria Assunta Cova
- Radiology Unit, Department of Medicine, Surgery and Health Sciences, Trieste University Hospital-ASUGI, University of Trieste, Trieste, Italy
| | - Agostino Accardo
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Paolo Manganotti
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, Trieste University Hospital-ASUGI, University of Trieste, Trieste, Italy.
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Omejc N, Peskar M, Miladinović A, Kavcic V, Džeroski S, Marusic U. On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features. Life (Basel) 2023; 13:life13020391. [PMID: 36836747 PMCID: PMC9965040 DOI: 10.3390/life13020391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 01/23/2023] [Accepted: 01/28/2023] [Indexed: 02/04/2023] Open
Abstract
The utilization of a non-invasive electroencephalogram (EEG) as an input sensor is a common approach in the field of the brain-computer interfaces (BCI). However, the collected EEG data pose many challenges, one of which may be the age-related variability of event-related potentials (ERPs), which are often used as primary EEG BCI signal features. To assess the potential effects of aging, a sample of 27 young and 43 older healthy individuals participated in a visual oddball study, in which they passively viewed frequent stimuli among randomly occurring rare stimuli while being recorded with a 32-channel EEG set. Two types of EEG datasets were created to train the classifiers, one consisting of amplitude and spectral features in time and another with extracted time-independent statistical ERP features. Among the nine classifiers tested, linear classifiers performed best. Furthermore, we show that classification performance differs between dataset types. When temporal features were used, maximum individuals' performance scores were higher, had lower variance, and were less affected overall by within-class differences such as age. Finally, we found that the effect of aging on classification performance depends on the classifier and its internal feature ranking. Accordingly, performance will differ if the model favors features with large within-class differences. With this in mind, care must be taken in feature extraction and selection to find the correct features and consequently avoid potential age-related performance degradation in practice.
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Affiliation(s)
- Nina Omejc
- Department of Knowledge Technologies, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
- Correspondence:
| | - Manca Peskar
- Institute for Kinesiology Research, Science and Research Centre Koper, 6000 Koper, Slovenia
- Biological Psychology and Neuroergonomics, Department of Psychology and Ergonomics, Faculty V: Mechanical Engineering and Transport Systems, Technische Universität Berlin, 10623 Berlin, Germany
| | - Aleksandar Miladinović
- Department of Ophthalmology, Institute for Maternal and Child Health-IRCCS Burlo Garofolo, 34137 Trieste, Italy
| | - Voyko Kavcic
- Institute of Gerontology, Wayne State University, Detroit, MI 48202, USA
- International Institute of Applied Gerontology, 1000 Ljubljana, Slovenia
| | - Sašo Džeroski
- Department of Knowledge Technologies, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
| | - Uros Marusic
- Institute for Kinesiology Research, Science and Research Centre Koper, 6000 Koper, Slovenia
- Department of Health Sciences, Alma Mater Europaea—ECM, 2000 Maribor, Slovenia
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Accardo A, Restivo L, Ajčević M, Miladinović A, Iscra K, Silveri G, Merlo M, Sinagra G. Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability. Med Biol Eng Comput 2022; 60:2655-2663. [PMID: 35809191 PMCID: PMC9365754 DOI: 10.1007/s11517-022-02618-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 06/15/2022] [Indexed: 11/29/2022]
Abstract
Diagnosis of etiology in early-stage ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) patients may be challenging. We aimed at investigating, by means of classification and regression tree (CART) modeling, the predictive power of heart rate variability (HRV) features together with clinical parameters to support the diagnosis in the early stage of IHD and DCM. The study included 263 IHD and 181 DCM patients, as well as 689 healthy subjects. A 24 h Holter monitoring was used and linear and non-linear HRV parameters were extracted considering both normal and ectopic beats (heart rate total variability signal). We used a CART algorithm to produce classification models based on HRV together with relevant clinical (age, sex, and left ventricular ejection fraction, LVEF) features. Among HRV parameters, MeanRR, SDNN, pNN50, LF, LF/HF, LFn, FD, Beta exp were selected by the CART algorithm and included in the produced models. The model based on pNN50, FD, sex, age, and LVEF features presented the highest accuracy (73.3%). The proposed approach based on HRV parameters, age, sex, and LVEF features highlighted the possibility to produce clinically interpretable models capable to differentiate IHD, DCM, and healthy subjects with accuracy which is clinically relevant in first steps of the IHD and DCM diagnostic process.
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Affiliation(s)
- Agostino Accardo
- Department Engineering and Architecture, University of Trieste, Trieste, Italy.
| | - Luca Restivo
- Cardiovascular Department, Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI) and University of Trieste, Trieste, Italy
| | - Miloš Ajčević
- Department Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Aleksandar Miladinović
- Department Engineering and Architecture, University of Trieste, Trieste, Italy
- Institute for Maternal and Child Health - IRCCS Burlo Garofolo, Trieste, Italy
| | - Katerina Iscra
- Department Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Giulia Silveri
- Department Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Marco Merlo
- Cardiovascular Department, Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI) and University of Trieste, Trieste, Italy
| | - Gianfranco Sinagra
- Cardiovascular Department, Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI) and University of Trieste, Trieste, Italy
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Dillen A, Lathouwers E, Miladinović A, Marusic U, Ghaffari F, Romain O, Meeusen R, De Pauw K. A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics. Front Hum Neurosci 2022; 16:949224. [PMID: 35966996 PMCID: PMC9364873 DOI: 10.3389/fnhum.2022.949224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups.
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Affiliation(s)
- Arnau Dillen
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotics Research Center, Vrije Universiteit Brussel, Brussels, Belgium
- Équipes Traitement de l'Information et Systèmes, CY Cergy Paris University, Cergy, France
| | - Elke Lathouwers
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotics Research Center, Vrije Universiteit Brussel, Brussels, Belgium
| | - Aleksandar Miladinović
- Institute for Kinesiology Research, Science and Research Centre Koper, Koper, Slovenia
- Institute for Maternal and Child Health - IRCCS Burlo Garofolo, Trieste, Italy
- Department Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Uros Marusic
- Institute for Kinesiology Research, Science and Research Centre Koper, Koper, Slovenia
- Department of Health Sciences, Alma Mater Europaea - ECM, Maribor, Slovenia
| | - Fakhreddine Ghaffari
- Équipes Traitement de l'Information et Systèmes, CY Cergy Paris University, Cergy, France
| | - Olivier Romain
- Équipes Traitement de l'Information et Systèmes, CY Cergy Paris University, Cergy, France
| | - Romain Meeusen
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotics Research Center, Vrije Universiteit Brussel, Brussels, Belgium
| | - Kevin De Pauw
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotics Research Center, Vrije Universiteit Brussel, Brussels, Belgium
- *Correspondence: Kevin De Pauw
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Ajčević M, Miladinović A, Furlanis G, Buoite Stella A, Naccarato M, Caruso P, Manganotti P, Accardo A. Wake-up Stroke Outcome Prediction by Interpretable Decision Tree Model. Stud Health Technol Inform 2022; 294:569-570. [PMID: 35612148 DOI: 10.3233/shti220527] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Outcome prediction in wake-up ischemic stroke (WUS) is important for guiding treatment strategies, in order to improve recovery and minimize disability. We aimed at producing an interpretable model to predict a good outcome (NIHSS 7-day<5) in thrombolysis treated WUS patients by using Classification and Regression Tree (CART) method. The study encompassed 104 WUS patients and we used a dataset consisting of demographic, clinical and neuroimaging features. The model was produced by CART with Gini split criterion and evaluated by using 5-fold cross-validation. The produced decision tree model was based on NIHSS at admission, ischemic core volume and age features. The predictive accuracy of model was 86.5% and the AUC-ROC was 0.88. In conclusion, in this preliminary study we identified interpretable model based on clinical and neuroimaging features to predict clinical outcome in thrombolysis treated wake-up stroke patients.
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Affiliation(s)
- Miloš Ajčević
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
| | | | - Giovanni Furlanis
- Department of Medicine, Surgery and Health Science, University of Trieste, Trieste, Italy
| | - Alex Buoite Stella
- Department of Medicine, Surgery and Health Science, University of Trieste, Trieste, Italy
| | - Marcello Naccarato
- Department of Medicine, Surgery and Health Science, University of Trieste, Trieste, Italy
| | - Paola Caruso
- Department of Medicine, Surgery and Health Science, University of Trieste, Trieste, Italy
| | - Paolo Manganotti
- Department of Medicine, Surgery and Health Science, University of Trieste, Trieste, Italy
| | - Agostino Accardo
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
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Ajčević M, Furlanis G, Miladinović A, Stella AB, Caruso P, Ukmar M, Cova M, Naccarato M, Accardo A, Manganotti P. EEG spectral parameters correlate with the extent of brain ischemia in hyper-acute ischemic stroke. J Neurol Sci 2021. [DOI: 10.1016/j.jns.2021.118722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Miladinović A, Ajčević M, Jarmolowska J, Marusic U, Colussi M, Silveri G, Battaglini PP, Accardo A. Effect of power feature covariance shift on BCI spatial-filtering techniques: A comparative study. Comput Methods Programs Biomed 2021; 198:105808. [PMID: 33157470 DOI: 10.1016/j.cmpb.2020.105808] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 10/12/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE The input data distributions of EEG-based BCI systems can change during intra-session transitions due to nonstationarity caused by features covariate shifts, thus compromising BCI performance. We aimed to identify the most robust spatial filtering approach, among most used methods, testing them on calibration dataset, and test dataset recorded 30 min afterwards. In addition, we also investigated if their performance improved after application of Stationary Subspace Analysis (SSA). METHODS We have recorded, in 17 healthy subjects, the calibration set at the beginning of the upper limb motor imagery BCI experiment and testing set separately 30 min afterwards. Both the calibration and test data were pre-processed and the BCI models were produced by using several spatial filtering approaches on the calibration set. Those models were subsequently evaluated on a test set. The differences between the accuracy estimated by cross-validation on the calibration dataset and the accuracy on the test dataset were investigated. The same procedure was performed with, and without SSA pre-processing step. RESULTS A significant reduction in accuracy on the test dataset was observed for CSP, SPoC and SpecRCSP approaches. For SLap and SpecCSP only a slight decreasing trend was observed, while FBCSP and FBCSPT largely maintained moderately high median accuracy >70%. In the case of application of SSA pre-processing, the differences between accuracy observed on calibration and test dataset were reduced. In addition, accuracy values both on calibration and test set were slightly higher in case of SSA pre-processing and also in this case FBCSP and FBCSPT presented slightly better performance compared to other methods. CONCLUSION The intrinsic signal nonstationarity characteristics, caused by covariance shifts of power features, reduced the accuracy of BCI model, therefore, suggesting that this evaluation framework should be considered for testing and simulating real life performance. FBCSP and FBSCPT approaches showed to be more robust to feature covariance shift. SSA can improve the models performance and reduce accuracy decline from calibration to test set.
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Affiliation(s)
- Aleksandar Miladinović
- Department of Engineering and Architecture, University of Trieste, Via Alfonso Valerio 10, 34127, Trieste, Italy.
| | - Miloš Ajčević
- Department of Engineering and Architecture, University of Trieste, Via Alfonso Valerio 10, 34127, Trieste, Italy
| | - Joanna Jarmolowska
- Department of Life Sciences, B.R.A.I.N. Center for Neuroscience, University of Trieste, Via Alexander Fleming 22, 34127 Trieste, Italy
| | - Uros Marusic
- Science and Research Centre Koper, Institute for Kinesiology Research, Garibaldijeva 1, 6000, Koper, Slovenia; Department of Health Sciences, Alma Mater Europaea - ECM, Slovenska ulica 17, 2000, Maribor, Slovenia
| | - Marco Colussi
- Department of Life Sciences, B.R.A.I.N. Center for Neuroscience, University of Trieste, Via Alexander Fleming 22, 34127 Trieste, Italy
| | - Giulia Silveri
- Department of Engineering and Architecture, University of Trieste, Via Alfonso Valerio 10, 34127, Trieste, Italy
| | - Piero Paolo Battaglini
- Department of Life Sciences, B.R.A.I.N. Center for Neuroscience, University of Trieste, Via Alexander Fleming 22, 34127 Trieste, Italy
| | - Agostino Accardo
- Department of Engineering and Architecture, University of Trieste, Via Alfonso Valerio 10, 34127, Trieste, Italy
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Simões M, Borra D, Santamaría-Vázquez E, Bittencourt-Villalpando M, Krzemiński D, Miladinović A, Schmid T, Zhao H, Amaral C, Direito B, Henriques J, Carvalho P, Castelo-Branco M. BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces. Front Neurosci 2020; 14:568104. [PMID: 33100959 PMCID: PMC7556208 DOI: 10.3389/fnins.2020.568104] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 08/24/2020] [Indexed: 01/13/2023] Open
Abstract
There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available datasets are usually limited by small number of participants with few BCI sessions. In this sense, the lack of large, comprehensive datasets with various individuals and multiple sessions has limited advances in the development of more effective data processing and analysis methods for BCI systems. This is particularly evident to explore the feasibility of deep learning methods that require large datasets. Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorder individuals undergoing 7 sessions of P300-based BCI joint-attention training, for a total of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challenge organized during MEDICON 2019 where, in two phases, teams from all over the world tried to achieve the best possible object-detection accuracy based on the P300 signals. This paper presents the characteristics of the dataset and the approaches followed by the 9 finalist teams during the competition. The winner obtained an average accuracy of 92.3% with a convolutional neural network based on EEGNet. The dataset is now publicly released and stands as a benchmark for future P300-based BCI algorithms based on multiple session data.
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Affiliation(s)
- Marco Simões
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal.,Centre for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Davide Borra
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Cesena, Italy
| | - Eduardo Santamaría-Vázquez
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red, Biomateriales y Nanomedicina, Madrid, Spain
| | | | | | - Dominik Krzemiński
- CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | | | | | - Thomas Schmid
- Machine Learning Group, Universität Leipzig, Leipzig, Germany
| | - Haifeng Zhao
- The University of Sydney, Camperdown, NSW, Australia
| | - Carlos Amaral
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Bruno Direito
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Jorge Henriques
- Centre for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Paulo Carvalho
- Centre for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
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Ajčević M, Furlanis G, Stella AB, Cillotto T, Caruso P, Ridolfi M, Lugnan C, Miladinović A, Ukmar M, Cova MA, Accardo A, Manganotti P, Naccarato M. A CT perfusion based model predicts outcome in wake-up stroke patients treated with recombinant tissue plasminogen activator. Physiol Meas 2020; 41:075011. [PMID: 32531770 DOI: 10.1088/1361-6579/ab9c70] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
OBJECTIVE Advanced neuroimaging has proved to be pivotal in the management of acute ischemic stroke. The use of CT perfusion (CTP) core and penumbra parameters to predict the outcome in wake-up stroke (WUS) patients in everyday clinical scenarios has not yet been investigated. The aim of our study was to investigate the predictive power of CTP parameters on functional and morphological outcomes in WUS patients treated with recombinant tissue plasminogen activator (rTPA). APPROACH We analyzed clinical data and processed CTP images of 83 consecutive WUS patients treated with rTPA. The predictive power of whole-brain CTP features and of the clinical stroke-related parameters to predict the National Institutes of Health Stroke Scale (NIHSS) score at the seventh day and ischemic lesion volume outcome was investigated by means of multivariate regression analysis as well as least absolute shrinkage and selection operator (LASSO) modeling. MAIN RESULTS Multivariate analysis showed that CTP core volume (β = 0.403, p = 0.000), NIHSS at admission (β = 0.323, p = 0.005) and Alberta Stroke Program Early CT (ASPECT) score (β = -0.224, p = 0.012) predict NIHSS at 7 days, while total hypoperfused volume (β = 0.542, p = 0.000) and core volume on CTP (β =0.441, p = 0.000) predict infarct lesion volume at follow-up CT. The LASSO modeling approach confirmed the significant predictive power of CTP core volume, total hypoperfused CTP volume, NIHSS at baseline and ASPECT score, producing a sparse model with adequate reliability (the root mean square error on a previously unseen testing dataset was 3.68). SIGNIFICANCE Our findings highlight the importance of CT multimodal imaging features for decision-making and prediction in the hyperacute phase of WUS. The predictive model supports the hypothesis that an irreversible necrotic core rather than the extent of the penumbra is the main prognostic factor in WUS patients treated with rTPA.
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Affiliation(s)
- Miloš Ajčević
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, Strada di Fiume, 447, 34149, Trieste, Italy. Department of Engineering and Architecture, University of Trieste, Via A. Valerio, 10, 34127, Trieste, Italy
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Miladinović A, Hajdarević B. [Endobronchial foreign bodies as a cause of bronchial carcinoma]. Plucne Bolesti Tuberk 1971; 23:344-6. [PMID: 5152405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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