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Guisande N, Montani F. Rényi entropy-complexity causality space: a novel neurocomputational tool for detecting scale-free features in EEG/iEEG data. Front Comput Neurosci 2024; 18:1342985. [PMID: 39081659 PMCID: PMC11287776 DOI: 10.3389/fncom.2024.1342985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 06/21/2024] [Indexed: 08/02/2024] Open
Abstract
Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforward means of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed the most variation across different modes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders.
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Affiliation(s)
| | - Fernando Montani
- Instituto de Física de La Plata (IFLP), Consejo Nacional de Investigaciones Científicas y Técnicas – Universidad Nacional de La Plata (CONICET-UNLP), La Plata, Buenos Aires, Argentina
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2
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Yi L, Xie G, Li Z, Li X, Zhang Y, Wu K, Shao G, Lv B, Jing H, Zhang C, Liang W, Sun J, Hao Z, Liang J. Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine. Front Neurosci 2023; 17:1205931. [PMID: 37694121 PMCID: PMC10483285 DOI: 10.3389/fnins.2023.1205931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
Depression is a common mental disorder that seriously affects patients' social function and daily life. Its accurate diagnosis remains a big challenge in depression treatment. In this study, we used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) and measured the whole brain EEG signals and forehead hemodynamic signals from 25 depression patients and 30 healthy subjects during the resting state. On one hand, we explored the EEG brain functional network properties, and found that the clustering coefficient and local efficiency of the delta and theta bands in patients were significantly higher than those in normal subjects. On the other hand, we extracted brain network properties, asymmetry, and brain oxygen entropy as alternative features, used a data-driven automated method to select features, and established a support vector machine model for automatic depression classification. The results showed the classification accuracy was 81.8% when using EEG features alone and increased to 92.7% when using hybrid EEG and fNIRS features. The brain network local efficiency in the delta band, hemispheric asymmetry in the theta band and brain oxygen sample entropy features differed significantly between the two groups (p < 0.05) and showed high depression distinguishing ability indicating that they may be effective biological markers for identifying depression. EEG, fNIRS and machine learning constitute an effective method for classifying depression at the individual level.
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Affiliation(s)
- Li Yi
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Guojun Xie
- Department of Psychiatry, The Third People’s Hospital of Foshan, Foshan, China
- Department of Psychiatry, The Third Affiliated Hospital of Foshan University, Foshan, China
| | - Zhihao Li
- School of Medicine, Foshan University, Foshan, China
| | - Xiaoling Li
- Department of Psychiatry, The Third People’s Hospital of Foshan, Foshan, China
- Department of Psychiatry, The Third Affiliated Hospital of Foshan University, Foshan, China
| | - Yizheng Zhang
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
| | - Guangjian Shao
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Biliang Lv
- School of Medicine, Foshan University, Foshan, China
| | - Huan Jing
- Department of Psychiatry, The Third People’s Hospital of Foshan, Foshan, China
- Department of Psychiatry, The Third Affiliated Hospital of Foshan University, Foshan, China
| | - Chunguo Zhang
- Department of Psychiatry, The Third People’s Hospital of Foshan, Foshan, China
- Department of Psychiatry, The Third Affiliated Hospital of Foshan University, Foshan, China
| | - Wenting Liang
- Department of Psychiatry, The Third People’s Hospital of Foshan, Foshan, China
- Department of Psychiatry, The Third Affiliated Hospital of Foshan University, Foshan, China
| | - Jinyan Sun
- School of Medicine, Foshan University, Foshan, China
| | - Zhifeng Hao
- College of Science, Shantou University, Shantou, China
| | - Jiaquan Liang
- Department of Psychiatry, The Third People’s Hospital of Foshan, Foshan, China
- Department of Psychiatry, The Third Affiliated Hospital of Foshan University, Foshan, China
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Lemoine É, Toffa D, Pelletier-Mc Duff G, Xu AQ, Jemel M, Tessier JD, Lesage F, Nguyen DK, Bou Assi E. Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography. Sci Rep 2023; 13:12650. [PMID: 37542101 PMCID: PMC10403587 DOI: 10.1038/s41598-023-39799-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/31/2023] [Indexed: 08/06/2023] Open
Abstract
Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy. Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure recurrence risk. However, EEG interpretation relies on the visual identification of interictal epileptiform discharges (IEDs) by neurologists, with limited sensitivity. Automated processing of EEG could increase its diagnostic yield and accessibility. The main objective was to develop a prediction model based on automated EEG processing to predict one-year seizure recurrence in patients undergoing routine EEG. We retrospectively selected a consecutive cohort of 517 patients undergoing routine EEG at our institution (training set) and a separate, temporally shifted cohort of 261 patients (testing set). We developed an automated processing pipeline to extract linear and non-linear features from the EEGs. We trained machine learning algorithms on multichannel EEG segments to predict one-year seizure recurrence. We evaluated the impact of IEDs and clinical confounders on performances and validated the performances on the testing set. The receiver operating characteristic area-under-the-curve for seizure recurrence after EEG in the testing set was 0.63 (95% CI 0.55-0.71). Predictions were still significantly above chance in EEGs with no IEDs. Our findings suggest that there are changes other than IEDs in the EEG signal embodying seizure propensity.
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Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Denahin Toffa
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Geneviève Pelletier-Mc Duff
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - An Qi Xu
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Mezen Jemel
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Jean-Daniel Tessier
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Frédéric Lesage
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Qc, Canada
- Centre de Recherche de l'institut de Cardiologie de Montréal, Montréal, Qc, Canada
| | - Dang K Nguyen
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Elie Bou Assi
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada.
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada.
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Ellis CA, Sendi MSE, Zhang R, Carbajal DA, Wang MD, Miller RL, Calhoun VD. Novel methods for elucidating modality importance in multimodal electrophysiology classifiers. Front Neuroinform 2023; 17:1123376. [PMID: 37006636 PMCID: PMC10050434 DOI: 10.3389/fninf.2023.1123376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/01/2023] [Indexed: 03/17/2023] Open
Abstract
IntroductionMultimodal classification is increasingly common in electrophysiology studies. Many studies use deep learning classifiers with raw time-series data, which makes explainability difficult, and has resulted in relatively few studies applying explainability methods. This is concerning because explainability is vital to the development and implementation of clinical classifiers. As such, new multimodal explainability methods are needed.MethodsIn this study, we train a convolutional neural network for automated sleep stage classification with electroencephalogram (EEG), electrooculogram, and electromyogram data. We then present a global explainability approach that is uniquely adapted for electrophysiology analysis and compare it to an existing approach. We present the first two local multimodal explainability approaches. We look for subject-level differences in the local explanations that are obscured by global methods and look for relationships between the explanations and clinical and demographic variables in a novel analysis.ResultsWe find a high level of agreement between methods. We find that EEG is globally the most important modality for most sleep stages and that subject-level differences in importance arise in local explanations that are not captured in global explanations. We further show that sex, followed by medication and age, had significant effects upon the patterns learned by the classifier.DiscussionOur novel methods enhance explainability for the growing field of multimodal electrophysiology classification, provide avenues for the advancement of personalized medicine, yield unique insights into the effects of demographic and clinical variables upon classifiers, and help pave the way for the implementation of multimodal electrophysiology clinical classifiers.
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Affiliation(s)
- Charles A. Ellis
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- *Correspondence: Charles A. Ellis,
| | - Mohammad S. E. Sendi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- McLean Hospital and Harvard Medical School, Boston, MA, United States
| | - Rongen Zhang
- Hankamer School of Business, Baylor University, Waco, TX, United States
| | - Darwin A. Carbajal
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - May D. Wang
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Robyn L. Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Vince D. Calhoun
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
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Joyce DW, Kormilitzin A, Smith KA, Cipriani A. Explainable artificial intelligence for mental health through transparency and interpretability for understandability. NPJ Digit Med 2023; 6:6. [PMID: 36653524 PMCID: PMC9849399 DOI: 10.1038/s41746-023-00751-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
The literature on artificial intelligence (AI) or machine learning (ML) in mental health and psychiatry lacks consensus on what "explainability" means. In the more general XAI (eXplainable AI) literature, there has been some convergence on explainability meaning model-agnostic techniques that augment a complex model (with internal mechanics intractable for human understanding) with a simpler model argued to deliver results that humans can comprehend. Given the differing usage and intended meaning of the term "explainability" in AI and ML, we propose instead to approximate model/algorithm explainability by understandability defined as a function of transparency and interpretability. These concepts are easier to articulate, to "ground" in our understanding of how algorithms and models operate and are used more consistently in the literature. We describe the TIFU (Transparency and Interpretability For Understandability) framework and examine how this applies to the landscape of AI/ML in mental health research. We argue that the need for understandablity is heightened in psychiatry because data describing the syndromes, outcomes, disorders and signs/symptoms possess probabilistic relationships to each other-as do the tentative aetiologies and multifactorial social- and psychological-determinants of disorders. If we develop and deploy AI/ML models, ensuring human understandability of the inputs, processes and outputs of these models is essential to develop trustworthy systems fit for deployment.
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Affiliation(s)
- Dan W. Joyce
- grid.416938.10000 0004 0641 5119University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK ,grid.10025.360000 0004 1936 8470Institute of Population Health, Department of Primary Care and Mental Health, University of Liverpool, Liverpool, L69 3GF UK
| | - Andrey Kormilitzin
- grid.416938.10000 0004 0641 5119University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK
| | - Katharine A. Smith
- grid.416938.10000 0004 0641 5119University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK ,grid.416938.10000 0004 0641 5119Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Warneford Hospital, Oxford, OX3 7JX UK ,grid.416938.10000 0004 0641 5119Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, OX3 7JX UK
| | - Andrea Cipriani
- grid.416938.10000 0004 0641 5119University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK ,grid.416938.10000 0004 0641 5119Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Warneford Hospital, Oxford, OX3 7JX UK ,grid.416938.10000 0004 0641 5119Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, OX3 7JX UK
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Khayretdinova M, Shovkun A, Degtyarev V, Kiryasov A, Pshonkovskaya P, Zakharov I. Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset. Front Aging Neurosci 2022; 14:1019869. [PMID: 36561135 PMCID: PMC9764861 DOI: 10.3389/fnagi.2022.1019869] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 11/02/2022] [Indexed: 12/12/2022] Open
Abstract
Introduction Brain age prediction has been shown to be clinically relevant, with errors in its prediction associated with various psychiatric and neurological conditions. While the prediction from structural and functional magnetic resonance imaging data has been feasible with high accuracy, whether the same results can be achieved with electroencephalography is unclear. Methods The current study aimed to create a new deep learning solution for brain age prediction using raw resting-state scalp EEG. To this end, we utilized the TD-BRAIN dataset, including 1,274 subjects (both healthy controls and individuals with various psychiatric disorders, with a total of 1,335 recording sessions). To achieve the best age prediction, we used data augmentation techniques to increase the diversity of the training set and developed a deep convolutional neural network model. Results The model's training was done with 10-fold cross-subject cross-validation, with the EEG recordings of the subjects used for training not considered to test the model. In training, using the relative rather than the absolute loss function led to a better mean absolute error of 5.96 years in cross-validation. We found that the best performance could be achieved when both eyes-open and eyes-closed states are used simultaneously. The frontocentral electrodes played the most important role in age prediction. Discussion The architecture and training method of the proposed deep convolutional neural networks (DCNN) improve state-of-the-art metrics in the age prediction task using raw resting-state EEG data by 13%. Given that brain age prediction might be a potential biomarker of numerous brain diseases, inexpensive and precise EEG-based estimation of brain age will be in demand for clinical practice.
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Höller Y, Jónsdóttir ST, Hannesdóttir AH, Ólafsson RP. EEG-responses to mood induction interact with seasonality and age. Front Psychiatry 2022; 13:950328. [PMID: 36016970 PMCID: PMC9396338 DOI: 10.3389/fpsyt.2022.950328] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
The EEG is suggested as a potential diagnostic and prognostic biomarker for seasonal affective disorder (SAD). As a pre-clinical form of SAD, seasonality is operationalized as seasonal variation in mood, appetite, weight, sleep, energy, and socializing. Importantly, both EEG biomarkers and seasonality interact with age. Inducing sad mood to assess cognitive vulnerability was suggested to improve the predictive value of summer assessments for winter depression. However, no EEG studies have been conducted on induced sad mood in relation to seasonality, and no studies so far have controlled for age. We recorded EEG and calculated bandpower in 114 participants during rest and during induced sad mood in summer. Participants were grouped by age and based on a seasonality score as obtained with the seasonal pattern assessment questionnaire (SPAQ). Participants with high seasonality scores showed significantly larger changes in EEG power from rest to sad mood induction, specifically in the alpha frequency range (p = 0.027), compared to participants with low seasonality scores. Furthermore, seasonality interacted significantly with age (p < 0.001), with lower activity in individuals with high seasonality scores that were older than 50 years but the opposite pattern in individuals up to 50 years. Effects of sad mood induction on brain activity are related to seasonality and can therefore be consider as potential predicting biomarkers for SAD. Future studies should control for age as a confounding factor, and more studies are needed to elaborate on the characteristics of EEG biomarkers in participants above 50 years.
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Affiliation(s)
- Yvonne Höller
- Faculty of Psychology, University of Akureyri, Akureyri, Iceland
| | - Sara Teresa Jónsdóttir
- Faculty of Psychology, University of Akureyri, Akureyri, Iceland.,Faculty of Psychology, University of Iceland, Reykjavík, Iceland
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Babiloni C, Noce G, Ferri R, Lizio R, Lopez S, Lorenzo I, Tucci F, Soricelli A, Zurrón M, Díaz F, Nobili F, Arnaldi D, Famà F, Buttinelli C, Giubilei F, Cipollini V, Marizzoni M, Güntekin B, Yıldırım E, Hanoğlu L, Yener G, Gündüz DH, Onorati P, Stocchi F, Vacca L, Maestú F, Frisoni GB, Del Percio C. Resting State Alpha Electroencephalographic Rhythms Are Affected by Sex in Cognitively Unimpaired Seniors and Patients with Alzheimer's Disease and Amnesic Mild Cognitive Impairment: A Retrospective and Exploratory Study. Cereb Cortex 2021; 32:2197-2215. [PMID: 34613369 DOI: 10.1093/cercor/bhab348] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 07/07/2021] [Accepted: 08/21/2021] [Indexed: 11/14/2022] Open
Abstract
In the present retrospective and exploratory study, we tested the hypothesis that sex may affect cortical sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms recorded in normal elderly (Nold) seniors and patients with Alzheimer's disease and mild cognitive impairment (ADMCI). Datasets in 69 ADMCI and 57 Nold individuals were taken from an international archive. The rsEEG rhythms were investigated at individual delta, theta, and alpha frequency bands and fixed beta (14-30 Hz) and gamma (30-40 Hz) bands. Each group was stratified into matched females and males. The sex factor affected the magnitude of rsEEG source activities in the Nold seniors. Compared with the males, the females were characterized by greater alpha source activities in all cortical regions. Similarly, the parietal, temporal, and occipital alpha source activities were greater in the ADMCI-females than the males. Notably, the present sex effects did not depend on core genetic (APOE4), neuropathological (Aβ42/phospho-tau ratio in the cerebrospinal fluid), structural neurodegenerative and cerebrovascular (MRI) variables characterizing sporadic AD-related processes in ADMCI seniors. These results suggest the sex factor may significantly affect neurophysiological brain neural oscillatory synchronization mechanisms underpinning the generation of dominant rsEEG alpha rhythms to regulate cortical arousal during quiet vigilance.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
- San Raffaele of Cassino, Cassino (FR), Italy
| | | | | | | | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | | | - Federico Tucci
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Andrea Soricelli
- IRCCS SDN, Napoli, Italy
- Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Montserrat Zurrón
- Departamento de Psicología Experimental, Facultad de Psicología, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Fernando Díaz
- Departamento de Psicología Experimental, Facultad de Psicología, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Flavio Nobili
- Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Italy
| | - Dario Arnaldi
- Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Italy
| | - Francesco Famà
- Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Virginia Cipollini
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
- REMER, Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab., Istanbul Medipol University, Istanbul, Turkey
| | - Ebru Yıldırım
- Istanbul Medipol University, Vocational School, Program of Electroneurophysiology, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Görsev Yener
- Izmir School of Economics, Faculty of Medicine, Izmir, Turkey
| | - Duygu Hünerli Gündüz
- Health Sciences Institute, Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Paolo Onorati
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | | | | | - Fernando Maestú
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad Complutense de Madrid, Madrid, Spain
| | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
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