1
|
Hernandez H, Baez S, Medel V, Moguilner S, Cuadros J, Santamaria-Garcia H, Tagliazucchi E, Valdes-Sosa PA, Lopera F, OchoaGómez JF, González-Hernández A, Bonilla-Santos J, Gonzalez-Montealegre RA, Aktürk T, Yıldırım E, Anghinah R, Legaz A, Fittipaldi S, Yener GG, Escudero J, Babiloni C, Lopez S, Whelan R, Lucas AAF, García AM, Huepe D, Caterina GD, Soto-Añari M, Birba A, Sainz-Ballesteros A, Coronel C, Herrera E, Abasolo D, Kilborn K, Rubido N, Clark R, Herzog R, Yerlikaya D, Güntekin B, Parra MA, Prado P, Ibanez A. Brain health in diverse settings: How age, demographics and cognition shape brain function. Neuroimage 2024; 295:120636. [PMID: 38777219 DOI: 10.1016/j.neuroimage.2024.120636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/17/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024] Open
Abstract
Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.
Collapse
Affiliation(s)
- Hernan Hernandez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sandra Baez
- Universidad de los Andes, Bogota, Colombia; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland
| | - Vicente Medel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Harvard Medical School, Boston, MA, USA
| | - Jhosmary Cuadros
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile; Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal 5001, Venezuela
| | - Hernando Santamaria-Garcia
- Pontificia Universidad Javeriana (PhD Program in Neuroscience) Bogotá, San Ignacio, Colombia; Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio Bogotá, San Ignacio, Colombia
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; University of Buenos Aires, Argentina
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences Technology of China, Chengdu, China; Cuban Neuroscience Center, La Habana, Cuba
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, University of Antioquia, Medellín, Colombia
| | | | | | | | | | - Tuba Aktürk
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Ebru Yıldırım
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Renato Anghinah
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil; Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center, University of Sao Paulo, Sao Paulo, Brazil
| | - Agustina Legaz
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Facultad de Psicología, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Görsev G Yener
- Faculty of Medicine, Izmir University of Economics, 35330, Izmir, Turkey; Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, Izmir, Turkey; Izmir Biomedicine and Genome Center, Izmir, Turkey
| | - Javier Escudero
- School of Engineering, Institute for Imaging, Data and Communications, University of Edinburgh, Scotland, UK
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino, (FR), Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy
| | - Robert Whelan
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Alberto A Fernández Lucas
- Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center, Universidad de San Andréss, Buenos Aires, Argentina; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - David Huepe
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez
| | - Gaetano Di Caterina
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK
| | | | - Agustina Birba
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | | | - Carlos Coronel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
| | - Eduar Herrera
- Departamento de Estudios Psicológicos, Universidad ICESI, Cali, Colombia
| | - Daniel Abasolo
- Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
| | - Kerry Kilborn
- School of Psychology, University of Glasgow, Glasgow, Scotland, UK
| | - Nicolás Rubido
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, AB24 3UE, UK
| | - Ruaridh Clark
- Centre for Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, UK
| | - Ruben Herzog
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris 75013, France
| | - Deniz Yerlikaya
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Bahar Güntekin
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey; Department of Biophysics, School of Medicine, Istanbul Medipol University, Turkey
| | - Mario A Parra
- Department of Psychological Sciences and Health, University of Strathclyde, United Kingdom and Associate Researcher of the Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Agustin Ibanez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA; Cognitive Neuroscience Center, Universidad de San Andrés and Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina; Trinity College Dublin, The University of Dublin, Dublin, Ireland.
| |
Collapse
|
2
|
Zauli FM, Del Vecchio M, Pigorini A, Russo S, Massimini M, Sartori I, Cardinale F, d'Orio P, Mikulan E. Localizing hidden Interictal Epileptiform Discharges with simultaneous intracerebral and scalp high-density EEG recordings. J Neurosci Methods 2024; 409:110193. [PMID: 38871302 DOI: 10.1016/j.jneumeth.2024.110193] [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: 12/31/2023] [Revised: 05/02/2024] [Accepted: 06/08/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND Scalp EEG is one of the main tools in the clinical evaluation of epilepsy. In some cases intracranial Interictal Epileptiform Discharges (IEDs) are not visible from the scalp. Recent studies have shown the feasibility of revealing them in the EEG if their timings are extracted from simultaneous intracranial recordings, but their potential for the localization of the epileptogenic zone is not yet well defined. NEW METHOD We recorded simultaneous high-density EEG (HD-EEG) and stereo-electroencephalography (SEEG) during interictal periods in 8 patients affected by drug-resistant focal epilepsy. We identified IEDs in the SEEG and systematically analyzed the time-locked signals on the EEG by means of evoked potentials, topographical analysis and Electrical Source Imaging (ESI). The dataset has been standardized and is being publicly shared. RESULTS Our results showed that IEDs that were not clearly visible at single-trials could be uncovered by averaging, in line with previous reports. They also showed that their topographical voltage distributions matched the position of the SEEG electrode where IEDs had been identified, and that ESI techniques can reconstruct it with an accuracy of ∼2 cm. Finally, the present dataset provides a reference to test the accuracy of different methods and parameters. COMPARISON WITH EXISTING METHODS Our study is the first to systematically compare ESI methods on simultaneously recorded IEDs, and to share a public resource with in-vivo data for their evaluation. CONCLUSIONS Simultaneous HD-EEG and SEEG recordings can unveil hidden IEDs whose origins can be reconstructed using topographical and ESI analyses, but results depend on the selected methods and parameters.
Collapse
Affiliation(s)
- Flavia Maria Zauli
- Department of Philosophy "P. Martinetti", Università degli Studi di Milano, Milan, Italy; Department of Biomedical and Clinical Sciences "L. Sacco", Università degli Studi di Milano, Milan, Italy; ASST GOM Niguarda, Piazza dell'Ospedale Maggiore 3, Milan, Italy
| | - Maria Del Vecchio
- Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Parma, Italy
| | - Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan, Italy; UOC Maxillo-facial Surgery and dentistry, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Simone Russo
- Department of Biomedical and Clinical Sciences "L. Sacco", Università degli Studi di Milano, Milan, Italy; Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences "L. Sacco", Università degli Studi di Milano, Milan, Italy; Istituto Di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Ivana Sartori
- ASST GOM Niguarda, Piazza dell'Ospedale Maggiore 3, Milan, Italy
| | - Francesco Cardinale
- ASST GOM Niguarda, Piazza dell'Ospedale Maggiore 3, Milan, Italy; Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Parma, Italy; Department of Medicine and Surgery, Unit of Neuroscience, Università degli Studi di Parma, Parma, Italy
| | - Piergiorgio d'Orio
- ASST GOM Niguarda, Piazza dell'Ospedale Maggiore 3, Milan, Italy; Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Parma, Italy; Department of Medicine and Surgery, Unit of Neuroscience, Università degli Studi di Parma, Parma, Italy
| | - Ezequiel Mikulan
- Department of Health Sciences, Università degli Studi di Milano, Milan, Italy.
| |
Collapse
|
3
|
Conti M, Bovenzi R, Palmieri MG, Placidi F, Stefani A, Mercuri NB, Albanese M. Early effect of onabotulinumtoxinA on EEG-based functional connectivity in patients with chronic migraine: A pilot study. Headache 2024. [PMID: 38837259 DOI: 10.1111/head.14750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/27/2024] [Accepted: 04/03/2024] [Indexed: 06/07/2024]
Abstract
OBJECTIVE In this pilot prospective cohort study, we aimed to evaluate, using high-density electroencephalography (HD-EEG), the longitudinal changes in functional connectivity (FC) in patients with chronic migraine (CM) treated with onabotulinumtoxinA (OBTA). BACKGROUND OBTA is a treatment for CM. Several studies have shown the modulatory action of OBTA on the central nervous system; however, research on migraine is limited. METHODS This study was conducted at the Neurology Unit of "Policlinico Tor Vergata," Rome, Italy, and included 12 adult patients with CM treated with OBTA and 15 healthy controls (HC). Patients underwent clinical scales at enrollment (T0) and 3 months (T1) from the start of treatment. HD-EEG was recorded using a 64-channel system in patients with CM at T0 and T1. A source reconstruction method was used to identify brain activity. FC in δ-θ-α-β-low-γ bands was analyzed using the weighted phase-lag index. FC changes between HCs and CM at T0 and T1 were assessed using cross-validation methods to estimate the results' reliability. RESULTS Compared to HCs at T0, patients with CM showed hyperconnected networks in δ (p = 0.046, area under the receiver operating characteristic curve [AUC: 0.76-0.98], Cohen's κ [0.65-0.93]) and β (p = 0.031, AUC [0.68-0.95], Cohen's κ [0.51-0.84]), mainly involving orbitofrontal, occipital, temporal pole and orbitofrontal, superior temporal, occipital, cingulate areas, and hypoconnected networks in α band (p = 0.029, AUC [0.80-0.99], Cohen's κ [0.42-0.77]), predominantly involving cingulate, temporal pole, and precuneus. Patients with CM at T1, compared to T0, showed hypoconnected networks in δ band (p = 0.032, AUC [0.73-0.99], Cohen's κ [0.53-0.90]) and hyperconnected networks in α band (p = 0.048, AUC [0.58-0.93], Cohen's κ [0.37-0.78]), involving the sensorimotor, orbitofrontal, cingulate, and temporal cortex. CONCLUSION These preliminary results showed that patients with CM presented disrupted EEG-FC compared to controls restored by a single session of OBTA treatment, suggesting a primary central modulatory action of OBTA.
Collapse
Affiliation(s)
- Matteo Conti
- Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Roberta Bovenzi
- Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | | | - Fabio Placidi
- Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Alessandro Stefani
- Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | | | - Maria Albanese
- Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
- Neurology Unit, Regional Referral Headache Center, University of Rome "Tor Vergata", Rome, Italy
| |
Collapse
|
4
|
Badalì C, Wollseiffen P, Schneider S. Shades of gravity - effects of planetary gravity levels on electrocortical activity and neurocognitive performance. Brain Struct Funct 2024; 229:1265-1277. [PMID: 38700553 DOI: 10.1007/s00429-024-02803-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/22/2024] [Indexed: 06/05/2024]
Abstract
The plans of international space agencies to return to the Moon and explore deep space, including Mars, highlight the challenges of human adaptation and stress the need for a thorough analysis of the factors that facilitate, limit and modify human performance under extreme environments. This study investigates the influence of partial gravity on behavioural (error rate and reaction time) and neuronal parameters (event-related potentials) through parabolic flights. Brain cortical activity was assessed using EEG from 18 participants who solved a neurocognitive task, consisting of a mental arithmetic task and an auditory oddball paradigm, during Earth (1G), Lunar (0.16G + 0.25G) and Martian gravity (0.38G + 0.5G) for 15 consecutive parabolas. Data shows higher electrocortical activity in Earth gravity compared to Lunar and Martian gravity in the parietal lobe. No differences in participants' performance were found among the gravity levels. Event-related potentials displayed gravity-dependent variations, though limited stimuli recording suggests caution in interpretation. Data suggests a threshold between Earth and Martian gravity within the different gravities responsible for physiological changes, but it seems to vary greatly between individuals. The altered neuronal communication could be explained with a model developed by Kohn and Ritzmann in 2018. The increasing intracranial pressure in weightlessness changes the properties of the cell membrane of neurons and leads to a depolarisation of the resting membrane potential. The findings underscore the individuality of physiological changes in response to gravity alterations, signalling the need for further investigations in future studies.
Collapse
Affiliation(s)
- Constance Badalì
- Institute of Movement and Neurosciences, Department of Exercise Neuroscience, German Sport University Cologne, Am Sportpark Müngersdorf 6, Cologne, D-50933, Germany.
| | - Petra Wollseiffen
- Institute of Movement and Neurosciences, Department of Exercise Neuroscience, German Sport University Cologne, Am Sportpark Müngersdorf 6, Cologne, D-50933, Germany
- Centre for Health and Integrative Physiology in Space (CHIPS), German Sport University, Cologne, Germany
| | - Stefan Schneider
- Institute of Movement and Neurosciences, Department of Exercise Neuroscience, German Sport University Cologne, Am Sportpark Müngersdorf 6, Cologne, D-50933, Germany
- Centre for Health and Integrative Physiology in Space (CHIPS), German Sport University, Cologne, Germany
| |
Collapse
|
5
|
Mou X, He C, Tan L, Yu J, Liang H, Zhang J, Tian Y, Yang YF, Xu T, Wang Q, Cao M, Chen Z, Hu CP, Wang X, Liu Q, Wu H. ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding. Sci Data 2024; 11:550. [PMID: 38811613 PMCID: PMC11137001 DOI: 10.1038/s41597-024-03398-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 05/21/2024] [Indexed: 05/31/2024] Open
Abstract
An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 13 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model. As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.
Collapse
Affiliation(s)
- Xinyu Mou
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Cuilin He
- Centre for Cognitive and Brain Sciences, Department of Psychology, Faculty of Social Sciences, University of Macau, Taipa, Macau SAR, China
| | - Liwei Tan
- Centre for Cognitive and Brain Sciences, Department of Psychology, Faculty of Social Sciences, University of Macau, Taipa, Macau SAR, China
| | - Junjie Yu
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Huadong Liang
- AI Research Institute, iFLYTEK Co., LTD, Hefei, China
| | - Jianyu Zhang
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yan Tian
- Centre for Cognitive and Brain Sciences, Department of Psychology, Faculty of Social Sciences, University of Macau, Taipa, Macau SAR, China
| | - Yu-Fang Yang
- Division of Experimental Psychology and Neuropsychology, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Ting Xu
- Center for the Integrative Developmental Neuroscience, Child Mind Institute, New York, NY, USA
| | - Qing Wang
- Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University, 600 S. Wanping Rd., Shanghai, 200030, China
| | - Miao Cao
- Australian National Imaging Facility and Swinburne Neuroimaging Facility, Swinburne University of Technology, Victoria, Australia
| | - Zijiao Chen
- Centre for Cognitive and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Kent Ridge, Singapore
| | - Chuan-Peng Hu
- School of Psychology, Nanjing Normal University, Nanjing, China
| | - Xindi Wang
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Quanying Liu
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences, Department of Psychology, Faculty of Social Sciences, University of Macau, Taipa, Macau SAR, China.
| |
Collapse
|
6
|
Deroche MLD, Wolfe J, Neumann S, Manning J, Hanna L, Towler W, Wilson C, Bien AG, Miller S, Schafer E, Gemignani J, Alemi R, Muthuraman M, Koirala N, Gracco VL. Cross-modal plasticity in children with cochlear implant: converging evidence from EEG and functional near-infrared spectroscopy. Brain Commun 2024; 6:fcae175. [PMID: 38846536 PMCID: PMC11154148 DOI: 10.1093/braincomms/fcae175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 04/02/2024] [Accepted: 05/17/2024] [Indexed: 06/09/2024] Open
Abstract
Over the first years of life, the brain undergoes substantial organization in response to environmental stimulation. In a silent world, it may promote vision by (i) recruiting resources from the auditory cortex and (ii) making the visual cortex more efficient. It is unclear when such changes occur and how adaptive they are, questions that children with cochlear implants can help address. Here, we examined 7-18 years old children: 50 had cochlear implants, with delayed or age-appropriate language abilities, and 25 had typical hearing and language. High-density electroencephalography and functional near-infrared spectroscopy were used to evaluate cortical responses to a low-level visual task. Evidence for a 'weaker visual cortex response' and 'less synchronized or less inhibitory activity of auditory association areas' in the implanted children with language delays suggests that cross-modal reorganization can be maladaptive and does not necessarily strengthen the dominant visual sense.
Collapse
Affiliation(s)
- Mickael L D Deroche
- Department of Psychology, Concordia University, Montreal, Quebec, Canada, H4B 1R6
| | - Jace Wolfe
- Hearts for Hearing Foundation, Oklahoma City, OK 73120, USA
| | - Sara Neumann
- Hearts for Hearing Foundation, Oklahoma City, OK 73120, USA
| | - Jacy Manning
- Hearts for Hearing Foundation, Oklahoma City, OK 73120, USA
| | - Lindsay Hanna
- Hearts for Hearing Foundation, Oklahoma City, OK 73120, USA
| | - Will Towler
- Hearts for Hearing Foundation, Oklahoma City, OK 73120, USA
| | - Caleb Wilson
- Department of Otolaryngology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Alexander G Bien
- Department of Otolaryngology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Sharon Miller
- Department of Audiology & Speech-Language Pathology, University of North Texas, Denton, TX 76201, USA
| | - Erin Schafer
- Department of Audiology & Speech-Language Pathology, University of North Texas, Denton, TX 76201, USA
| | - Jessica Gemignani
- Department of Developmental and Social Psychology, University of Padova, 35131 Padua, Italy
| | - Razieh Alemi
- Department of Psychology, Concordia University, Montreal, Quebec, Canada, H4B 1R6
| | - Muthuraman Muthuraman
- Section of Neural Engineering with Signal Analytics and Artificial Intelligence, Department of Neurology, University Hospital Würzburg, 97080 Würzburg, Germany
| | | | | |
Collapse
|
7
|
Kouti M, Ansari-Asl K, Namjoo E. EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints. Med Biol Eng Comput 2024:10.1007/s11517-024-03125-9. [PMID: 38771431 DOI: 10.1007/s11517-024-03125-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 05/11/2024] [Indexed: 05/22/2024]
Abstract
One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on L 1 and L 2 norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.
Collapse
Affiliation(s)
- Mayadeh Kouti
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
- Department of Electrical Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Karim Ansari-Asl
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
| | - Ehsan Namjoo
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| |
Collapse
|
8
|
Bi J, Gao Y, Peng Z, Ma Y. Classification of motor imagery using chaotic entropy based on sub-band EEG source localization. J Neural Eng 2024; 21:036016. [PMID: 38722315 DOI: 10.1088/1741-2552/ad4914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 05/09/2024] [Indexed: 05/18/2024]
Abstract
Objective.Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy.Approach.To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy feature extraction method based on sub-band ESL. Firstly, the preprocessed EEG signals were filtered into 8 sub-bands. Each sub-band signal was source localized respectively to reveal the activation patterns of specific frequency bands of the EEG signals and the activities of specific brain regions in the MI task. Then, approximate entropy, fuzzy entropy and permutation entropy were extracted from the source signal as features to quantify the complexity and randomness of the signal. Finally, the classification of different MI tasks was achieved using support vector machine.Main result.The proposed method was validated on two MI public datasets (brain-computer interface (BCI) competition III IVa, BCI competition IV 2a) and the results showed that the classification accuracies were higher than the existing methods.Significance.The spatial resolution of the signal was improved by sub-band EEG localization in the paper, which provided a new idea for EEG MI research.
Collapse
Affiliation(s)
- Jicheng Bi
- College of Automation, Hangzhou Dianzi University, Hangzhou, People's Republic of China
| | - Yunyuan Gao
- College of Automation, Hangzhou Dianzi University, Hangzhou, People's Republic of China
| | - Zheng Peng
- College of Automation, Hangzhou Dianzi University, Hangzhou, People's Republic of China
| | - Yuliang Ma
- College of Automation, Hangzhou Dianzi University, Hangzhou, People's Republic of China
| |
Collapse
|
9
|
Dayarian N, Khadem A. A hybrid boundary element-finite element approach for solving the EEG forward problem in brain modeling. Front Syst Neurosci 2024; 18:1327674. [PMID: 38764980 PMCID: PMC11099220 DOI: 10.3389/fnsys.2024.1327674] [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: 10/25/2023] [Accepted: 02/22/2024] [Indexed: 05/21/2024] Open
Abstract
This article introduces a hybrid BE-FE method for solving the EEG forward problem, leveraging the strengths of both the Boundary Element Method (BEM) and Finite Element Method (FEM). FEM accurately models complex and anisotropic tissue properties for realistic head geometries, while BEM excels in handling isotropic tissue regions and dipolar sources efficiently. The proposed hybrid method divides regions into homogeneous boundary element (BE) regions that include sources and heterogeneous anisotropic finite element (FE) regions. So, BEM models the brain, including dipole sources, and FEM models other head layers. Validation includes inhomogeneous isotropic/anisotropic three- and four-layer spherical head models, and a four-layer MRI-based realistic head model. Results for six dipole eccentricities and two orientations are computed using BEM, FEM, and hybrid BE-FE method. Statistical analysis, comparing error criteria of RDM and MAG, reveals notable improvements using the hybrid FE-BE method. In the spherical head model, the hybrid BE-FE method compared with FEM demonstrates enhancements of at least 1.05 and 38.31% in RDM and MAG criteria, respectively. Notably, in the anisotropic four-layer head model, improvements reach a maximum of 88.3% for RDM and 93.27% for MAG over FEM. Moreover, in the anisotropic four-layer realistic head model, the proposed hybrid method exhibits 55.4% improvement in RDM and 89.3% improvement in MAG compared to FEM. These findings underscore the proposed method is a promising approach for solving the realistic EEG forward problems, advancing neuroimaging techniques and enhancing understanding of brain function.
Collapse
Affiliation(s)
| | - Ali Khadem
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| |
Collapse
|
10
|
Ramezani Z, André V, Khizroev S. Modeling the effect of magnetoelectric nanoparticles on neuronal electrical activity: An analog circuit approach. Biointerphases 2024; 19:031001. [PMID: 38738941 DOI: 10.1116/5.0199163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/22/2024] [Indexed: 05/14/2024] Open
Abstract
This paper introduces a physical neuron model that incorporates magnetoelectric nanoparticles (MENPs) as an essential electrical circuit component to wirelessly control local neural activity. Availability of such a model is important as MENPs, due to their magnetoelectric effect, can wirelessly and noninvasively modulate neural activity, which, in turn, has implications for both finding cures for neurological diseases and creating a wireless noninvasive high-resolution brain-machine interface. When placed on a neuronal membrane, MENPs act as magnetic-field-controlled finite-size electric dipoles that generate local electric fields across the membrane in response to magnetic fields, thus allowing to controllably activate local ion channels and locally initiate an action potential. Herein, the neuronal electrical characteristic description is based on ion channel activation and inhibition mechanisms. A MENP-based memristive Hodgkin-Huxley circuit model is extracted by combining the Hodgkin-Huxley model and an equivalent circuit model for a single MENP. In this model, each MENP becomes an integral part of the neuron, thus enabling wireless local control of the neuron's electric circuit itself. Furthermore, the model is expanded to include multiple MENPs to describe collective effects in neural systems.
Collapse
Affiliation(s)
- Zeinab Ramezani
- Department of Electrical and Computer Engineering, College of Engineering, University of Miami, Miami, Florida 33146
| | - Victoria André
- Department of Biomedical Engineering, College of Engineering, University of Miami, Miami, Florida 33146
| | - Sakhrat Khizroev
- Department of Electrical and Computer Engineering, College of Engineering, University of Miami, Miami, Florida 33146
| |
Collapse
|
11
|
Huang G, Liu K, Liang J, Cai C, Gu ZH, Qi F, Li Y, Yu ZL, Wu W. Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:6423-6437. [PMID: 36215381 DOI: 10.1109/tnnls.2022.3209925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this article, a novel data-synthesized spatiotemporally convolutional encoder-decoder network (DST-CedNet) method is proposed for ESI. The DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a CedNet to learn a robust mapping from the measured electroencephalography/magnetoencephalography (E/MEG) signals to the brain activity. In particular, by incorporating prior knowledge regarding dynamical brain activities, a novel data synthesis strategy is devised to generate large-scale samples for effectively training CedNet. This stands in contrast to traditional ESI methods where the prior information is often enforced via constraints primarily aimed for mathematical convenience. Extensive numerical experiments as well as analysis of a real MEG and epilepsy EEG dataset demonstrate that the DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.
Collapse
|
12
|
Ghin F, Eggert E, Gholamipourbarogh N, Talebi N, Beste C. Response stopping under conflict: The integrative role of representational dynamics associated with the insular cortex. Hum Brain Mapp 2024; 45:e26643. [PMID: 38664992 PMCID: PMC11046082 DOI: 10.1002/hbm.26643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 02/02/2024] [Accepted: 02/18/2024] [Indexed: 04/29/2024] Open
Abstract
Coping with distracting inputs during goal-directed behavior is a common challenge, especially when stopping ongoing responses. The neural basis for this remains debated. Our study explores this using a conflict-modulation Stop Signal task, integrating group independent component analysis (group-ICA), multivariate pattern analysis (MVPA), and EEG source localization analysis. Consistent with previous findings, we show that stopping performance is better in congruent (nonconflicting) trials than in incongruent (conflicting) trials. Conflict effects in incongruent trials compromise stopping more due to the need for the reconfiguration of stimulus-response (S-R) mappings. These cognitive dynamics are reflected by four independent neural activity patterns (ICA), each coding representational content (MVPA). It is shown that each component was equally important in predicting behavioral outcomes. The data support an emerging idea that perception-action integration in action-stopping involves multiple independent neural activity patterns. One pattern relates to the precuneus (BA 7) and is involved in attention and early S-R processes. Of note, three other independent neural activity patterns were associated with the insular cortex (BA13) in distinct time windows. These patterns reflect a role in early attentional selection but also show the reiterated processing of representational content relevant for stopping in different S-R mapping contexts. Moreover, the insular cortex's role in automatic versus complex response selection in relation to stopping processes is shown. Overall, the insular cortex is depicted as a brain hub, crucial for response selection and cancellation across both straightforward (automatic) and complex (conditional) S-R mappings, providing a neural basis for general cognitive accounts on action control.
Collapse
Affiliation(s)
- Filippo Ghin
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTU DresdenDresdenGermany
| | - Elena Eggert
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTU DresdenDresdenGermany
| | - Negin Gholamipourbarogh
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTU DresdenDresdenGermany
| | - Nasibeh Talebi
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTU DresdenDresdenGermany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTU DresdenDresdenGermany
| |
Collapse
|
13
|
Russo S, Claar L, Marks L, Krishnan G, Furregoni G, Zauli FM, Hassan G, Solbiati M, d’Orio P, Mikulan E, Sarasso S, Rosanova M, Sartori I, Bazhenov M, Pigorini A, Massimini M, Koch C, Rembado I. Thalamic feedback shapes brain responses evoked by cortical stimulation in mice and humans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578243. [PMID: 38352535 PMCID: PMC10862802 DOI: 10.1101/2024.01.31.578243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Cortical stimulation with single pulses is a common technique in clinical practice and research. However, we still do not understand the extent to which it engages subcortical circuits which contribute to the associated evoked potentials (EPs). Here we find that cortical stimulation generates remarkably similar EPs in humans and mice, with a late component similarly modulated by the subject's behavioral state. We optogenetically dissect the underlying circuit in mice, demonstrating that the late component of these EPs is caused by a thalamic hyperpolarization and rebound. The magnitude of this late component correlates with the bursting frequency and synchronicity of thalamic neurons, modulated by the subject's behavioral state. A simulation of the thalamo-cortical circuit highlights that both intrinsic thalamic currents as well as cortical and thalamic GABAergic neurons contribute to this response profile. We conclude that the cortical stimulation engages cortico-thalamo-cortical circuits highly preserved across different species and stimulation modalities.
Collapse
Affiliation(s)
- Simone Russo
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan 20157, Italy
- Department of Philosophy ‘Piero Martinetti’, University of Milan, Milan, Italy
- Brain and Consciousness, Allen Institute, Seattle, United States
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Leslie Claar
- Brain and Consciousness, Allen Institute, Seattle, United States
| | - Lydia Marks
- Brain and Consciousness, Allen Institute, Seattle, United States
| | - Giri Krishnan
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Giulia Furregoni
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan 20157, Italy
| | - Flavia Maria Zauli
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan 20157, Italy
- Department of Philosophy ‘Piero Martinetti’, University of Milan, Milan, Italy
- ASST Grande Ospedale Metropolitano Niguarda, “C. Munari” Epilepsy Surgery Centre, Department of Neuroscience, Italy
| | - Gabriel Hassan
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan 20157, Italy
- Department of Philosophy ‘Piero Martinetti’, University of Milan, Milan, Italy
| | - Michela Solbiati
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan 20157, Italy
- ASST Grande Ospedale Metropolitano Niguarda, “C. Munari” Epilepsy Surgery Centre, Department of Neuroscience, Italy
| | - Piergiorgio d’Orio
- ASST Grande Ospedale Metropolitano Niguarda, “C. Munari” Epilepsy Surgery Centre, Department of Neuroscience, Italy
- University of Parma, Parma 43121, Italy
| | - Ezequiel Mikulan
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan 20157, Italy
| | - Simone Sarasso
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan 20157, Italy
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan 20157, Italy
| | - Ivana Sartori
- ASST Grande Ospedale Metropolitano Niguarda, “C. Munari” Epilepsy Surgery Centre, Department of Neuroscience, Italy
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan 20122, Italy
- UOC Maxillo-facial Surgery and dentistry, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan 20157, Italy
- Istituto Di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan 20122, Italy
- Azrieli Program in Brain, Mind and Consciousness, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario M5G 1M1, Canada
| | - Christof Koch
- Brain and Consciousness, Allen Institute, Seattle, United States
| | - Irene Rembado
- Brain and Consciousness, Allen Institute, Seattle, United States
| |
Collapse
|
14
|
Proverbio AM, Cesati F. Neural correlates of recalled sadness, joy, and fear states: a source reconstruction EEG study. Front Psychiatry 2024; 15:1357770. [PMID: 38638416 PMCID: PMC11024723 DOI: 10.3389/fpsyt.2024.1357770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
Introduction The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification. Methods The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs). Results Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states. Discussion In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.
Collapse
Affiliation(s)
- Alice Mado Proverbio
- Cognitive Electrophysiology Lab, Department of Psychology, University of Milano-Bicocca, Milan, Italy
- NEURO-MI Milan Center for Neuroscience, Milan, Italy
| | - Federico Cesati
- Cognitive Electrophysiology Lab, Department of Psychology, University of Milano-Bicocca, Milan, Italy
| |
Collapse
|
15
|
Nadalizadeh F, Rajabioun M, Feyzi A. Driving fatigue detection based on brain source activity and ARMA model. Med Biol Eng Comput 2024; 62:1017-1030. [PMID: 38117429 DOI: 10.1007/s11517-023-02983-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 11/28/2023] [Indexed: 12/21/2023]
Abstract
Fatigue among drivers is a significant issue in society, and according to organizational reports, it substantially contributes to accidents. So accurate fatigue detection in drivers plays a crucial role in reducing the number of people fatalities or injured resulting from accidents. Several methods are proposed for fatigue driver recognition among which electroencephalography (EEG) is one. This paper proposed a method for fatigue recognition by EEG signals with extracted features from source and sensor spaces. The proposed method starts with preprocessing by applying filtering and artifact rejection. Then source localization methods are applied to EEG signals for active source extraction. A multivariate autoregressive (MVAR) model is fitted to selected sources, and a dual Kalman filter is applied to estimate the source activity and their relationships. Then multivariate autoregressive moving average (ARMA) is fitted between EEG and source activity signals. Features are extracted from model parameters, source relationship matrix, and wavelet transform of EEG and source activity signals. The novelty of this approach is the use of ARMA model between source activities (as input) and EEG signals (as output) and feature extraction from source relations. Relevant features are selected using a combination of RelifF and neighborhood component analysis (NCA) methods. Three classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayesian (NB) classifiers, are employed to classify drivers. To improve performance, the final label for fatigue detection is calculated by combining these classifiers using the voting method. The results demonstrate that the proposed method accurately recognizes and classifies fatigued drivers with the ensemble classifiers in comparison with other methods.
Collapse
Affiliation(s)
- Fahimeh Nadalizadeh
- Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Mehdi Rajabioun
- Department of Engineering, Mamaghan Branch, Islamic Azad University, Mamaghan, Iran.
| | - Amirreza Feyzi
- Department of Electrical and Computer Engineering, Tabriz University, Tabriz, Iran
| |
Collapse
|
16
|
Hindriks R. Characterization of Second-Order Mixing Effects in Reconstructed Cross-Spectra of Random Neural Fields. Brain Topogr 2024:10.1007/s10548-024-01040-8. [PMID: 38472533 DOI: 10.1007/s10548-024-01040-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 02/06/2024] [Indexed: 03/14/2024]
Abstract
Functional connectivity in electroencephalography (EEG) and magnetoencephalography (MEG) data is commonly assessed by using measures that are insensitive to instantaneously interacting sources and as such would not give rise to false positive interactions caused by instantaneous mixing of true source signals (first-order mixing). Recent studies, however, have drawn attention to the fact that such measures are still susceptible to instantaneous mixing from lagged sources (i.e. second-order mixing) and that this can lead to a large number of false positive interactions. In this study we relate first- and second-order mixing effects on the cross-spectra of reconstructed source activity to the properties of the resolution operators that are used for the reconstruction. We derive two identities that relate first- and second-order mixing effects to the transformation properties of measurement and source configurations and exploit them to establish several basic properties of signal mixing. First, we provide a characterization of the configurations that are maximally and minimally sensitive to second-order mixing. It turns out that second-order mixing effects are maximal when the measurement locations are far apart and the sources coincide with the measurement locations. Second, we provide a description of second-order mixing effects in the vicinity of the measurement locations in terms of the local geometry of the point-spread functions of the resolution operator. Third, we derive a version of Lagrange's identity for cross-talk functions that establishes the existence of a trade-off between the magnitude of first- and second-order mixing effects. It also shows that, whereas the magnitude of first-order mixing is determined by the inner product of cross-talk functions, the magnitude of second-order mixing is determined by a generalized cross-product of cross-talk functions (the wedge product) which leads to an intuitive geometric understanding of the trade-off. All results are derived within the general framework of random neural fields on cortical manifolds.
Collapse
Affiliation(s)
- Rikkert Hindriks
- Department of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| |
Collapse
|
17
|
Chang YJ, Chen YI, Yeh HC, Santacruz SR. Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics. Sci Rep 2024; 14:5145. [PMID: 38429297 PMCID: PMC10907713 DOI: 10.1038/s41598-024-54593-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 02/14/2024] [Indexed: 03/03/2024] Open
Abstract
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the behaviors, investigations of neural population dynamics are mainly limited to single-scale analysis. Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal populations. Here we introduce a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales. Our model not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. We also show that the NBGNet robustly predicts held-out data across a long time scale (2 weeks) without retraining. We further validate the effective connectivity defined from our model by demonstrating that neural connectivity during motor behaviour agrees with the established neuroanatomical hierarchy of motor control in the literature. The NBGNet approach opens the door to revealing a comprehensive understanding of brain computation, where network mechanisms of multi-scale activity are critical.
Collapse
Affiliation(s)
- Yin-Jui Chang
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Yuan-I Chen
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Hsin-Chih Yeh
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
- Texas Materials Institute, The University of Texas at Austin, Austin, TX, USA
| | - Samantha R Santacruz
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
- Institute for Neuroscience, The University of Texas at Austin, Austin, TX, USA.
- Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
| |
Collapse
|
18
|
Moradi N, Goodyear BG, Sotero RC. Deep EEG source localization via EMD-based fMRI high spatial frequency. PLoS One 2024; 19:e0299284. [PMID: 38427616 PMCID: PMC10906834 DOI: 10.1371/journal.pone.0299284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 02/07/2024] [Indexed: 03/03/2024] Open
Abstract
Brain imaging with a high-spatiotemporal resolution is crucial for accurate brain-function mapping. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two popular neuroimaging modalities with complementary features that record brain function with high temporal and spatial resolution, respectively. One popular non-invasive way to obtain data with both high spatial and temporal resolutions is to combine the fMRI activation map and EEG data to improve the spatial resolution of the EEG source localization. However, using the whole fMRI map may cause spurious results for the EEG source localization, especially for deep brain regions. Considering the head's conductivity, deep regions' sources with low activity are unlikely to be detected by the EEG electrodes at the scalp. In this study, we use fMRI's high spatial-frequency component to identify the local high-intensity activations that are most likely to be captured by the EEG. The 3D Empirical Mode Decomposition (3D-EMD), a data-driven method, is used to decompose the fMRI map into its spatial-frequency components. Different validation measurements for EEG source localization show improved performance for the EEG inverse-modeling informed by the fMRI's high-frequency spatial component compared to the fMRI-informed EEG source-localization methods. The level of improvement varies depending on the voxels' intensity and their distribution. Our experimental results also support this conclusion.
Collapse
Affiliation(s)
- Narges Moradi
- Biomedical Engineering Department, University of Calgary, Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Bradley G. Goodyear
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Roberto C. Sotero
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| |
Collapse
|
19
|
Lahtinen J, Koulouri A, Rampp S, Wellmer J, Wolters C, Pursiainen S. Standardized hierarchical adaptive Lp regression for noise robust focal epilepsy source reconstructions. Clin Neurophysiol 2024; 159:24-40. [PMID: 38244372 DOI: 10.1016/j.clinph.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/02/2023] [Accepted: 12/02/2023] [Indexed: 01/22/2024]
Abstract
OBJECTIVE To investigate the ability of standardization to reduce source localization errors and measurement noise uncertainties for hierarchical Bayesian algorithms with L1- and L2-norms as priors in electroencephalography and magnetoencephalography of focal epilepsy. METHODS Description of the standardization methodology relying on the Hierarchical Bayesian framework, referred to as the Standardized Hierarchical Adaptive Lp-norm Regularization (SHALpR). The performance was tested using real data from two focal epilepsy patients. Simulated data that resembled the available real data was constructed for further localization and noise robustness investigation. RESULTS The proposed algorithms were compared to their non-standardized counterparts, Standardized low-resolution brain electromagnetic tomography, Standardized Shrinking LORETA-FOCUSS, and Dynamic statistical parametric maps. Based on the simulations, the standardized Hierarchical adaptive algorithm using L2-norm was noise robust for 10 dB signal-to-noise ratio (SNR), whereas the L1-norm prior worked robustly also with 5 dB SNR. The accuracy of the standardized L1-normed methodology to localize focal activity was under 1 cm for both patients. CONCLUSIONS Numerical results of the proposed methodology display improved localization and noise robustness. The proposed methodology also outperformed the compared methods when dealing with real data. SIGNIFICANCE The proposed standardized methodology, especially when employing the L1-norm, could serve as a valuable assessment tool in surgical decision-making.
Collapse
Affiliation(s)
- Joonas Lahtinen
- Faculty of Information Technology and Communication Sciences, Tampere University, Tampere 33720, Finland.
| | - Alexandra Koulouri
- Faculty of Information Technology and Communication Sciences, Tampere University, Tampere 33720, Finland.
| | - Stefan Rampp
- Department of Neurosurgery, University Hospital Halle (Saale), Halle 06097, Germany; Department of Neurosurgery, University Hospital Erlangen, Erlangen 91054, Germany; Department of Neuroradiology, University Hospital Erlangen, Erlangen 91054, Germany.
| | - Jörg Wellmer
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University, Bochum44892, Germany.
| | - Carsten Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster 48149, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster 48149, Germany.
| | - Sampsa Pursiainen
- Faculty of Information Technology and Communication Sciences, Tampere University, Tampere 33720, Finland.
| |
Collapse
|
20
|
Hirata A, Niitsu M, Phang CR, Kodera S, Kida T, Rashed EA, Fukunaga M, Sadato N, Wasaka T. High-resolution EEG source localization in personalized segmentation-free head model with multi-dipole fitting. Phys Med Biol 2024; 69:055013. [PMID: 38306964 DOI: 10.1088/1361-6560/ad25c3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/02/2024] [Indexed: 02/04/2024]
Abstract
Objective. Electroencephalograms (EEGs) are often used to monitor brain activity. Several source localization methods have been proposed to estimate the location of brain activity corresponding to EEG readings. However, only a few studies evaluated source localization accuracy from measured EEG using personalized head models in a millimeter resolution. In this study, based on a volume conductor analysis of a high-resolution personalized human head model constructed from magnetic resonance images, a finite difference method was used to solve the forward problem and to reconstruct the field distribution.Approach. We used a personalized segmentation-free head model developed using machine learning techniques, in which the abrupt change of electrical conductivity occurred at the tissue interface is suppressed. Using this model, a smooth field distribution was obtained to address the forward problem. Next, multi-dipole fitting was conducted using EEG measurements for each subject (N= 10 male subjects, age: 22.5 ± 0.5), and the source location and electric field distribution were estimated.Main results.For measured somatosensory evoked potential for electrostimulation to the wrist, a multi-dipole model with lead field matrix computed with the volume conductor model was found to be superior than a single dipole model when using personalized segmentation-free models (6/10). The correlation coefficient between measured and estimated scalp potentials was 0.89 for segmentation-free head models and 0.71 for conventional segmented models. The proposed method is straightforward model development and comparable localization difference of the maximum electric field from the target wrist reported using fMR (i.e. 16.4 ± 5.2 mm) in previous study. For comparison, DUNEuro based on sLORETA was (EEG: 17.0 ± 4.0 mm). In addition, somatosensory evoked magnetic fields obtained by Magnetoencephalography was 25.3 ± 8.5 mm using three-layer sphere and sLORETA.Significance. For measured EEG signals, our procedures using personalized head models demonstrated that effective localization of the somatosensory cortex, which is located in a non-shallower cortex region. This method may be potentially applied for imaging brain activity located in other non-shallow regions.
Collapse
Affiliation(s)
- Akimasa Hirata
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Masamune Niitsu
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Chun Ren Phang
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Sachiko Kodera
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Tetsuo Kida
- Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai 480-0392, Japan
| | - Essam A Rashed
- Graduate School of Information Science, University of Hyogo, Kobe 650-0047, Japan
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Toshiaki Wasaka
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| |
Collapse
|
21
|
Avigdor T, Abdallah C, Afnan J, Cai Z, Rammal S, Grova C, Frauscher B. Consistency of electrical source imaging in presurgical evaluation of epilepsy across different vigilance states. Ann Clin Transl Neurol 2024; 11:389-403. [PMID: 38217279 PMCID: PMC10863930 DOI: 10.1002/acn3.51959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/24/2023] [Accepted: 11/18/2023] [Indexed: 01/15/2024] Open
Abstract
OBJECTIVE The use of electrical source imaging (ESI) in assessing the source of interictal epileptic discharges (IEDs) is gaining increasing popularity in presurgical work-up of patients with drug-resistant focal epilepsy. While vigilance affects the ability to locate IEDs and identify the epileptogenic zone, we know little about its impact on ESI. METHODS We studied overnight high-density electroencephalography recordings in focal drug-resistant epilepsy. IEDs were marked visually in each vigilance state, and examined in the sensor and source space. ESIs were calculated and compared between all vigilance states and the clinical ground truth. Two conditions were considered within each vigilance state, an unequalized and an equalized number of IEDs. RESULTS The number, amplitude, and duration of IEDs were affected by the vigilance state, with N3 sleep presenting the highest number, amplitude, and duration for both conditions (P < 0.001), while signal-to-noise ratio only differed in the unequalized condition (P < 0.001). The vigilance state did not affect channel involvement (P > 0.05). ESI maps showed no differences in distance, quality, extent, or maxima distances compared to the clinical ground truth for both conditions (P > 0.05). Only when an absolute reference (wakefulness) was used, the channel involvement (P < 0.05) and ESI source extent (P < 0.01) were impacted during rapid-eye-movement (REM) sleep. Clustering of amplitude-sensitive and -insensitive ESI maps pointed to amplitude rather than the spatial profile as the driver (P < 0.05). INTERPRETATION IED ESI results are stable across vigilance states, including REM sleep, if controlled for amplitude and IED number. ESI is thus stable and invariant to the vigilance state.
Collapse
Affiliation(s)
- Tamir Avigdor
- Analytical Neurophysiology LabMontreal Neurological Institute and Hospital, McGill UniversityMontrealQuebecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontrealCanada
| | - Chifaou Abdallah
- Analytical Neurophysiology LabMontreal Neurological Institute and Hospital, McGill UniversityMontrealQuebecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontrealCanada
| | - Jawata Afnan
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontrealCanada
| | - Zhengchen Cai
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealQuebecCanada
| | - Saba Rammal
- Analytical Neurophysiology LabMontreal Neurological Institute and Hospital, McGill UniversityMontrealQuebecCanada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontrealCanada
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of PhysicsConcordia UniversityMontrealQuebecCanada
| | - Birgit Frauscher
- Analytical Neurophysiology LabMontreal Neurological Institute and Hospital, McGill UniversityMontrealQuebecCanada
- Department of NeurologyDuke University Medical CenterDurhamNorth CarolinaUSA
- Department of Biomedical EngineeringDuke Pratt School of EngineeringDurhamNorth CarolinaUSA
| |
Collapse
|
22
|
Issabekov G, Matsumoto T, Hoshi H, Fukasawa K, Ichikawa S, Shigihara Y. Resting-state brain activity distinguishes patients with generalised epilepsy from others. Seizure 2024; 115:50-58. [PMID: 38183828 DOI: 10.1016/j.seizure.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/14/2023] [Accepted: 01/01/2024] [Indexed: 01/08/2024] Open
Abstract
PURPOSE Epilepsy is a prevalent neurological disorder characterised by repetitive seizures. It is categorised into three types: generalised epilepsy (GE), focal epilepsy (FE), and combined generalised and focal epilepsy. Correctly subtyping the epilepsy is important to select appropriate treatments. The types are mainly determined (i.e., diagnosed) by their semiologies supported by clinical examinations, such as electroencephalography and magnetoencephalography (MEG). Although these examinations are traditionally based on visual inspections of interictal epileptic discharges (IEDs), which are not always visible, alternative analyses have been anticipated. We examined if resting-state brain activities can distinguish patients with GE, which would help us to diagnose the type of epilepsy. METHODS The 5 min resting-state brain activities acquired using MEG were obtained retrospectively from 15 patients with GE. The cortical source of the activities was estimated at each frequency band from delta to high-frequency oscillation (HFO). These estimated activities were compared with reference datasets from 133 healthy individuals and control data from 29 patients with FE. RESULTS Patients with GE showed larger theta in the occipital, alpha in the left temporal, HFO in the rostral deep regions, and smaller HFO in the caudal ventral regions. Their area under the curves of the receiver operating characteristic curves was around 0.8-0.9. The distinctive pattern was not found for data from FE. CONCLUSION Patients with GE show distinctive resting-state brain activity, which could be a potential biomarker and used complementarily to classical analysis based on the visual inspection of IEDs.
Collapse
Affiliation(s)
- Galymzhan Issabekov
- Precision Medicine Centre, Kumagaya General Hospital, Kumagaya 360-8567, Japan
| | - Takahiro Matsumoto
- Department of Neurosurgery, Kumagaya General Hospital, Kumagaya 360-8567, Japan
| | - Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro 080-0833, Japan
| | - Keisuke Fukasawa
- Clinical Laboratory, Kumagaya General Hospital, Kumagaya 360-8567, Japan
| | - Sayuri Ichikawa
- Clinical Laboratory, Kumagaya General Hospital, Kumagaya 360-8567, Japan
| | - Yoshihito Shigihara
- Precision Medicine Centre, Kumagaya General Hospital, Kumagaya 360-8567, Japan; Precision Medicine Centre, Hokuto Hospital, Obihiro 080-0833, Japan.
| |
Collapse
|
23
|
Silva Pereira S, Özer EE, Sebastian-Galles N. Complexity of STG signals and linguistic rhythm: a methodological study for EEG data. Cereb Cortex 2024; 34:bhad549. [PMID: 38236741 DOI: 10.1093/cercor/bhad549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 02/06/2024] Open
Abstract
The superior temporal and the Heschl's gyri of the human brain play a fundamental role in speech processing. Neurons synchronize their activity to the amplitude envelope of the speech signal to extract acoustic and linguistic features, a process known as neural tracking/entrainment. Electroencephalography has been extensively used in language-related research due to its high temporal resolution and reduced cost, but it does not allow for a precise source localization. Motivated by the lack of a unified methodology for the interpretation of source reconstructed signals, we propose a method based on modularity and signal complexity. The procedure was tested on data from an experiment in which we investigated the impact of native language on tracking to linguistic rhythms in two groups: English natives and Spanish natives. In the experiment, we found no effect of native language but an effect of language rhythm. Here, we compare source projected signals in the auditory areas of both hemispheres for the different conditions using nonparametric permutation tests, modularity, and a dynamical complexity measure. We found increasing values of complexity for decreased regularity in the stimuli, giving us the possibility to conclude that languages with less complex rhythms are easier to track by the auditory cortex.
Collapse
Affiliation(s)
- Silvana Silva Pereira
- Center for Brain and Cognition, Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08005 Barcelona, Spain
| | - Ege Ekin Özer
- Center for Brain and Cognition, Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08005 Barcelona, Spain
| | - Nuria Sebastian-Galles
- Center for Brain and Cognition, Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08005 Barcelona, Spain
| |
Collapse
|
24
|
Mao X, Zhang Z, Yang Y, Chen Y, Wang Y, Wang W. Characteristics of different Mandarin pronunciation element perception: evidence based on a multifeature paradigm for recording MMN and P3a components of phonemic changes in speech sounds. Front Neurosci 2024; 17:1277129. [PMID: 38264493 PMCID: PMC10804857 DOI: 10.3389/fnins.2023.1277129] [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: 08/14/2023] [Accepted: 12/18/2023] [Indexed: 01/25/2024] Open
Abstract
Background As a tonal language, Mandarin Chinese has the following pronunciation elements for each syllable: the vowel, consonant, tone, duration, and intensity. Revealing the characteristics of auditory-related cortical processing of these different pronunciation elements is interesting. Methods A Mandarin pronunciation multifeature paradigm was designed, during which a standard stimulus and five different phonemic deviant stimuli were presented. The electroencephalogram (EEG) data were recorded with 256-electrode high-density EEG equipment. Time-domain and source localization analyses were conducted to demonstrate waveform characteristics and locate the sources of the cortical processing of mismatch negativity (MMN) and P3a components following different stimuli. Results Vowel and consonant differences elicited distinct MMN and P3a components, but tone and duration differences did not. Intensity differences elicited distinct MMN components but not P3a components. For MMN and P3a components, the activated cortical areas were mainly in the frontal-temporal lobe. However, the regions and intensities of the cortical activation were significantly different among the components for the various deviant stimuli. The activated cortical areas of the MMN and P3a components elicited by vowels and consonants seemed to be larger and show more intense activation. Conclusion The auditory processing centers use different auditory-related cognitive resources when processing different Mandarin pronunciation elements. Vowels and consonants carry more information for speech comprehension; moreover, more neurons in the cortex may be involved in the recognition and cognitive processing of these elements.
Collapse
Affiliation(s)
- Xiang Mao
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Ziyue Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Yijing Yang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Yu Chen
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Yue Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Wei Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| |
Collapse
|
25
|
Brinkmann BH. Technical Considerations in EEG Source Imaging. J Clin Neurophysiol 2024; 41:2-7. [PMID: 38181382 DOI: 10.1097/wnp.0000000000001029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2024] Open
Abstract
SUMMARY EEG source imaging is an established technique for identifying the origin of interictal and ictal epileptiform discharges in patients with epilepsy, and it is an important tool in neurophysiology research. Accurate and reliable EEG source imaging requires appropriate choices of how the head, skull, and scalp are modeled, and understanding of the different approaches to modeling is important to guide these choices. Similarly, numerous different approaches to modeling the electrical sources within the brain exist, and appropriate understanding of the strengths and limitations of each are essential to obtaining accurate, reliable, and interpretable solutions. This review aims to describe the essential theoretical basis for these head and source models while also discussing the practical implications of each in clinical or research applications.
Collapse
Affiliation(s)
- Benjamin H Brinkmann
- Departments of Neurology and Physiology and Biomedical Engineering, Mayo Clinic, Alfred 9-441C, SMH; 200 First Street SW, Rochester, Minnesota, U.S.A
| |
Collapse
|
26
|
Sanchez-Bornot J, Sotero RC, Kelso JAS, Şimşek Ö, Coyle D. Solving large-scale MEG/EEG source localisation and functional connectivity problems simultaneously using state-space models. Neuroimage 2024; 285:120458. [PMID: 37993002 DOI: 10.1016/j.neuroimage.2023.120458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 09/28/2023] [Accepted: 11/14/2023] [Indexed: 11/24/2023] Open
Abstract
State-space models are widely employed across various research disciplines to study unobserved dynamics. Conventional estimation techniques, such as Kalman filtering and expectation maximisation, offer valuable insights but incur high computational costs in large-scale analyses. Sparse inverse covariance estimators can mitigate these costs, but at the expense of a trade-off between enforced sparsity and increased estimation bias, necessitating careful assessment in low signal-to-noise ratio (SNR) situations. To address these challenges, we propose a three-fold solution: (1) Introducing multiple penalised state-space (MPSS) models that leverage data-driven regularisation; (2) Developing novel algorithms derived from backpropagation, gradient descent, and alternating least squares to solve MPSS models; (3) Presenting a K-fold cross-validation extension for evaluating regularisation parameters. We validate this MPSS regularisation framework through lower and more complex simulations under varying SNR conditions, including a large-scale synthetic magneto- and electro-encephalography (MEG/EEG) data analysis. In addition, we apply MPSS models to concurrently solve brain source localisation and functional connectivity problems for real event-related MEG/EEG data, encompassing thousands of sources on the cortical surface. The proposed methodology overcomes the limitations of existing approaches, such as constraints to small-scale and region-of-interest analyses. Thus, it may enable a more accurate and detailed exploration of cognitive brain functions.
Collapse
Affiliation(s)
- Jose Sanchez-Bornot
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee campus, Derry∼Londonderry, United Kingdom.
| | - Roberto C Sotero
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - J A Scott Kelso
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee campus, Derry∼Londonderry, United Kingdom; Human Brain & Behavior laboratory, Center for Complex Systems & Brain Sciences, Florida Atlantic University, Boca Raton, FL, USA
| | - Özgür Şimşek
- Bath Institute for the Augmented Human, University of Bath, Bath, BA2 7AY, United Kingdom
| | - Damien Coyle
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee campus, Derry∼Londonderry, United Kingdom; Bath Institute for the Augmented Human, University of Bath, Bath, BA2 7AY, United Kingdom
| |
Collapse
|
27
|
Mora-Gonzalez J, Esteban-Cornejo I, Solis-Urra P, Rodriguez-Ayllon M, Cadenas-Sanchez C, Hillman CH, Kramer AF, Catena A, Ortega FB. The effects of an exercise intervention on neuroelectric activity and executive function in children with overweight/obesity: The ActiveBrains randomized controlled trial. Scand J Med Sci Sports 2024; 34:e14486. [PMID: 37691352 DOI: 10.1111/sms.14486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/11/2023] [Accepted: 08/21/2023] [Indexed: 09/12/2023]
Abstract
OBJECTIVE To investigate whether a 20-week aerobic and resistance exercise program induces changes in brain current density underlying working memory and inhibitory control in children with overweight/obesity. METHODS A total of 67 children (10.00 ± 1.10 years) were randomized into an exercise or control group. Electroencephalography (EEG)-based current density (μA/mm2 ) was estimated using standardized low-resolution brain electromagnetic tomography (sLORETA) during a working memory task (Delayed non-matched-to-sample task, DNMS) and inhibitory control task (Modified flanker task, MFT). In DNMS, participants had to memorize four stimuli (Pokemons) and then select between two of them, one of which had not been previously shown. In MFT, participants had to indicate whether the centered cow (i.e., target) of five faced the right or left. RESULTS The exercise group had significantly greater increases in brain activation in comparison with the control group during the encoding phase of DNMS, particularly during retention of second stimuli in temporal and frontal areas (peak t = from 3.4 to 3.8, cluster size [k] = from 11 to 39), during the retention of the third stimuli in frontal areas (peak t = from 3.7 to 3.9, k = from 15 to 26), and during the retention of the fourth stimuli in temporal and occipital areas (peak t = from 2.7 to 4.3, k = from 13 to 101). In MFT, the exercise group presented a lower current density change in the middle frontal gyrus (peak t = -4.1, k = 5). No significant change was observed between groups for behavioral performance (p ≥ 0.05). CONCLUSION A 20-week exercise program modulates brain activity which might provide a positive influence on working memory and inhibitory control in children with overweight/obesity.
Collapse
Affiliation(s)
- Jose Mora-Gonzalez
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
| | - Irene Esteban-Cornejo
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
| | - Patricio Solis-Urra
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Faculty of Education and Social Sciences, Universidad Andres Bello, Viña del Mar, Chile
| | - María Rodriguez-Ayllon
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Cristina Cadenas-Sanchez
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
| | - Charles H Hillman
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA
- Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, Massachusetts, USA
| | - Arthur F Kramer
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA
- Beckman Institute, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Andrés Catena
- School of Psychology, University of Granada, Granada, Spain
| | - Francisco B Ortega
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| |
Collapse
|
28
|
Pitetzis D, Frantzidis C, Psoma E, Ketseridou SN, Deretzi G, Kalogera-Fountzila A, Bamidis PD, Spilioti M. The Pre-Interictal Network State in Idiopathic Generalized Epilepsies. Brain Sci 2023; 13:1671. [PMID: 38137119 PMCID: PMC10741409 DOI: 10.3390/brainsci13121671] [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: 10/29/2023] [Revised: 11/24/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Generalized spike wave discharges (GSWDs) are the typical electroencephalographic findings of Idiopathic Generalized Epilepsies (IGEs). These discharges are either interictal or ictal and recent evidence suggests differences in their pathogenesis. The aim of this study is to investigate, through functional connectivity analysis, the pre-interictal network state in IGEs, which precedes the formation of the interictal GSWDs. A high-density electroencephalogram (HD-EEG) was recorded in twenty-one patients with IGEs, and cortical connectivity was analyzed based on lagged coherence and individual anatomy. Graph theory analysis was used to estimate network features, assessed using the characteristic path length and clustering coefficient. The functional connectivity analysis identified two distinct networks during the pre-interictal state. These networks exhibited reversed connectivity attributes, reflecting synchronized activity at 3-4 Hz (delta2), and desynchronized activity at 8-10.5 Hz (alpha1). The delta2 network exhibited a statistically significant (p < 0.001) decrease in characteristic path length and an increase in the mean clustering coefficient. In contrast, the alpha1 network showed opposite trends in these features. The nodes influencing this state were primarily localized in the default mode network (DMN), dorsal attention network (DAN), visual network (VIS), and thalami. In conclusion, the coupling of two networks defined the pre-interictal state in IGEs. This state might be considered as a favorable condition for the generation of interictal GSWDs.
Collapse
Affiliation(s)
- Dimitrios Pitetzis
- Department of Neurology, Papageorgiou General Hospital, 56403 Thessaloniki, Greece;
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
| | - Christos Frantzidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
| | - Elizabeth Psoma
- Department of Radiology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece; (E.P.); (A.K.-F.)
| | - Smaranda Nafsika Ketseridou
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
| | - Georgia Deretzi
- Department of Neurology, Papageorgiou General Hospital, 56403 Thessaloniki, Greece;
| | - Anna Kalogera-Fountzila
- Department of Radiology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece; (E.P.); (A.K.-F.)
| | - Panagiotis D. Bamidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
| | - Martha Spilioti
- 1st Department of Neurology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece;
| |
Collapse
|
29
|
Horrillo-Maysonnial A, Avigdor T, Abdallah C, Mansilla D, Thomas J, von Ellenrieder N, Royer J, Bernhardt B, Grova C, Gotman J, Frauscher B. Targeted density electrode placement achieves high concordance with traditional high-density EEG for electrical source imaging in epilepsy. Clin Neurophysiol 2023; 156:262-271. [PMID: 37704552 DOI: 10.1016/j.clinph.2023.08.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/27/2023] [Accepted: 08/12/2023] [Indexed: 09/15/2023]
Abstract
OBJECTIVE High-density (HD) electroencephalography (EEG) is increasingly used in presurgical epilepsy evaluation, but it is demanding in time and resources. To overcome these issues, we compared EEG source imaging (ESI) solutions with a targeted density and HD-EEG montage. METHODS HD-EEGs from patients undergoing presurgical evaluation were analyzed. A low-density recording was created by selecting the 25 electrodes of a standard montage from the 83 electrodes of the HD-EEG and adding 8-11 electrodes around the electrode with the highest amplitude interictal epileptiform discharges. The ESI solution from this "targeted" montage was compared to that from the HD-EEG using the distance between peak vertices, sublobar concordance and a qualitative similarity measure. RESULTS Fifty-eight foci of forty-three patients were included. The median distance between the peak vertices of the two montages was 13.2 mm, irrespective of focus' location. Tangential generators (n = 5/58) showed a higher distance than radial generators (p = 0.04). We found sublobar concordance in 54/58 of the foci (93%). Map similarity, assessed by an epileptologist, had a median score of 4/5. CONCLUSIONS ESI solutions obtained from a targeted density montage show high concordance with those calculated from HD-EEG. SIGNIFICANCE Requiring significantly fewer electrodes, targeted density EEG allows obtaining similar ESI solutions as traditional HD-EEG montage.
Collapse
Affiliation(s)
- A Horrillo-Maysonnial
- Clinical Neurophysiology Section, Clínica Universidad de Navarra, Pamplona, Spain; IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain; Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - T Avigdor
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Canada.
| | - C Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Canada.
| | - D Mansilla
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - J Thomas
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - N von Ellenrieder
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - J Royer
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - B Bernhardt
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - C Grova
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Canada; Multimodal Functional Imaging Lab, PERFORM Center, Department of Physics, Concordia University, Montreal, QC, Canada.
| | - J Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Department of Neurology, Duke University Medical Center, Durham, NC, United States; Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, United States.
| |
Collapse
|
30
|
Conti M, Guerra A, Pierantozzi M, Bovenzi R, D'Onofrio V, Simonetta C, Cerroni R, Liguori C, Placidi F, Mercuri NB, Di Giuliano F, Schirinzi T, Stefani A. Band-Specific Altered Cortical Connectivity in Early Parkinson's Disease and its Clinical Correlates. Mov Disord 2023; 38:2197-2208. [PMID: 37860930 DOI: 10.1002/mds.29615] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/25/2023] [Accepted: 09/11/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND Functional connectivity (FC) has shown promising results in assessing the pathophysiology and identifying early biomarkers of neurodegenerative disorders, such as Parkinson's disease (PD). OBJECTIVES In this study, we aimed to assess possible resting-state FC abnormalities in early-stage PD patients using high-density electroencephalography (EEG) and to detect their clinical relationship with motor and non-motor PD symptoms. METHODS We enrolled 26 early-stage levodopa naïve PD patients and a group of 20 healthy controls (HC). Data were recorded with 64-channels EEG system and a source-reconstruction method was used to identify brain-region activity. FC was calculated using the weighted phase-lag index in θ, α, and β bands. Additionally, we quantified the unbalancing between β and lower frequencies through a novel index (β-functional ratio [FR]). Statistical analysis was conducted using a network-based statistical approach. RESULTS PD patients showed hypoconnected networks in θ and α band, involving prefrontal-limbic-temporal and frontoparietal areas, respectively, and a hyperconnected network in the β frequency band, involving sensorimotor-frontal areas. The θ FC network was negatively related to Non-Motor Symptoms Scale scores and α FC to the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III gait subscore, whereas β FC and β-FR network were positively linked to the bradykinesia subscore. Changes in θ FC and β-FR showed substantial reliability and high accuracy, precision, sensitivity, and specificity in discriminating PD and HC. CONCLUSIONS Frequency-specific FC changes in PD likely reflect the dysfunction of distinct cortical networks, which occur from the early stage of the disease. These abnormalities are involved in the pathophysiology of specific motor and non-motor PD symptoms, including gait, bradykinesia, mood, and cognition. © 2023 International Parkinson and Movement Disorder Society.
Collapse
Affiliation(s)
- Matteo Conti
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Andrea Guerra
- Parkinson and Movement Disorders Unit, Study Centre on Neurodegeneration (CESNE), Department of Neuroscience, University of Padova, Padua, Italy
| | - Mariangela Pierantozzi
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Roberta Bovenzi
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Valentina D'Onofrio
- Parkinson and Movement Disorders Unit, Study Centre on Neurodegeneration (CESNE), Department of Neuroscience, University of Padova, Padua, Italy
| | - Clara Simonetta
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Rocco Cerroni
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Claudio Liguori
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Fabio Placidi
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Nicola Biagio Mercuri
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Francesca Di Giuliano
- Neuroradiology Unit, Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy
| | - Tommaso Schirinzi
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Alessandro Stefani
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| |
Collapse
|
31
|
McFadden J. Razor sharp: The role of Occam's razor in science. Ann N Y Acad Sci 2023; 1530:8-17. [PMID: 38018886 PMCID: PMC10952609 DOI: 10.1111/nyas.15086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Occam's razor-the principle of simplicity-has recently been attacked as a cultural bias without rational foundation. Increasingly, belief in pseudoscience and mysticism is growing. I argue that inclusion of Occam's razor is an essential factor that distinguishes science from superstition and pseudoscience. I also describe how the razor is embedded in Bayesian inference and argue that science is primarily the means to discover the simplest descriptions of our world.
Collapse
Affiliation(s)
- Johnjoe McFadden
- Leverhulme Quantum Biology Doctoral Training CentreUniversity of SurreyGuildfordUK
| |
Collapse
|
32
|
Greenblatt AS, Beniczky S, Nascimento FA. Pitfalls in scalp EEG: Current obstacles and future directions. Epilepsy Behav 2023; 149:109500. [PMID: 37931388 DOI: 10.1016/j.yebeh.2023.109500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023]
Abstract
Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.
Collapse
Affiliation(s)
- Adam S Greenblatt
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund and Aarhus University Hospital, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Fábio A Nascimento
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
| |
Collapse
|
33
|
Aoki Y, Kazui H, Pascual-Marqui RD, Bruña R, Yoshiyama K, Wada T, Kanemoto H, Suzuki Y, Suehiro T, Satake Y, Yamakawa M, Hata M, Canuet L, Ishii R, Iwase M, Ikeda M. Normalized Power Variance: A new Field Orthogonal to Power in EEG Analysis. Clin EEG Neurosci 2023; 54:611-619. [PMID: 35345930 DOI: 10.1177/15500594221088736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
To date, electroencephalogram (EEG) has been used in the diagnosis of epilepsy, dementia, and disturbance of consciousness via the inspection of EEG waves and identification of abnormal electrical discharges and slowing of basic waves. In addition, EEG power analysis combined with a source estimation method like exact-low-resolution-brain-electromagnetic-tomography (eLORETA), which calculates the power of cortical electrical activity from EEG data, has been widely used to investigate cortical electrical activity in neuropsychiatric diseases. However, the recently developed field of mathematics "information geometry" indicates that EEG has another dimension orthogonal to power dimension - that of normalized power variance (NPV). In addition, by introducing the idea of information geometry, a significantly faster convergent estimator of NPV was obtained. Research into this NPV coordinate has been limited thus far. In this study, we applied this NPV analysis of eLORETA to idiopathic normal pressure hydrocephalus (iNPH) patients prior to a cerebrospinal fluid (CSF) shunt operation, where traditional power analysis could not detect any difference associated with CSF shunt operation outcome. Our NPV analysis of eLORETA detected significantly higher NPV values at the high convexity area in the beta frequency band between 17 shunt responders and 19 non-responders. Considering our present and past research findings about NPV, we also discuss the advantage of this application of NPV representing a sensitive early warning signal of cortical impairment. Overall, our findings demonstrated that EEG has another dimension - that of NPV, which contains a lot of information about cortical electrical activity that can be useful in clinical practice.
Collapse
Affiliation(s)
- Yasunori Aoki
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
- Department of Psychiatry, Nippon Life Hospital, Osaka, Japan
| | - Hiroaki Kazui
- Department of Neuropsychiatry, Kochi Medical School, Kochi University, Kochi, Japan
| | - Roberto D Pascual-Marqui
- The KEY Institute for Brain-Mind Research, University Hospital of Psychiatry, Zurich, Switzerland
| | - Ricardo Bruña
- UCM-UPM Centre for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Electrical Engineering, La Laguna University, Tenerife, Spain
| | - Kenji Yoshiyama
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Tamiki Wada
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Hideki Kanemoto
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yukiko Suzuki
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Takashi Suehiro
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yuto Satake
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Maki Yamakawa
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Masahiro Hata
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Leonides Canuet
- Neurology department, Nuestra Senora del Rosario hospital, Madrid, Spain
| | - Ryouhei Ishii
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
- Graduate School of Comprehensive Rehabilitation, Osaka Prefecture University, Osaka, Japan
| | - Masao Iwase
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| |
Collapse
|
34
|
Vallarino E, Hincapié AS, Jerbi K, Leahy RM, Pascarella A, Sorrentino A, Sommariva S. Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation. Neuroimage 2023; 281:120356. [PMID: 37703939 DOI: 10.1016/j.neuroimage.2023.120356] [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: 04/15/2023] [Revised: 08/25/2023] [Accepted: 08/30/2023] [Indexed: 09/15/2023] Open
Abstract
The accurate characterization of cortical functional connectivity from Magnetoencephalography (MEG) data remains a challenging problem due to the subjective nature of the analysis, which requires several decisions at each step of the analysis pipeline, such as the choice of a source estimation algorithm, a connectivity metric and a cortical parcellation, to name but a few. Recent studies have emphasized the importance of selecting the regularization parameter in minimum norm estimates with caution, as variations in its value can result in significant differences in connectivity estimates. In particular, the amount of regularization that is optimal for MEG source estimation can actually be suboptimal for coherence-based MEG connectivity analysis. In this study, we expand upon previous work by examining a broader range of commonly used connectivity metrics, including the imaginary part of coherence, corrected imaginary part of Phase Locking Value, and weighted Phase Lag Index, within a larger and more realistic simulation scenario. Our results show that the best estimate of connectivity is achieved using a regularization parameter that is 1 or 2 orders of magnitude smaller than the one that yields the best source estimation. This remarkable difference may imply that previous work assessing source-space connectivity using minimum-norm may have benefited from using less regularization, as this may have helped reduce false positives. Importantly, we provide the code for MEG data simulation and analysis, offering the research community a valuable open source tool for informed selections of the regularization parameter when using minimum-norm for source space connectivity analyses.
Collapse
Affiliation(s)
| | - Ana Sofia Hincapié
- Computational and Cognitive Neuroscience Lab, Psychology Department, Université de Montréal, Montréal, Québec, Canada
| | - Karim Jerbi
- Computational and Cognitive Neuroscience Lab, Psychology Department, Université de Montréal, Montréal, Québec, Canada; MEG Center, Psychology Department, Université de Montréal, Montréal, Québec, Canada; MILA (Quebec Artificial Intelligence Institute), Montréal, Québec, Canada; Unique Center (Québec Neuro-AI Research Center), Montréal, Québec, Canada
| | - Richard M Leahy
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Annalisa Pascarella
- Istituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Ricerche, Roma, Italy
| | | | - Sara Sommariva
- Dipartimento di Matematica, Università di Genova, Genova, Italy
| |
Collapse
|
35
|
Jafari M, Shaabani M, Hosseini SR, Ashayeri H, Bakhshi E, Haghgoo HA. Modification of cortical electrical activity in stroke survivors with abnormal subjective visual vertical: An eLORETA study. Heliyon 2023; 9:e22194. [PMID: 38027645 PMCID: PMC10661540 DOI: 10.1016/j.heliyon.2023.e22194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 09/16/2023] [Accepted: 11/06/2023] [Indexed: 12/01/2023] Open
Abstract
Objectives Balance impairment is among the main complications of stroke. The gravity-based subjective vertical (SV) is considered an important reference for upright posture and navigation affected by stroke. The correlation between injury location and pathological perception of verticality remains controversial. This study aimed to evaluate the cortico-cortical network of vertical perception among patients with the right hemisphere stroke and abnormal visual-vertical perception compared with healthy individuals. Materials and methods This observational cross-sectional study included 40 patients with the right hemisphere stroke and 35 healthy participants. All patients had abnormal visual-vertical perception. The EEG connectivity analysis was conducted through the exact low-resolution brain electromagnetic tomography analysis (eLORETA). Results Stroke survivors manifested a power spectral density that reduced within the beta-2 frequency band in the left hemisphere and increased within the beta-3 frequency band in the right hemisphere compared with controls (p < 0.01). The lagged-phase synchronization was increased within alpha-1, beta-2, and beta-3 bands and decreased in stroke survivors compared with controls in the vestibular network involved in visual-vertical perception (p < 0.01). Conclusion The results of this study demonstrated variations in the function and functional connectivity of cortical areas involved in the visual-vertical perception that are mainly located in the vestibular cortex.
Collapse
Affiliation(s)
- Meymaneh Jafari
- Department of Audiology, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Moslem Shaabani
- Department of Audiology, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Seyed Ruhollah Hosseini
- Department of Psychology, Faculty of Education Sciences and Psychology, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Hassan Ashayeri
- Rehabilitation Research Center, Department of Basic Sciences, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Enayatollah Bakhshi
- Department of Biostatistics, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Hojjat Allah Haghgoo
- Department of Occupational Therapy. University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| |
Collapse
|
36
|
Yang Y, Yang H, Yu C, Ni F, Yu T, Luo R. Alterations in the topological organization of the default-mode network in Tourette syndrome. BMC Neurol 2023; 23:390. [PMID: 37899454 PMCID: PMC10614376 DOI: 10.1186/s12883-023-03421-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/05/2023] [Indexed: 10/31/2023] Open
Abstract
BACKGROUND The exact pathophysiology of TS is still elusive. Previous studies have identified default mode networks (DMN) abnormalities in patients with TS. However, these literatures investigated the neural activity during the tic suppression, not a true resting-state. Therefore, this study aimed to reveal the neural mechanism of Tourette's syndrome (TS) from the perspective of topological organization and functional connectivity within the DMN by electroencephalography (EEG) in resting-state. METHODS The study was conducted by analyzing the EEG data of TS patients with graph theory approaches. Thirty children with TS and thirty healthy controls (HCs) were recruited, and all subjects underwent resting-state EEG data acquisition. Functional connectivity within the DMN was calculated, and network properties were measured. RESULTS A significantly lower connectivity in the neural activity of the TS patients in the β band was found between the bilateral posterior cingulate cortex/retrosplenial cortex (t = -3.02, p < 0.05). Compared to HCs, the TS patients' local topological properties (degree centrality) in the left temporal lobe in the γ band were changed, while the global topological properties (global efficiency and local efficiency) in DMN exhibited no significant differences. It was also demonstrated that the degree centrality of the left temporal lobe in the γ band was positively related to the Yale Global Tic Severity Scale scores (r = 0.369, p = 0.045). CONCLUSIONS The functional connectivity and topological properties of the DMN of TS patients were disrupted, and abnormal DMN topological property alterations might affect the severity of tic in TS patients. The abnormal topological properties of the DMN in TS patients may be due to abnormal functional connectivity alterations. The findings provide novel insight into the neural mechanism of TS patients.
Collapse
Affiliation(s)
- Yue Yang
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Hua Yang
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Chunmei Yu
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Fang Ni
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Tao Yu
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Rong Luo
- Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Sichuan University, Chengdu, 610041, China.
| |
Collapse
|
37
|
Qiao R, Zhang H, Tian Y. EEG cortical network reveals the temporo-spatial mechanism of visual search. Brain Res Bull 2023; 203:110758. [PMID: 37704055 DOI: 10.1016/j.brainresbull.2023.110758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/06/2023] [Accepted: 09/11/2023] [Indexed: 09/15/2023]
Abstract
This study aims to explore a method based on brain networks for implicit attention by using wavelet coherence as feature to identify individual targets in the visual field, find the optimal classification rhythm and time window, and investigate the relationship between the optimal rhythm and N2pc event-related potential. The study uses a weighted minimum norm estimate to locate the sources of the scalp EEG and reconstructs the source time series. The functional connectivity between brain areas during the visual search process is evaluated using wavelet coherence analysis, and a lateral difference network is constructed based on the difference in coherence values between the left and right visual fields. A support vector machine classifier is trained based on the wavelet coherence network features to identify the target in the left or right visual field. We also extract N2pc from the source activity data of the parieto-occipital brain region and record the time period in which N2pc occurred. The study finds that the best classification performance is achieved in the theta rhythm from 200 to 400 ms and achieved an average classification accuracy of 87% (chance level: 51.07%) in a serial search task. And this time window corresponds to the time period when N2pc appeared. The results show that the use of wavelet coherence analysis to evaluate the functional connectivity between brain areas during the visual search process provides a new approach for analyzing brain activity. The study's findings regarding the relationship between the N2pc and theta rhythm and the effectiveness of using wavelet coherence network features based on the theta rhythm for visual search classification contribute to the understanding of the neural mechanisms underlying visual search.
Collapse
Affiliation(s)
- Rui Qiao
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Haiyong Zhang
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Yin Tian
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Institute for Advanced Sciences,Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing 400064, China.
| |
Collapse
|
38
|
Oliveira R, De Lucia M, Lutti A. Single-subject electroencephalography measurement of interhemispheric transfer time for the in-vivo estimation of axonal morphology. Hum Brain Mapp 2023; 44:4859-4874. [PMID: 37470446 PMCID: PMC10472916 DOI: 10.1002/hbm.26420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 06/12/2023] [Accepted: 06/26/2023] [Indexed: 07/21/2023] Open
Abstract
Assessing axonal morphology in vivo opens new avenues for the combined study of brain structure and function. A novel approach has recently been introduced to estimate the morphology of axonal fibers from the combination of magnetic resonance imaging (MRI) data and electroencephalography (EEG) measures of the interhemispheric transfer time (IHTT). In the original study, the IHTT measures were computed from EEG data averaged across a group, leading to bias of the axonal morphology estimates. Here, we seek to estimate axonal morphology from individual measures of IHTT, obtained from EEG data acquired in a visual evoked potential experiment. Subject-specific IHTTs are computed in a data-driven framework with minimal a priori constraints, based on the maximal peak of neural responses to visual stimuli within periods of statistically significant evoked activity in the inverse solution space. The subject-specific IHTT estimates ranged from 8 to 29 ms except for one participant and the between-session variability was comparable to between-subject variability. The mean radius of the axonal radius distribution, computed from the IHTT estimates and the MRI data, ranged from 0 to 1.09 μm across subjects. The change in axonal g-ratio with axonal radius ranged from 0.62 to 0.81 μm-α . The single-subject measurement of the IHTT yields estimates of axonal morphology that are consistent with histological values. However, improvement of the repeatability of the IHTT estimates is required to improve the specificity of the single-subject axonal morphology estimates.
Collapse
Affiliation(s)
- Rita Oliveira
- Laboratory for Research in Neuroimaging, Department of Clinical NeuroscienceLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Marzia De Lucia
- Laboratory for Research in Neuroimaging, Department of Clinical NeuroscienceLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical NeuroscienceLausanne University Hospital and University of LausanneLausanneSwitzerland
| |
Collapse
|
39
|
Pham MD, D’Angiulli A, Dehnavi MM, Chhabra R. From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems? Brain Sci 2023; 13:1316. [PMID: 37759917 PMCID: PMC10526461 DOI: 10.3390/brainsci13091316] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
We examine the challenging "marriage" between computational efficiency and biological plausibility-A crucial node in the domain of spiking neural networks at the intersection of neuroscience, artificial intelligence, and robotics. Through a transdisciplinary review, we retrace the historical and most recent constraining influences that these parallel fields have exerted on descriptive analysis of the brain, construction of predictive brain models, and ultimately, the embodiment of neural networks in an enacted robotic agent. We study models of Spiking Neural Networks (SNN) as the central means enabling autonomous and intelligent behaviors in biological systems. We then provide a critical comparison of the available hardware and software to emulate SNNs for investigating biological entities and their application on artificial systems. Neuromorphics is identified as a promising tool to embody SNNs in real physical systems and different neuromorphic chips are compared. The concepts required for describing SNNs are dissected and contextualized in the new no man's land between cognitive neuroscience and artificial intelligence. Although there are recent reviews on the application of neuromorphic computing in various modules of the guidance, navigation, and control of robotic systems, the focus of this paper is more on closing the cognition loop in SNN-embodied robotics. We argue that biologically viable spiking neuronal models used for electroencephalogram signals are excellent candidates for furthering our knowledge of the explainability of SNNs. We complete our survey by reviewing different robotic modules that can benefit from neuromorphic hardware, e.g., perception (with a focus on vision), localization, and cognition. We conclude that the tradeoff between symbolic computational power and biological plausibility of hardware can be best addressed by neuromorphics, whose presence in neurorobotics provides an accountable empirical testbench for investigating synthetic and natural embodied cognition. We argue this is where both theoretical and empirical future work should converge in multidisciplinary efforts involving neuroscience, artificial intelligence, and robotics.
Collapse
Affiliation(s)
- Martin Do Pham
- Department of Computer Science, University of Toronto, Toronto, ON M5S 1A1, Canada; (M.D.P.); (M.M.D.)
| | - Amedeo D’Angiulli
- Department of Neuroscience, Carleton University, Ottawa, ON K1S 5B6, Canada;
| | - Maryam Mehri Dehnavi
- Department of Computer Science, University of Toronto, Toronto, ON M5S 1A1, Canada; (M.D.P.); (M.M.D.)
| | - Robin Chhabra
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
| |
Collapse
|
40
|
Theodosiadou G, Arnaoutoglou DG, Nannis I, Katsimentes S, Sirakoulis GC, Kyriacou GA. Direct Estimation of Equivalent Bioelectric Sources Based on Huygens' Principle. Bioengineering (Basel) 2023; 10:1063. [PMID: 37760165 PMCID: PMC10525174 DOI: 10.3390/bioengineering10091063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023] Open
Abstract
An estimation of the electric sources in the heart was conducted using a novel method, based on Huygens' Principle, aiming at a direct estimation of equivalent bioelectric sources over the heart's surface in real time. The main scope of this work was to establish a new, fast approach to the solution of the inverse electrocardiography problem. The study was based on recorded electrocardiograms (ECGs). Based on Huygens' Principle, measurements obtained from the surfaceof a patient's thorax were interpolated over the surface of the employed volume conductor model and considered as secondary Huygens' sources. These sources, being non-zero only over the surface under study, were employed to determine the weighting factors of the eigenfunctions' expansion, describing the generated voltage distribution over the whole conductor volume. With the availability of the potential distribution stemming from measurements, the electromagnetics reciprocity theorem is applied once again to yield the equivalent sources over the pericardium. The methodology is self-validated, since the surface potentials calculated from these equivalent sources are in very good agreement with ECG measurements. The ultimate aim of this effort is to create a tool providing the equivalent epicardial voltage or current sources in real time, i.e., during the ECG measurements with multiple electrodes.
Collapse
Affiliation(s)
| | | | | | | | | | - George A. Kyriacou
- Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece; (G.T.); (D.G.A.); (I.N.); (S.K.); (G.C.S.)
| |
Collapse
|
41
|
Badalì C, Wollseiffen P, Schneider S. Under pressure-the influence of hypergravity on electrocortical activity and neurocognitive performance. Exp Brain Res 2023; 241:2249-2259. [PMID: 37542004 PMCID: PMC10471660 DOI: 10.1007/s00221-023-06677-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 07/25/2023] [Indexed: 08/06/2023]
Abstract
The effects of hypergravity and the associated increased pressure on the human body have not yet been studied in detail, but are of great importance for the safety of astronauts on space missions and could have a long-term impact on rehabilitation strategies for neurological patients. Considering the plans of international space agencies with the exploration of Mars and Moon, it is important to explore the effects of both extremes, weightlessness and hypergravity. During parabolic flights, a flight manoeuvre that artificially creates weightlessness and hypergravity, electrocortical activity as well as behavioural parameters (error rate and reaction time) and neuronal parameters (event-related potentials P300 and N200) were examined with an electroencephalogram. Thirteen participants solved a neurocognitive task (mental arithmetic task as a primary task and oddball paradigm as a secondary task) within normal as well as hypergravity condition in fifteen consecutive parabolas for 22 s each. No changes between the different gravity levels could be observed for the behavioural parameters and cortical current density. A significantly lower P300 amplitude was observed in 1 G, triggered by the primary task and the target sound of the oddball paradigm. The N200, provoked by the sounds of the oddball paradigm, revealed a higher amplitude in 1.8 G. A model established by Kohn et al. (2018) describing changes in neural communication with decreasing gravity can be used here as an explanatory approach. The fluid shift increases the intracranial pressure, decreases membrane viscosity and influences the open state probability of ion channels. This leads to an increase in the resting membrane potential, and the threshold for triggering an action potential can be reached more easily. The question now arises whether the observed changes are linear or whether they depend on a specific threshold.
Collapse
Affiliation(s)
- Constance Badalì
- Institute of Movement and Neurosciences, German Sport University Cologne, Am Sportpark Müngersdorf 6, 50933, Cologne, Germany.
| | - Petra Wollseiffen
- Institute of Movement and Neurosciences, German Sport University Cologne, Am Sportpark Müngersdorf 6, 50933, Cologne, Germany
- Centre for Health and Integrative Physiology in Space (CHIPS), German Sport University Cologne, Cologne, Germany
| | - Stefan Schneider
- Institute of Movement and Neurosciences, German Sport University Cologne, Am Sportpark Müngersdorf 6, 50933, Cologne, Germany
- Centre for Health and Integrative Physiology in Space (CHIPS), German Sport University Cologne, Cologne, Germany
| |
Collapse
|
42
|
Miao Z, Zhao M, Zhang X, Ming D. LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interfaces and interpretability. Neuroimage 2023; 276:120209. [PMID: 37269957 DOI: 10.1016/j.neuroimage.2023.120209] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/05/2023] [Accepted: 05/30/2023] [Indexed: 06/05/2023] Open
Abstract
Electroencephalography (EEG)-based brain-computer interfaces (BCIs) pose a challenge for decoding due to their low spatial resolution and signal-to-noise ratio. Typically, EEG-based recognition of activities and states involves the use of prior neuroscience knowledge to generate quantitative EEG features, which may limit BCI performance. Although neural network-based methods can effectively extract features, they often encounter issues such as poor generalization across datasets, high predicting volatility, and low model interpretability. To address these limitations, we propose a novel lightweight multi-dimensional attention network, called LMDA-Net. By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net is able to effectively integrate features from multiple dimensions, resulting in improved classification performance across various BCI tasks. LMDA-Net was evaluated on four high-impact public datasets, including motor imagery (MI) and P300-Speller, and was compared with other representative models. The experimental results demonstrate that LMDA-Net outperforms other representative methods in terms of classification accuracy and predicting volatility, achieving the highest accuracy in all datasets within 300 training epochs. Ablation experiments further confirm the effectiveness of the channel attention module and the depth attention module. To facilitate an in-depth understanding of the features extracted by LMDA-Net, we propose class-specific neural network feature interpretability algorithms that are suitable for evoked responses and endogenous activities. By mapping the output of the specific layer of LMDA-Net to the time or spatial domain through class activation maps, the resulting feature visualizations can provide interpretable analysis and establish connections with EEG time-spatial analysis in neuroscience. In summary, LMDA-Net shows great potential as a general decoding model for various EEG tasks.
Collapse
Affiliation(s)
- Zhengqing Miao
- State Key Laboratory of Precision Measuring Technology and Instruments, School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, China.
| | - Meirong Zhao
- State Key Laboratory of Precision Measuring Technology and Instruments, School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, China.
| | - Xin Zhang
- Laboratory of Neural Engineering and Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, China; Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.
| | - Dong Ming
- Laboratory of Neural Engineering and Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, China; Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.
| |
Collapse
|
43
|
Paz-Linares D, Gonzalez-Moreira E, Areces-Gonzalez A, Wang Y, Li M, Martinez-Montes E, Bosch-Bayard J, Bringas-Vega ML, Valdes-Sosa M, Valdes-Sosa PA. Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG. Sci Rep 2023; 13:11466. [PMID: 37454235 PMCID: PMC10349891 DOI: 10.1038/s41598-023-38513-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.
Collapse
Affiliation(s)
- Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Eduardo Gonzalez-Moreira
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Electrical Engineering, Central University "Marta Abreu" of Las Villas, Santa Clara, Cuba
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Technical Sciences, University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Rio, Cuba
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Jorge Bosch-Bayard
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
- McGill Centre for Integrative Neurosciences MCIN, Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Mitchell Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba.
| |
Collapse
|
44
|
Cuadros J, Z-Rivera L, Castro C, Whitaker G, Otero M, Weinstein A, Martínez-Montes E, Prado P, Zañartu M. DIVA Meets EEG: Model Validation Using Formant-Shift Reflex. APPLIED SCIENCES (BASEL, SWITZERLAND) 2023; 13:7512. [PMID: 38435340 PMCID: PMC10906992 DOI: 10.3390/app13137512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
The neurocomputational model 'Directions into Velocities of Articulators' (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions.
Collapse
Affiliation(s)
- Jhosmary Cuadros
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal 5001, Venezuela
| | - Lucía Z-Rivera
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile
| | - Christian Castro
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile
| | - Grace Whitaker
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
| | - Mónica Otero
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago 8420524, Chile
- Centro Basal Ciencia & Vida, Universidad San Sebastián, Santiago 8580000, Chile
| | - Alejandro Weinstein
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile
| | | | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago 7510602, Chile
| | - Matías Zañartu
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
| |
Collapse
|
45
|
Legaz A, Prado P, Moguilner S, Báez S, Santamaría-García H, Birba A, Barttfeld P, García AM, Fittipaldi S, Ibañez A. Social and non-social working memory in neurodegeneration. Neurobiol Dis 2023; 183:106171. [PMID: 37257663 PMCID: PMC11177282 DOI: 10.1016/j.nbd.2023.106171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/08/2023] [Accepted: 05/24/2023] [Indexed: 06/02/2023] Open
Abstract
Although social functioning relies on working memory, whether a social-specific mechanism exists remains unclear. This undermines the characterization of neurodegenerative conditions with both working memory and social deficits. We assessed working memory domain-specificity across behavioral, electrophysiological, and neuroimaging dimensions in 245 participants. A novel working memory task involving social and non-social stimuli with three load levels was assessed across controls and different neurodegenerative conditions with recognized impairments in: working memory and social cognition (behavioral-variant frontotemporal dementia); general cognition (Alzheimer's disease); and unspecific patterns (Parkinson's disease). We also examined resting-state theta oscillations and functional connectivity correlates of working memory domain-specificity. Results in controls and all groups together evidenced increased working memory demands for social stimuli associated with frontocinguloparietal theta oscillations and salience network connectivity. Canonical frontal theta oscillations and executive-default mode network anticorrelation indexed non-social stimuli. Behavioral-variant frontotemporal dementia presented generalized working memory deficits related to posterior theta oscillations, with social stimuli linked to salience network connectivity. In Alzheimer's disease, generalized working memory impairments were related to temporoparietal theta oscillations, with non-social stimuli linked to the executive network. Parkinson's disease showed spared working memory performance and canonical brain correlates. Findings support a social-specific working memory and related disease-selective pathophysiological mechanisms.
Collapse
Affiliation(s)
- Agustina Legaz
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Universidad Nacional de Córdoba, Facultad de Psicología, Córdoba, Argentina
| | - Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile; Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Sebastián Moguilner
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, United States; Trinity College Dublin (TCD), Dublin, Ireland
| | | | - Hernando Santamaría-García
- Pontificia Universidad Javeriana, Medical School, Physiology and Psychiatry Departments, Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Agustina Birba
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina; Facultad de Psicología, Universidad de La Laguna, Tenerife, Spain; Instituto Universitario de Neurociencia, Universidad de La Laguna, Tenerife, Spain
| | - Pablo Barttfeld
- Cognitive Science Group. Instituto de Investigaciones Psicológicas (IIPsi), CONICET UNC, Facultad de Psicología, Universidad Nacional de Córdoba, Boulevard de la Reforma esquina Enfermera Gordillo, CP 5000. Córdoba, Argentina
| | - Adolfo M García
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, United States; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile; Trinity College Dublin (TCD), Dublin, Ireland
| | - Sol Fittipaldi
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, United States; Trinity College Dublin (TCD), Dublin, Ireland.
| | - Agustín Ibañez
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, United States; Trinity College Dublin (TCD), Dublin, Ireland.
| |
Collapse
|
46
|
Delatolas T, Antonakakis M, Wolters CH, Zervakis M. EEG Source Analysis with a Convolutional Neural Network and Finite Element Analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083731 DOI: 10.1109/embc40787.2023.10340742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
To reconstruct the electrophysiological activity of brain responses, source analysis is performed through the solution of the forward and inverse problems. The former contains a unique solution while the latter is ill-posed. In this regard, many algorithms have been suggested relying on different prior information for solving the inverse problem. Recently, neural networks have been used to deal with source analysis. However, their underlying training for inverse solutions is based on suboptimal forward modeling. In this work, we propose a CNN that is able to reconstruct EEG brain activity. To train our proposed CNN, a skull-conductivity calibrated and white matter anisotropic head model. Based on this model, we generate simulated EEG data and used them to train our CNN. We first evaluate the performance of our CNN using the simulated EEG data while a realistic application with somatosensory evoked potentials follows. From the results, we observed that the CCN correctly localized the P20/N20 component at the subject-specific Brodmann area 3b and it can potentially localize deeper sources. A comparison is also presented with well-known inverse solutions (single dipole scans and sLORETA) showing similar localization performance. Through these results, an emerging potential for real applications appears on the basis of realistic head modeling.
Collapse
|
47
|
Liu C, Downey RJ, Mu Y, Richer N, Hwang J, Shah VA, Sato SD, Clark DJ, Hass CJ, Manini TM, Seidler RD, Ferris DP. Comparison of EEG Source Localization Using Simplified and Anatomically Accurate Head Models in Younger and Older Adults. IEEE Trans Neural Syst Rehabil Eng 2023; 31:2591-2602. [PMID: 37252873 PMCID: PMC10336858 DOI: 10.1109/tnsre.2023.3281356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Accuracy of electroencephalography (EEG) source localization relies on the volume conduction head model. A previous analysis of young adults has shown that simplified head models have larger source localization errors when compared with head models based on magnetic resonance images (MRIs). As obtaining individual MRIs may not always be feasible, researchers often use generic head models based on template MRIs. It is unclear how much error would be introduced using template MRI head models in older adults that likely have differences in brain structure compared to young adults. The primary goal of this study was to determine the error caused by using simplified head models without individual-specific MRIs in both younger and older adults. We collected high-density EEG during uneven terrain walking and motor imagery for 15 younger (22±3 years) and 21 older adults (74±5 years) and obtained [Formula: see text]-weighted MRI for each individual. We performed equivalent dipole fitting after independent component analysis to obtain brain source locations using four forward modeling pipelines with increasing complexity. These pipelines included: 1) a generic head model with template electrode positions or 2) digitized electrode positions, 3) individual-specific head models with digitized electrode positions using simplified tissue segmentation, or 4) anatomically accurate segmentation. We found that when compared to the anatomically accurate individual-specific head models, performing dipole fitting with generic head models led to similar source localization discrepancies (up to 2 cm) for younger and older adults. Co-registering digitized electrode locations to the generic head models reduced source localization discrepancies by ∼ 6 mm. Additionally, we found that source depths generally increased with skull conductivity for the representative young adult but not as much for the older adult. Our results can help inform a more accurate interpretation of brain areas in EEG studies when individual MRIs are unavailable.
Collapse
|
48
|
Cao J, Bulger E, Shinn-Cunningham B, Grover P, Kainerstorfer JM. Diffuse Optical Tomography Spatial Prior for EEG Source Localization in Human Visual Cortex. Neuroimage 2023:120210. [PMID: 37311535 DOI: 10.1016/j.neuroimage.2023.120210] [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: 04/27/2023] [Accepted: 05/30/2023] [Indexed: 06/15/2023] Open
Abstract
Electroencephalography (EEG) and diffuse optical tomography (DOT) are imaging methods which are widely used for neuroimaging. While the temporal resolution of EEG is high, the spatial resolution is typically limited. DOT, on the other hand, has high spatial resolution, but the temporal resolution is inherently limited by the slow hemodynamics it measures. In our previous work, we showed using computer simulations that when using the results of DOT reconstruction as the spatial prior for EEG source reconstruction, high spatio-temporal resolution could be achieved. In this work, we experimentally validate the algorithm by alternatingly flashing two visual stimuli at a speed that is faster than the temporal resolution of DOT. We show that the joint reconstruction using both EEG and DOT clearly resolves the two stimuli temporally, and the spatial confinement is drastically improved in comparison to reconstruction using EEG alone.
Collapse
Affiliation(s)
- Jiaming Cao
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Eli Bulger
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Barbara Shinn-Cunningham
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Pulkit Grover
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Jana M Kainerstorfer
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, Pennsylvania, United States.
| |
Collapse
|
49
|
Ferrand M, Baumann C, Aron O, Vignal JP, Jonas J, Tyvaert L, Colnat-Coulbois S, Koessler L, Maillard L. Intracerebral Correlates of Scalp EEG Ictal Discharges Based on Simultaneous Stereo-EEG Recordings. Neurology 2023; 100:e2045-e2059. [PMID: 36963841 PMCID: PMC10186237 DOI: 10.1212/wnl.0000000000207135] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/18/2023] [Indexed: 03/26/2023] Open
Abstract
BACKGROUND AND OBJECTIVES It remains unknown to what extent ictal scalp EEG can accurately predict the localization of the intracerebral seizure onset in presurgical evaluation of drug-resistant epilepsies. In this study, we aimed to define homogeneous ictal scalp EEG profiles (based on their first ictal abnormality) and assess their localizing value using simultaneously recorded scalp EEG and stereo-EEG. METHODS We retrospectively included consecutive patients with drug-resistant focal epilepsy who had simultaneous stereo-EEG and scalp EEG recordings of at least 1 seizure in the epileptology unit in Nancy, France. We analyzed 1 seizure per patient and used hierarchical cluster analysis to group similar seizure profiles on scalp EEG and then performed a descriptive analysis of their intracerebral correlates. RESULTS We enrolled 129 patients in this study. The hierarchical cluster analysis showed 6 profiles on scalp EEG first modification. None were specific to a single intracerebral localization. The "normal EEG" and "blurred EEG" clusters (early muscle artifacts) comprised only 5 patients each and corresponded to no preferential intracerebral localization. The "temporal discharge" cluster (n = 46) was characterized by theta or delta discharges on ipsilateral anterior temporal scalp electrodes and corresponded to a preferential mesial temporal intracerebral localization. The "posterior discharge" cluster (n = 42) was characterized by posterior ipsilateral or contralateral rhythmic alpha discharges or slow waves on scalp and corresponded to a preferential temporal localization. However, this profile was the statistically most frequent scalp EEG correlate of occipital and parietal seizures. The "diffuse suppression" cluster (n = 9) was characterized by a bilateral and diffuse background activity suppression on scalp and corresponded to mesial, and particularly insulo-opercular, localization. Finally, the "frontal discharge" cluster (n = 22) was characterized by bilateral frontal rhythmic fast activity or preictal spike on scalp and corresponded to preferential ventrodorsal frontal intracerebral localizations. DISCUSSION The hierarchical cluster analysis identified 6 seizure profiles regarding the first abnormality on scalp EEG. None of them were specific of a single intracerebral localization. Nevertheless, the strong relationships between the "temporal," "frontal," "diffuse suppression," and "posterior" profiles and intracerebral discharge localizations may contribute to hierarchize hypotheses derived from ictal scalp EEG analysis regarding intracerebral seizure onset.
Collapse
Affiliation(s)
- Mickaël Ferrand
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Cédric Baumann
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Olivier Aron
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Jean-Pierre Vignal
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Jacques Jonas
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Louise Tyvaert
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Sophie Colnat-Coulbois
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Laurent Koessler
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Louis Maillard
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France.
| |
Collapse
|
50
|
Hecker L, Tebartz van Elst L, Kornmeier J. Source localization using recursively applied and projected MUSIC with flexible extent estimation. Front Neurosci 2023; 17:1170862. [PMID: 37255753 PMCID: PMC10225686 DOI: 10.3389/fnins.2023.1170862] [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: 02/21/2023] [Accepted: 04/17/2023] [Indexed: 06/01/2023] Open
Abstract
Magneto- and electroencephalography (M/EEG) are widespread techniques to measure neural activity in-vivo at a high temporal resolution but low spatial resolution. Locating the neural sources underlying the M/EEG poses an inverse problem, which is ill-posed. We developed a new method based on Recursive Application of Multiple Signal Classification (MUSIC). Our proposed method is able to recover not only the locations but, in contrast to other inverse solutions, also the extent of active brain regions flexibly (FLEX-MUSIC). This is achieved by allowing it to search not only for single dipoles but also dipole clusters of increasing extent to find the best fit during each recursion. FLEX-MUSIC achieved the highest accuracy for both single dipole and extended sources compared to all other methods tested. Remarkably, FLEX-MUSIC was capable to accurately estimate the level of sparsity in the source space (r = 0.82), whereas all other approaches tested failed to do so (r ≤ 0.18). The average computation time of FLEX-MUSIC was considerably lower compared to a popular Bayesian approach and comparable to that of another recursive MUSIC approach and eLORETA. FLEX-MUSIC produces only few errors and was capable to reliably estimate the extent of sources. The accuracy and low computation time of FLEX-MUSIC renders it an improved technique to solve M/EEG inverse problems both in neuroscience research and potentially in pre-surgery diagnostic in epilepsy.
Collapse
Affiliation(s)
- Lukas Hecker
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Freiburg, Germany
- Department of Psychosomatic Medicine and Psychotherapy, University of Freiburg Medical Center, Freiburg, Germany
- Perception and Cognition Lab, Institute for Frontier Areas of Psychology and Mental Health, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ludger Tebartz van Elst
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jürgen Kornmeier
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Freiburg, Germany
- Perception and Cognition Lab, Institute for Frontier Areas of Psychology and Mental Health, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| |
Collapse
|