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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] [MESH Headings] [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.
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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.
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Marino M, Mantini D. Human brain imaging with high-density electroencephalography: Techniques and applications. J Physiol 2024. [PMID: 39173191 DOI: 10.1113/jp286639] [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: 05/04/2024] [Accepted: 07/30/2024] [Indexed: 08/24/2024] Open
Abstract
Electroencephalography (EEG) is a technique for non-invasively measuring neuronal activity in the human brain using electrodes placed on the participant's scalp. With the advancement of digital technologies, EEG analysis has evolved over time from the qualitative analysis of amplitude and frequency modulations to a comprehensive analysis of the complex spatiotemporal characteristics of the recorded signals. EEG is now considered a powerful tool for measuring neural processes in the same time frame in which they happen (i.e. the subsecond range). However, it is commonly argued that EEG suffers from low spatial resolution, which makes it difficult to localize the generators of EEG activity accurately and reliably. Today, the availability of high-density EEG (hdEEG) systems, combined with methods for incorporating information on head anatomy and sophisticated source-localization algorithms, has transformed EEG into an important neuroimaging tool. hdEEG offers researchers and clinicians a rich and varied range of applications. It can be used not only for investigating neural correlates in motor and cognitive neuroscience experiments, but also for clinical diagnosis, particularly in the detection of epilepsy and the characterization of neural impairments in a wide range of neurological disorders. Notably, the integration of hdEEG systems with other physiological recordings, such as kinematic and/or electromyography data, might be especially beneficial to better understand the neuromuscular mechanisms associated with deconditioning in ageing and neuromotor disorders, by mapping the neurokinematic and neuromuscular connectivity patterns directly in the brain.
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Affiliation(s)
- Marco Marino
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
- Department of General Psychology, University of Padua, Padua, Italy
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Belgium
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Liu Y, Su H, Li C. Effect of Inverse Solutions, Connectivity Measures, and Node Sizes on EEG Source Network: A Simultaneous EEG Study. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2644-2653. [PMID: 39024075 DOI: 10.1109/tnsre.2024.3430312] [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: 07/20/2024]
Abstract
Brain network provides an essential perspective for studying normal and pathological brain activities. Reconstructing the brain network in the source space becomes more needed, for example, as a target in non-invasive neuromodulation. Precise estimating source activities from the scalp EEG is still challenging because it is an ill-posed question and because of the volume conduction effect. There is no consensus on how to reconstruct the EEG source network. This study uses simultaneous scalp EEG and stereo-EEG to investigate the effect of inverse solutions, connectivity measures, and node sizes on the reconstruction of the source network. We evaluated the performance of different methods on both source activity and network. Numerical simulation was also carried out for comparison. The weighted phase-lag index (wPLI) method achieved significantly better performance on the reconstructed networks in source space than five other connectivity measures (directed transfer function (DTF), partial directed coherence (PDC), efficient effective connectivity (EEC), Pearson correlation coefficient (PCC), and amplitude envelope correlation (AEC)). There is no significant difference between the inverse solutions (standardized low-resolution brain electromagnetic tomography (sLORETA), weighted minimum norm estimate (wMNE), and linearly constrained minimum variance (LCMV) beamforming) on the reconstructed source networks. The source network based on signal phases can fit intracranial activities better than signal waveform properties or causality. Our study provides a basis for reconstructing source space networks from scalp EEG, especially for future neuromodulation research.
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Afnan J, Cai Z, Lina JM, Abdallah C, Delaire E, Avigdor T, Ros V, Hedrich T, von Ellenrieder N, Kobayashi E, Frauscher B, Gotman J, Grova C. EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy. Hum Brain Mapp 2024; 45:e26720. [PMID: 38994740 PMCID: PMC11240147 DOI: 10.1002/hbm.26720] [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: 01/15/2024] [Revised: 04/16/2024] [Accepted: 05/06/2024] [Indexed: 07/13/2024] Open
Abstract
Electro/Magneto-EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth-weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high-density EEG (HD-EEG) simulations of epileptic activity and actual MEG/HD-EEG recordings from patients with focal epilepsy. We incorporated depth-weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD-EEG involving a wide range of spatial extents and signal-to-noise ratio (SNR) levels, before investigating EMSI on clinical HD-EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth-weighted cMEM and depth-weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth-weighted cMEM improved the localization when compared to cMEM and depth-weighted MNE, whereas depth-weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD-EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth-weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD-EEG and clinical MEG and HD-EEG for epilepsy patients.
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Affiliation(s)
- Jawata Afnan
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Zhengchen Cai
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Jean-Marc Lina
- Physnum Team, Centre De Recherches Mathématiques, Montréal, Québec, Canada
- Electrical Engineering Department, École De Technologie Supérieure, Montréal, Québec, Canada
- Center for Advanced Research in Sleep Medicine, Sacré-Coeur Hospital, Montréal, Québec, Canada
| | - Chifaou Abdallah
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Edouard Delaire
- Multimodal Functional Imaging Lab, Department of Physics and Concordia School of Health, Concordia University, Montréal, Québec, Canada
| | - Tamir Avigdor
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Victoria Ros
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Tanguy Hedrich
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
| | - Nicolas von Ellenrieder
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Eliane Kobayashi
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jean Gotman
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Physnum Team, Centre De Recherches Mathématiques, Montréal, Québec, Canada
- Multimodal Functional Imaging Lab, Department of Physics and Concordia School of Health, Concordia University, Montréal, Québec, Canada
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5
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Jacobsen NA, Ferris DP. Exploring Electrocortical Signatures of Gait Adaptation: Differential Neural Dynamics in Slow and Fast Gait Adapters. eNeuro 2024; 11:ENEURO.0515-23.2024. [PMID: 38871456 PMCID: PMC11242882 DOI: 10.1523/eneuro.0515-23.2024] [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: 12/06/2023] [Revised: 05/13/2024] [Accepted: 05/27/2024] [Indexed: 06/15/2024] Open
Abstract
Individuals exhibit significant variability in their ability to adapt locomotor skills, with some adapting quickly and others more slowly. Differences in brain activity likely contribute to this variability, but direct neural evidence is lacking. We investigated individual differences in electrocortical activity that led to faster locomotor adaptation rates. We recorded high-density electroencephalography while young, neurotypical adults adapted their walking on a split-belt treadmill and grouped them based on how quickly they restored their gait symmetry. Results revealed unique spectral signatures within the posterior parietal, bilateral sensorimotor, and right visual cortices that differ between fast and slow adapters. Specifically, fast adapters exhibited lower alpha power in the posterior parietal and right visual cortices during early adaptation, associated with quicker attainment of steady-state step length symmetry. Decreased posterior parietal alpha may reflect enhanced spatial attention, sensory integration, and movement planning to facilitate faster locomotor adaptation. Conversely, slow adapters displayed greater alpha and beta power in the right visual cortex during late adaptation, suggesting potential differences in visuospatial processing. Additionally, fast adapters demonstrated reduced spectral power in the bilateral sensorimotor cortices compared with slow adapters, particularly in the theta band, which may suggest variations in perception of the split-belt perturbation. These findings suggest that alpha and beta oscillations in the posterior parietal and visual cortices and theta oscillations in the sensorimotor cortex are related to the rate of gait adaptation.
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Affiliation(s)
- Noelle A Jacobsen
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida 32611-6131
| | - Daniel P Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida 32611-6131
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6
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Pedapati EV, Ethridge LE, Liu Y, Liu R, Sweeney JA, DeStefano LA, Miyakoshi M, Razak K, Schmitt LM, Moore DR, Gilbert DL, Wu SW, Smith E, Shaffer RC, Dominick KC, Horn PS, Binder D, Erickson CA. Frontal Cortex Hyperactivation and Gamma Desynchrony in Fragile X Syndrome: Correlates of Auditory Hypersensitivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.13.598957. [PMID: 38915683 PMCID: PMC11195233 DOI: 10.1101/2024.06.13.598957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Fragile X syndrome (FXS) is an X-linked disorder that often leads to intellectual disability, anxiety, and sensory hypersensitivity. While sound sensitivity (hyperacusis) is a distressing symptom in FXS, its neural basis is not well understood. It is postulated that hyperacusis may stem from temporal lobe hyperexcitability or dysregulation in top-down modulation. Studying the neural mechanisms underlying sound sensitivity in FXS using scalp electroencephalography (EEG) is challenging because the temporal and frontal regions have overlapping neural projections that are difficult to differentiate. To overcome this challenge, we conducted EEG source analysis on a group of 36 individuals with FXS and 39 matched healthy controls. Our goal was to characterize the spatial and temporal properties of the response to an auditory chirp stimulus. Our results showed that males with FXS exhibit excessive activation in the frontal cortex in response to the stimulus onset, which may reflect changes in top-down modulation of auditory processing. Additionally, during the chirp stimulus, individuals with FXS demonstrated a reduction in typical gamma phase synchrony, along with an increase in asynchronous gamma power, across multiple regions, most strongly in temporal cortex. Consistent with these findings, we observed a decrease in the signal-to-noise ratio, estimated by the ratio of synchronous to asynchronous gamma activity, in individuals with FXS. Furthermore, this ratio was highly correlated with performance in an auditory attention task. Compared to controls, males with FXS demonstrated elevated bidirectional frontotemporal information flow at chirp onset. The evidence indicates that both temporal lobe hyperexcitability and disruptions in top-down regulation play a role in auditory sensitivity disturbances in FXS. These findings have the potential to guide the development of therapeutic targets and back-translation strategies.
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Affiliation(s)
- Ernest V Pedapati
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lauren E Ethridge
- Department of Pediatrics, Section on Developmental and Behavioral Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Department of Psychology, University of Oklahoma, Norman, OK, United States
| | - Yanchen Liu
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Rui Liu
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lisa A DeStefano
- Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Makoto Miyakoshi
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Khaleel Razak
- Department of Psychology, University of California, Riverside, Riverside, CA, United States
| | - Lauren M Schmitt
- Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - David R Moore
- Communication Sciences Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Manchester Centre for Audiology and Deafness, University of Manchester, Manchester, UK
| | - Donald L Gilbert
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Steve W Wu
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Elizabeth Smith
- Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Rebecca C Shaffer
- Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Kelli C Dominick
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Paul S Horn
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Devin Binder
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, United States
| | - Craig A Erickson
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, United States
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Jacobsen NA, Ferris DP. Electrocortical theta activity may reflect sensory prediction errors during adaptation to a gradual gait perturbation. PeerJ 2024; 12:e17451. [PMID: 38854799 PMCID: PMC11162180 DOI: 10.7717/peerj.17451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 05/03/2024] [Indexed: 06/11/2024] Open
Abstract
Locomotor adaptation to abrupt and gradual perturbations are likely driven by fundamentally different neural processes. The aim of this study was to quantify brain dynamics associated with gait adaptation to a gradually introduced gait perturbation, which typically results in smaller behavioral errors relative to an abrupt perturbation. Loss of balance during standing and walking elicits transient increases in midfrontal theta oscillations that have been shown to scale with perturbation intensity. We hypothesized there would be no significant change in anterior cingulate theta power (4-7 Hz) with respect to pre-adaptation when a gait perturbation is introduced gradually because the gradual perturbation acceleration and stepping kinematic errors are small relative to an abrupt perturbation. Using mobile electroencephalography (EEG), we measured gait-related spectral changes near the anterior cingulate, posterior cingulate, sensorimotor, and posterior parietal cortices as young, neurotypical adults (n = 30) adapted their gait to an incremental split-belt treadmill perturbation. Most cortical clusters we examined (>70%) did not exhibit changes in electrocortical activity between 2-50 Hz. However, we did observe gait-related theta synchronization near the left anterior cingulate cortex during strides with the largest errors, as measured by step length asymmetry. These results suggest gradual adaptation with small gait asymmetry and perturbation magnitude may not require significant cortical resources beyond normal treadmill walking. Nevertheless, the anterior cingulate may remain actively engaged in error monitoring, transmitting sensory prediction error information via theta oscillations.
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Affiliation(s)
- Noelle A. Jacobsen
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
| | - Daniel Perry Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
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Song Y, Shahdadian S, Armstrong E, Brock E, Conrad SE, Acord S, Johnson YR, Marks W, Papadelis C. Spatiotemporal dynamics of cortical somatosensory network in typically developing children. Cereb Cortex 2024; 34:bhae230. [PMID: 38836408 PMCID: PMC11151116 DOI: 10.1093/cercor/bhae230] [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: 05/21/2023] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 06/06/2024] Open
Abstract
Sense of touch is essential for our interactions with external objects and fine control of hand actions. Despite extensive research on human somatosensory processing, it is still elusive how involved brain regions interact as a dynamic network in processing tactile information. Few studies probed temporal dynamics of somatosensory information flow and reported inconsistent results. Here, we examined cortical somatosensory processing through magnetic source imaging and cortico-cortical coupling dynamics. We recorded magnetoencephalography signals from typically developing children during unilateral pneumatic stimulation. Neural activities underlying somatosensory evoked fields were mapped with dynamic statistical parametric mapping, assessed with spatiotemporal activation analysis, and modeled by Granger causality. Unilateral pneumatic stimulation evoked prominent and consistent activations in the contralateral primary and secondary somatosensory areas but weaker and less consistent activations in the ipsilateral primary and secondary somatosensory areas. Activations in the contralateral primary motor cortex and supramarginal gyrus were also consistently observed. Spatiotemporal activation and Granger causality analysis revealed initial serial information flow from contralateral primary to supramarginal gyrus, contralateral primary motor cortex, and contralateral secondary and later dynamic and parallel information flows between the consistently activated contralateral cortical areas. Our study reveals the spatiotemporal dynamics of cortical somatosensory processing in the normal developing brain.
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Affiliation(s)
- Yanlong Song
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, 1500 Cooper St., Fort Worth, TX 76104, United States
- Department of Bioengineering, University of Texas at Arlington, 500 UTA Blvd., Arlington, TX 76010, United States
- Departments of Physical Medicine and Rehabilitation and Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, United States
| | - Sadra Shahdadian
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, 1500 Cooper St., Fort Worth, TX 76104, United States
- Department of Bioengineering, University of Texas at Arlington, 500 UTA Blvd., Arlington, TX 76010, United States
| | - Eryn Armstrong
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, 1500 Cooper St., Fort Worth, TX 76104, United States
| | - Emily Brock
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, 1500 Cooper St., Fort Worth, TX 76104, United States
| | - Shannon E Conrad
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, 1500 Cooper St., Fort Worth, TX 76104, United States
| | - Stephanie Acord
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, 1500 Cooper St., Fort Worth, TX 76104, United States
| | - Yvette R Johnson
- NEST Developmental Follow-up Center, Neonatology, Cook Children’s Health Care System, 1521 Cooper St., Fort Worth, TX 76104, United States
- Department of Pediatrics, Burnett School of Medicine, Texas Christian University, TCU Box 297085, Fort Worth, TX 76129, United States
| | - Warren Marks
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, 1500 Cooper St., Fort Worth, TX 76104, United States
| | - Christos Papadelis
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, 1500 Cooper St., Fort Worth, TX 76104, United States
- Department of Bioengineering, University of Texas at Arlington, 500 UTA Blvd., Arlington, TX 76010, United States
- Department of Pediatrics, Burnett School of Medicine, Texas Christian University, TCU Box 297085, Fort Worth, TX 76129, United States
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Kaneko N, Yokoyama M, Nakazawa K, Yokoyama H. Accurate digitization of EEG electrode locations by electromagnetic tracking system: The proposed head rotation method and comparison against optical system. MethodsX 2024; 12:102766. [PMID: 38808097 PMCID: PMC11131068 DOI: 10.1016/j.mex.2024.102766] [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: 03/24/2024] [Accepted: 05/15/2024] [Indexed: 05/30/2024] Open
Abstract
Electroencephalogram (EEG) electrode digitization is crucial for accurate EEG source estimation, and several commercial systems are available for this purpose. The present study aimed to evaluate the digitizing accuracy of electromagnetic and optical systems. Additionally, we introduced a novel rotation method for the electromagnetic system and compared its accuracy with the conventional method of electromagnetic and optical systems. In the conventional method, the operator moves around a stationary participant to digitize, while the participant does not move their head or body. In contrast, in our proposed rotation method with an electromagnetic system, the operator rotates the participant sitting on a swivel chair to digitize in a consistent position. We showed high localization accuracy in both the optical and electromagnetic systems, with an average localization error of less than 3.6 mm. Comparisons of the digitization methods revealed that the electromagnetic system demonstrates superior digitizing accuracy compared to the optical system. Notably, the proposed rotational method is the most accurate among the three methods, which can be attributed to the consistent positioning of EEG electrode digitization within the electromagnetic field. Considering the affordability of the electromagnetic system, our findings provide valuable insights for researchers aiming for precise EEG source estimation.•The study compares the accuracy of electromagnetic and optical systems for EEG electrode digitization, introducing a novel rotation method for improved consistency and precision.•The electromagnetic system, especially with the proposed rotation method, achieves superior digitizing accuracy over the optical system.•Highlighting the cost-effectiveness and precision of the electromagnetic system with the rotation method, this research offers significant insights for achieving precise EEG source estimation.
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Affiliation(s)
- Naotsugu Kaneko
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
| | - Moeka Yokoyama
- Sportology Center, Graduate School of Medicine, Juntendo University, Tokyo 113-8421, Japan
| | - Kimitaka Nakazawa
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
| | - Hikaru Yokoyama
- Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan
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10
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Vitkova V, Ristori D, Cheron G, Bazan A, Cebolla AM. Long-lasting negativity in the left motoric brain structures during word memory inhibition in the Think/No-Think paradigm. Sci Rep 2024; 14:10907. [PMID: 38740808 DOI: 10.1038/s41598-024-60378-y] [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: 10/16/2023] [Accepted: 04/22/2024] [Indexed: 05/16/2024] Open
Abstract
In this study, we investigated the electrical brain responses in a high-density EEG array (64 electrodes) elicited specifically by the word memory cue in the Think/No-Think paradigm in 46 participants. In a first step, we corroborated previous findings demonstrating sustained and reduced brain electrical frontal and parietal late potentials elicited by memory cues following the No-Think (NT) instructions as compared to the Think (T) instructions. The topographical analysis revealed that such reduction was significant 1000 ms after memory cue onset and that it was long-lasting for 1000 ms. In a second step, we estimated the underlying brain generators with a distributed method (swLORETA) which does not preconceive any localization in the gray matter. This method revealed that the cognitive process related to the inhibition of memory retrieval involved classical motoric cerebral structures with the left primary motor cortex (M1, BA4), thalamus, and premotor cortex (BA6). Also, the right frontal-polar cortex was involved in the T condition which we interpreted as an indication of its role in the maintaining of a cognitive set during remembering, by the selection of one cognitive mode of processing, Think, over the other, No-Think, across extended periods of time, as it might be necessary for the successful execution of the Think/No-Think task.
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Affiliation(s)
- Viktoriya Vitkova
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium
- InterPsy Laboratory, Université de Lorraine, Nancy, France
| | - Dominique Ristori
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium
| | - Guy Cheron
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium
| | - Ariane Bazan
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium
- InterPsy Laboratory, Université de Lorraine, Nancy, France
| | - Ana Maria Cebolla
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium.
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11
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Cai Z, Jiang X, Bagić A, Worrell GA, Richardson M, He B. Spontaneous HFO Sequences Reveal Propagation Pathways for Precise Delineation of Epileptogenic Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592202. [PMID: 38746136 PMCID: PMC11092614 DOI: 10.1101/2024.05.02.592202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Epilepsy, a neurological disorder affecting millions worldwide, poses great challenges in precisely delineating the epileptogenic zone - the brain region generating seizures - for effective treatment. High-frequency oscillations (HFOs) are emerging as promising biomarkers; however, the clinical utility is hindered by the difficulties in distinguishing pathological HFOs from non- epileptiform activities at single electrode and single patient resolution and understanding their dynamic role in epileptic networks. Here, we introduce an HFO-sequencing approach to analyze spontaneous HFOs traversing cortical regions in 40 drug-resistant epilepsy patients. This data- driven method automatically detected over 8.9 million HFOs, pinpointing pathological HFO- networks, and unveiled intricate millisecond-scale spatiotemporal dynamics, stability, and functional connectivity of HFOs in prolonged intracranial EEG recordings. These HFO sequences demonstrated a significant improvement in localization of epileptic tissue, with an 818.47% increase in concordance with seizure-onset zone (mean error: 2.92 mm), compared to conventional benchmarks. They also accurately predicted seizure outcomes for 90% AUC based on pre-surgical information using generalized linear models. Importantly, this mapping remained reliable even with short recordings (mean standard deviation: 3.23 mm for 30-minute segments). Furthermore, HFO sequences exhibited distinct yet highly repetitive spatiotemporal patterns, characterized by pronounced synchrony and predominant inward information flow from periphery towards areas involved in propagation, suggesting a crucial role for excitation-inhibition balance in HFO initiation and progression. Together, these findings shed light on the intricate organization of epileptic network and highlight the potential of HFO-sequencing as a translational tool for improved diagnosis, surgical targeting, and ultimately, better outcomes for vulnerable patients with drug-resistant epilepsy. One Sentence Summary Pathological fast brain oscillations travel like traffic along varied routes, outlining recurrently visited neural sites emerging as critical hotspots in epilepsy network.
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Obert DP, Killing D, Happe T, Tamas P, Altunkaya A, Dragovic SZ, Kreuzer M, Schneider G, Fenzl T. Substance specific EEG patterns in mice undergoing slow anesthesia induction. BMC Anesthesiol 2024; 24:167. [PMID: 38702608 PMCID: PMC11067159 DOI: 10.1186/s12871-024-02552-3] [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: 03/06/2024] [Accepted: 04/26/2024] [Indexed: 05/06/2024] Open
Abstract
The exact mechanisms and the neural circuits involved in anesthesia induced unconsciousness are still not fully understood. To elucidate them valid animal models are necessary. Since the most commonly used species in neuroscience are mice, we established a murine model for commonly used anesthetics/sedatives and evaluated the epidural electroencephalographic (EEG) patterns during slow anesthesia induction and emergence. Forty-four mice underwent surgery in which we inserted a central venous catheter and implanted nine intracranial electrodes above the prefrontal, motor, sensory, and visual cortex. After at least one week of recovery, mice were anesthetized either by inhalational sevoflurane or intravenous propofol, ketamine, or dexmedetomidine. We evaluated the loss and return of righting reflex (LORR/RORR) and recorded the electrocorticogram. For spectral analysis we focused on the prefrontal and visual cortex. In addition to analyzing the power spectral density at specific time points we evaluated the changes in the spectral power distribution longitudinally. The median time to LORR after start anesthesia ranged from 1080 [1st quartile: 960; 3rd quartile: 1080]s under sevoflurane anesthesia to 1541 [1455; 1890]s with ketamine. Around LORR sevoflurane as well as propofol induced a decrease in the theta/alpha band and an increase in the beta/gamma band. Dexmedetomidine infusion resulted in a shift towards lower frequencies with an increase in the delta range. Ketamine induced stronger activity in the higher frequencies. Our results showed substance-specific changes in EEG patterns during slow anesthesia induction. These patterns were partially identical to previous observations in humans, but also included significant differences, especially in the low frequencies. Our study emphasizes strengths and limitations of murine models in neuroscience and provides an important basis for future studies investigating complex neurophysiological mechanisms.
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Affiliation(s)
- David P Obert
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts's General Hospital, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - David Killing
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Tom Happe
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Philipp Tamas
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Alp Altunkaya
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Srdjan Z Dragovic
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Matthias Kreuzer
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Gerhard Schneider
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany
| | - Thomas Fenzl
- School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, 81675, Munich, Germany.
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13
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Wang Y, Li D, Widjaja J, Guo R, Cai L, Yan R, Ozsoy S, Allocca G, Fang J, Dong Y, Tseng GC, Huang C, Huang YH. An Electroencephalogram Signature of Melanin-Concentrating Hormone Neuron Activities Predicts Cocaine Seeking. Biol Psychiatry 2024:S0006-3223(24)01257-5. [PMID: 38677639 DOI: 10.1016/j.biopsych.2024.04.009] [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: 11/30/2023] [Revised: 04/02/2024] [Accepted: 04/15/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND Identifying biomarkers that predict substance use disorder propensity may better strategize antiaddiction treatment. Melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus critically mediate interactions between sleep and substance use; however, their activities are largely obscured in surface electroencephalogram (EEG) measures, hindering the development of biomarkers. METHODS Surface EEG signals and real-time calcium (Ca2+) activities of lateral hypothalamus MCH neurons (Ca2+MCH) were simultaneously recorded in male and female adult rats. Mathematical modeling and machine learning were then applied to predict Ca2+MCH using EEG derivatives. The robustness of the predictions was tested across sex and treatment conditions. Finally, features extracted from the EEG-predicted Ca2+MCH either before or after cocaine experience were used to predict future drug-seeking behaviors. RESULTS An EEG waveform derivative-a modified theta-delta-theta peak ratio (EEGTDT ratio)-accurately tracked real-time Ca2+MCH in rats. The prediction was robust during rapid eye movement sleep (REMS), persisted through vigilance states, sleep manipulations, and circadian phases, and was consistent across sex. Moreover, cocaine self-administration and long-term withdrawal altered EEGTDT ratio, suggesting shortening and circadian redistribution of synchronous MCH neuron activities. In addition, features of EEGTDT ratio indicative of prolonged synchronous MCH neuron activities predicted lower subsequent cocaine seeking. EEGTDT ratio also exhibited advantages over conventional REMS measures for the predictions. CONCLUSIONS The identified EEGTDT ratio may serve as a noninvasive measure for assessing MCH neuron activities in vivo and evaluating REMS; it may also serve as a potential biomarker for predicting drug use propensity.
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Affiliation(s)
- Yao Wang
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Danyang Li
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | | | - Rong Guo
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Li Cai
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Rongzhen Yan
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Sahin Ozsoy
- Somnivore Pty. Ltd., Bacchus Marsh, Victoria, Australia
| | - Giancarlo Allocca
- Somnivore Pty. Ltd., Bacchus Marsh, Victoria, Australia; Department of Pharmacology and Therapeutics, University of Melbourne, Melbourne, Victoria, Australia; Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Jidong Fang
- Department of Psychiatry and Behavioral Health, Penn State College of Medicine, Hershey, Pennsylvania
| | - Yan Dong
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - George C Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Chengcheng Huang
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Yanhua H Huang
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania.
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Liu K, Peng S, Liang C, Yu Z, Xiao B, Wang G, Wu W. VSSI-GGD: A Variation Sparse EEG Source Imaging Approach Based on Generalized Gaussian Distribution. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1524-1534. [PMID: 38564353 DOI: 10.1109/tnsre.2024.3383452] [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: 04/04/2024]
Abstract
Electroencephalographic (EEG) source imaging (ESI) is a powerful method for studying brain functions and surgical resection of epileptic foci. However, accurately estimating the location and extent of brain sources remains challenging due to noise and background interference in EEG signals. To reconstruct extended brain sources, we propose a new ESI method called Variation Sparse Source Imaging based on Generalized Gaussian Distribution (VSSI-GGD). VSSI-GGD uses the generalized Gaussian prior as a sparse constraint on the spatial variation domain and embeds it into the Bayesian framework for source estimation. Using a variational technique, we approximate the intractable true posterior with a Gaussian density. Through convex analysis, the Bayesian inference problem is transformed entirely into a series of regularized L2p -norm ( ) optimization problems, which are efficiently solved with the ADMM algorithm. Imaging results of numerical simulations and human experimental dataset analysis reveal the superior performance of VSSI-GGD, which provides higher spatial resolution with clear boundaries compared to benchmark algorithms. VSSI-GGD can potentially serve as an effective and robust spatiotemporal EEG source imaging method. The source code of VSSI-GGD is available at https://github.com/Mashirops/VSSI-GGD.git.
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15
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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.
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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
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16
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Wang Y, Li D, Widjaja J, Guo R, Cai L, Yan R, Ozsoy S, Allocca G, Fang J, Dong Y, Tseng GC, Huang C, Huang YH. An EEG Signature of MCH Neuron Activities Predicts Cocaine Seeking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.586887. [PMID: 38586019 PMCID: PMC10996698 DOI: 10.1101/2024.03.27.586887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Background Identifying biomarkers that predict substance use disorder (SUD) propensity may better strategize anti-addiction treatment. The melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus (LH) critically mediates interactions between sleep and substance use; however, their activities are largely obscured in surface electroencephalogram (EEG) measures, hindering the development of biomarkers. Methods Surface EEG signals and real-time Ca2+ activities of LH MCH neurons (Ca2+MCH) were simultaneously recorded in male and female adult rats. Mathematical modeling and machine learning were then applied to predict Ca2+MCH using EEG derivatives. The robustness of the predictions was tested across sex and treatment conditions. Finally, features extracted from the EEG-predicted Ca2+MCH either before or after cocaine experience were used to predict future drug-seeking behaviors. Results An EEG waveform derivative - a modified theta-to-delta ratio (EEG Ratio) - accurately tracks real-time Ca2+MCH in rats. The prediction was robust during rapid eye movement sleep (REMS), persisted through REMS manipulations, wakefulness, circadian phases, and was consistent across sex. Moreover, cocaine self-administration and long-term withdrawal altered EEG Ratio suggesting shortening and circadian redistribution of synchronous MCH neuron activities. In addition, features of EEG Ratio indicative of prolonged synchronous MCH neuron activities predicted lower subsequent cocaine seeking. EEG Ratio also exhibited advantages over conventional REMS measures for the predictions. Conclusions The identified EEG Ratio may serve as a non-invasive measure for assessing MCH neuron activities in vivo and evaluating REMS; it may also serve as a potential biomarker predicting drug use propensity.
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Affiliation(s)
- Yao Wang
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15219; 15260; 15213
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15219; 15260; 15213
| | - Danyang Li
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15219; 15260; 15213
| | | | - Rong Guo
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15219; 15260; 15213
| | - Li Cai
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15219; 15260; 15213
| | - Rongzhen Yan
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15219; 15260; 15213
| | - Sahin Ozsoy
- Somnivore Pty. Ltd., Bacchus Marsh, VIC, Australia 3340
| | - Giancarlo Allocca
- Somnivore Pty. Ltd., Bacchus Marsh, VIC, Australia 3340
- Department of Pharmacology and Therapeutics, The University of Melbourne, VIC, Australia 3010
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, VIC, Australia
| | - Jidong Fang
- Department of Psychiatry and Behavioral Health, Penn State College of Medicine, Hershey, PA 17033
| | - Yan Dong
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15219; 15260; 15213
| | - George C. Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15219; 15260; 15213
| | - Chengcheng Huang
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15219; 15260; 15213
| | - Yanhua H. Huang
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15219; 15260; 15213
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Luo H, Huang X, Li Z, Tian W, Fang K, Liu T, Wang S, Tang B, Hu J, Yuan TF, Cao L. An Electroencephalography Profile of Paroxysmal Kinesigenic Dyskinesia. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306321. [PMID: 38227367 DOI: 10.1002/advs.202306321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/24/2023] [Indexed: 01/17/2024]
Abstract
Paroxysmal kinesigenic dyskinesia (PKD) is associated with a disturbance of neural circuit and network activities, while its neurophysiological characteristics have not been fully elucidated. This study utilized the high-density electroencephalogram (hd-EEG) signals to detect abnormal brain activity of PKD and provide a neural biomarker for its clinical diagnosis and PKD progression monitoring. The resting hd-EEGs are recorded from two independent datasets and then source-localized for measuring the oscillatory activities and function connectivity (FC) patterns of cortical and subcortical regions. The abnormal elevation of theta oscillation in wildly brain regions represents the most remarkable physiological feature for PKD and these changes returned to healthy control level in remission patients. Another remarkable feature of PKD is the decreased high-gamma FCs in non-remission patients. Subtype analyses report that increased theta oscillations may be related to the emotional factors of PKD, while the decreased high-gamma FCs are related to the motor symptoms. Finally, the authors established connectome-based predictive modelling and successfully identified the remission state in PKD patients in dataset 1 and dataset 2. The findings establish a clinically relevant electroencephalography profile of PKD and indicate that hd-EEG can provide robust neural biomarkers to evaluate the prognosis of PKD.
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Affiliation(s)
- Huichun Luo
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Xiaojun Huang
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Ziyi Li
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Wotu Tian
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Kan Fang
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
- Department of Neurology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Taotao Liu
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Shige Wang
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Beisha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Hunan Province, 410008, China
| | - Ji Hu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Ti-Fei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
- Co-innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu, 226019, China
- Institute of Mental Health and drug discovery, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325000, China
| | - Li Cao
- Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
- Shanghai Neurological Rare Disease Biobank and Precision Diagnostic Technical Service Platform, Shanghai, China
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18
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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.
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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
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Hong X, Farmer C, Kozhemiako N, Holmes GL, Thompson L, Manwaring S, Thurm A, Buckley A. Differences in Sleep EEG Coherence and Spindle Metrics in Toddlers With and Without Language Delay: A Prospective Observational Study. RESEARCH SQUARE 2024:rs.3.rs-3904113. [PMID: 38410470 PMCID: PMC10896365 DOI: 10.21203/rs.3.rs-3904113/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Background Sleep plays a crucial role in early language development, and sleep disturbances are common in children with neurodevelopmental disorders. Examining sleep microarchitecture in toddlers with and without language delays can offer key insights into neurophysiological abnormalities associated with atypical neurodevelopmental trajectories and potentially aid in early detection and intervention. Methods Here, we investigated electroencephalogram (EEG) coherence and sleep spindles in 16 toddlers with language delay (LD) compared with a group of 39 typically developing (TD) toddlers. The sample was majority male (n = 34, 62%). Participants were aged 12-to-22 months at baseline, and 34 (LD, n=11; TD, n=23) participants were evaluated again at 36 months of age. Results LD toddlers demonstrated increased EEG coherence compared to TD toddlers, with differences most prominent during slow-wave sleep. Within the LD group, lower expressive language skills were associated with higher coherence in REM sleep. Within the TD group, lower expressive language skills were associated with higher coherence in slow-wave sleep. Sleep spindle density, duration, and frequency changed between baseline and follow-up for both groups, with the LD group demonstrating a smaller magnitude of change than the TD group. The direction of change was frequency-dependent for both groups. Conclusions These findings indicate that atypical sleep EEG connectivity and sleep spindle development can be detected in toddlers between 12 and 36 months and offers insights into neurophysiological mechanisms underlying the etiology of neurodevelopmental disorders. Trial registration https://clinicaltrials.gov/study/NCT01339767; Registration date: 4/20/2011.
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Affiliation(s)
- Xinyi Hong
- National Institute of Mental Health Division of Intramural Research Programs: National Institute of Mental Health Intramural Research Program
| | - Cristan Farmer
- National Institute of Mental Health Intramural Research Program
| | | | | | - Lauren Thompson
- Washington State University Elson S Floyd College of Medicine
| | - Stacy Manwaring
- University of Utah Department of Communication Sciences and Disorders
| | - Audrey Thurm
- National Institute of Mental Health Intramural Research Program
| | - Ashura Buckley
- National Institute of Mental Health Intramural Research Program
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Siemann J, Kroeger A, Bender S, Muthuraman M, Siniatchkin M. Segregated Dynamical Networks for Biological Motion Perception in the Mu and Beta Range Underlie Social Deficits in Autism. Diagnostics (Basel) 2024; 14:408. [PMID: 38396447 PMCID: PMC10887711 DOI: 10.3390/diagnostics14040408] [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: 11/19/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/25/2024] Open
Abstract
OBJECTIVE Biological motion perception (BMP) correlating with a mirror neuron system (MNS) is attenuated in underage individuals with autism spectrum disorder (ASD). While BMP in typically-developing controls (TDCs) encompasses interconnected MNS structures, ASD data hint at segregated form and motion processing. This coincides with less fewer long-range connections in ASD than TDC. Using BMP and electroencephalography (EEG) in ASD, we characterized directionality and coherence (mu and beta frequencies). Deficient BMP may stem from desynchronization thereof in MNS and may predict social-communicative deficits in ASD. Clinical considerations thus profit from brain-behavior associations. METHODS Point-like walkers elicited BMP using 15 white dots (walker vs. scramble in 21 ASD (mean: 11.3 ± 2.3 years) vs. 23 TDC (mean: 11.9 ± 2.5 years). Dynamic Imaging of Coherent Sources (DICS) characterized the underlying EEG time-frequency causality through time-resolved Partial Directed Coherence (tPDC). Support Vector Machine (SVM) classification validated the group effects (ASD vs. TDC). RESULTS TDC showed MNS sources and long-distance paths (both feedback and bidirectional); ASD demonstrated distinct from and motion sources, predominantly local feedforward connectivity, and weaker coherence. Brain-behavior correlations point towards dysfunctional networks. SVM successfully classified ASD regarding EEG and performance. CONCLUSION ASD participants showed segregated local networks for BMP potentially underlying thwarted complex social interactions. Alternative explanations include selective attention and global-local processing deficits. SIGNIFICANCE This is the first study applying source-based connectivity to reveal segregated BMP networks in ASD regarding structure, cognition, frequencies, and temporal dynamics that may explain socio-communicative aberrancies.
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Affiliation(s)
- Julia Siemann
- Department of Child and Adolescent Psychiatry and Psychotherapy Bethel, Evangelical Hospital Bielefeld, 33617 Bielefeld, Germany;
| | - Anne Kroeger
- Clinic of Child and Adolescent Psychiatry, Goethe-University of Frankfurt am Main, 60389 Frankfurt, Germany (S.B.)
| | - Stephan Bender
- Clinic of Child and Adolescent Psychiatry, Goethe-University of Frankfurt am Main, 60389 Frankfurt, Germany (S.B.)
- Department for Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Neural Engineering with Signal Analytics and Artificial Intelligence (NESA-AI), University Clinic Würzburg, 97080 Würzburg, Germany;
| | - Michael Siniatchkin
- Department of Child and Adolescent Psychiatry and Psychotherapy Bethel, Evangelical Hospital Bielefeld, 33617 Bielefeld, Germany;
- University Clinic of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, 52074 Aachen, Germany
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Chen Z, Yang R, Huang M, Li F, Lu G, Wang Z. EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification. Comput Biol Med 2024; 169:107901. [PMID: 38159400 DOI: 10.1016/j.compbiomed.2023.107901] [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: 09/08/2023] [Revised: 12/11/2023] [Accepted: 12/23/2023] [Indexed: 01/03/2024]
Abstract
Because of the intricate topological structure and connection of the human brain, extracting deep spatial features from electroencephalograph (EEG) signals is a challenging and time-consuming task. The extraction of topological spatial information plays a crucial role in EEG classification, and the architecture of the spatial convolution greatly affects the performance and complexity of convolutional neural network (CNN) based EEG classification models. In this study, a progressive convolution CNN architecture named EEGProgress is proposed, aiming to efficiently extract the topological spatial information of EEG signals from multi-scale levels (electrode, brain region, hemisphere, global) with superior speed. To achieve this, the raw EEG data is permuted using the empirical topological permutation rule, integrating the EEG data with numerous topological properties. Subsequently, the spatial features are extracted by a progressive feature extractor including prior, electrode, region, and hemisphere convolution blocks, progressively extracting the deep spatial features with reduced parameters and speed. Finally, the comparison and ablation experiments under both cross-subject and within-subject scenarios are conducted on a public dataset to verify the performance of the proposed EEGProgress and the effectiveness of the topological permutation. The results demonstrate the superior feature extraction ability of the proposed EEGProgress, with an average increase of 4.02% compared to other CNN-based EEG classification models under both cross-subject and within-subject scenarios. Furthermore, with the obtained average testing time, FLOPs, and parameters, the proposed EEGProgress outperforms other comparison models in terms of model complexity.
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Affiliation(s)
- Zhige Chen
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Rui Yang
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
| | - Mengjie Huang
- Design School, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.
| | - Fumin Li
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Guoping Lu
- School of Electrical Engineering, Nantong University, Nantong 226019, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, United Kingdom
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22
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Sourav S, Kekunnaya R, Bottari D, Shareef I, Pitchaimuthu K, Röder B. Sound suppresses earliest visual cortical processing after sight recovery in congenitally blind humans. Commun Biol 2024; 7:118. [PMID: 38253781 PMCID: PMC10803735 DOI: 10.1038/s42003-023-05749-3] [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: 04/11/2023] [Accepted: 12/28/2023] [Indexed: 01/24/2024] Open
Abstract
Neuroscientific research has consistently shown more extensive non-visual activity in the visual cortex of congenitally blind humans compared to sighted controls; a phenomenon known as crossmodal plasticity. Whether or not crossmodal activation of the visual cortex retracts if sight can be restored is still unknown. The present study, involving a rare group of sight-recovery individuals who were born pattern vision blind, employed visual event-related potentials to investigate persisting crossmodal modulation of the initial visual cortical processing stages. Here we report that the earliest, stimulus-driven retinotopic visual cortical activity (<100 ms) was suppressed in a spatially specific manner in sight-recovery individuals when concomitant sounds accompanied visual stimulation. In contrast, sounds did not modulate the earliest visual cortical response in two groups of typically sighted controls, nor in a third control group of sight-recovery individuals who had suffered a transient phase of later (rather than congenital) visual impairment. These results provide strong evidence for persisting crossmodal activity in the visual cortex after sight recovery following a period of congenital visual deprivation. Based on the time course of this modulation, we speculate on a role of exuberant crossmodal thalamic input which may arise during a sensitive phase of brain development.
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Affiliation(s)
- Suddha Sourav
- Biological Psychology and Neuropsychology, University of Hamburg, Hamburg, Germany.
| | - Ramesh Kekunnaya
- Jasti V Ramanamma Children's Eye Care Center, Child Sight Institute, L V Prasad Eye Institute, Hyderabad, India
| | - Davide Bottari
- Biological Psychology and Neuropsychology, University of Hamburg, Hamburg, Germany
- IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Idris Shareef
- Jasti V Ramanamma Children's Eye Care Center, Child Sight Institute, L V Prasad Eye Institute, Hyderabad, India
| | - Kabilan Pitchaimuthu
- Biological Psychology and Neuropsychology, University of Hamburg, Hamburg, Germany
- Jasti V Ramanamma Children's Eye Care Center, Child Sight Institute, L V Prasad Eye Institute, Hyderabad, India
- Department of Medicine and Optometry, Linnaeus University, Kalmar, Sweden
| | - Brigitte Röder
- Biological Psychology and Neuropsychology, University of Hamburg, Hamburg, Germany
- Jasti V Ramanamma Children's Eye Care Center, Child Sight Institute, L V Prasad Eye Institute, Hyderabad, India
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23
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Erickson B, Rich R, Shankar S, Kim B, Driscoll N, Mentzelopoulos G, Fernandez-Nuñez G, Vitale F, Medaglia JD. Evaluating and benchmarking the EEG signal quality of high-density, dry MXene-based electrode arrays against gelled Ag/AgCl electrodes. J Neural Eng 2024; 21:016005. [PMID: 38081060 PMCID: PMC10788783 DOI: 10.1088/1741-2552/ad141e] [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: 06/14/2023] [Revised: 09/17/2023] [Accepted: 12/11/2023] [Indexed: 01/13/2024]
Abstract
Objective.To evaluate the signal quality of dry MXene-based electrode arrays (also termed 'MXtrodes') for electroencephalographic (EEG) recordings where gelled Ag/AgCl electrodes are a standard.Approach.We placed 4 × 4 MXtrode arrays and gelled Ag/AgCl electrodes on different scalp locations. The scalp was cleaned with alcohol and rewetted with saline before application. We recorded from both electrode types simultaneously while participants performed a vigilance task.Main results.The root mean squared amplitude of MXtrodes was slightly higher than that of Ag/AgCl electrodes (.24-1.94 uV). Most MXtrode pairs had slightly lower broadband spectral coherence (.05 to .1 dB) and Delta- and Theta-band timeseries correlation (.05 to .1 units) compared to the Ag/AgCl pair (p< .001). However, the magnitude of correlation and coherence was high across both electrode types. Beta-band timeseries correlation and spectral coherence were higher between neighboring MXtrodes in the array (.81 to .84 units) than between any other pair (.70 to .75 units). This result suggests the close spacing of the nearest MXtrodes (3 mm) more densely sampled high spatial-frequency topographies. Event-related potentials were more similar between MXtrodes (ρ⩾ .95) than equally spaced Ag/AgCl electrodes (ρ⩽ .77,p< .001). Dry MXtrode impedance (x̄= 5.15 KΩ cm2) was higher and more variable than gelled Ag/AgCl electrodes (x̄= 1.21 KΩ cm2,p< .001). EEG was also recorded on the scalp across diverse hair types.Significance.Dry MXene-based electrodes record EEG at a quality comparable to conventional gelled Ag/AgCl while requiring minimal scalp preparation and no gel. MXtrodes can record independent signals at a spatial density four times higher than conventional electrodes, including through hair, thus opening novel opportunities for research and clinical applications that could benefit from dry and higher-density configurations.
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Affiliation(s)
- Brian Erickson
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Ryan Rich
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Sneha Shankar
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, United States of America
| | - Brian Kim
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Nicolette Driscoll
- Laboratory of Electronics Research, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Georgios Mentzelopoulos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, United States of America
| | - Guadalupe Fernandez-Nuñez
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - John D Medaglia
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Drexel University, Philadelphia, PA 19104, United States of America
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24
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Ye S, Bagić A, He B. Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy. Brain Topogr 2024; 37:152-168. [PMID: 38112884 PMCID: PMC10771380 DOI: 10.1007/s10548-023-01025-z] [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: 05/15/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023]
Abstract
The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.
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Affiliation(s)
- Shuai Ye
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA
| | - Anto Bagić
- Department of Neurology, University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh Medical School, Pittsburgh, PA, USA
| | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA.
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Lin S, Jiang J, Huang K, Li L, He X, Du P, Wu Y, Liu J, Li X, Huang Z, Zhou Z, Yu Y, Gao J, Lei M, Wu H. Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces. ACS NANO 2023; 17:24487-24513. [PMID: 38064282 DOI: 10.1021/acsnano.3c06781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.
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Affiliation(s)
- Sen Lin
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Jingjing Jiang
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Kai Huang
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Lei Li
- National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
| | - Xian He
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Peng Du
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Yufeng Wu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Junchen Liu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xilin Li
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Advanced Institute for Brain and Intelligence, Guangxi University, Nanning 530004, China
| | - Zhibao Huang
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Zenan Zhou
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Yuanhang Yu
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Jiaxin Gao
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Ming Lei
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Hui Wu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
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Liu Y, Wang N, Su X, Zhao T, Zhang J, Geng Y, Wang N, Zhou M, Zhang G, Huang L. Classification of cognitive impairment in older adults based on brain functional state measurement data via hierarchical clustering analysis. Front Aging Neurosci 2023; 15:1198481. [PMID: 38161594 PMCID: PMC10757366 DOI: 10.3389/fnagi.2023.1198481] [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: 04/03/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Cognitive impairment (CI) is a common degenerative condition in the older population. However, the current methods for assessing CI are not based on brain functional state, which leads to delayed diagnosis, limiting the initiatives towards achieving early interventions. Methods A total of one hundred and forty-nine community-dwelling older adults were recruited. Montreal Cognitive Assessment (MoCA) and Mini-Mental State Exam (MMSE) were used to screen for CI, while brain functional was assessed by brain functional state measurement (BFSM) based on electroencephalogram. Bain functional state indicators associated with CI were selected by lasso and logistic regression models (LRM). We then classified the CI participants based on the selected variables using hierarchical clustering analysis. Results Eighty-one participants with CI detected by MoCA were divided into five groups. Cluster 1 had relatively lower brain functional states. Cluster 2 had highest mental task-switching index (MTSi, 13.7 ± 3.4), Cluster 3 had the highest sensory threshold index (STi, 29.9 ± 7.7), Cluster 4 had high mental fatigue index (MFi) and cluster 5 had the highest mental refractory period index (MRPi), and external apprehension index (EAi) (21.6 ± 4.4, 35.4 ± 17.7, respectively). Thirty-three participants with CI detected by MMSE were divided into 3 categories. Cluster 1 had the highest introspective intensity index (IIi, 63.4 ± 20.0), anxiety tendency index (ATi, 67.2 ± 13.6), emotional resistance index (ERi, 50.2 ± 11.9), and hypoxia index (Hi, 41.8 ± 8.3). Cluster 2 had the highest implicit cognitive threshold index (ICTi, 87.2 ± 12.7), and cognitive efficiency index (CEi, 213.8 ± 72.0). Cluster 3 had higher STi. The classifications both showed well intra-group consistency and inter-group variability. Conclusion In our study, BFSM-based classification can be used to identify clinically and brain-functionally relevant CI subtypes, by which clinicians can perform personalized early rehabilitation.
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Affiliation(s)
- Yangxiaoxue Liu
- Medical School of Chinese PLA, Beijing, China
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, China
| | - Na Wang
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xinling Su
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Tianshu Zhao
- Medical School of Chinese PLA, Beijing, China
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, China
| | - Jiali Zhang
- Medical School of Chinese PLA, Beijing, China
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, China
| | - Yuhan Geng
- Medical School of Chinese PLA, Beijing, China
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Ning Wang
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Ming Zhou
- Medical School of Chinese PLA, Beijing, China
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Gongzi Zhang
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Liping Huang
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
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Artoni F, Cometa A, Dalise S, Azzollini V, Micera S, Chisari C. Cortico-muscular connectivity is modulated by passive and active Lokomat-assisted Gait. Sci Rep 2023; 13:21618. [PMID: 38062035 PMCID: PMC10703891 DOI: 10.1038/s41598-023-48072-x] [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: 04/06/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
The effects of robotic-assisted gait (RAG) training, besides conventional therapy, on neuroplasticity mechanisms and cortical integration in locomotion are still uncertain. To advance our knowledge on the matter, we determined the involvement of motor cortical areas in the control of muscle activity in healthy subjects, during RAG with Lokomat, both with maximal guidance force (100 GF-passive RAG) and without guidance force (0 GF-active RAG) as customary in rehabilitation treatments. We applied a novel cortico-muscular connectivity estimation procedure, based on Partial Directed Coherence, to jointly study source localized EEG and EMG activity during rest (standing) and active/passive RAG. We found greater cortico-cortical connectivity, with higher path length and tendency toward segregation during rest than in both RAG conditions, for all frequency bands except for delta. We also found higher cortico-muscular connectivity in distal muscles during swing (0 GF), and stance (100 GF), highlighting the importance of direct supraspinal control to maintain balance, even when gait is supported by a robotic exoskeleton. Source-localized connectivity shows that this control is driven mainly by the parietal and frontal lobes. The involvement of many cortical areas also in passive RAG (100 GF) justifies the use of the 100 GF RAG training for neurorehabilitation, with the aim of enhancing cortical-muscle connections and driving neural plasticity in neurological patients.
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Affiliation(s)
- Fiorenzo Artoni
- Department of Clinical Neurosciences, University of Genève, Faculty of Medicine, 1211, Geneva, Switzerland.
- Ago Neurotechnologies Sàrl, 1201, Geneva, Switzerland.
| | - Andrea Cometa
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025, Pontedera, Italy
- University School for Advanced Studies IUSS Pavia, 27100, Pavia, Italy
| | - Stefania Dalise
- Unit of Neurorehabilitation, Pisa University Hospital, Pisa, Italy
| | - Valentina Azzollini
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Silvestro Micera
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025, Pontedera, Italy
- Translational Neural Engineering Laboratory (TNE), École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Carmelo Chisari
- Unit of Neurorehabilitation, Pisa University Hospital, Pisa, Italy
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
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28
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Wei Y, Wang X, Luo R, Mai X, Li S, Meng J. Decoding movement frequencies and limbs based on steady-state movement-related rhythms from noninvasive EEG. J Neural Eng 2023; 20:066019. [PMID: 37816342 DOI: 10.1088/1741-2552/ad01de] [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: 06/03/2023] [Accepted: 10/10/2023] [Indexed: 10/12/2023]
Abstract
Objective.Decoding different types of movements noninvasively from electroencephalography (EEG) is an essential topic in neural engineering, especially in brain-computer interface. Although the widely used sensorimotor rhythm (SMR) is efficient in limb decoding, it lacks efficacy in decoding movement frequencies. Accumulating evidence supports the notion that the movement frequency is encoded in the steady-state movement-related rhythm (SSMRR). Our study has two primary objectives: firstly, to investigate the spatial-spectral representation of SSMRR in EEG during voluntary movements; secondly, to assess whether movement frequencies and limbs can be effectively decoded based on SSMRR.Approach.To comprehensively examine the representation of SSMRR, we investigated the frequency characteristics and spatial patterns associated with various rhythmic finger movements. Coherence analysis was performed between the sensor or source domain EEG and finger movements recorded by data gloves. A fusion model based on spectral SNR features and filter-bank common spatial pattern features was utilized to decode movement frequencies and limbs.Main results.At the group-level, sensor domain, and source domain coherence maps demonstrated that the accurate movement frequency (f0) and its first harmonic (f1) were encoded in the contralateral motor cortex. For the four-class classification, including two movement frequencies for both hands, the decoding accuracies for externally paced and internally paced movements were 73.14 ± 15.86% and 66.30 ± 17.26% (averaged across ten subjects, chance levels at 31.05% and 30.96%). Notably, the average results of five subjects with the highest decoding accuracies reached 87.21 ± 7.44% and 80.44 ± 7.99%.Significance.Our results verified the EEG representation of SSMRR and proved that the movement frequency and limb could be effectively decoded based on spatial-spectral features extracted from SSMRR. We suggest that SSMRR can serve as a complement to SMR to expand the range of decodable movement types and the approaches of limb decoding.
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Affiliation(s)
- Yuxuan Wei
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xu Wang
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Ruijie Luo
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Ximing Mai
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Songwei Li
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Jianjun Meng
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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Kenefati G, Rockholt MM, Ok D, McCartin M, Zhang Q, Sun G, Maslinski J, Wang A, Chen B, Voigt EP, Chen ZS, Wang J, Doan LV. Changes in alpha, theta, and gamma oscillations in distinct cortical areas are associated with altered acute pain responses in chronic low back pain patients. Front Neurosci 2023; 17:1278183. [PMID: 37901433 PMCID: PMC10611481 DOI: 10.3389/fnins.2023.1278183] [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: 08/15/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
Introduction Chronic pain negatively impacts a range of sensory and affective behaviors. Previous studies have shown that the presence of chronic pain not only causes hypersensitivity at the site of injury but may also be associated with pain-aversive experiences at anatomically unrelated sites. While animal studies have indicated that the cingulate and prefrontal cortices are involved in this generalized hyperalgesia, the mechanisms distinguishing increased sensitivity at the site of injury from a generalized site-nonspecific enhancement in the aversive response to nociceptive inputs are not well known. Methods We compared measured pain responses to peripheral mechanical stimuli applied to a site of chronic pain and at a pain-free site in participants suffering from chronic lower back pain (n = 15) versus pain-free control participants (n = 15) by analyzing behavioral and electroencephalographic (EEG) data. Results As expected, participants with chronic pain endorsed enhanced pain with mechanical stimuli in both back and hand. We further analyzed electroencephalographic (EEG) recordings during these evoked pain episodes. Brain oscillations in theta and alpha bands in the medial orbitofrontal cortex (mOFC) were associated with localized hypersensitivity, while increased gamma oscillations in the anterior cingulate cortex (ACC) and increased theta oscillations in the dorsolateral prefrontal cortex (dlPFC) were associated with generalized hyperalgesia. Discussion These findings indicate that chronic pain may disrupt multiple cortical circuits to impact nociceptive processing.
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Affiliation(s)
- George Kenefati
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Interdisciplinary Pain Research Program, New York University Grossman School of Medicine, New York, NY, United States
| | - Mika M. Rockholt
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Interdisciplinary Pain Research Program, New York University Grossman School of Medicine, New York, NY, United States
| | - Deborah Ok
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - Michael McCartin
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - Qiaosheng Zhang
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Interdisciplinary Pain Research Program, New York University Grossman School of Medicine, New York, NY, United States
| | - Guanghao Sun
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Interdisciplinary Pain Research Program, New York University Grossman School of Medicine, New York, NY, United States
| | - Julia Maslinski
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Interdisciplinary Pain Research Program, New York University Grossman School of Medicine, New York, NY, United States
| | - Aaron Wang
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Interdisciplinary Pain Research Program, New York University Grossman School of Medicine, New York, NY, United States
| | - Baldwin Chen
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Interdisciplinary Pain Research Program, New York University Grossman School of Medicine, New York, NY, United States
| | - Erich P. Voigt
- Department of Otolaryngology-Head and Neck Surgery, New York University Grossman School of Medicine, New York, NY, United States
| | - Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neuroscience and Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, United States
| | - Jing Wang
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Interdisciplinary Pain Research Program, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neuroscience and Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
| | - Lisa V. Doan
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Interdisciplinary Pain Research Program, New York University Grossman School of Medicine, New York, NY, United States
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Downey RJ, Ferris DP. iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG. SENSORS (BASEL, SWITZERLAND) 2023; 23:8214. [PMID: 37837044 PMCID: PMC10574843 DOI: 10.3390/s23198214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023]
Abstract
The goal of this study was to test a novel approach (iCanClean) to remove non-brain sources from scalp EEG data recorded in mobile conditions. We created an electrically conductive phantom head with 10 brain sources, 10 contaminating sources, scalp, and hair. We tested the ability of iCanClean to remove artifacts while preserving brain activity under six conditions: Brain, Brain + Eyes, Brain + Neck Muscles, Brain + Facial Muscles, Brain + Walking Motion, and Brain + All Artifacts. We compared iCanClean to three other methods: Artifact Subspace Reconstruction (ASR), Auto-CCA, and Adaptive Filtering. Before and after cleaning, we calculated a Data Quality Score (0-100%), based on the average correlation between brain sources and EEG channels. iCanClean consistently outperformed the other three methods, regardless of the type or number of artifacts present. The most striking result was for the condition with all artifacts simultaneously present. Starting from a Data Quality Score of 15.7% (before cleaning), the Brain + All Artifacts condition improved to 55.9% after iCanClean. Meanwhile, it only improved to 27.6%, 27.2%, and 32.9% after ASR, Auto-CCA, and Adaptive Filtering. For context, the Brain condition scored 57.2% without cleaning (reasonable target). We conclude that iCanClean offers the ability to clear multiple artifact sources in real time and could facilitate human mobile brain-imaging studies with EEG.
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Affiliation(s)
| | - Daniel P. Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA;
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Kumar A, Lin CC, Kuo SH, Pan MK. Physiological Recordings of the Cerebellum in Movement Disorders. CEREBELLUM (LONDON, ENGLAND) 2023; 22:985-1001. [PMID: 36070135 PMCID: PMC10354710 DOI: 10.1007/s12311-022-01473-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/27/2022] [Indexed: 06/15/2023]
Abstract
The cerebellum plays an important role in movement disorders, specifically in symptoms of ataxia, tremor, and dystonia. Understanding the physiological signals of the cerebellum contributes to insights into the pathophysiology of these movement disorders and holds promise in advancing therapeutic development. Non-invasive techniques such as electroencephalogram and magnetoencephalogram can record neural signals with high temporal resolution at the millisecond level, which is uniquely suitable to interrogate cerebellar physiology. These techniques have recently been implemented to study cerebellar physiology in healthy subjects as well as individuals with movement disorders. In the present review, we focus on the current understanding of cerebellar physiology using these techniques to study movement disorders.
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Affiliation(s)
- Ami Kumar
- Department of Neurology, Columbia University Irving Medical Center and the New York Presbyterian Hospital, 650 W 168thStreet, Room 305, New York, NY, 10032, USA
- Initiative for Columbia Ataxia and Tremor, Columbia University Irving Medical Center, New York, NY, USA
| | - Chih-Chun Lin
- Department of Neurology, Columbia University Irving Medical Center and the New York Presbyterian Hospital, 650 W 168thStreet, Room 305, New York, NY, 10032, USA
- Initiative for Columbia Ataxia and Tremor, Columbia University Irving Medical Center, New York, NY, USA
| | - Sheng-Han Kuo
- Department of Neurology, Columbia University Irving Medical Center and the New York Presbyterian Hospital, 650 W 168thStreet, Room 305, New York, NY, 10032, USA.
- Initiative for Columbia Ataxia and Tremor, Columbia University Irving Medical Center, New York, NY, USA.
| | - Ming-Kai Pan
- Cerebellar Research Center, National Taiwan University Hospital, Yun-Lin Branch, Yun-Lin, 64041, Taiwan.
- Department and Graduate Institute of Pharmacology, National Taiwan University College of Medicine, Taipei, 10051, Taiwan.
- Department of Medical Research, National Taiwan University Hospital, Taipei, 10002, Taiwan.
- Institute of Biomedical Sciences, Academia Sinica, Taipei City, 11529, Taiwan.
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Galaz Prieto F, Lahtinen J, Samavaki M, Pursiainen S. Multi-compartment head modeling in EEG: Unstructured boundary-fitted tetra meshing with subcortical structures. PLoS One 2023; 18:e0290715. [PMID: 37729152 PMCID: PMC10511141 DOI: 10.1371/journal.pone.0290715] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/12/2023] [Indexed: 09/22/2023] Open
Abstract
This paper introduces an automated approach for generating a finite element (FE) discretization of a multi-compartment human head model for electroencephalographic (EEG) source localization. We aim to provide an adaptable FE mesh generation tool for EEG studies. Our technique relies on recursive solid angle labeling of a surface segmentation coupled with smoothing, refinement, inflation, and optimization procedures to enhance the mesh quality. In this study, we performed numerical meshing experiments with the three-layer Ary sphere and a magnetic resonance imaging (MRI)-based multi-compartment head segmentation which incorporates a comprehensive set of subcortical brain structures. These experiments are motivated, on one hand, by the sensitivity of non-invasive subcortical source localization to modeling errors and, on the other hand, by the present lack of open EEG software pipelines to discretize all these structures. Our approach was found to successfully produce an unstructured and boundary-fitted tetrahedral mesh with a sub-one-millimeter fitting error, providing the desired accuracy for the three-dimensional anatomical details, EEG lead field matrix, and source localization. The mesh generator applied in this study has been implemented in the open MATLAB-based Zeffiro Interface toolbox for forward and inverse processing in EEG and it allows for graphics processing unit acceleration.
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Affiliation(s)
- Fernando Galaz Prieto
- Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Pirkanmaa, Finland
| | - Joonas Lahtinen
- Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Pirkanmaa, Finland
| | - Maryam Samavaki
- Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Pirkanmaa, Finland
| | - Sampsa Pursiainen
- Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Pirkanmaa, Finland
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Chikara RK, Jahromi S, Tamilia E, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Electromagnetic source imaging predicts surgical outcome in children with focal cortical dysplasia. Clin Neurophysiol 2023; 153:88-101. [PMID: 37473485 PMCID: PMC10528204 DOI: 10.1016/j.clinph.2023.06.015] [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: 02/14/2023] [Revised: 05/25/2023] [Accepted: 06/15/2023] [Indexed: 07/22/2023]
Abstract
OBJECTIVE To evaluate the diagnostic accuracy of electromagnetic source imaging (EMSI) in localizing spikes and predict surgical outcome in children with drug resistant epilepsy (DRE) due to focal cortical dysplasia (FCD). METHODS We retrospectively analyzed magnetoencephalography (MEG) and high-density (HD-EEG) data from 23 children with FCD-associated DRE who underwent intracranial EEG and surgery. We localized spikes using equivalent current dipole (ECD) fitting, dipole clustering, and dynamical statistical parametric mapping (dSPM) on EMSI, electric source imaging (ESI), and magnetic source imaging (MSI). We calculated the distance from the seizure onset zone (DSOZ) and resection (DRES). We estimated receiver operating characteristic (ROC) curves with Youden's index (J) to predict outcome. RESULTS EMSI presented shorter DSOZ (15.18 ± 9.06 mm) and DRES (8.56 ± 6.24 mm) compared to ESI (DSOZ: 25.04 ± 16.20 mm, p < 0.009; DRES: 18.88 ± 17.30 mm, p < 0.03) and MSI (DSOZ: 23.37 ± 8.98 mm, p < 0.03; DRES: 15.51 ± 10.11 mm, p < 0.02) for clustering in patients with good outcome. Clustering showed shorter DSOZ and DRES compared to ECD fitting and dSPM (p < 0.05). EMSI had higher performance as outcome predictor (J = 70.63%) compared to ESI (J = 41.27%) and MSI (J = 33.33%) for clustering. CONCLUSIONS EMSI provides superior localization and improved predictive performance than individual modalities. SIGNIFICANCE EMSI can help the surgical planning and facilitate the localization of epileptogenic foci.
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Affiliation(s)
- Rupesh Kumar Chikara
- Jane and John Justin Institute for Mind Health, Neuroscience Research, Cook Children's Health Care System, Fort Worth, TX, USA; Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA
| | - Saeed Jahromi
- Jane and John Justin Institute for Mind Health, Neuroscience Research, Cook Children's Health Care System, Fort Worth, TX, USA; Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Steve M Stufflebeam
- Athinoula Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health, Neuroscience Research, Cook Children's Health Care System, Fort Worth, TX, USA; Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA; School of Medicine, Texas Christian University, Fort Worth, TX, USA.
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Jacobsen NA, Ferris DP. Electrocortical activity correlated with locomotor adaptation during split-belt treadmill walking. J Physiol 2023; 601:3921-3944. [PMID: 37522890 PMCID: PMC10528133 DOI: 10.1113/jp284505] [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: 02/07/2023] [Accepted: 07/11/2023] [Indexed: 08/01/2023] Open
Abstract
Locomotor adaptation is crucial for daily gait adjustments to changing environmental demands and obstacle avoidance. Mobile brain imaging with high-density electroencephalography (EEG) now permits quantification of electrocortical dynamics during human locomotion. To determine the brain areas involved in human locomotor adaptation, we recorded high-density EEG from healthy, young adults during split-belt treadmill walking. We incorporated a dual-electrode EEG system and neck electromyography to decrease motion and muscle artefacts. Voluntary movement preparation and execution have been linked to alpha (8-13 Hz) and beta band (13-30 Hz) desynchronizations in the sensorimotor and posterior parietal cortices, whereas theta band (4-7 Hz) modulations in the anterior cingulate have been correlated with movement error monitoring. We hypothesized that relative to normal walking, split-belt walking would elicit: (1) decreases in alpha and beta band power in sensorimotor and posterior parietal cortices, reflecting enhanced motor flexibility; and (2) increases in theta band power in anterior cingulate cortex, reflecting instability and balance errors that will diminish with practice. We found electrocortical activity in multiple regions that was associated with stages of gait adaptation. Data indicated that sensorimotor and posterior parietal cortices had decreased alpha and beta band spectral power during early adaptation to split-belt treadmill walking that gradually returned to pre-adaptation levels by the end of the adaptation period. Our findings emphasize that multiple brain areas are involved in adjusting gait under changing environmental demands during human walking. Future studies could use these findings on healthy, young participants to identify dysfunctional supraspinal mechanisms that may be impairing gait adaptation. KEY POINTS: Identifying the location and time course of electrical changes in the brain correlating with gait adaptation increases our understanding of brain function and provides targets for brain stimulation interventions. Using high-density EEG in combination with 3D biomechanics, we found changes in neural oscillations localized near the sensorimotor, posterior parietal and cingulate cortices during split-belt treadmill adaptation. These findings suggest that multiple cortical mechanisms may be associated with locomotor adaptation, and their temporal dynamics can be quantified using mobile EEG. Results from this study can serve as a reference model to examine brain dynamics in individuals with movement disorders that cause gait asymmetry and reduced gait adaptation.
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Affiliation(s)
- Noelle A Jacobsen
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
| | - Daniel P Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
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Scano A, Guanziroli E, Brambilla C, Amendola C, Pirovano I, Gasperini G, Molteni F, Spinelli L, Molinari Tosatti L, Rizzo G, Re R, Mastropietro A. A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation. Healthcare (Basel) 2023; 11:2282. [PMID: 37628480 PMCID: PMC10454517 DOI: 10.3390/healthcare11162282] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/02/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.
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Affiliation(s)
- Alessandro Scano
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via A. Corti 12, 20133 Milan, Italy; (C.B.); (L.M.T.)
| | - Eleonora Guanziroli
- Villa Beretta Rehabilitation Center, Via N. Sauro 17, 23845 Costa Masnaga, Italy; (E.G.); (G.G.); (F.M.)
| | - Cristina Brambilla
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via A. Corti 12, 20133 Milan, Italy; (C.B.); (L.M.T.)
| | - Caterina Amendola
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.A.); (R.R.)
| | - Ileana Pirovano
- Institute of Biomedical Technologies (ITB), Italian National Research Council (CNR), Via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (G.R.); (A.M.)
| | - Giulio Gasperini
- Villa Beretta Rehabilitation Center, Via N. Sauro 17, 23845 Costa Masnaga, Italy; (E.G.); (G.G.); (F.M.)
| | - Franco Molteni
- Villa Beretta Rehabilitation Center, Via N. Sauro 17, 23845 Costa Masnaga, Italy; (E.G.); (G.G.); (F.M.)
| | - Lorenzo Spinelli
- Institute for Photonics and Nanotechnology (IFN), Italian National Research Council (CNR), Piazza Leonardo da Vinci 32, 20133 Milan, Italy;
| | - Lorenzo Molinari Tosatti
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via A. Corti 12, 20133 Milan, Italy; (C.B.); (L.M.T.)
| | - Giovanna Rizzo
- Institute of Biomedical Technologies (ITB), Italian National Research Council (CNR), Via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (G.R.); (A.M.)
| | - Rebecca Re
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.A.); (R.R.)
- Institute for Photonics and Nanotechnology (IFN), Italian National Research Council (CNR), Piazza Leonardo da Vinci 32, 20133 Milan, Italy;
| | - Alfonso Mastropietro
- Institute of Biomedical Technologies (ITB), Italian National Research Council (CNR), Via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (G.R.); (A.M.)
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Singer N, Poker G, Dunsky-Moran N, Nemni S, Reznik Balter S, Doron M, Baker T, Dagher A, Zatorre RJ, Hendler T. Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation. Neuroimage 2023; 276:120183. [PMID: 37225112 PMCID: PMC10300238 DOI: 10.1016/j.neuroimage.2023.120183] [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: 11/20/2022] [Revised: 04/06/2023] [Accepted: 05/22/2023] [Indexed: 05/26/2023] Open
Abstract
Reward processing is essential for our mental-health and well-being. In the current study, we developed and validated a scalable, fMRI-informed EEG model for monitoring reward processing related to activation in the ventral-striatum (VS), a significant node in the brain's reward system. To develop this EEG-based model of VS-related activation, we collected simultaneous EEG/fMRI data from 17 healthy individuals while listening to individually-tailored pleasurable music - a highly rewarding stimulus known to engage the VS. Using these cross-modal data, we constructed a generic regression model for predicting the concurrently acquired Blood-Oxygen-Level-Dependent (BOLD) signal from the VS using spectro-temporal features from the EEG signal (termed hereby VS-related-Electrical Finger Print; VS-EFP). The performance of the extracted model was examined using a series of tests that were applied on the original dataset and, importantly, an external validation dataset collected from a different group of 14 healthy individuals who underwent the same EEG/FMRI procedure. Our results showed that the VS-EFP model, as measured by simultaneous EEG, predicted BOLD activation in the VS and additional functionally relevant regions to a greater extent than an EFP model derived from a different anatomical region. The developed VS-EFP was also modulated by musical pleasure and predictive of the VS-BOLD during a monetary reward task, further indicating its functional relevance. These findings provide compelling evidence for the feasibility of using EEG alone to model neural activation related to the VS, paving the way for future use of this scalable neural probing approach in neural monitoring and self-guided neuromodulation.
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Affiliation(s)
- Neomi Singer
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada; Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, 6 Weizman St. Tel Aviv, 64239, Israel; Sagol school of Neuroscience, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel
| | - Gilad Poker
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, 6 Weizman St. Tel Aviv, 64239, Israel
| | - Netta Dunsky-Moran
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, 6 Weizman St. Tel Aviv, 64239, Israel; Sagol school of Neuroscience, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel
| | - Shlomi Nemni
- Sagol school of Neuroscience, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel; School of Psychological Sciences, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel
| | - Shira Reznik Balter
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, 6 Weizman St. Tel Aviv, 64239, Israel
| | - Maayan Doron
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada; Sackler School of Medicine, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel
| | - Travis Baker
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada; Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
| | - Alain Dagher
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada
| | - Robert J Zatorre
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada; International Laboratory for Brain, Music, and Sound Research (BRAMS), Canada
| | - Talma Hendler
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, 6 Weizman St. Tel Aviv, 64239, Israel; Sagol school of Neuroscience, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel; School of Psychological Sciences, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel; Sackler School of Medicine, Tel-Aviv University, PO Box 39040, Tel Aviv 6997801, Israel.
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Xu H, Cao K, Chen H, Abudusalamu A, Wu W, Xue Y. Emotional brain network decoded by biological spiking neural network. Front Neurosci 2023; 17:1200701. [PMID: 37496741 PMCID: PMC10366476 DOI: 10.3389/fnins.2023.1200701] [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: 04/05/2023] [Accepted: 05/30/2023] [Indexed: 07/28/2023] Open
Abstract
Introduction Emotional disorders are essential manifestations of many neurological and psychiatric diseases. Nowadays, researchers try to explore bi-directional brain-computer interface techniques to help the patients. However, the related functional brain areas and biological markers are still unclear, and the dynamic connection mechanism is also unknown. Methods To find effective regions related to different emotion recognition and intervention, our research focuses on finding emotional EEG brain networks using spiking neural network algorithm with binary coding. We collected EEG data while human participants watched emotional videos (fear, sadness, happiness, and neutrality), and analyzed the dynamic connections between the electrodes and the biological rhythms of different emotions. Results The analysis has shown that the local high-activation brain network of fear and sadness is mainly in the parietal lobe area. The local high-level brain network of happiness is in the prefrontal-temporal lobe-central area. Furthermore, the α frequency band could effectively represent negative emotions, while the α frequency band could be used as a biological marker of happiness. The decoding accuracy of the three emotions reached 86.36%, 95.18%, and 89.09%, respectively, fully reflecting the excellent emotional decoding performance of the spiking neural network with self- backpropagation. Discussion The introduction of the self-backpropagation mechanism effectively improves the performance of the spiking neural network model. Different emotions exhibit distinct EEG networks and neuro-oscillatory-based biological markers. These emotional brain networks and biological markers may provide important hints for brain-computer interface technique exploration to help related brain disease recovery.
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Affiliation(s)
- Hubo Xu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China
- Department of Pharmacology, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Kexin Cao
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China
- Department of Pharmacology, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Hongguang Chen
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
| | - Awuti Abudusalamu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China
- Department of Pharmacology, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Wei Wu
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yanxue Xue
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
- Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, Peking University, Beijing, China
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Koirala N, Deroche MLD, Wolfe J, Neumann S, Bien AG, Doan D, Goldbeck M, Muthuraman M, Gracco VL. Dynamic networks differentiate the language ability of children with cochlear implants. Front Neurosci 2023; 17:1141886. [PMID: 37409105 PMCID: PMC10318154 DOI: 10.3389/fnins.2023.1141886] [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: 01/10/2023] [Accepted: 05/29/2023] [Indexed: 07/07/2023] Open
Abstract
Background Cochlear implantation (CI) in prelingually deafened children has been shown to be an effective intervention for developing language and reading skill. However, there is a substantial proportion of the children receiving CI who struggle with language and reading. The current study-one of the first to implement electrical source imaging in CI population was designed to identify the neural underpinnings in two groups of CI children with good and poor language and reading skill. Methods Data using high density electroencephalography (EEG) under a resting state condition was obtained from 75 children, 50 with CIs having good (HL) or poor language skills (LL) and 25 normal hearing (NH) children. We identified coherent sources using dynamic imaging of coherent sources (DICS) and their effective connectivity computing time-frequency causality estimation based on temporal partial directed coherence (TPDC) in the two CI groups compared to a cohort of age and gender matched NH children. Findings Sources with higher coherence amplitude were observed in three frequency bands (alpha, beta and gamma) for the CI groups when compared to normal hearing children. The two groups of CI children with good (HL) and poor (LL) language ability exhibited not only different cortical and subcortical source profiles but also distinct effective connectivity between them. Additionally, a support vector machine (SVM) algorithm using these sources and their connectivity patterns for each CI group across the three frequency bands was able to predict the language and reading scores with high accuracy. Interpretation Increased coherence in the CI groups suggest overall that the oscillatory activity in some brain areas become more strongly coupled compared to the NH group. Moreover, the different sources and their connectivity patterns and their association to language and reading skill in both groups, suggest a compensatory adaptation that either facilitated or impeded language and reading development. The neural differences in the two groups of CI children may reflect potential biomarkers for predicting outcome success in CI children.
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Affiliation(s)
- Nabin Koirala
- Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, United States
| | | | - Jace Wolfe
- Hearts for Hearing Foundation, Oklahoma City, OK, United States
| | - Sara Neumann
- Hearts for Hearing Foundation, Oklahoma City, OK, United States
| | - Alexander G. Bien
- Department of Otolaryngology – Head and Neck Surgery, University of Oklahoma Medical Center, Oklahoma City, OK, United States
| | - Derek Doan
- University of Oklahoma College of Medicine, Oklahoma City, OK, United States
| | - Michael Goldbeck
- University of Oklahoma College of Medicine, Oklahoma City, OK, United States
| | - Muthuraman Muthuraman
- Department of Neurology, Neural Engineering with Signal Analytics and Artificial Intelligence (NESA-AI), Universitätsklinikum Würzburg, Würzburg, Germany
| | - Vincent L. Gracco
- Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, United States
- School of Communication Sciences and Disorders, McGill University, Montreal, QC, Canada
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Rockholt MM, Kenefati G, Doan LV, Chen ZS, Wang J. In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand? Front Neurosci 2023; 17:1186418. [PMID: 37389362 PMCID: PMC10301750 DOI: 10.3389/fnins.2023.1186418] [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: 03/14/2023] [Accepted: 05/12/2023] [Indexed: 07/01/2023] Open
Abstract
Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.
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Affiliation(s)
- Mika M. Rockholt
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - George Kenefati
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - Lisa V. Doan
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neuroscience & Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, United States
| | - Jing Wang
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neuroscience & Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, United States
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Stangl M, Maoz SL, Suthana N. Mobile cognition: imaging the human brain in the 'real world'. Nat Rev Neurosci 2023; 24:347-362. [PMID: 37046077 PMCID: PMC10642288 DOI: 10.1038/s41583-023-00692-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2023] [Indexed: 04/14/2023]
Abstract
Cognitive neuroscience studies in humans have enabled decades of impactful discoveries but have primarily been limited to recording the brain activity of immobile participants in a laboratory setting. In recent years, advances in neuroimaging technologies have enabled recordings of human brain activity to be obtained during freely moving behaviours in the real world. Here, we propose that these mobile neuroimaging methods can provide unique insights into the neural mechanisms of human cognition and contribute to the development of novel treatments for neurological and psychiatric disorders. We further discuss the challenges associated with studying naturalistic human behaviours in complex real-world settings as well as strategies for overcoming them. We conclude that mobile neuroimaging methods have the potential to bring about a new era of cognitive neuroscience in which neural mechanisms can be studied with increased ecological validity and with the ability to address questions about natural behaviour and cognitive processes in humans engaged in dynamic real-world experiences.
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Affiliation(s)
- Matthias Stangl
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behaviour, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Sabrina L Maoz
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behaviour, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA.
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Frauscher B, Bénar CG, Engel JJ, Grova C, Jacobs J, Kahane P, Wiebe S, Zjilmans M, Dubeau F. Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy. Epilepsy Behav 2023; 143:109221. [PMID: 37119580 DOI: 10.1016/j.yebeh.2023.109221] [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: 03/18/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023]
Abstract
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.
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Affiliation(s)
- B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - C G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - J Jr Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - C Grova
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of Physics, Concordia University, Montreal, QC, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, QC, Canada; Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
| | - J Jacobs
- Department of Pediatric and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - P Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institute Neurosciences, Department of Neurology, 38000 Grenoble, France
| | - S Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - M Zjilmans
- Stichting Epilepsie Instellingen Nederland, The Netherlands; Brain Center, University Medical Center Utrecht, The Netherlands
| | - F Dubeau
- Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
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Lamoš M, Bočková M, Goldemundová S, Baláž M, Chrastina J, Rektor I. The effect of deep brain stimulation in Parkinson's disease reflected in EEG microstates. NPJ Parkinsons Dis 2023; 9:63. [PMID: 37069159 PMCID: PMC10110608 DOI: 10.1038/s41531-023-00508-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 04/03/2023] [Indexed: 04/19/2023] Open
Abstract
Mechanisms of deep brain stimulation (DBS) on cortical networks were explored mainly by fMRI. Advanced analysis of high-density EEG is a source of additional information and may provide clinically useful biomarkers. The presented study evaluates EEG microstates in Parkinson's disease and the effect of DBS of the subthalamic nucleus (STN). The association between revealed spatiotemporal dynamics of brain networks and changes in oscillatory activity and clinical examination were assessed. Thirty-seven patients with Parkinson's disease treated by STN-DBS underwent two sessions (OFF and ON stimulation conditions) of resting-state EEG. EEG microstates were analyzed in patient recordings and in a matched healthy control dataset. Microstate parameters were then compared across groups and were correlated with clinical and neuropsychological scores. Of the five revealed microstates, two differed between Parkinson's disease patients and healthy controls. Another microstate differed between ON and OFF stimulation conditions in the patient group and restored parameters in the ON stimulation state toward to healthy values. The mean beta power of that microstate was the highest in patients during the OFF stimulation condition and the lowest in healthy controls; sources were localized mainly in the supplementary motor area. Changes in microstate parameters correlated with UPDRS and neuropsychological scores. Disease specific alterations in the spatiotemporal dynamics of large-scale brain networks can be described by EEG microstates. The approach can reveal changes reflecting the effect of DBS on PD motor symptoms as well as changes probably related to non-motor symptoms not influenced by DBS.
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Grants
- NU21-04-00445 Agentura Pro Zdravotnický Výzkum České Republiky (Czech Health Research Council)
- NU21-04-00445 Agentura Pro Zdravotnický Výzkum České Republiky (Czech Health Research Council)
- NU21-04-00445 Agentura Pro Zdravotnický Výzkum České Republiky (Czech Health Research Council)
- NU21-04-00445 Agentura Pro Zdravotnický Výzkum České Republiky (Czech Health Research Council)
- NU21-04-00445 Agentura Pro Zdravotnický Výzkum České Republiky (Czech Health Research Council)
- NU21-04-00445 Agentura Pro Zdravotnický Výzkum České Republiky (Czech Health Research Council)
- LM2018129 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LM2018129 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LM2018129 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LM2018129 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LM2018129 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LM2018129 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
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Affiliation(s)
- Martin Lamoš
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Martina Bočková
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
- Movement Disorders Center, First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic
| | - Sabina Goldemundová
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Marek Baláž
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
- Movement Disorders Center, First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic
| | - Jan Chrastina
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
- Department of Neurosurgery, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic
| | - Ivan Rektor
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
- Movement Disorders Center, First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic.
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43
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Ternisien E, Cecchin T, Colnat-Coulbois S, Maillard LG, Koessler L. Extracting the Invisible: Mesial Temporal Source Detection in Simultaneous EEG and SEEG Recordings. Brain Topogr 2023; 36:192-209. [PMID: 36732440 DOI: 10.1007/s10548-023-00940-5] [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: 10/25/2022] [Accepted: 01/19/2023] [Indexed: 02/04/2023]
Abstract
Epileptic source detection relies mainly on visual expertise of scalp EEG signals, but it is recognised that epileptic discharges can escape to this expertise due to a deep localization of the brain sources that induce a very low, even negative, signal to noise ratio. In this methodological study, we aimed to investigate the feasibility of extracting deep mesial temporal sources that were invisible in scalp EEG signals using blind source separation (BSS) methods (infomax ICA, extended infomax ICA, and JADE) combined with a statistical measure (kurtosis). We estimated the effect of different methodological and physiological parameters that could alter or improve the extraction. Using nine well-defined mesial epileptic networks (1949 spikes) obtained from seven patients and simultaneous EEG-SEEG recordings, the first independent component extracted from the scalp EEG signals was validated in mean from 46 to 80% according to the different parameters. The three BSS methods equally performed (no significant difference) and no influence of the number of scalp electrodes used was found. At the opposite, the number and amplitude of spikes included in the averaging before the extraction modified the performance. Anyway, despite their invisibility in scalp EEG signals, this study demonstrates that deep source extraction is feasible under certain conditions and with the use of common signal analysis toolboxes. This finding confirms the crucial need to continue the signal analysis of scalp EEG recordings which contains subcortical signals that escape to expert visual analysis but could be found by signal processing.
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Affiliation(s)
| | | | - Sophie Colnat-Coulbois
- Université de Lorraine, CNRS, CRAN, Nancy, France.,Université de Lorraine, CHRU-Nancy, Service de Neurochirurgie, Nancy, France
| | - Louis Georges Maillard
- Université de Lorraine, CNRS, CRAN, Nancy, France.,Université de Lorraine, CHRU-Nancy, Service de Neurologie, Nancy, France
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Chirumamilla VC, Hitchings L, Mulkey SB, Anwar T, Baker R, Larry Maxwell G, De Asis-Cruz J, Kapse K, Limperopoulos C, du Plessis A, Govindan RB. Functional brain network properties of healthy full-term newborns quantified by scalp and source-reconstructed EEG. Clin Neurophysiol 2023; 147:72-80. [PMID: 36731349 PMCID: PMC9975070 DOI: 10.1016/j.clinph.2023.01.005] [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: 10/27/2022] [Revised: 12/20/2022] [Accepted: 01/01/2023] [Indexed: 01/24/2023]
Abstract
OBJECTIVE Identifying the functional brain network properties of term low-risk newborns using high-density EEG (HD-EEG) and comparing these properties with those of established functional magnetic resonance image (fMRI) - based networks. METHODS HD-EEG was collected from 113 low-risk term newborns before delivery hospital discharge and within 72 hours of birth. Functional brain networks were reconstructed using coherence at the scalp and source levels in delta, theta, alpha, beta, and gamma frequency bands. These networks were characterized for the global and local network architecture. RESULTS Source-level networks in all the frequency bands identified the presence of the efficient small world (small-world propensity (SWP) > 0.6) architecture with four distinct modules linked by hub regions and rich-club (coefficient > 1) topology. The modular regions included primary, association, limbic, paralimbic, and subcortical regions, which have been demonstrated in fMRI studies. In contrast, scalp-level networks did not display consistent small world architecture (SWP < 0.6), and also identified only 2-3 modules in each frequency band.The modular regions of the scalp-network primarily included frontal and occipital regions. CONCLUSIONS Our findings show that EEG sources in low-risk newborns corroborate fMRI-based connectivity results. SIGNIFICANCE EEG source analysis characterizes functional connectivity at the bedside of low-risk newborn infants soon after birth.
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Affiliation(s)
| | - Laura Hitchings
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, USA
| | - Sarah B Mulkey
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, USA; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA; Department of Neurology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Tayyba Anwar
- Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA; Department of Neurology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA; Department of Neurology, Children's National Hospital, Washington, DC, USA
| | - Robin Baker
- Inova Women's and Children's Hospital, Fairfax, VA, USA; Fairfax Neonatal Associates, Fairfax, VA, USA
| | | | | | - Kushal Kapse
- Developing Brain Institute, Children's National Hospital, Washington, DC, USA
| | - Catherine Limperopoulos
- Developing Brain Institute, Children's National Hospital, Washington, DC, USA; Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC, USA
| | - Adre du Plessis
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, USA; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - R B Govindan
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, USA; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
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Hatlestad-Hall C, Bruña R, Liljeström M, Renvall H, Heuser K, Taubøll E, Maestú F, Haraldsen IH. Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough? Clin Neurophysiol 2023; 150:1-16. [PMID: 36972647 DOI: 10.1016/j.clinph.2023.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 02/03/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities. METHODS EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested. RESULTS The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated. CONCLUSIONS Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data. SIGNIFICANCE Characterisation of functional brain networks derived from low-density EEG warrants careful consideration.
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Affiliation(s)
| | - Ricardo Bruña
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Radiology, Universidad Complutense de Madrid, Madrid, Spain
| | - Mia Liljeström
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Kjell Heuser
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Erik Taubøll
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Fernando Maestú
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | - Ira H Haraldsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway; BrainSymph AS, Oslo, Norway
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Makowka S, Mory LN, Mouthon M, Mancini C, Guggisberg AG, Chabwine JN. EEG Beta functional connectivity decrease in the left amygdala correlates with the affective pain in fibromyalgia: A pilot study. PLoS One 2023; 18:e0281986. [PMID: 36802404 PMCID: PMC9943002 DOI: 10.1371/journal.pone.0281986] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 02/07/2023] [Indexed: 02/23/2023] Open
Abstract
Fibromyalgia (FM) is a major chronic pain disease with prominent affective disturbances, and pain-associated changes in neurotransmitters activity and in brain connectivity. However, correlates of affective pain dimension lack. The primary goal of this correlational cross-sectional case-control pilot study was to find electrophysiological correlates of the affective pain component in FM. We examined the resting-state EEG spectral power and imaginary coherence in the beta (β) band (supposedly indexing the GABAergic neurotransmission) in 16 female patients with FM and 11 age-adjusted female controls. FM patients displayed lower functional connectivity in the High β (Hβ, 20-30 Hz) sub-band than controls (p = 0.039) in the left basolateral complex of the amygdala (p = 0.039) within the left mesiotemporal area, in particular, in correlation with a higher affective pain component level (r = 0.50, p = 0.049). Patients showed higher Low β (Lβ, 13-20 Hz) relative power than controls in the left prefrontal cortex (p = 0.001), correlated with ongoing pain intensity (r = 0.54, p = 0.032). For the first time, GABA-related connectivity changes correlated with the affective pain component are shown in the amygdala, a region highly involved in the affective regulation of pain. The β power increase in the prefrontal cortex could be compensatory to pain-related GABAergic dysfunction.
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Affiliation(s)
- Soline Makowka
- Faculty of Science and Medicine, Department of Neuroscience and Movement Science, Laboratory for Neurorehabilitation Science, Medicine Section, University of Fribourg, Fribourg, Switzerland
| | - Lliure-Naima Mory
- Faculty of Science and Medicine, Department of Neuroscience and Movement Science, Laboratory for Neurorehabilitation Science, Medicine Section, University of Fribourg, Fribourg, Switzerland
- Neurorehabilitation Division, Fribourg Hospital Meyriez/Murten, Fribourg, Switzerland
| | - Michael Mouthon
- Faculty of Science and Medicine, Department of Neuroscience and Movement Science, Laboratory for Neurorehabilitation Science, Medicine Section, University of Fribourg, Fribourg, Switzerland
| | - Christian Mancini
- Faculty of Science and Medicine, Department of Neuroscience and Movement Science, Laboratory for Neurorehabilitation Science, Medicine Section, University of Fribourg, Fribourg, Switzerland
| | - Adrian G. Guggisberg
- Department of Clinical Neuroscience, Division of Neurorehabilitation, Geneva University Hospital, Geneva, Switzerland
| | - Joelle Nsimire Chabwine
- Faculty of Science and Medicine, Department of Neuroscience and Movement Science, Laboratory for Neurorehabilitation Science, Medicine Section, University of Fribourg, Fribourg, Switzerland
- Neurorehabilitation Division, Fribourg Hospital Meyriez/Murten, Fribourg, Switzerland
- * E-mail:
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Van de Steen F, Pinotsis D, Devos W, Colenbier N, Bassez I, Friston K, Marinazzo D. Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex. PLoS Comput Biol 2022; 18:e1009988. [PMID: 36574458 PMCID: PMC9829170 DOI: 10.1371/journal.pcbi.1009988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 01/09/2023] [Accepted: 12/16/2022] [Indexed: 12/29/2022] Open
Abstract
During resting-state EEG recordings, alpha activity is more prominent over the posterior cortex in eyes-closed (EC) conditions compared to eyes-open (EO). In this study, we characterized the difference in spectra between EO and EC conditions using dynamic causal modelling. Specifically, we investigated the role of intrinsic and extrinsic connectivity-within the visual cortex-in generating EC-EO alpha power differences over posterior electrodes. The primary visual cortex (V1) and the bilateral middle temporal visual areas (V5) were equipped with bidirectional extrinsic connections using a canonical microcircuit. The states of four intrinsically coupled subpopulations-within each occipital source-were also modelled. Using Bayesian model selection, we tested whether modulations of the intrinsic connections in V1, V5 or extrinsic connections (or a combination thereof) provided the best evidence for the data. In addition, using parametric empirical Bayes (PEB), we estimated group averages under the winning model. Bayesian model selection showed that the winning model contained both extrinsic connectivity modulations, as well as intrinsic connectivity modulations in all sources. The PEB analysis revealed increased extrinsic connectivity during EC. Overall, we found a reduction in the inhibitory intrinsic connections during EC. The results suggest that the intrinsic modulations in V5 played the most important role in producing EC-EO alpha differences, suggesting an intrinsic disinhibition in higher order visual cortex, during EC resting state.
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Affiliation(s)
- Frederik Van de Steen
- Department of Data Analysis, Ghent University, Ghent, Belgium
- Vrije Universiteit Brussel, AIMS laboratory, Brussel, Belgium
- The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
- * E-mail:
| | - Dimitris Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City—University of London, London, United Kingdom
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Wouter Devos
- Department of Data Analysis, Ghent University, Ghent, Belgium
| | | | - Iege Bassez
- Department of Data Analysis, Ghent University, Ghent, Belgium
| | - Karl Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
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48
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Hybart RL, Ferris DP. Embodiment for Robotic Lower-Limb Exoskeletons: A Narrative Review. IEEE Trans Neural Syst Rehabil Eng 2022; PP:10.1109/TNSRE.2022.3229563. [PMID: 37015690 PMCID: PMC10267288 DOI: 10.1109/tnsre.2022.3229563] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Research on embodiment of objects external to the human body has revealed important information about how the human nervous system interacts with robotic lower limb exoskeletons. Typical robotic exoskeleton control approaches view the controllers as an external agent intending to move in coordination with the human. However, principles of embodiment suggest that the exoskeleton controller should ideally coordinate with the human such that the nervous system can adequately model the input-output dynamics of the exoskeleton controller. Measuring embodiment of exoskeletons should be a necessary step in the exoskeleton development and prototyping process. Researchers need to establish high fidelity quantitative measures of embodiment, rather than relying on current qualitative survey measures. Mobile brain imaging techniques, such as high-density electroencephalography, is likely to provide a deeper understanding of embodiment during human-machine interactions and advance exoskeleton research and development. In this review we show why future exoskeleton research should include quantitative measures of embodiment as a metric of success.
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Bae S, Lim HK, Jeong Y, Kim SG, Park SM, Shon YM, Suh M. Deep brain stimulation of the anterior nuclei of the thalamus can alleviate seizure severity and induce hippocampal GABAergic neuronal changes in a pilocarpine-induced epileptic mouse brain. Cereb Cortex 2022; 32:5530-5543. [PMID: 35258078 DOI: 10.1093/cercor/bhac033] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/17/2022] [Accepted: 01/24/2022] [Indexed: 01/25/2023] Open
Abstract
Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) has been widely used as an effective treatment for refractory temporal lobe epilepsy. Despite its promising clinical outcome, the exact mechanism of how ANT-DBS alleviates seizure severity has not been fully understood, especially at the cellular level. To assess effects of DBS, the present study examined electroencephalography (EEG) signals and locomotor behavior changes and conducted immunohistochemical analyses to examine changes in neuronal activity, number of neurons, and neurogenesis of inhibitory neurons in different hippocampal subregions. ANT-DBS alleviated seizure activity, abnormal locomotor behaviors, reduced theta-band, increased gamma-band EEG power in the interictal state, and increased the number of neurons in the dentate gyrus (DG). The number of parvalbumin- and somatostatin-expressing inhibitory neurons was recovered to the level in DG and CA1 of naïve mice. Notably, BrdU-positive inhibitory neurons were increased. In conclusion, ANT-DBS not only could reduce the number of seizures, but also could induce neuronal changes in the hippocampus, which is a key region involved in chronic epileptogenesis. Importantly, our results suggest that ANT-DBS may lead to hippocampal subregion-specific cellular recovery of GABAergic inhibitory neurons.
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Affiliation(s)
- Sungjun Bae
- Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon 16419, South Korea.,Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.,IMNEWRUN Inc., N Center Bldg. A 5F, Sungkyunkwan University, Suwon 16419, South Korea
| | - Hyun-Kyoung Lim
- Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon 16419, South Korea.,Biomedical Institute for Convergence at SKKU (BICS), Sungkyunkwan University, Suwon 16419, South Korea
| | - Yoonyi Jeong
- Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon 16419, South Korea.,Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, South Korea
| | - Seong-Gi Kim
- Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon 16419, South Korea.,Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, South Korea
| | - Sung-Min Park
- Department of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, South Korea
| | - Young-Min Shon
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea.,Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Suwon 16419, South Korea
| | - Minah Suh
- Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon 16419, South Korea.,Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.,IMNEWRUN Inc., N Center Bldg. A 5F, Sungkyunkwan University, Suwon 16419, South Korea.,Biomedical Institute for Convergence at SKKU (BICS), Sungkyunkwan University, Suwon 16419, South Korea.,Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Suwon 16419, South Korea
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50
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Wang RWY, Liu IN. Temporal and electroencephalography dynamics of surreal marketing. Front Neurosci 2022; 16:949008. [PMID: 36389218 PMCID: PMC9648353 DOI: 10.3389/fnins.2022.949008] [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: 05/20/2022] [Accepted: 08/25/2022] [Indexed: 11/28/2022] Open
Abstract
Event-related spectral perturbation analysis was employed in this study to explore whether surreal image designs containing metaphors could influence product marketing effects, including consumers' product curiosity, product comprehension, product preference, and purchase intention. A total of 30 healthy participants aged 21-30 years were recruited. Neurophysiological findings revealed that lower gamma, beta, and theta spectral powers were evoked in the right insula (Brodmann Area 13) by surreal marketing images. This was associated, behaviorally, with the manifestation of higher product curiosity and purchase intention. Based on previous research, the brain functions of this area include novelty, puzzle-solving, and cravings for reward caused by cognitive overload.
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Affiliation(s)
- Regina W. Y. Wang
- Department of Design, National Taiwan University of Science and Technology, Taipei City, Taiwan
- Design Perceptual Awareness Laboratory, Taiwan Building Technology Center, National Taiwan University of Science and Technology, Taipei City, Taiwan
| | - I-Ning Liu
- Department of Design, National Taiwan University of Science and Technology, Taipei City, Taiwan
- Design Perceptual Awareness Laboratory, Taiwan Building Technology Center, National Taiwan University of Science and Technology, Taipei City, Taiwan
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