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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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Taberna GA, Samogin J, Zhao M, Marino M, Guarnieri R, Cuartas Morales E, Ganzetti M, Liu Q, Mantini D. Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data. Comput Biol Med 2024; 178:108704. [PMID: 38852398 DOI: 10.1016/j.compbiomed.2024.108704] [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: 11/10/2023] [Revised: 05/28/2024] [Accepted: 06/01/2024] [Indexed: 06/11/2024]
Abstract
INTRODUCTION High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets. FINDINGS Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software. CONCLUSIONS NET is freely available (https://github.com/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows.
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Affiliation(s)
- Gaia Amaranta Taberna
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001, Leuven, Belgium
| | - Jessica Samogin
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001, Leuven, Belgium
| | - Mingqi Zhao
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001, Leuven, Belgium; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, 730000, Lanzhou, PR China
| | - Marco Marino
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001, Leuven, Belgium; Department of General Psychology, University of Padova, 35131, Padova, Italy
| | - Roberto Guarnieri
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001, Leuven, Belgium
| | - Ernesto Cuartas Morales
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001, Leuven, Belgium; Dirección Académica, Universidad Nacional de Colombia, Sede de La Paz, La Paz, 202017, Colombia
| | - Marco Ganzetti
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001, Leuven, Belgium; Roche Pharma Research and Early Development (pRED), pRED Data & Analytics, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, 4070, Basel, Switzerland
| | - Quanying Liu
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001, Leuven, Belgium; Department of Biomedical Engineering, Southern University of Science and Technology, 518055, Shenzhen, PR China
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001, Leuven, Belgium; KU Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium.
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Vorwerk J, Wolters CH, Baumgarten D. Global sensitivity of EEG source analysis to tissue conductivity uncertainties. Front Hum Neurosci 2024; 18:1335212. [PMID: 38532791 PMCID: PMC10963400 DOI: 10.3389/fnhum.2024.1335212] [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: 11/08/2023] [Accepted: 01/22/2024] [Indexed: 03/28/2024] Open
Abstract
Introduction To reliably solve the EEG inverse problem, accurate EEG forward solutions based on a detailed, individual volume conductor model of the head are essential. A crucial-but often neglected-aspect in generating a volume conductor model is the choice of the tissue conductivities, as these may vary from subject to subject. In this study, we investigate the sensitivity of EEG forward and inverse solutions to tissue conductivity uncertainties for sources distributed over the whole cortex surface. Methods We employ a detailed five-compartment head model distinguishing skin, skull, cerebrospinal fluid, gray matter, and white matter, where we consider uncertainties of skin, skull, gray matter, and white matter conductivities. We use the finite element method (FEM) to calculate EEG forward solutions and goal function scans (GFS) as inverse approach. To be able to generate the large number of EEG forward solutions, we employ generalized polynomial chaos (gPC) expansions. Results For sources up to a depth of 4 cm, we find the strongest influence on the signal topography of EEG forward solutions for the skull conductivity and a notable effect for the skin conductivity. For even deeper sources, e.g., located deep in the longitudinal fissure, we find an increasing influence of the white matter conductivity. The conductivity variations translate to varying source localizations particularly for quasi-tangential sources on sulcal walls, whereas source localizations of quasi-radial sources on the top of gyri are less affected. We find a strong correlation between skull conductivity and the variation of source localizations and especially the depth of the reconstructed source for quasi-tangential sources. We furthermore find a clear but weaker correlation between depth of the reconstructed source and the skin conductivity. Discussion Our results clearly show the influence of tissue conductivity uncertainties on EEG source analysis. We find a particularly strong influence of skull and skin conductivity uncertainties.
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Affiliation(s)
- Johannes Vorwerk
- Institute of Electrical and Biomedical Engineering, UMIT TIROL—Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
| | - Carsten H. Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Daniel Baumgarten
- Institute of Electrical and Biomedical Engineering, UMIT TIROL—Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
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Noor MS, Howell B, McIntyre CC. Role of the volume conductor on simulations of local field potential recordings from deep brain stimulation electrodes. PLoS One 2023; 18:e0294512. [PMID: 38011104 PMCID: PMC10681243 DOI: 10.1371/journal.pone.0294512] [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: 04/20/2023] [Accepted: 11/02/2023] [Indexed: 11/29/2023] Open
Abstract
OBJECTIVE Local field potential (LFP) recordings from deep brain stimulation (DBS) electrodes are commonly used in research analyses, and are beginning to be used in clinical practice. Computational models of DBS LFPs provide tools for investigating the biophysics and neural synchronization that underlie LFP signals. However, technical standards for DBS LFP model parameterization remain to be established. Therefore, the goal of this study was to evaluate the role of the volume conductor (VC) model complexity on simulated LFP signals in the subthalamic nucleus (STN). APPROACH We created a detailed human head VC model that explicitly represented the inhomogeneity and anisotropy associated with 12 different tissue structures. This VC model represented our "gold standard" for technical detail and electrical realism. We then incrementally decreased the complexity of the VC model and quantified the impact on the simulated LFP recordings. Identical STN neural source activity was used when comparing the different VC model variants. Results Ignoring tissue anisotropy reduced the simulated LFP amplitude by ~12%, while eliminating soft tissue heterogeneity had a negligible effect on the recordings. Simplification of the VC model to consist of a single homogenous isotropic tissue medium with a conductivity of 0.215 S/m contributed an additional ~3% to the error. SIGNIFICANCE Highly detailed VC models do generate different results than simplified VC models. However, with errors in the range of ~15%, the use of a well-parameterized simple VC model is likely to be acceptable in most contexts for DBS LFP modeling.
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Affiliation(s)
- M. Sohail Noor
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Bryan Howell
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Cameron C. McIntyre
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
- Department of Neurosurgery, Duke University, Durham, NC, United States of America
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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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Azizollahi H, Aarabi A, Kazemi K, Wallois F. Assessing the effects of head modelling errors and measurement noise on EEG source localization accuracy in preterm newborns: A single-subject study. Eur J Neurosci 2023; 58:2746-2765. [PMID: 37448164 DOI: 10.1111/ejn.16060] [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: 10/12/2022] [Revised: 05/22/2023] [Accepted: 05/27/2023] [Indexed: 07/15/2023]
Abstract
The accuracy of electroencephalogram (EEG) source localization is compromised because of head modelling errors. In this study, we investigated the effect of inaccuracy in the conductivity of head tissues and head model structural deficiencies on the accuracy of EEG source analysis in premature neonates. A series of EEG forward and inverse simulations was performed by introducing structural deficiencies into the reference head models to generate test models, which were then used to investigate head modelling errors caused by cerebrospinal fluid (CSF) exclusion, lack of grey matter (GM)-white matter (WM) distinction, fontanel exclusion and inaccuracy in skull conductivity. The modelling errors were computed between forward and inverse solutions obtained using the reference and test models generated for each deficiency. Our results showed that the exclusion of CSF from the head model had a strong widespread effect on the accuracy of the EEG source localization with position errors lower than 4.17 mm. The GM and WM distinction also caused strong localization errors (up to 3.5 mm). The exclusion of fontanels from the head model also strongly affected the accuracy of the EEG source localization for sources located beneath the fontanels with a maximum localization error of 4.37 mm. Similarly, inaccuracies in the skull conductivity caused errors in EEG forward and inverse modelling in sources beneath cranial bones. Our results indicate that the accuracy of EEG source imaging in premature neonates can be largely improved by using head models, which include not only the brain, skull and scalp but also the CSF, GM, WM and fontanels.
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Affiliation(s)
- Hamed Azizollahi
- GRAMFC, Inserm U1105, University Research Center (CURS), CHU AMIENS-SITE SUD, Amiens, France
| | - Ardalan Aarabi
- Laboratory of Functional Neuroscience and Pathologies (LNFP UR 4559), University Research Center, University Hospital, Amiens, France
- Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
| | - Kamran Kazemi
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Fabrice Wallois
- GRAMFC, Inserm U1105, University Research Center (CURS), CHU AMIENS-SITE SUD, Amiens, France
- EFSN Pediatric (Pediatric Nervous System Functional Investigation Unit), Department of Pediatrics, CHU AMIENS-SITE SUD, Amiens, France
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Pereira M, Fernandes SR, Miranda PC, de Carvalho M. Lumbar trans-spinal direct current stimulation: A modeling-experimental approach to dorsal root ganglia stimulation. Front Neurosci 2022; 16:1041932. [PMID: 36570853 PMCID: PMC9773993 DOI: 10.3389/fnins.2022.1041932] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 11/23/2022] [Indexed: 12/14/2022] Open
Abstract
Introduction The excitability of spinal motor neurons (MN) can be altered through subthreshold currents, such as transcutaneous spinal direct-current stimulation (tsDCS). Current evidence shows that tsDCS can interfere with ascending somatosensory pathways and lower motor neurons' (LMN) excitability, which points to its therapeutic potential for repairing altered spinal responses. We aim to define the best tsDCS montage for maximizing the electric field (E-field) in the lumbar spinal cord (L-SC) by computer modeling; and to apply this montage to measure the effect on LMN excitability and somatosensory evoked potentials (SSEP). Methods A human volume conductor model was obtained from an available database. The E-field distribution was calculated considering three different electrode settings aiming at maximizing the field at L-SC and right dorsal root ganglia (DRG). The best electrode setting was then selected and applied in a blind crossover pseudo-randomized study including 14 subjects. tsDCS was delivered for 15 min (cathodal vs. sham) over L2 vertebra level (4 mA, 144 mC/cm2), and its effect on F-waves, H-reflex (including homosynaptic depression, HD) and SSEPs was investigated in the lower limbs. Results All simulated montages showed higher current density and E-field magnitudes between the electrodes (>0.15 V/m), with a major longitudinal component and with rostral-caudal direction. The induced E-field involved the sensory ganglia and was maximum in the right T8-left L2 montage, which was the one selected for the experimental protocol. We disclosed a statistically significant increase of the H-reflex amplitude at 0.1 Hz, after cathodal tsDCS (c-tsDCS) on both sides. No other significant change was observed. Discussion Our results can suggest the c-tsDCS applied to the L-SC and DRG can modulate synaptic efficiency increasing lower motor neurons response to Ia fibers excitation. The possible implications of our findings for treating clinical conditions will be addressed in future studies.
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Affiliation(s)
- Mariana Pereira
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
| | - Sofia Rita Fernandes
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal,Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Pedro Cavaleiro Miranda
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Mamede de Carvalho
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal,Departamento de Neurociências e Saúde Mental, Hospital de Santa Maria - Centro Hospitalar Lisboa Norte, Lisboa, Portugal,*Correspondence: Mamede de Carvalho,
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Patient-specific solution of the electrocorticography forward problem in deforming brain. Neuroimage 2022; 263:119649. [PMID: 36167268 DOI: 10.1016/j.neuroimage.2022.119649] [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: 09/30/2021] [Revised: 08/25/2022] [Accepted: 09/23/2022] [Indexed: 11/22/2022] Open
Abstract
Invasive intracranial electroencephalography (iEEG), or electrocorticography (ECoG), measures electric potential directly on the surface of the brain and can be used to inform treatment planning for epilepsy surgery. Combined with numerical modeling it can further improve accuracy of epilepsy surgery planning. Accurate solution of the iEEG forward problem, which is a crucial prerequisite for solving the iEEG inverse problemin epilepsy seizure onset zone localization, requires accurate representation of the patient's brain geometry and tissue electrical conductivity after implantation of electrodes. However, implantation of subdural grid electrodes causes the brain to deform, which invalidates preoperatively acquired image data. Moreover, postoperative magnetic resonance imaging (MRI) is incompatible with implanted electrodes and computed tomography (CT) has insufficient range of soft tissue contrast, which precludes both MRI and CT from being used to obtain the deformed postoperative geometry. In this paper, we present a biomechanics-based image warping procedure using preoperative MRI for tissue classification and postoperative CT for locating implanted electrodes to perform non-rigid registration of the preoperative image data to the postoperative configuration. We solve the iEEG forward problem on the predicted postoperative geometry using the finite element method (FEM) which accounts for patient-specific inhomogeneity and anisotropy of tissue conductivity. Results for the simulation of a current source in the brain show large differences in electric potential predicted by the models based on the original images and the deformed images corresponding to the brain geometry deformed by placement of invasive electrodes. Computation of the lead field matrix (useful for solution of the iEEG inverse problem) also showed significant differences between the different models. The results suggest that rapid and accurate solution of the forward problem in a deformed brain for a given patient is achievable.
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Kashyap R, Bhattacharjee S, Bharath RD, Venkatasubramanian G, Udupa K, Bashir S, Oishi K, Desmond JE, Chen SHA, Guan C. Variation of cerebrospinal fluid in specific regions regulates focality in transcranial direct current stimulation. Front Hum Neurosci 2022; 16:952602. [PMID: 36118967 PMCID: PMC9479459 DOI: 10.3389/fnhum.2022.952602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundConventionally, transcranial direct current stimulation (tDCS) aims to focalize the current reaching the target region-of-interest (ROI). The focality can be quantified by the dose-target-determination-index (DTDI). Despite having a uniform tDCS setup, some individuals receive focal stimulation (high DTDI) while others show reduced focality (“non-focal”). The volume of cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) underlying each ROI govern the tDCS current distribution inside the brain, thereby regulating focality.AimTo determine the regional volume parameters that differentiate the focal and non-focal groups.MethodsT1-weighted images of the brain from 300 age-sex matched adults were divided into three equal groups- (a) Young (20 ≤ × < 40 years), (b) Middle (40 ≤ × < 60 years), and (c) Older (60 ≤ × < 80 years). For each group, inter and intra-hemispheric montages with electrodes at (1) F3 and right supraorbital region (F3-RSO), and (2) CP5 and Cz (CP5-Cz) were simulated, targeting the left- Dorsolateral Prefrontal Cortex (DLPFC) and -Inferior Parietal Lobule (IPL), respectively. Both montages were simulated for two current doses (1 and 2 mA). For each individual head simulated for a tDCS configuration (montage and dose), the current density at each region-of-interest (ROI) and their DTDI were calculated. The individuals were categorized into two groups- (1) Focal (DTDI ≥ 0.75), and (2) Non-focal (DTDI < 0.75). The regional volume of CSF, GM, and WM of all the ROIs was determined. For each tDCS configuration and ROI, three 3-way analysis of variance was performed considering- (i) GM, (ii) WM, and (iii) CSF as the dependent variable (DV). The age group, sex, and focality group were the between-subject factors. For a given ROI, if any of the 3 DV’s showed a significant main effect or interaction involving the focality group, then that ROI was classified as a “focal ROI.”ResultsRegional CSF was the principal determinant of focality. For interhemispheric F3-RSO montage, interaction effect (p < 0.05) of age and focality was observed at Left Caudate Nucleus, with the focal group exhibiting higher CSF volume. The CSF volume of focal ROI correlated positively (r ∼ 0.16, p < 0.05) with the current density at the target ROI (DLPFC). For intrahemispheric CP5-Cz montage, a significant (p < 0.05) main effect was observed at the left pre- and post-central gyrus, with the focal group showing lower CSF volume. The CSF volume correlated negatively (r ∼ –0.16, p < 0.05) with current density at left IPL. The results were consistent for both current doses.ConclusionThe CSF channels the flow of tDCS current between electrodes with focal ROIs acting like reservoirs of current. The position of focal ROI in the channel determines the stimulation intensity at the target ROI. For focal stimulation in interhemispheric F3-RSO, the proximity of focal ROI reserves the current density at the target ROI (DLPFC). In contrast, for intrahemispheric montage (CP5-Cz), the far-end location of focal ROI reduces the current density at the target (IPL).
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Affiliation(s)
- Rajan Kashyap
- Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
- *Correspondence: Rajan Kashyap,
| | - Sagarika Bhattacharjee
- Department of Neurophysiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
- Psychology, School of Social Sciences (SSS), Nanyang Technological University, Singapore, Singapore
| | - Rose Dawn Bharath
- Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - Ganesan Venkatasubramanian
- InSTAR Program, Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - Kaviraja Udupa
- Department of Neurophysiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Shahid Bashir
- Neuroscience Center, King Fahad Specialist Hospital Dammam, Dammam, Saudi Arabia
| | - Kenichi Oishi
- The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - John E. Desmond
- The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - S. H. Annabel Chen
- Psychology, School of Social Sciences (SSS), Nanyang Technological University, Singapore, Singapore
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore
- Lee Kong Chian School of Medicine (LKC Medicine), Nanyang Technological University, Singapore, Singapore
- National Institute of Education, Nanyang Technological University, Singapore, Singapore
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
- Cuntai Guan,
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Giri A, Kumar L, Kurwale N, Gandhi TK. Anatomical harmonics basis based brain source localization with application to epilepsy. Sci Rep 2022; 12:11240. [PMID: 35787640 PMCID: PMC9253096 DOI: 10.1038/s41598-022-14500-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 06/08/2022] [Indexed: 11/23/2022] Open
Abstract
Brain Source Localization (BSL) using Electroencephalogram (EEG) has been a useful noninvasive modality for the diagnosis of epileptogenic zones, study of evoked related potentials, and brain disorders. The inverse solution of BSL is limited by high computational cost and localization error. The performance is additionally limited by head shape assumption and the corresponding harmonics basis function. In this work, an anatomical harmonics basis (Spherical Harmonics (SH), and more particularly Head Harmonics (H2)) based BSL is presented. The spatio-temporal four shell head model is formulated in SH and H2 domain. The anatomical harmonics domain formulation leads to dimensionality reduction and increased contribution of source eigenvalues, resulting in decreased computation and increased accuracy respectively. The performance of spatial subspace based Multiple Signal Classification (MUSIC) and Recursively Applied and Projected (RAP)-MUSIC method is compared with the proposed SH and H2 counterparts on simulated data. SH and H2 domain processing effectively resolves the problem of high computational cost without sacrificing the inverse source localization accuracy. The proposed H2 MUSIC was additionally validated for epileptogenic zone localization on clinical EEG data. The proposed framework offers an effective solution to clinicians in automated and time efficient seizure localization.
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Affiliation(s)
- Amita Giri
- Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi, India
| | - Lalan Kumar
- Department of Electrical Engineering and Bharti School of Telecommunication, Indian Institute of Technology - Delhi, New Delhi, India.
| | - Nilesh Kurwale
- Deenanath Mangeshkar Hospital and Research Center, Pune, Maharashtra, India
| | - Tapan K Gandhi
- Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi, India.
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Shamo: A Tool for Electromagnetic Modeling, Simulation and Sensitivity Analysis of the Head. Neuroinformatics 2022; 20:811-824. [PMID: 35266105 DOI: 10.1007/s12021-022-09574-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/10/2022] [Indexed: 12/31/2022]
Abstract
Accurate electromagnetic modeling of the head of a subject is of main interest in the fields of source reconstruction and brain stimulation. Those processes rely heavily on the quality of the model and, even though the geometry of the tissues can be extracted from magnetic resonance images (MRI) or computed tomography (CT), their physical properties such as the electrical conductivity are difficult to measure with non intrusive techniques. In this paper, we propose a tool to assess the uncertainty in the model parameters, the tissue conductivity, as well as compute a parametric forward models for electroencephalography (EEG) and transcranial direct current stimulation (tDCS) current distribution.
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12
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Piastra MC, Oostenveld R, Schoffelen JM, Piai V. Estimating the influence of stroke lesions on MEG source reconstruction. Neuroimage 2022; 260:119422. [PMID: 35781078 DOI: 10.1016/j.neuroimage.2022.119422] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 05/20/2022] [Accepted: 06/18/2022] [Indexed: 11/28/2022] Open
Abstract
Source reconstruction of magnetoencephalography (MEG) has been used to assess brain reorganization after brain damage, such as stroke. Lesions result in parts of the brain having an electrical conductivity that differs from the normal values. The effect this has on the forward solutions (i.e., the propagation of electric currents and magnetic fields generated by cortical activity) is well predictable. However, their influence on source localization results is not well characterized and understood. This is specifically a concern for patient studies with asymmetric (i.e., within one hemisphere) lesions focusing on asymmetric and lateralized brain activity, such as language. In particular, it is good practice to consider the level of geometrical detail that is necessary to compute and interpret reliable source reconstruction results. To understand the effect of lesions on source estimates and propose recommendations to researchers working with clinical data, in this study we consider the trade off between improved accuracy and the additional effort to compute more realistic head models, with the aim to answer the question whether the additional effort is worth it. We simulated and analyzed the effects of a stroke lesion (i.e., an asymmetrically distributed CSF-filled cavity) in the head model with three different sizes and locations when performing MEG source reconstruction using a finite element method (FEM). We compared the effect of the lesion with a homogeneous head model that neglects the lesion. We computed displacement and attenuation/amplification maps to quantify the localization errors and signal magnitude modulation. We conclude that brain lesions leading to asymmetrically distributed CSF-filled cavities should be modeled when performing MEG source reconstruction, especially when investigating deep sources or post-stroke hemispheric lateralization of functions. The strongest effects are not only visible in perilesional areas, but can extend up to 20 mm from the lesion. Bigger lesions lead to stronger effects impacting larger areas, independently from the lesion location. Lastly, we conclude that more priority should be given to usability and accessibility of the required computational tools, to allow researchers with less technical expertise to use the improved methods that are available but currently not widely adopted yet.
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Affiliation(s)
- Maria Carla Piastra
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Nijmegen, The Netherlands; Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, Faculty of Science and Technology, University of Twente, Enschede, The Netherlands.
| | - Robert Oostenveld
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands; NatMEG, Karolinska Institutet, Stockholm, Sweden
| | - Jan Mathijs Schoffelen
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Vitória Piai
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands; Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Medical Psychology, Nijmegen, The Netherlands
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13
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Zhao M, Bonassi G, Samogin J, Taberna GA, Porcaro C, Pelosin E, Avanzino L, Mantini D. Assessing Neurokinematic and Neuromuscular Connectivity During Walking Using Mobile Brain-Body Imaging. Front Neurosci 2022; 16:912075. [PMID: 35720696 PMCID: PMC9204106 DOI: 10.3389/fnins.2022.912075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
Gait is a common but rather complex activity that supports mobility in daily life. It requires indeed sophisticated coordination of lower and upper limbs, controlled by the nervous system. The relationship between limb kinematics and muscular activity with neural activity, referred to as neurokinematic and neuromuscular connectivity (NKC/NMC) respectively, still needs to be elucidated. Recently developed analysis techniques for mobile high-density electroencephalography (hdEEG) recordings have enabled investigations of gait-related neural modulations at the brain level. To shed light on gait-related neurokinematic and neuromuscular connectivity patterns in the brain, we performed a mobile brain/body imaging (MoBI) study in young healthy participants. In each participant, we collected hdEEG signals and limb velocity/electromyography signals during treadmill walking. We reconstructed neural signals in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–50 Hz) frequency bands, and assessed the co-modulations of their power envelopes with myogenic/velocity envelopes. Our results showed that the myogenic signals have larger discriminative power in evaluating gait-related brain-body connectivity with respect to kinematic signals. A detailed analysis of neuromuscular connectivity patterns in the brain revealed robust responses in the alpha and beta bands over the lower limb representation in the primary sensorimotor cortex. There responses were largely contralateral with respect to the body sensor used for the analysis. By using a voxel-wise analysis of variance on the NMC images, we revealed clear modulations across body sensors; the variability across frequency bands was relatively lower, and below significance. Overall, our study demonstrates that a MoBI platform based on hdEEG can be used for the investigation of gait-related brain-body connectivity. Future studies might involve more complex walking conditions to gain a better understanding of fundamental neural processes associated with gait control, or might be conducted in individuals with neuromotor disorders to identify neural markers of abnormal gait.
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Affiliation(s)
- Mingqi Zhao
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | - Gaia Bonassi
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, Azienda Sanitaria Locale Chiavarese, Genoa, Italy
| | - Jessica Samogin
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | | | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Institute of Cognitive Sciences and Technologies—National Research Council, Rome, Italy
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Laura Avanzino
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- *Correspondence: Dante Mantini,
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14
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Heise KF, Rueda-Delgado L, Chalavi S, King BR, Monteiro TS, Edden RAE, Mantini D, Swinnen SP. The interaction between endogenous GABA, functional connectivity, and behavioral flexibility is critically altered with advanced age. Commun Biol 2022; 5:426. [PMID: 35523951 PMCID: PMC9076638 DOI: 10.1038/s42003-022-03378-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 04/19/2022] [Indexed: 01/16/2023] Open
Abstract
The flexible adjustment of ongoing behavior challenges the nervous system’s dynamic control mechanisms and has shown to be specifically susceptible to age-related decline. Previous work links endogenous gamma-aminobutyric acid (GABA) with behavioral efficiency across perceptual and cognitive domains, with potentially the strongest impact on those behaviors that require a high level of dynamic control. Our analysis integrated behavior and modulation of interhemispheric phase-based connectivity during dynamic motor-state transitions with endogenous GABA concentration in adult human volunteers. We provide converging evidence for age-related differences in the preferred state of endogenous GABA concentration for more flexible behavior. We suggest that the increased interhemispheric connectivity observed in the older participants represents a compensatory neural mechanism caused by phase-entrainment in homotopic motor cortices. This mechanism appears to be most relevant in the presence of a less optimal tuning of the inhibitory tone as observed during healthy aging to uphold the required flexibility of behavioral action. Future work needs to validate the relevance of this interplay between neural connectivity and GABAergic inhibition for other domains of flexible human behavior. An integrated analysis of GABA concentration, interhemispheric phase-based connectivity behavior and in healthy human participants revealed a task-specific association between GABA concentration and motor-cortical connectivity. These results shed light on how GABA is involved in neural oscillations and behavioral performance.
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Affiliation(s)
- Kirstin-Friederike Heise
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium. .,KU Leuven Brain Institute, Leuven, Belgium.
| | - Laura Rueda-Delgado
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium.,School of Psychology, Trinity College Dublin, Dublin, 2, Ireland
| | - Sima Chalavi
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium.,KU Leuven Brain Institute, Leuven, Belgium
| | - Bradley R King
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium.,KU Leuven Brain Institute, Leuven, Belgium.,Department of Health & Kinesiology, College of Health, University of Utah, Salt Lake City, UT, USA
| | - Thiago Santos Monteiro
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium.,KU Leuven Brain Institute, Leuven, Belgium
| | - Richard A E Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Dante Mantini
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Stephan P Swinnen
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium.,KU Leuven Brain Institute, Leuven, Belgium
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15
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Connectivity in Large-Scale Resting-State Brain Networks Is Related to Motor Learning: A High-Density EEG Study. Brain Sci 2022; 12:brainsci12050530. [PMID: 35624919 PMCID: PMC9138969 DOI: 10.3390/brainsci12050530] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/12/2022] [Accepted: 04/19/2022] [Indexed: 11/28/2022] Open
Abstract
Previous research has shown that resting-state functional connectivity (rsFC) between different brain regions (seeds) is related to motor learning and motor memory consolidation. Using high-density electroencephalography (hdEEG), we addressed this question from a brain network perspective. Specifically, we examined frequency-dependent functional connectivity in resting-state networks from twenty-nine young healthy participants before and after they were trained on a motor sequence learning task. Consolidation was assessed with an overnight retest on the motor task. Our results showed training-related decreases in gamma-band connectivity within the motor network, and between the motor and functionally distinct resting-state networks including the attentional network. Brain-behavior correlation analyses revealed that baseline beta, delta, and theta rsFC were related to subsequent motor learning and memory consolidation such that lower connectivity within the motor network and between the motor and several distinct resting-state networks was correlated with better learning and overnight consolidation. Lastly, training-related increases in beta-band connectivity between the motor and the visual networks were related to greater consolidation. Altogether, our results indicate that connectivity in large-scale resting-state brain networks is related to—and modulated by—motor learning and memory consolidation processes. These finding corroborate previous seed-based connectivity research and provide evidence that frequency-dependent functional connectivity in resting-state networks is critically linked to motor learning and memory consolidation.
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16
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Zhao M, Bonassi G, Samogin J, Taberna GA, Pelosin E, Nieuwboer A, Avanzino L, Mantini D. Frequency-dependent modulation of neural oscillations across the gait cycle. Hum Brain Mapp 2022; 43:3404-3415. [PMID: 35384123 PMCID: PMC9248303 DOI: 10.1002/hbm.25856] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/08/2022] [Accepted: 03/22/2022] [Indexed: 12/14/2022] Open
Abstract
Balance and walking are fundamental to support common daily activities. Relatively accurate characterizations of normal and impaired gait features were attained at the kinematic and muscular levels. Conversely, the neural processes underlying gait dynamics still need to be elucidated. To shed light on gait‐related modulations of neural activity, we collected high‐density electroencephalography (hdEEG) signals and ankle acceleration data in young healthy participants during treadmill walking. We used the ankle acceleration data to segment each gait cycle in four phases: initial double support, right leg swing, final double support, left leg swing. Then, we processed hdEEG signals to extract neural oscillations in alpha, beta, and gamma bands, and examined event‐related desynchronization/synchronization (ERD/ERS) across gait phases. Our results showed that ERD/ERS modulations for alpha, beta, and gamma bands were strongest in the primary sensorimotor cortex (M1), but were also found in premotor cortex, thalamus and cerebellum. We observed a modulation of neural oscillations across gait phases in M1 and cerebellum, and an interaction between frequency band and gait phase in premotor cortex and thalamus. Furthermore, an ERD/ERS lateralization effect was present in M1 for the alpha and beta bands, and in the cerebellum for the beta and gamma bands. Overall, our findings demonstrate that an electrophysiological source imaging approach based on hdEEG can be used to investigate dynamic neural processes of gait control. Future work on the development of mobile hdEEG‐based brain–body imaging platforms may enable overground walking investigations, with potential applications in the study of gait disorders.
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Affiliation(s)
- Mingqi Zhao
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | - Gaia Bonassi
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, Chiavari, Italy
| | - Jessica Samogin
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | | | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health, University of Genova, Genova, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Laura Avanzino
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
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17
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Putzolu M, Samogin J, Cosentino C, Mezzarobba S, Bonassi G, Lagravinese G, Vato A, Mantini D, Avanzino L, Pelosin E. Neural oscillations during motor imagery of complex gait: an HdEEG study. Sci Rep 2022; 12:4314. [PMID: 35279682 PMCID: PMC8918338 DOI: 10.1038/s41598-022-07511-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 01/20/2022] [Indexed: 11/15/2022] Open
Abstract
The aim of this study was to investigate differences between usual and complex gait motor imagery (MI) task in healthy subjects using high-density electroencephalography (hdEEG) with a MI protocol. We characterized the spatial distribution of α- and β-bands oscillations extracted from hdEEG signals recorded during MI of usual walking (UW) and walking by avoiding an obstacle (Dual-Task, DT). We applied a source localization algorithm to brain regions selected from a large cortical-subcortical network, and then we analyzed α and β bands Event-Related Desynchronizations (ERDs). Nineteen healthy subjects visually imagined walking on a path with (DT) and without (UW) obstacles. Results showed in both gait MI tasks, α- and β-band ERDs in a large cortical-subcortical network encompassing mostly frontal and parietal regions. In most of the regions, we found α- and β-band ERDs in the DT compared with the UW condition. Finally, in the β band, significant correlations emerged between ERDs and scores in imagery ability tests. Overall we detected MI gait-related α- and β-band oscillations in cortical and subcortical areas and significant differences between UW and DT MI conditions. A better understanding of gait neural correlates may lead to a better knowledge of pathophysiology of gait disturbances in neurological diseases.
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18
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Gomez A, Escobar-Huertas J, Linero D, Cardenas F, Garzón-Alvarado D. Simulation of the Electrical Stimulation of the Rat Brain Using Sleep Frequencies: A Finite Element Modeling Approach. J Theor Biol 2022; 542:111093. [DOI: 10.1016/j.jtbi.2022.111093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/09/2022] [Accepted: 03/14/2022] [Indexed: 11/30/2022]
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19
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Samogin J, Rueda Delgado L, Taberna GA, Swinnen SP, Mantini D. Age-related differences of frequency-dependent functional connectivity in brain networks and their link to motor performance. Brain Connect 2022; 12:686-698. [DOI: 10.1089/brain.2021.0135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Jessica Samogin
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Laura Rueda Delgado
- Trinity College Institute of Neuroscience, 71434, Dublin, Ireland
- Cumulus Neuroscience, Ltd. , Dublin, Ireland
| | - Gaia Amaranta Taberna
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Stephan P. Swinnen
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Leuven Brain Institute , Leuven, Belgium
| | - Dante Mantini
- Leuven, Belgium
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
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20
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Lee S, Lee C, Kim E, Ko SA, Kim SN, Choy YB, Im CH. In-vivo estimation of tissue electrical conductivities of a rabbit eye for precise simulation of electric field distributions during ocular iontophoresis. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3540. [PMID: 34672120 DOI: 10.1002/cnm.3540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 09/21/2021] [Accepted: 10/16/2021] [Indexed: 06/13/2023]
Abstract
Precise estimation of electrical conductivity of the eyes is important for the accurate analysis of electric field distributions in the eyes during ocular iontophoresis. In this study, we estimated the tissue electrical conductivities of a rabbit eye, which has been widely employed for neuro-ophthalmological experiments, through an in vivo experiment for the first time. Electrical potentials were measured at multiple locations on the skin, while weak currents were transmitted into the skin via two surface electrodes attached to the skin around the eye. A finite element model was constructed to calculate the electric potentials at the measurement locations. The conductivity values of different tissues were then estimated using an optimization procedure to minimize the difference between the measured and calculated electric potentials. The accuracy of the estimated tissue conductivity values of the rabbit eye was validated by comparing the measured and calculated electric potential values for different electrode montages. Further multi-physical analyses of iontophoretic drug delivery to the rabbit eye showed a significant influence of the conductivity profile on the resultant particle distribution. Overall, our results provide an important reference for the tissue electrical conductivity values of the rabbit eye, which could be further utilized for designing new medical devices for delivering electric fields to the eyes, such as transorbital and transscleral electrical stimulations.
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Affiliation(s)
- Sangjun Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
- Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea
| | - Chany Lee
- Department of Structure & Function of Neural Network, Korea Brain Research Institute, Daegu, Republic of Korea
| | - Euijin Kim
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Song Ah Ko
- Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Se-Na Kim
- Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Young Bin Choy
- Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
- Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
- Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea
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21
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Zhao M, Bonassi G, Guarnieri R, Pelosin E, Nieuwboer A, Avanzino L, Mantini D. A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data. J Neural Eng 2021; 18. [PMID: 34874319 DOI: 10.1088/1741-2552/ac4084] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/06/2021] [Indexed: 11/12/2022]
Abstract
Objective.Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g. ocular, myogenic) and non-biological (e.g. movement-related) artifacts, which-depending on their extent-may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for the attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts.Approach.In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during an auditory stimulation, as well as the event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches.Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts.Significance.Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contributes to the further development of mobile brain/body imaging applications.
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Affiliation(s)
- Mingqi Zhao
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium
| | - Gaia Bonassi
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, Azienda Sanitaria Locale Chiavarese, 16043 Chiavari, Italy
| | - Roberto Guarnieri
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium.,Icometrix, 3012 Leuven, Belgium
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health, University of Genova, 16132 Genova, Italy.,Ospedale Policlinico San Martino, IRCCS, 16132 Genoa, Italy
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, KU Leuven, 3001 Leuven, Belgium
| | - Laura Avanzino
- Ospedale Policlinico San Martino, IRCCS, 16132 Genoa, Italy.,Department of Experimental Medicine, Section of Human Physiology, University of Genoa, 16132 Genoa, Italy
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126 Venice, Italy
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22
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Anderson DN, Charlebois CM, Smith EH, Arain AM, Davis TS, Rolston JD. Probabilistic comparison of gray and white matter coverage between depth and surface intracranial electrodes in epilepsy. Sci Rep 2021; 11:24155. [PMID: 34921176 PMCID: PMC8683494 DOI: 10.1038/s41598-021-03414-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/23/2021] [Indexed: 11/20/2022] Open
Abstract
In this study, we quantified the coverage of gray and white matter during intracranial electroencephalography in a cohort of epilepsy patients with surface and depth electrodes. We included 65 patients with strip electrodes (n = 12), strip and grid electrodes (n = 24), strip, grid, and depth electrodes (n = 7), or depth electrodes only (n = 22). Patient-specific imaging was used to generate probabilistic gray and white matter maps and atlas segmentations. Gray and white matter coverage was quantified using spherical volumes centered on electrode centroids, with radii ranging from 1 to 15 mm, along with detailed finite element models of local electric fields. Gray matter coverage was highly dependent on the chosen radius of influence (RoI). Using a 2.5 mm RoI, depth electrodes covered more gray matter than surface electrodes; however, surface electrodes covered more gray matter at RoI larger than 4 mm. White matter coverage and amygdala and hippocampal coverage was greatest for depth electrodes at all RoIs. This study provides the first probabilistic analysis to quantify coverage for different intracranial recording configurations. Depth electrodes offer increased coverage of gray matter over other recording strategies if the desired signals are local, while subdural grids and strips sample more gray matter if the desired signals are diffuse.
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Affiliation(s)
- Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA. .,Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT, USA.
| | - Chantel M Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Elliot H Smith
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Amir M Arain
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Tyler S Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA. .,Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
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23
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Wang H, Sun W, Zhang J, Yan Z, Wang C, Wang L, Liu T, Li C, Chen D, Shintaro F, Wu J, Yan T. Influence of layered skull modeling on the frequency sensitivity and target accuracy in simulations of transcranial current stimulation. Hum Brain Mapp 2021; 42:5345-5356. [PMID: 34390079 PMCID: PMC8519867 DOI: 10.1002/hbm.25622] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 06/29/2021] [Accepted: 07/31/2021] [Indexed: 11/27/2022] Open
Abstract
With the development of electrical stimulation technology, especially the emergence of temporally interfering (TI) stimulation, it is necessary to discuss the influence of current frequency on stimulation intensity. Accurate skull modeling is important for transcranial current stimulation (tCS) simulation prediction because of its large role in dispersing current. In this study, we simulated different frequencies of transcranial alternating current stimulation (tACS) and TI stimulation in single-layer and layered skull model, compared the electric field via error parameters such as the relative difference measure and relative magnification factor. Pearson correlation analysis and t-test were used to measure the differences in envelope amplitude. The results showed that the intensity of electric field in the brain generated by per unit of stimulation current will increase with current frequency, and the layered skull model had a better response to frequency. An obvious pattern difference was found between the electric fields of the layered and single-layer skull individualized models. For TI stimulation, the Pearson correlation coefficient between the envelope distribution of the layered skull model and the single-layer skull was only 0.746 in the individualized model, which is clearly lower than the correlation coefficient of 0.999 determined from the spherical model. Higher carrier frequencies seemed to be easier to generate a large enough brain electric field envelope in TI stimulation. In conclusion, we recommend using layered skull models instead of single-layer skull models in tCS (particularly TI stimulation) simulation studies in order to improve the accuracy of the prediction of stimulus intensity and stimulus target.
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Affiliation(s)
- Heng Wang
- School of Mechatronical EngineeringBeijing Institute of TechnologyBeijingChina
| | - Weiqian Sun
- School of Life ScienceBeijing Institute of TechnologyBeijingChina
| | - Jianxu Zhang
- School of Mechatronical EngineeringBeijing Institute of TechnologyBeijingChina
| | - Zilong Yan
- School of Mechatronical EngineeringBeijing Institute of TechnologyBeijingChina
| | - Chenyu Wang
- School of Mechatronical EngineeringBeijing Institute of TechnologyBeijingChina
| | - Luyao Wang
- School of Mechatronical EngineeringBeijing Institute of TechnologyBeijingChina
| | - Tiantian Liu
- School of Life ScienceBeijing Institute of TechnologyBeijingChina
| | - Chunlin Li
- School of Biomedical EngineeringCapital Medical UniversityBeijingChina
| | - Duanduan Chen
- School of Life ScienceBeijing Institute of TechnologyBeijingChina
| | | | - Jinglong Wu
- School of Mechatronical EngineeringBeijing Institute of TechnologyBeijingChina
- Department of Neurology and NeuroscienceOkayama UniversityOkayamaJapan
| | - Tianyi Yan
- School of Life ScienceBeijing Institute of TechnologyBeijingChina
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24
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Wang T, Kleiven S, Li X. Influence of Anisotropic White Matter on Electroosmotic Flow Induced by Direct Current. Front Bioeng Biotechnol 2021; 9:689020. [PMID: 34485253 PMCID: PMC8414365 DOI: 10.3389/fbioe.2021.689020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 08/03/2021] [Indexed: 11/13/2022] Open
Abstract
Treatment of cerebral edema remains a major challenge in clinical practice and new innovative therapies are needed. This study presents a novel approach for mitigating cerebral edema by inducing bulk fluid transport utilizing the brain’s electroosmotic property using an anatomically detailed finite element head model incorporating anisotropy in the white matter (WM). Three representative anisotropic conductivity algorithms are employed for the WM and compared with isotropic WM. The key results are (1) the electroosmotic flow (EOF) is driven from the edema region to the subarachnoid space under an applied electric field with its magnitude linearly correlated to the electric field and direction following current flow pathways; (2) the extent of EOF distribution variation correlates highly with the degree of the anisotropic ratio of the WM regions; (3) the directions of the induced EOF in the anisotropic models deviate from its isotropically defined pathways and tend to move along the principal fiber direction. The results suggest WM anisotropy should be incorporated in head models for more reliable EOF evaluations for cerebral edema mitigation and demonstrate the promise of the electroosmosis based approach to be developed as a new therapy for edema treatment as evaluated with enhanced head models incorporating WM anisotropy.
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Affiliation(s)
- Teng Wang
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden
| | - Svein Kleiven
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden
| | - Xiaogai Li
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden
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25
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Charlebois CM, Caldwell DJ, Rampersad SM, Janson AP, Ojemann JG, Brooks DH, MacLeod RS, Butson CR, Dorval AD. Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation. Front Neurosci 2021; 15:691701. [PMID: 34408621 PMCID: PMC8365306 DOI: 10.3389/fnins.2021.691701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 07/12/2021] [Indexed: 11/13/2022] Open
Abstract
Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications.
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Affiliation(s)
- Chantel M Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States
| | - David J Caldwell
- Department of Bioengineering, University of Washington, Seattle, WA, United States.,Center for Neurotechnology, University of Washington, Seattle, WA, United States.,Medical Scientist Training Program, University of Washington, Seattle, WA, United States
| | - Sumientra M Rampersad
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Andrew P Janson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Dana H Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Rob S MacLeod
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States
| | - Christopher R Butson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States.,Department of Neurology, Neurosurgery and Psychiatry, University of Utah, Salt Lake City, UT, United States
| | - Alan D Dorval
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
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26
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Wang T, Kleiven S, Li X. Electroosmosis Based Novel Treatment Approach for Cerebral Edema. IEEE Trans Biomed Eng 2021; 68:2645-2653. [PMID: 33338011 DOI: 10.1109/tbme.2020.3045916] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Cerebral edema characterized as an abnormal accumulation of interstitial fluid has not been treated effectively. We propose a novel edema treatment approach to drive edematous fluid out of the brain by direct current utilizing brain tissue's electroosmotic property. METHODS A finite element (FE) head model is developed and employed to assess the feasibility of the approach. First, the capacity of the model for electric field prediction is validated against human experiments. Second, two electrode configurations (S and D-montage) are designed to evaluate the distribution of the electric field, electroosmotic flow (EOF), current density, and temperature across the brain under an applied direct current. RESULTS The S-montage is shown to induce an average EOF velocity of 7e-4 mm/s underneath the anode by a voltage of 15 V, and the D-montage induces a velocity of 9e-4 mm/s by a voltage of 5 V. Meanwhile, the brain temperature in both configurations is below 38 °C, which is within the safety range. Further, the magnitude of EOF is proportional to the electric field, and the EOF direction follows the current flow from anode to cathode. The EOF velocity in the white matter is significantly higher than that in the gray matter under the anode where the fluid is to be drawn out. CONCLUSION The proposed electroosmosis based approach allows alleviating brain edema within the critical time window by direct current. SIGNIFICANCE The approach may be further developed as a new treatment solely or as a complement to existing conventional treatments of edema.
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27
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Guarnieri R, Zhao M, Taberna GA, Ganzetti M, Swinnen SP, Mantini D. RT-NET: real-time reconstruction of neural activity using high-density electroencephalography. Neuroinformatics 2021; 19:251-266. [PMID: 32720212 PMCID: PMC8004510 DOI: 10.1007/s12021-020-09479-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
High-density electroencephalography (hdEEG) has been successfully used for large-scale investigations of neural activity in the healthy and diseased human brain. Because of their high computational demand, analyses of source-projected hdEEG data are typically performed offline. Here, we present a real-time noninvasive electrophysiology toolbox, RT-NET, which has been specifically developed for online reconstruction of neural activity using hdEEG. RT-NET relies on the Lab Streaming Layer for acquiring raw data from a large number of EEG amplifiers and for streaming the processed data to external applications. RT-NET estimates a spatial filter for artifact removal and source activity reconstruction using a calibration dataset. This spatial filter is then applied to the hdEEG data as they are acquired, thereby ensuring low latencies and computation times. Overall, our analyses show that RT-NET can estimate real-time neural activity with performance comparable to offline analysis methods. It may therefore enable the development of novel brain–computer interface applications such as source-based neurofeedback.
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Affiliation(s)
- Roberto Guarnieri
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium
| | - Mingqi Zhao
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium
| | - Gaia Amaranta Taberna
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium
| | - Marco Ganzetti
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium.,Roche Pharmaceutical Research and Early Development, Roche Innovation Center, 4051, Basel, Switzerland
| | - Stephan P Swinnen
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium.,Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium. .,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126, Venice, Italy.
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28
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Taberna GA, Samogin J, Marino M, Mantini D. Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies. Brain Sci 2021; 11:brainsci11060741. [PMID: 34204868 PMCID: PMC8226780 DOI: 10.3390/brainsci11060741] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/23/2021] [Accepted: 05/31/2021] [Indexed: 11/16/2022] Open
Abstract
Recent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurate head model, defining how neural currents propagate from the cortex to the sensors placed over the scalp. To the best of our knowledge, no study has been conducted yet to systematically test to what extent head modeling accuracy impacts on EEG-RSN reconstruction. To address this question, we used 256-channel hdEEG data collected in a group of young healthy participants at rest. We first estimated functional connectivity in EEG-RSNs by means of band-limited power envelope correlations, using neural activity estimated with an optimized analysis workflow. Then, we defined a series of head models with different levels of complexity, specifically testing the effect of different electrode positioning techniques and head tissue segmentation methods. We observed that robust EEG-RSNs can be obtained using a realistic head model, and that inaccuracies due to head tissue segmentation impact on RSN reconstruction more than those due to electrode positioning. Additionally, we found that EEG-RSN robustness to head model variations had space and frequency specificity. Overall, our results may contribute to defining a benchmark for assessing the reliability of hdEEG functional connectivity measures.
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Affiliation(s)
- Gaia Amaranta Taberna
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (G.A.T.); (J.S.); (M.M.)
| | - Jessica Samogin
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (G.A.T.); (J.S.); (M.M.)
| | - Marco Marino
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (G.A.T.); (J.S.); (M.M.)
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126 Venice, Italy
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (G.A.T.); (J.S.); (M.M.)
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126 Venice, Italy
- Correspondence: ; Tel.: +32-16-37-29-09
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29
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Taberna GA, Samogin J, Mantini D. Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction. Neuroinformatics 2021; 19:585-596. [PMID: 33506384 PMCID: PMC8566646 DOI: 10.1007/s12021-020-09504-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2020] [Indexed: 01/15/2023]
Abstract
In the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.
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Affiliation(s)
- Gaia Amaranta Taberna
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium
| | - Jessica Samogin
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium. .,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy.
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30
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Colella M, Paffi A, De Santis V, Apollonio F, Liberti M. Effect of skin conductivity on the electric field induced by transcranial stimulation techniques in different head models. Phys Med Biol 2021; 66:035010. [PMID: 33496268 DOI: 10.1088/1361-6560/abcde7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
This study aims at quantifying the effect that using different skin conductivity values has on the estimation of the electric (E)-field distribution induced by transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) in the brain of two anatomical models. The induced E-field was calculated with numerical simulations inside MIDA and Duke models, assigning to the skin a conductivity value estimated from a multi-layered skin model and three values taken from literature. The effect of skin conductivity variations on the local E-field induced by tDCS in the brain was up to 70%. In TMS, minor local differences, in the order of 20%, were obtained in regions of interest for the onset of possible side effects. Results suggested that an accurate model of the skin is necessary in all numerical studies that aim at precisely estimating the E-field induced during TMS and tDCS applications. This also highlights the importance of further experimental studies on human skin characterization, especially at low frequencies.
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Affiliation(s)
- Micol Colella
- Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome 'La Sapienza', Rome, Italy
| | - Alessandra Paffi
- Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome 'La Sapienza', Rome, Italy
| | - Valerio De Santis
- Department of Industrial and Information Engineering and Economics (DIIEE), University of L'Aquila, L'Aquila, Italy
| | - Francesca Apollonio
- Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome 'La Sapienza', Rome, Italy
| | - Micaela Liberti
- Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome 'La Sapienza', Rome, Italy
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31
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Schrader S, Antonakakis M, Rampp S, Engwer C, Wolters CH. A novel method for calibrating head models to account for variability in conductivity and its evaluation in a sphere model. Phys Med Biol 2020; 65:245043. [PMID: 33113524 DOI: 10.1088/1361-6560/abc5aa] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The accuracy in electroencephalography (EEG) and combined EEG and magnetoencephalography (MEG) source reconstructions as well as in optimized transcranial electric stimulation (TES) depends on the conductive properties assigned to the head model, and most importantly on individual skull conductivity. In this study, we present an automatic pipeline to calibrate head models with respect to skull conductivity based on the reconstruction of the P20/N20 response using somatosensory evoked potentials and fields. In order to validate in a well-controlled setup without interplay with numerical errors, we evaluate the accuracy of this algorithm in a 4-layer spherical head model using realistic noise levels as well as dipole sources at different eccentricities with strengths and orientations related to somatosensory experiments. Our results show that the reference skull conductivity can be reliably reconstructed for sources resembling the generator of the P20/N20 response. In case of erroneous assumptions on scalp conductivity, the resulting skull conductivity parameter counterbalances this effect, so that EEG source reconstructions using the fitted skull conductivity parameter result in lower errors than when using the standard value. We propose an automatized procedure to calibrate head models which only relies on non-invasive modalities that are available in a standard MEG laboratory, measures under in vivo conditions and in the low frequency range of interest. Calibrated head modeling can improve EEG and combined EEG/MEG source analysis as well as optimized TES.
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Affiliation(s)
- S Schrader
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
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32
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Antonakakis M, Schrader S, Aydin Ü, Khan A, Gross J, Zervakis M, Rampp S, Wolters CH. Inter-Subject Variability of Skull Conductivity and Thickness in Calibrated Realistic Head Models. Neuroimage 2020; 223:117353. [DOI: 10.1016/j.neuroimage.2020.117353] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 08/19/2020] [Accepted: 09/05/2020] [Indexed: 01/11/2023] Open
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33
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Azizollahi H, Aarabi A, Wallois F. Effect of structural complexities in head modeling on the accuracy of EEG source localization in neonates. J Neural Eng 2020; 17:056004. [PMID: 32942266 DOI: 10.1088/1741-2552/abb994] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Neonatal electroencephalography (EEG) source localization is highly prone to errors due to head modeling deficiencies. In this study, we investigated the effect of head model complexities on the accuracy of EEG source localization in full term neonates using a realistic volume conductor head model. APPROACH We performed numerical simulations to investigate source localization errors caused by cerebrospinal fluid (CSF) and fontanel exclusion and gray matter (GM)/white matter (WM) distinction using the finite element method. MAIN RESULTS Our results showed that the exclusion of CSF from the head model could cause significant localization errors mostly for sources closer to the inner surface of the skull. With a less pronounced effect compared to the CSF exclusion, the discrimination between GM and WM also widely affected all sources, especially those located in deeper structures. The exclusion of the fontanels from the head model led to source localization errors for sources located in areas beneath the fontanels. Our finding clearly shows that the CSF inclusion and GM/WM distinction in EEG inverse modeling can substantially reduce EEG source localization errors. Moreover, fontanels should be included in neonatal head models, particularly in source localization applications, in which sources of interest are located beneath or in vicinity of fontanels. SIGNIFICANCE Our findings have practical implications for a better understanding of the impact of head model complexities on the accuracy of EEG source localization in neonates.
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Affiliation(s)
- Hamed Azizollahi
- GRAMFC, Inserm U1105, University Research Center (CURS), CHU AMIENS - SITE SUD, Amiens, France
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34
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Samogin J, Marino M, Porcaro C, Wenderoth N, Dupont P, Swinnen SP, Mantini D. Frequency-dependent functional connectivity in resting state networks. Hum Brain Mapp 2020; 41:5187-5198. [PMID: 32840936 PMCID: PMC7670639 DOI: 10.1002/hbm.25184] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 08/07/2020] [Indexed: 12/27/2022] Open
Abstract
Functional magnetic resonance imaging studies have documented the resting human brain to be functionally organized in multiple large‐scale networks, called resting‐state networks (RSNs). Other brain imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), have been used for investigating the electrophysiological basis of RSNs. To date, it is largely unclear how neural oscillations measured with EEG and MEG are related to functional connectivity in the resting state. In addition, it remains to be elucidated whether and how the observed neural oscillations are related to the spatial distribution of the network nodes over the cortex. To address these questions, we examined frequency‐dependent functional connectivity between the main nodes of several RSNs, spanning large part of the cortex. We estimated connectivity using band‐limited power correlations from high‐density EEG data collected in healthy participants. We observed that functional interactions within RSNs are characterized by a specific combination of neuronal oscillations in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–80 Hz) bands, which highly depend on the position of the network nodes. This finding may contribute to a better understanding of the mechanisms through which neural oscillations support functional connectivity in the brain.
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Affiliation(s)
- Jessica Samogin
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium
| | - Marco Marino
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Camillo Porcaro
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Rome, Italy.,Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK.,Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.,Research in Advanced Neurorehabilitation, S. Anna Istitute, Crotone, Italy
| | - Nicole Wenderoth
- Neural Control of Movement Lab, Department of Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Patrick Dupont
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium.,Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium
| | - Stephan P Swinnen
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
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35
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A Lead Field Two-Domain Model for Longitudinal Neural Tracts-Analytical Framework and Implications for Signal Bandwidth. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:5436807. [PMID: 32565881 PMCID: PMC7275970 DOI: 10.1155/2020/5436807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 04/07/2020] [Accepted: 04/21/2020] [Indexed: 11/18/2022]
Abstract
Somatosensory evoked potentials are a well-established tool for assessing volley conduction in afferent neural pathways. However, from a clinical perspective, recording of spinal signals is still a demanding task due to the low amplitudes compared to relevant noise sources. Computer modeling is a powerful tool for gaining insight into signal genesis and, thus, for promoting future innovations in signal extraction. However, due to the complex structure of neural pathways, modeling is computationally demanding. We present a theoretical framework which allows computing the electric potential generated by a single axon in a body surface lead by the convolution of the neural lead field function with a propagating action potential term. The signal generated by a large cohort of axons was obtained by convoluting a single axonal signal with the statistical distribution of temporal dispersion of individual axonal signals. For establishing the framework, analysis was based on an analytical model. Our approach was further adopted for a numerical computation of body surface neuropotentials employing the lead field theory. Double convolution allowed straightforward analysis in the frequency domain. The highest frequency components occurred at the cellular membrane. A bandpass type spectral shape and a peak frequency of 1800 Hz was observed. The volume conductor transmitting the signal to the recording lead acted as an additional bandpass reducing the axonal peak frequency from 200 Hz to 500 Hz. The superposition of temporally dispersed axonal signals acted as an additional low-pass filter further reducing the compound action potential peak frequency from 90 Hz to 170 Hz. Our results suggest that the bandwidth of spinal evoked potentials might be narrower than the bandwidth requested by current clinical guidelines. The present findings will allow the optimization of noise suppression. Furthermore, our theoretical framework allows the adaptation in numerical methods and application in anatomically realistic geometries in future studies.
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36
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Wang S, Itthipuripat S, Ku Y. Encoding strategy mediates the effect of electrical stimulation over posterior parietal cortex on visual short-term memory. Cortex 2020; 128:203-217. [PMID: 32361592 DOI: 10.1016/j.cortex.2020.03.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 02/08/2020] [Accepted: 03/10/2020] [Indexed: 01/20/2023]
Abstract
Over past decades, converging neuroimaging and electrophysiological findings have suggested a crucial role of posterior parietal cortex (PPC) in supporting the storage capacity of visual short-term memory (VSTM). Moreover, a few recent studies have shown that electrical stimulation over PPC can enhance VSTM capacity, making it a promising method for improving VSTM function. However, the reliability of these results is still in question because null findings have also been observed. Among studies that reported significant effects, some found increased VSTM capacity only in people with low capacity. Here, we hypothesized that subjects' encoding strategy might be a key source of these variable results. To directly test this hypothesis, we stimulated PPC using transcranial direct-current stimulation (tDCS) in male and female human subjects instructed to employ different encoding strategies during a VSTM recall task. We found that VSTM capacity was higher in subjects who were instructed to remember all items in the supra-capacity array of visual stimuli (i.e., the remember-all group), compared to subjects who were told to focus on a subset of these stimuli (i.e., the remember-subset group). As predicted, anodal tDCS over PPC significantly enhanced VSTM capacity only in the remember-subset group, but not in the remember-all group. Additionally, no effect of encoding strategy or its interaction with electrical stimulation was found on VSTM precision. Together, these results suggest that encoding strategy has a selective influence on VSTM capacity and this influence of encoding strategy mediates the effect of electrical stimulation over PPC on VSTM function.
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Affiliation(s)
- Sisi Wang
- Guangdong Provincial Key Laboratory of Social Cognitive Neuroscience and Mental Health, Department of Psychology, Sun Yat-Sen University, Guangzhou, China; Peng Cheng Laboratory, Shenzhen, China; Shanghai Key Laboratory of Brain Functional Genomics, Shanghai Changning-ECNU Mental Health Center, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Department of Psychology, Vanderbilt University, Nashville, TN, USA.
| | - Sirawaj Itthipuripat
- Department of Psychology, Vanderbilt University, Nashville, TN, USA; Learning Institute, King Mongkut's University of Technology Thonburi, Bangkok, Thailand; Futuristic Research in Enigmatic Aesthetics Knowledge Laboratory, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
| | - Yixuan Ku
- Guangdong Provincial Key Laboratory of Social Cognitive Neuroscience and Mental Health, Department of Psychology, Sun Yat-Sen University, Guangzhou, China; Peng Cheng Laboratory, Shenzhen, China; Shanghai Key Laboratory of Brain Functional Genomics, Shanghai Changning-ECNU Mental Health Center, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; NYU-ECNU Institute of Brain and Cognitive Science, NYU Shanghai and Collaborative Innovation Center for Brain Science, Shanghai, China.
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37
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Zhao M, Marino M, Samogin J, Swinnen SP, Mantini D. Hand, foot and lip representations in primary sensorimotor cortex: a high-density electroencephalography study. Sci Rep 2019; 9:19464. [PMID: 31857602 PMCID: PMC6923477 DOI: 10.1038/s41598-019-55369-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Accepted: 11/22/2019] [Indexed: 11/09/2022] Open
Abstract
The primary sensorimotor cortex plays a major role in the execution of movements of the contralateral side of the body. The topographic representation of different body parts within this brain region is commonly investigated through functional magnetic resonance imaging (fMRI). However, fMRI does not provide direct information about neuronal activity. In this study, we used high-density electroencephalography (hdEEG) to map the representations of hand, foot, and lip movements in the primary sensorimotor cortex, and to study their neural signatures. Specifically, we assessed the event-related desynchronization (ERD) in the cortical space. We found that the performance of hand, foot, and lip movements elicited an ERD in beta and gamma frequency bands. The primary regions showing significant beta- and gamma-band ERD for hand and foot movements, respectively, were consistent with previously reported using fMRI. We observed relatively weaker ERD for lip movements, which may be explained by the fact that less fine movement control was required. Overall, our study demonstrated that ERD based on hdEEG data can support the study of motor-related neural processes, with relatively high spatial resolution. An interesting avenue may be the use of hdEEG for deeper investigations into the pathophysiology of neuromotor disorders.
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Affiliation(s)
- Mingqi Zhao
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001, Leuven, Belgium
| | - Marco Marino
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126, Venice, Italy
| | - Jessica Samogin
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001, Leuven, Belgium
| | - Stephan P Swinnen
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001, Leuven, Belgium.,Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001, Leuven, Belgium. .,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126, Venice, Italy.
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38
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Vorwerk J, Brock AA, Anderson DN, Rolston JD, Butson CR. A retrospective evaluation of automated optimization of deep brain stimulation parameters. J Neural Eng 2019; 16:064002. [PMID: 31344689 PMCID: PMC7759010 DOI: 10.1088/1741-2552/ab35b1] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
OBJECTIVE We performed a retrospective analysis of an optimization algorithm for the computation of patient-specific multipolar stimulation configurations employing multiple independent current/voltage sources. We evaluated whether the obtained stimulation configurations align with clinical data and whether the optimized stimulation configurations have the potential to lead to an equal or better stimulation of the target region as manual programming, while reducing the time required for programming sessions. APPROACH For three patients (five electrodes) diagnosed with essential tremor, we derived optimized multipolar stimulation configurations using an approach that is suitable for the application in clinical practice. To evaluate the automatically derived stimulation settings, we compared them to the results of the monopolar review. MAIN RESULTS We observe a good agreement between the findings of the monopolar review and the optimized stimulation configurations, with the algorithm assigning the maximal voltage in the optimized multipolar pattern to the contact that was found to lead to the best therapeutic effect in the clinical monopolar review in all cases. Additionally, our simulation results predict that the optimized stimulation settings lead to the activation of an equal or larger volume fraction of the target compared to the manually determined settings in all cases. SIGNIFICANCE Our results demonstrate the feasibility of an automatic determination of optimal DBS configurations and motivate a further evaluation of the applied optimization algorithm.
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Affiliation(s)
- Johannes Vorwerk
- Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, Utah, USA,Institute of Electrical and Biomedical Engineering, UMIT - Private University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
| | - Andrea A. Brock
- Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, Utah, USA,Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Daria N. Anderson
- Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, Utah, USA,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - John D. Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - Christopher R. Butson
- Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, Utah, USA,Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA,Departments of Psychiatry and Neurology, University of Utah, Salt Lake City, Utah, USA,Correspondig Author.
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39
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Fernandes SR, Pereira M, Salvador R, Miranda PC, de Carvalho M. Cervical trans-spinal direct current stimulation: a modelling-experimental approach. J Neuroeng Rehabil 2019; 16:123. [PMID: 31653265 PMCID: PMC6815068 DOI: 10.1186/s12984-019-0589-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 09/06/2019] [Indexed: 11/18/2022] Open
Abstract
Background Trans-spinal direct current stimulation (tsDCS) is a non-invasive technique with promising neuromodulatory effects on spinal cord (SC) circuitry. Computational studies are essential to guide effective tsDCS protocols for specific clinical applications. This study aims to combine modelling and experimental studies to determine the electrode montage that maximizes electric field (E-field) delivery during cervical tsDCS. Methods Current and E-field distributions in the cervical SC were predicted for four electrode montages in a human realistic model using computational methods. A double-blind crossover and randomized exploratory study was conducted using the montage that maximized E-field delivery. tsDCS was applied for 15 min in 10 healthy subjects (anodal, cathodal, sham, with polarity assigned to the cervical electrode), with a current intensity of 2.5 mA, resulting in a total current charge density delivery of 90 mC/cm2. Upper limb motor (transcranial magnetic stimulation) and sensory evoked potentials (MEP, SEP), M-waves, H-reflex and F-wave responses were analysed. Central and peripheral conduction times were determined using MEP. Repeated measures ANOVA and Friedman test were used for statistical analysis (significance level α = 0.05). Results All montages presented higher current density and E-field magnitudes in the cervical SC region between the electrodes. However, electrodes at C3 and T3 spinous processes (C3-T3) originated the highest E-field magnitude (0.50 V/m). Using C3-T3 montage we observed significant changes in N9 SEP latency (p = 0.006), but significance did not persist in pairwise comparisons (sham-anodal: p = 0.022; sham-cathodal: p = 0.619; anodal-cathodal: p = 0.018; α = 0.017, Bonferroni corrected). MEP latency and central motor conduction time (CMCT) modified significantly on stimulation (p = 0.007 and p = 0.015, respectively). In addition, pairwise comparisons confirmed significant differences between sham and cathodal conditions after Bonferroni correction for MEP latency (sham-anodal: p = 0.868; sham-cathodal: p = 0.011; anodal-cathodal: p = 0.023) and CMCT (sham-anodal: p = 0.929; sham-cathodal: p = 0.010; anodal-cathodal: p = 0.034). Conclusions Computational models predicted higher E-field delivery in the cervical SC for the C3-T3 montage. Polarity-dependent effects in motor responses were reported using this montage consistent with spinal motor modulation. tsDCS experimental protocol designs should be guided by modelling studies to improve effectiveness.
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Affiliation(s)
- Sofia Rita Fernandes
- Instituto de Fisiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisbon, Portugal. .,Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016, Lisbon, Portugal.
| | - Mariana Pereira
- Instituto de Fisiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisbon, Portugal
| | - Ricardo Salvador
- Neuroelectrics, Avinguda Tibidabo, 47 bis, 08035, Barcelona, Spain
| | - Pedro Cavaleiro Miranda
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016, Lisbon, Portugal
| | - Mamede de Carvalho
- Instituto de Fisiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisbon, Portugal.,Departamento de Neurociências e Saúde Mental, Hospital de Santa Maria - Centro Hospitalar Lisboa Norte, Avenida Professor Egas Moniz, 1649-035, Lisbon, Portugal
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40
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Marino M, Arcara G, Porcaro C, Mantini D. Hemodynamic Correlates of Electrophysiological Activity in the Default Mode Network. Front Neurosci 2019; 13:1060. [PMID: 31636535 PMCID: PMC6788217 DOI: 10.3389/fnins.2019.01060] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 09/20/2019] [Indexed: 12/16/2022] Open
Abstract
Hemodynamic fluctuations in the default mode network (DMN), observed through functional magnetic resonance imaging (fMRI), have been linked to electrophysiological oscillations detected by electroencephalography (EEG). It has been reported that, among the electrophysiological oscillations, those in the alpha frequency range (8–13 Hz) are the most dominant during resting state. We hypothesized that DMN spatial configuration closely depends on the specific neuronal oscillations considered, and that alpha oscillations would mainly correlate with increased blood oxygen-level dependent (BOLD) signal in the DMN. To test this hypothesis, we used high-density EEG (hdEEG) data simultaneously collected with fMRI scanning in 20 healthy volunteers at rest. We first detected the DMN from source reconstructed hdEEG data for multiple frequency bands, and we then mapped the correlation between temporal profile of hdEEG-derived DMN activity and fMRI–BOLD signals on a voxel-by-voxel basis. In line with our hypothesis, we found that the correlation map associated with alpha oscillations, more than with any other frequency bands, displayed a larger overlap with DMN regions. Overall, our study provided further evidence for a primary role of alpha oscillations in supporting DMN functioning. We suggest that simultaneous EEG–fMRI may represent a powerful tool to investigate the neurophysiological basis of human brain networks.
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Affiliation(s)
- Marco Marino
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Giorgio Arcara
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Camillo Porcaro
- Institute of Cognitive Sciences and Technologies (ISTC) - National Research Council (CNR), Rome, Italy.,S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy.,Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.,Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium
| | - Dante Mantini
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy.,Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium
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41
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Samogin J, Liu Q, Marino M, Wenderoth N, Mantini D. Shared and connection-specific intrinsic interactions in the default mode network. Neuroimage 2019; 200:474-481. [DOI: 10.1016/j.neuroimage.2019.07.007] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Revised: 04/24/2019] [Accepted: 07/04/2019] [Indexed: 10/26/2022] Open
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42
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Bächinger M, Lehner R, Thomas F, Hanimann S, Balsters J, Wenderoth N. Human motor fatigability as evoked by repetitive movements results from a gradual breakdown of surround inhibition. eLife 2019; 8:46750. [PMID: 31524600 PMCID: PMC6746551 DOI: 10.7554/elife.46750] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 08/22/2019] [Indexed: 01/08/2023] Open
Abstract
Motor fatigability emerges when demanding tasks are executed over an extended period of time. Here, we used repetitive low-force movements that cause a gradual reduction in movement speed (or 'motor slowing') to study the central component of fatigability in healthy adults. We show that motor slowing is associated with a gradual increase of net excitability in the motor network and, specifically, in primary motor cortex (M1), which results from overall disinhibition. Importantly, we link performance decrements to a breakdown of surround inhibition in M1, which is associated with high coactivation of antagonistic muscle groups. This is consistent with the model that a loss of inhibitory control might broaden the tuning of population vectors such that movement patterns become more variable, ill-timed and effortful. We propose that the release of inhibition in M1 is an important mechanism underpinning motor fatigability and, potentially, also pathological fatigue as frequently observed in patients with brain disorders.
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Affiliation(s)
- Marc Bächinger
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Zurich, Switzerland.,Neuroscience Center Zurich (ZNZ), University of Zurich, Federal Institute of Technology Zurich, University and Balgrist Hospital Zurich, Zurich, Switzerland
| | - Rea Lehner
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Zurich, Switzerland.,Neuroscience Center Zurich (ZNZ), University of Zurich, Federal Institute of Technology Zurich, University and Balgrist Hospital Zurich, Zurich, Switzerland
| | - Felix Thomas
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Zurich, Switzerland.,Neuroscience Center Zurich (ZNZ), University of Zurich, Federal Institute of Technology Zurich, University and Balgrist Hospital Zurich, Zurich, Switzerland
| | - Samira Hanimann
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Zurich, Switzerland
| | - Joshua Balsters
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Zurich, Switzerland.,Department of Psychology, Royal Holloway University of London, Egham, United Kingdom
| | - Nicole Wenderoth
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Zurich, Switzerland.,Neuroscience Center Zurich (ZNZ), University of Zurich, Federal Institute of Technology Zurich, University and Balgrist Hospital Zurich, Zurich, Switzerland
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43
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Antonakakis M, Schrader S, Wollbrink A, Oostenveld R, Rampp S, Haueisen J, Wolters CH. The effect of stimulation type, head modeling, and combined EEG and MEG on the source reconstruction of the somatosensory P20/N20 component. Hum Brain Mapp 2019; 40:5011-5028. [PMID: 31397966 PMCID: PMC6865415 DOI: 10.1002/hbm.24754] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 07/23/2019] [Accepted: 07/28/2019] [Indexed: 11/06/2022] Open
Abstract
Modeling and experimental parameters influence the Electro- (EEG) and Magnetoencephalography (MEG) source analysis of the somatosensory P20/N20 component. In a sensitivity group study, we compare P20/N20 source analysis due to different stimulation type (Electric-Wrist [EW], Braille-Tactile [BT], or Pneumato-Tactile [PT]), measurement modality (combined EEG/MEG - EMEG, EEG, or MEG) and head model (standard or individually skull-conductivity calibrated including brain anisotropic conductivity). Considerable differences between pairs of stimulation types occurred (EW-BT: 8.7 ± 3.3 mm/27.1° ± 16.4°, BT-PT: 9 ± 5 mm/29.9° ± 17.3°, and EW-PT: 9.8 ± 7.4 mm/15.9° ± 16.5° and 75% strength reduction of BT or PT when compared to EW) regardless of the head model used. EMEG has nearly no localization differences to MEG, but large ones to EEG (16.1 ± 4.9 mm), while source orientation differences are non-negligible to both EEG (14° ± 3.7°) and MEG (12.5° ± 10.9°). Our calibration results show a considerable inter-subject variability (3.1-14 mS/m) for skull conductivity. The comparison due to different head model show localization differences smaller for EMEG (EW: 3.4 ± 2.4 mm, BT: 3.7 ± 3.4 mm, and PT: 5.9 ± 6.8 mm) than for EEG (EW: 8.6 ± 8.3 mm, BT: 11.8 ± 6.2 mm, and PT: 10.5 ± 5.3 mm), while source orientation differences for EMEG (EW: 15.4° ± 6.3°, BT: 25.7° ± 15.2° and PT: 14° ± 11.5°) and EEG (EW: 14.6° ± 9.5°, BT: 16.3° ± 11.1° and PT: 12.9° ± 8.9°) are in the same range. Our results show that stimulation type, modality and head modeling all have a non-negligible influence on the source reconstruction of the P20/N20 component. The complementary information of both modalities in EMEG can be exploited on the basis of detailed and individualized head models.
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Affiliation(s)
- Marios Antonakakis
- Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Muenster, Germany
| | - Sophie Schrader
- Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Muenster, Germany
| | - Andreas Wollbrink
- Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Muenster, Germany
| | - Robert Oostenveld
- Donders Institute, Radboud University, Nijmegen, Netherlands.,Karolinska Institute, Stockholm, Sweden
| | - Stefan Rampp
- Department of Neurosurgery, University Hospital Erlangen, Erlangen, Germany
| | - Jens Haueisen
- Institute for Biomedical Engineering and Informatics, Technical University of Ilmenau, Ilmenau, Germany
| | - Carsten H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Muenster, Germany.,Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
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44
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Vorwerk J, Aydin Ü, Wolters CH, Butson CR. Influence of Head Tissue Conductivity Uncertainties on EEG Dipole Reconstruction. Front Neurosci 2019; 13:531. [PMID: 31231178 PMCID: PMC6558618 DOI: 10.3389/fnins.2019.00531] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 05/08/2019] [Indexed: 11/28/2022] Open
Abstract
Reliable EEG source analysis depends on sufficiently detailed and accurate head models. In this study, we investigate how uncertainties inherent to the experimentally determined conductivity values of the different conductive compartments influence the results of EEG source analysis. In a single source scenario, the superficial and focal somatosensory P20/N20 component, we analyze the influence of varying conductivities on dipole reconstructions using a generalized polynomial chaos (gPC) approach. We find that in particular the conductivity uncertainties for skin and skull have a significant influence on the EEG inverse solution, leading to variations in source localization by several centimeters. The conductivity uncertainties for gray and white matter were found to have little influence on the source localization, but a strong influence on the strength and orientation of the reconstructed source, respectively. As the CSF conductivity is most accurately determined of all conductivities in a realistic head model, CSF conductivity uncertainties had a negligible influence on the source reconstruction. This small uncertainty is a further benefit of distinguishing the CSF in realistic volume conductor models.
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Affiliation(s)
- Johannes Vorwerk
- Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
- Institute of Electrical and Biomedical Engineering, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
| | - Ümit Aydin
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montreal, QC, Canada
| | - Carsten H. Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Christopher R. Butson
- Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States
- Departments of Biomedical Engineering, Neurology, and Psychiatry, University of Utah, Salt Lake City, UT, United States
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, UT, United States
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45
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Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topogr 2019; 32:825-858. [PMID: 31054104 PMCID: PMC6708046 DOI: 10.1007/s10548-019-00710-2] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 04/13/2019] [Indexed: 01/01/2023]
Abstract
Electromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the whole-skull, the spongiform layer of the skull, isotropic, perpendicularly- and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. This large variation should be acknowledged, and care should be taken when creating volume conductor models, ideally constructing them on an individual basis, rather than assuming them from the literature. When personalised models are unavailable, it is suggested weighted average means from the current meta-analysis are used. Assigning conductivity as: 0.41 S/m for the scalp, 0.02 S/m for the whole skull, or when better modelled as a three-layer skull 0.048 S/m for the spongiform layer, 0.007 S/m for the inner compact and 0.005 S/m for the outer compact, as well as 1.71 S/m for the CSF, 0.47 S/m for the grey matter, 0.22 S/m for WM and 50.4 for the BSCR.
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46
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Chella F, Marzetti L, Stenroos M, Parkkonen L, Ilmoniemi RJ, Romani GL, Pizzella V. The impact of improved MEG-MRI co-registration on MEG connectivity analysis. Neuroimage 2019; 197:354-367. [PMID: 31029868 DOI: 10.1016/j.neuroimage.2019.04.061] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 04/13/2019] [Accepted: 04/23/2019] [Indexed: 02/07/2023] Open
Abstract
Co-registration between structural head images and functional MEG data is needed for anatomically-informed MEG data analysis. Despite the efforts to minimize the co-registration error, conventional landmark- and surface-based strategies for co-registering head and MEG device coordinates achieve an accuracy of typically 5-10 mm. Recent advances in instrumentation and technical solutions, such as the development of hybrid ultra-low-field (ULF) MRI-MEG devices or the use of 3D-printed individualized foam head-casts, promise unprecedented co-registration accuracy, i.e., 2 mm or better. In the present study, we assess through simulations the impact of such an improved co-registration on MEG connectivity analysis. We generated synthetic MEG recordings for pairs of connected cortical sources with variable locations. We then assessed the capability to reconstruct source-level connectivity from these recordings for 0-15-mm co-registration error, three levels of head modeling detail (one-, three- and four-compartment models), two source estimation techniques (linearly constrained minimum-variance beamforming and minimum-norm estimation MNE) and five separate connectivity metrics (imaginary coherency, phase-locking value, amplitude-envelope correlation, phase-slope index and frequency-domain Granger causality). We found that beamforming can better take advantage of an accurate co-registration than MNE. Specifically, when the co-registration error was smaller than 3 mm, the relative error in connectivity estimates was down to one-third of that observed with typical co-registration errors. MNE provided stable results for a wide range of co-registration errors, while the performance of beamforming rapidly degraded as the co-registration error increased. Furthermore, we found that even moderate co-registration errors (>6 mm, on average) essentially decrease the difference of four- and three- or one-compartment models. Hence, a precise co-registration is important if one wants to take full advantage of highly accurate head models for connectivity analysis. We conclude that an improved co-registration will be beneficial for reliable connectivity analysis and effective use of highly accurate head models in future MEG connectivity studies.
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Affiliation(s)
- Federico Chella
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy; Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy.
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy; Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, FI, 00076, Aalto, Finland
| | - Lauri Parkkonen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, FI, 00076, Aalto, Finland
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, FI, 00076, Aalto, Finland
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy; Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy
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47
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Pillain A, Rahmouni L, Andriulli F. Handling anisotropic conductivities in the EEG forward problem with a symmetric formulation. Phys Med Biol 2019; 64:035022. [PMID: 30577034 DOI: 10.1088/1361-6560/aafaaf] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The electroencephalography (EEG) forward problem, the computation of the electric potential generated by a known electric current source configuration in the brain, is a key step of EEG source analysis. In this problem, it is often desired to model the anisotropic conductivity profiles of the skull and of the white matter. These profiles, however, cannot be handled by standard surface integral formulations and the use of volume finite elements is required. Leveraging on the representation theorem using an anisotropic fundamental solution, this paper proposes a modified symmetric formulation for solving the EEG forward problem by a surface integral equation which can take into account anisotropic conductivity profiles. A set of numerical results is presented to corroborate theoretical treatments and to show the impact of the proposed approach on both canonical and real case scenarios.
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Affiliation(s)
- Axelle Pillain
- Computational Electromagnetics Research Laboratory, IMT Atlantique, Brest, France
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48
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Wang S, Itthipuripat S, Ku Y. Electrical Stimulation Over Human Posterior Parietal Cortex Selectively Enhances the Capacity of Visual Short-Term Memory. J Neurosci 2019; 39:528-536. [PMID: 30459222 PMCID: PMC6335754 DOI: 10.1523/jneurosci.1959-18.2018] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 10/31/2018] [Accepted: 11/09/2018] [Indexed: 11/21/2022] Open
Abstract
Visual short-term memory (VSTM) provides an on-line mental space for incoming sensory information to be temporally maintained to carry out complex behavioral tasks. Despite its essential functions, the capacity at which VSTM could maintain sensory information is limited (i.e., VSTM can hold only about three to four visual items at once). Moreover, the quality of sensory representation (i.e., precision) degrades as more information has to be maintained in VSTM. Correlational evidence suggests that the level and the pattern of neural activity measured in the posterior parietal cortex (PPC) track both VSTM capacity and precision. However, the causal contributions of the PPC to these different VSTM operations are unclear. Here, we tested whether stimulating the PPC with transcranial direct current stimulation (tDCS) could increase VSTM capacity or precision. We found that stimulating the PPC in male and female human participants selectively enhanced VSTM capacity when the number of memory items exceeded capacity limit, without significant effects on VSTM precision. Moreover, this enhancement of VSTM capacity is region specific as stimulating the prefrontal cortex did not change VSTM capacity or precision. Null stimulation effects in the sensory memory condition confirmed that the tDCS-induced enhancement of VSTM capacity was not simply due to changes in sensory or attentional processes. Altogether, these results provide causal evidence suggesting that the PPC has a more dominant role in supporting the storage capacity of VSTM compared with maintaining the quality of sensory representations. Furthermore, tDCS could be used as a promising noninvasive method to enhance this PPC VSTM-related function.SIGNIFICANCE STATEMENT Correlational evidence from neuroimaging and electrophysiology suggests that the posterior parietal cortex (PPC) supports the storage capacity of visual short-term memory (VSTM) and the precision of sensory representations maintained in VSTM. However, the causal contributions of the PPC to these different VSTM functions were unclear. Here, we found that electrical stimulation over the PPC selectively enhanced VSTM capacity without changing VSTM precision. Overall, our findings suggest that the PPC has a dominant and causal role in supporting the storage capacity of VSTM.
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Affiliation(s)
- Sisi Wang
- Shanghai Key Laboratory of Brain Functional Genomics, Shanghai Changning-ECNU Mental Health Center, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, People's Republic of China
- Department of Psychology, Center for Integrative and Cognitive Neuroscience, and Interdisciplinary Program in Neuroscience, Vanderbilt University, Nashville, Tennessee 37235
| | - Sirawaj Itthipuripat
- Department of Psychology, Center for Integrative and Cognitive Neuroscience, and Interdisciplinary Program in Neuroscience, Vanderbilt University, Nashville, Tennessee 37235
- Learning Institute, and
- Futuristic Research in Enigmatic Aesthetics Knowledge Laboratory, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand, and
| | - Yixuan Ku
- Shanghai Key Laboratory of Brain Functional Genomics, Shanghai Changning-ECNU Mental Health Center, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, People's Republic of China,
- NYU-ECNU Institute of Brain and Cognitive Science, NYU Shanghai and Collaborative Innovation Center for Brain Science, Shanghai 200062, People's Republic of China
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49
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Latikka J, Eskola H. The Resistivity of Human Brain Tumours In Vivo. Ann Biomed Eng 2019; 47:706-713. [PMID: 30610409 DOI: 10.1007/s10439-018-02189-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 12/13/2018] [Indexed: 10/27/2022]
Abstract
The histological structure of tumour tissues differs from healthy brain tissues. It can therefore be assumed that there are differences also in the electrical characteristics of these tissues. The electrical characteristics of the tissues define how electric current is distributed within volume conductors, such as the human body or head. Incorrect values affect, for example, the accuracy of impedance tomography or EEG source localisation. However, no controlled experimental data for human in vivo brain tumour resistivity values have been reported thus far. We have developed a controlled method for detecting the electrical resistivities of living brain tissue and investigated different types of brain tumours. The measurements were taken during brain surgeries conducted to remove the tumours. For analysis purposes, the tumours were divided into the following categories: meningiomas, low-grade gliomas, high-grade gliomas (glioblastomas) and other tumours or lesions. The averages of the measured resistivity values were 530 Ω-cm for meningiomas, 160 Ω-cm for low-grade gliomas, and 498 Ω-cm for high-grade gliomas. The differences in high- and low-grade glioma values and meningioma and low-grade glioma values were statistically highly significant. The tumour values were also compared to surrounding healthy brain tissues, and the difference ranged from 40 to 330%. The results suggest that certain tumour types have different electronic properties and that the resistivity values could be used to distinguish tumour tissue from surrounding healthy tissue and to identify and classify certain brain tumour types.
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Affiliation(s)
- J Latikka
- Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland.
| | - H Eskola
- Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
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50
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Liu A, Vöröslakos M, Kronberg G, Henin S, Krause MR, Huang Y, Opitz A, Mehta A, Pack CC, Krekelberg B, Berényi A, Parra LC, Melloni L, Devinsky O, Buzsáki G. Immediate neurophysiological effects of transcranial electrical stimulation. Nat Commun 2018; 9:5092. [PMID: 30504921 PMCID: PMC6269428 DOI: 10.1038/s41467-018-07233-7] [Citation(s) in RCA: 251] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Accepted: 10/18/2018] [Indexed: 12/19/2022] Open
Abstract
Noninvasive brain stimulation techniques are used in experimental and clinical fields for their potential effects on brain network dynamics and behavior. Transcranial electrical stimulation (TES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), has gained popularity because of its convenience and potential as a chronic therapy. However, a mechanistic understanding of TES has lagged behind its widespread adoption. Here, we review data and modelling on the immediate neurophysiological effects of TES in vitro as well as in vivo in both humans and other animals. While it remains unclear how typical TES protocols affect neural activity, we propose that validated models of current flow should inform study design and artifacts should be carefully excluded during signal recording and analysis. Potential indirect effects of TES (e.g., peripheral stimulation) should be investigated in more detail and further explored in experimental designs. We also consider how novel technologies may stimulate the next generation of TES experiments and devices, thus enhancing validity, specificity, and reproducibility.
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Affiliation(s)
- Anli Liu
- New York University Comprehensive Epilepsy Center, 223 34th Street, New York, NY, 10016, USA.
- Department of Neurology, NYU Langone Health, 222 East 41st Street, 14th Floor, New York, NY, 10016, USA.
| | - Mihály Vöröslakos
- MTA-SZTE 'Momentum' Oscillatory Neuronal Networks Research Group, Department of Physiology, Faculty of Medicine, University of Szeged, 10 Dom sq., Szeged, H-6720, Hungary
- New York University Neuroscience Institute, 435 East 30th Street, New York, NY, 10016, USA
| | - Greg Kronberg
- Department of Biomedical Engineering, City College of New York, 160 Convent Ave, New York, NY, 10031, USA
| | - Simon Henin
- New York University Comprehensive Epilepsy Center, 223 34th Street, New York, NY, 10016, USA
- Department of Neurology, NYU Langone Health, 222 East 41st Street, 14th Floor, New York, NY, 10016, USA
| | - Matthew R Krause
- Montreal Neurological Institute, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Yu Huang
- Department of Biomedical Engineering, City College of New York, 160 Convent Ave, New York, NY, 10031, USA
| | - Alexander Opitz
- Department of Biomedical Engineering of Minnesota, 312 Church St. SE, Minneapolis, MN, 55455, USA
| | - Ashesh Mehta
- Department of Neurosurgery, Hofstra Northwell School of Medicine, 611 Northern Blvd, Great Neck, NY, 11021, USA
- Feinstein Institute for Medical Research, Hofstra Northwell School of Medicine, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Christopher C Pack
- Montreal Neurological Institute, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Bart Krekelberg
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Avenue, Newark, NJ, 07102, USA
| | - Antal Berényi
- MTA-SZTE 'Momentum' Oscillatory Neuronal Networks Research Group, Department of Physiology, Faculty of Medicine, University of Szeged, 10 Dom sq., Szeged, H-6720, Hungary
| | - Lucas C Parra
- Department of Biomedical Engineering, City College of New York, 160 Convent Ave, New York, NY, 10031, USA
| | - Lucia Melloni
- New York University Comprehensive Epilepsy Center, 223 34th Street, New York, NY, 10016, USA
- Department of Neurology, NYU Langone Health, 222 East 41st Street, 14th Floor, New York, NY, 10016, USA
- Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, 60322, Frankfurt am Main, Germany
| | - Orrin Devinsky
- New York University Comprehensive Epilepsy Center, 223 34th Street, New York, NY, 10016, USA
- Department of Neurology, NYU Langone Health, 222 East 41st Street, 14th Floor, New York, NY, 10016, USA
| | - György Buzsáki
- New York University Neuroscience Institute, 435 East 30th Street, New York, NY, 10016, USA.
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