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Ma YY, Gao Y, Wu HQ, Liang XY, Li Y, Lu H, Liu CZ, Ning XL. OPM-MEG Measuring Phase Synchronization on Source Time Series: Application in Rhythmic Median Nerve Stimulation. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1426-1434. [PMID: 38530717 DOI: 10.1109/tnsre.2024.3381173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
The magnetoencephalogram (MEG) based on array optically pumped magnetometers (OPMs) has the potential of replacing conventional cryogenic superconducting quantum interference device. Phase synchronization is a common method for measuring brain oscillations and functional connectivity. Verifying the feasibility and fidelity of OPM-MEG in measuring phase synchronization will help its widespread application in the study of aforementioned neural mechanisms. The analysis method on source-level time series can weaken the influence of instantaneous field spread effect. In this paper, the OPM-MEG was used for measuring the evoked responses of 20Hz rhythmic and arrhythmic median nerve stimulation, and the inter-trial phase synchronization (ITPS) and inter-reginal phase synchronization (IRPS) of primary somatosensory cortex (SI) and secondary somatosensory cortex (SII) were analysed. The results find that under rhythmic condition, the evoked responses of SI and SII show continuous oscillations and the effect of resetting phase. The values of ITPS and IRPS significantly increase at the stimulation frequency of 20Hz and its harmonic of 40Hz, whereas the arrhythmic stimulation does not exhibit this phenomenon. Moreover, in the initial stage of stimulation, the ITPS and IRPS values are significantly higher at Mu rhythm in the rhythmic condition compared to arrhythmic. In conclusion, the results demonstrate the ability of OPM-MEG in measuring phase pattern and functional connectivity on source-level, and may also prove beneficial for the study on the mechanism of rhythmic stimulation therapy for rehabilitation.
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Buschermöhle Y, Höltershinken MB, Erdbrügger T, Radecke JO, Sprenger A, Schneider TR, Lencer R, Gross J, Wolters CH. Comparing the performance of beamformer algorithms in estimating orientations of neural sources. iScience 2024; 27:109150. [PMID: 38420593 PMCID: PMC10901088 DOI: 10.1016/j.isci.2024.109150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/12/2023] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
The efficacy of transcranial electric stimulation (tES) to effectively modulate neuronal activity depends critically on the spatial orientation of the targeted neuronal population. Therefore, precise estimation of target orientation is of utmost importance. Different beamforming algorithms provide orientation estimates; however, a systematic analysis of their performance is still lacking. For fixed brain locations, EEG and MEG data from sources with randomized orientations were simulated. The orientation was then estimated (1) with an EEG and (2) with a combined EEG-MEG approach. Three commonly used beamformer algorithms were evaluated with respect to their abilities to estimate the correct orientation: Unit-Gain (UG), Unit-Noise-Gain (UNG), and Array-Gain (AG) beamformer. Performance depends on the signal-to-noise ratios for the modalities and on the chosen beamformer. Overall, the UNG and AG beamformers appear as the most reliable. With increasing noise, the UG estimate converges to a vector determined by the leadfield, thus leading to insufficient orientation estimates.
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Affiliation(s)
- Yvonne Buschermöhle
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
| | - Malte B Höltershinken
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Institute for Analysis and Numerics, University of Münster, 48149 Münster, Germany
| | - Tim Erdbrügger
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Institute for Analysis and Numerics, University of Münster, 48149 Münster, Germany
| | - Jan-Ole Radecke
- Department of Psychiatry and Psychotherapy, University of Lübeck, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Andreas Sprenger
- Center of Brain, Behavior and Metabolism, University of Lübeck, 23562 Lübeck, Germany
- Department of Neurology, University of Lübeck, 23562 Lübeck, Germany
- Institute of Psychology II, University of Lübeck, 23562 Lübeck, Germany
| | - Till R Schneider
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Rebekka Lencer
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
- Department of Psychiatry and Psychotherapy, University of Lübeck, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, 23562 Lübeck, Germany
- Institute of Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
| | - Carsten H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
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Santo-Angles A, Temudo A, Babushkin V, Sreenivasan KK. Effective connectivity of working memory performance: a DCM study of MEG data. Front Hum Neurosci 2024; 18:1339728. [PMID: 38501039 PMCID: PMC10944968 DOI: 10.3389/fnhum.2024.1339728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/12/2024] [Indexed: 03/20/2024] Open
Abstract
Visual working memory (WM) engages several nodes of a large-scale network that includes frontal, parietal, and visual regions; however, little is understood about how these regions interact to support WM behavior. In particular, it is unclear whether network dynamics during WM maintenance primarily represent feedforward or feedback connections. This question has important implications for current debates about the relative roles of frontoparietal and visual regions in WM maintenance. In the current study, we investigated the network activity supporting WM using MEG data acquired while healthy subjects performed a multi-item delayed estimation WM task. We used computational modeling of behavior to discriminate correct responses (high accuracy trials) from two different types of incorrect responses (low accuracy and swap trials), and dynamic causal modeling of MEG data to measure effective connectivity. We observed behaviorally dependent changes in effective connectivity in a brain network comprising frontoparietal and early visual areas. In comparison with high accuracy trials, frontoparietal and frontooccipital networks showed disrupted signals depending on type of behavioral error. Low accuracy trials showed disrupted feedback signals during early portions of WM maintenance and disrupted feedforward signals during later portions of maintenance delay, while swap errors showed disrupted feedback signals during the whole delay period. These results support a distributed model of WM that emphasizes the role of visual regions in WM storage and where changes in large scale network configurations can have important consequences for memory-guided behavior.
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Affiliation(s)
- Aniol Santo-Angles
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Center for Brain and Health, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Ainsley Temudo
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Vahan Babushkin
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Kartik K. Sreenivasan
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Center for Brain and Health, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
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Silva Pereira S, Özer EE, Sebastian-Galles N. Complexity of STG signals and linguistic rhythm: a methodological study for EEG data. Cereb Cortex 2024; 34:bhad549. [PMID: 38236741 DOI: 10.1093/cercor/bhad549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 02/06/2024] Open
Abstract
The superior temporal and the Heschl's gyri of the human brain play a fundamental role in speech processing. Neurons synchronize their activity to the amplitude envelope of the speech signal to extract acoustic and linguistic features, a process known as neural tracking/entrainment. Electroencephalography has been extensively used in language-related research due to its high temporal resolution and reduced cost, but it does not allow for a precise source localization. Motivated by the lack of a unified methodology for the interpretation of source reconstructed signals, we propose a method based on modularity and signal complexity. The procedure was tested on data from an experiment in which we investigated the impact of native language on tracking to linguistic rhythms in two groups: English natives and Spanish natives. In the experiment, we found no effect of native language but an effect of language rhythm. Here, we compare source projected signals in the auditory areas of both hemispheres for the different conditions using nonparametric permutation tests, modularity, and a dynamical complexity measure. We found increasing values of complexity for decreased regularity in the stimuli, giving us the possibility to conclude that languages with less complex rhythms are easier to track by the auditory cortex.
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Affiliation(s)
- Silvana Silva Pereira
- Center for Brain and Cognition, Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08005 Barcelona, Spain
| | - Ege Ekin Özer
- Center for Brain and Cognition, Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08005 Barcelona, Spain
| | - Nuria Sebastian-Galles
- Center for Brain and Cognition, Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08005 Barcelona, Spain
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van de Velden D, Heide EC, Bouter C, Bucerius J, Riedel CH, Focke NK. Effects of inverse methods and spike phases on interictal high-density EEG source reconstruction. Clin Neurophysiol 2023; 156:4-13. [PMID: 37832322 DOI: 10.1016/j.clinph.2023.08.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 08/15/2023] [Accepted: 08/24/2023] [Indexed: 10/15/2023]
Abstract
OBJECTIVE To determine the effect of inverse methods and timepoints of interictal epileptic discharges (IEDs) used for high-density electric source imaging (hd-ESI) in pharmacoresistant focal epilepsies. METHODS We retrospectively evaluated the hd-ESI and [18F]fluorodeoxyglucose positron emission tomography (18FDG-PET) of 21 operated patients with pharmacoresistant focal epilepsy (Engel I). Volumetric hd-ESI was performed with three different inverse methods such as the inverse solution linearly constrained minimum variance (LCMV, a beamformer method), standardized low resolution electromagnetic tomography (sLORETA) and weighted minimum-norm estimation (wMNE) and at different IED phases. Hd-ESI accuracy was determined by volumetric overlap and distance between hd-ESI source maximum, as well as 18FDG-PET hypometabolic region relative to the resection zone (RZ). RESULTS In our cohort, the shortest distances and greatest volumetric overlaps to the RZ were found in the half-rise and peak-phase for all inverse methods. The distance to the RZ was not different between the centroid of the clinical hypothesis-based cluster and the source maximum in peak-phase. However, the distance of the hypothesis-based cluster was significantly shorter compared to the cluster selected by the smallest p-value. CONCLUSIONS Hd-ESI provides the greatest accuracy in determining the RZ at the IED half-rise and peak-phase for all applied inverse methods, whereby sLORETA and LCMV were equally accurate. SIGNIFICANCE Our results offer guidance in selecting inverse methods and IED phases for hd-ESI, compare the performance of hd-ESI and 18FDG-PET and encourage future studies in investigating the relationship between interictal ESI and 18FDG-PET hypometabolism.
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Affiliation(s)
- Daniel van de Velden
- University Medical Center Göttingen, Clinic for Neurology, 37075 Göttingen, Germany.
| | - Ev-Christin Heide
- University Medical Center Göttingen, Clinic for Neurology, 37075 Göttingen, Germany
| | - Caroline Bouter
- University Medical Center Göttingen, Department of Nuclear Medicine, 37075 Göttingen, Germany
| | - Jan Bucerius
- University Medical Center Göttingen, Department of Nuclear Medicine, 37075 Göttingen, Germany
| | - Christian H Riedel
- University Medical Center Göttingen, Institute for Diagnostic and Interventional Neuroradiology, 37075 Göttingen, Germany
| | - Niels K Focke
- University Medical Center Göttingen, Clinic for Neurology, 37075 Göttingen, Germany.
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Lv Y, Zhang JJ, Wang K, Ju L, Zhang H, Zhao Y, Pan Y, Gong J, Wang X, Fong KNK. Determining the Optimal Stimulation Sessions for TMS-Induced Recovery of Upper Extremity Motor Function Post Stroke: A Randomized Controlled Trial. Brain Sci 2023; 13:1662. [PMID: 38137110 PMCID: PMC10741851 DOI: 10.3390/brainsci13121662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023] Open
Abstract
To find out the optimal treatment sessions of repetitive transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) for upper extremity dysfunction after stroke during the 6-week treatment and to explore its mechanism using motor-evoked potentials (MEPs) and resting-state functional magnetic resonance imaging (rs-fMRI), 72 participants with upper extremity motor dysfunction after ischemic stroke were randomly divided into the control group, 10-session, 20-session, and 30-session rTMS groups. Low-frequency (1 Hz) rTMS over the contralesional M1 was applied in all rTMS groups. The motor function of the upper extremity was assessed before and after treatment. In addition, MEPs and rs-fMRI data were analyzed to detect its effect on brain reorganization. After 6 weeks of treatment, there were significant differences in the Fugl-Meyer Assessment of the upper extremity and the Wolf Motor Function Test scores between the 10-session group and the 30-session group and between the 20- and 30-session groups and the control group, while there was no significant difference between the 20-session group and the 30-session group. Meanwhile, no significant difference was found between the 10-session group and the control group. The 20-session group of rTMS decreased the excitability of the contralesional corticospinal tract represented by the amplitudes of MEPs and enhanced the functional connectivity of the ipsilesional M1 or premotor cortex with the the precentral gyrus, postcentral gyrus, and cingulate gyrus, etc. In conclusion, the 20-session of rTMS protocol is the optimal treatment sessions of TMS for upper extremity dysfunction after stroke during the 6-week treatment. The potential mechanism is related to its influence on the excitability of the corticospinal tract and the remodeling of corticomotor functional networks.
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Affiliation(s)
- Yichen Lv
- School of Rehabilitation Medicine, Binzhou Medical University, Yantai 264000, China
- Department of Rehabilitation Medicine, Clinical Medical College, Yangzhou University, Yangzhou 225001, China
| | - Jack Jiaqi Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Kui Wang
- Department of Rehabilitation Medicine, Clinical Medical College, Yangzhou University, Yangzhou 225001, China
| | - Leilei Ju
- Department of Rehabilitation Medicine, Clinical Medical College, Yangzhou University, Yangzhou 225001, China
| | - Hongying Zhang
- Department of Medical Imaging, Clinical Medical College, Yangzhou University, Yangzhou 225001, China
| | - Yuehan Zhao
- School of Rehabilitation Medicine, Binzhou Medical University, Yantai 264000, China
- Department of Rehabilitation Medicine, Clinical Medical College, Yangzhou University, Yangzhou 225001, China
| | - Yao Pan
- School of Rehabilitation Medicine, Binzhou Medical University, Yantai 264000, China
- Department of Rehabilitation Medicine, Clinical Medical College, Yangzhou University, Yangzhou 225001, China
| | - Jianwei Gong
- School of Rehabilitation Medicine, Binzhou Medical University, Yantai 264000, China
| | - Xin Wang
- Department of Rehabilitation Medicine, Clinical Medical College, Yangzhou University, Yangzhou 225001, China
| | - Kenneth N. K. Fong
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
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Zheng L, Liao P, Wu X, Cao M, Cui W, Lu L, Xu H, Zhu L, Lyu B, Wang X, Teng P, Wang J, Vogrin S, Plummer C, Luan G, Gao JH. An artificial intelligence-based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography. J Neural Eng 2023; 20:046036. [PMID: 37615416 DOI: 10.1088/1741-2552/acef92] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023]
Abstract
Objective.Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy evaluation. However, the clinical utility of MEG mapping for localising epileptic foci is limited by its low efficiency, high labour requirements, and considerable interoperator variability. To address these obstacles, we proposed a novel artificial intelligence-based automated magnetic source imaging (AMSI) pipeline for automated detection and localisation of epileptic sources from MEG data.Approach.To expedite the analysis of clinical MEG data from patients with epilepsy and reduce human bias, we developed an autolabelling method, a deep-learning model based on convolutional neural networks and a hierarchical clustering method based on a perceptual hash algorithm, to enable the coregistration of MEG and magnetic resonance imaging, the detection and clustering of epileptic activity, and the localisation of epileptic sources in a highly automated manner. We tested the capability of the AMSI pipeline by assessing MEG data from 48 epilepsy patients.Main results.The AMSI pipeline was able to rapidly detect interictal epileptiform discharges with 93.31% ± 3.87% precision based on a 35-patient dataset (with sevenfold patientwise cross-validation) and robustly rendered accurate localisation of epileptic activity with a lobar concordance of 87.18% against interictal and ictal stereo-electroencephalography findings in a 13-patient dataset. We also showed that the AMSI pipeline accomplishes the necessary processes and delivers objective results within a much shorter time frame (∼12 min) than traditional manual processes (∼4 h).Significance.The AMSI pipeline promises to facilitate increased utilisation of MEG data in the clinical analysis of patients with epilepsy.
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Affiliation(s)
- Li Zheng
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
| | - Pan Liao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
| | - Xiuwen Wu
- Changping Laboratory, Beijing, People's Republic of China
- Center for Biomedical Engineering, University of Science and Technology of China, Anhui, People's Republic of China
| | - Miao Cao
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
| | - Wei Cui
- Center for Biomedical Engineering, University of Science and Technology of China, Anhui, People's Republic of China
| | - Lingxi Lu
- Center for the Cognitive Science of Language, Beijing Language and Culture University, Beijing, People's Republic of China
| | - Hui Xu
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Linlin Zhu
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Bingjiang Lyu
- Changping Laboratory, Beijing, People's Republic of China
| | - Xiongfei Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
- Beijing Key Laboratory of Epilepsy, Capital Medical University, Beijing, People's Republic of China
| | - Pengfei Teng
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jing Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Simon Vogrin
- Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia
| | - Chris Plummer
- Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia
| | - Guoming Luan
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
- Beijing Key Laboratory of Epilepsy, Capital Medical University, Beijing, People's Republic of China
| | - Jia-Hong Gao
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
- McGovern Institute for Brain Research, Peking University, Beijing, People's Republic of China
- National Biomedical Imaging Center, Peking University, Beijing, People's Republic of China
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O'Reilly JA, Zhu JD, Sowman PF. Localized estimation of electromagnetic sources underlying event-related fields using recurrent neural networks. J Neural Eng 2023; 20:046035. [PMID: 37567215 DOI: 10.1088/1741-2552/acef94] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/10/2023] [Indexed: 08/13/2023]
Abstract
Objective. To use a recurrent neural network (RNN) to reconstruct neural activity responsible for generating noninvasively measured electromagnetic signals.Approach. Output weights of an RNN were fixed as the lead field matrix from volumetric source space computed using the boundary element method with co-registered structural magnetic resonance images and magnetoencephalography (MEG). Initially, the network was trained to minimise mean-squared-error loss between its outputs and MEG signals, causing activations in the penultimate layer to converge towards putative neural source activations. Subsequently, L1 regularisation was applied to the final hidden layer, and the model was fine-tuned, causing it to favour more focused activations. Estimated source signals were then obtained from the outputs of the last hidden layer. We developed and validated this approach with simulations before applying it to real MEG data, comparing performance with beamformers, minimum-norm estimate, and mixed-norm estimate source reconstruction methods.Main results. The proposed RNN method had higher output signal-to-noise ratios and comparable correlation and error between estimated and simulated sources. Reconstructed MEG signals were also equal or superior to the other methods regarding their similarity to ground-truth. When applied to MEG data recorded during an auditory roving oddball experiment, source signals estimated with the RNN were generally biophysically plausible and consistent with expectations from the literature.Significance. This work builds on recent developments of RNNs for modelling event-related neural responses by incorporating biophysical constraints from the forward model, thus taking a significant step towards greater biological realism and introducing the possibility of exploring how input manipulations may influence localised neural activity.
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Affiliation(s)
- Jamie A O'Reilly
- School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Judy D Zhu
- School of Psychological Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Paul F Sowman
- School of Psychological Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
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Pellegrini F, Delorme A, Nikulin V, Haufe S. Identifying good practices for detecting inter-regional linear functional connectivity from EEG. Neuroimage 2023; 277:120218. [PMID: 37307866 PMCID: PMC10374983 DOI: 10.1016/j.neuroimage.2023.120218] [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: 03/14/2023] [Revised: 05/12/2023] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear. In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC. Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipelines that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies. We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis of EEG data recorded during motor imagery.
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Affiliation(s)
- Franziska Pellegrini
- Charité-Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany; Bernstein Center for Computational Neuroscience, Philippstraße 13, Berlin, 10117, Germany.
| | - Arnaud Delorme
- Swartz Center for Computational Neuroscience, 9500 Gilman Dr., La Jolla, California, 92903-0559, United States
| | - Vadim Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Stephanstraße 1a, Leipzig, 04103, Germany
| | - Stefan Haufe
- Charité-Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany; Bernstein Center for Computational Neuroscience, Philippstraße 13, Berlin, 10117, Germany; Technische Universität Berlin, Straße des 17. Juni 135, Berlin, 10623, Germany; Physikalisch-Technische Bundesanstalt Braunschweig und Berlin, Abbestraße 2-12, Berlin, 10587, Germany
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10
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Rubinstein DY, Eisenberg DP, Carver FW, Holroyd T, Apud JA, Coppola R, Berman KF. Spatiotemporal Alterations in Working Memory-Related Beta Band Neuromagnetic Activity of Patients With Schizophrenia On and Off Antipsychotic Medication: Investigation With MEG. Schizophr Bull 2023; 49:669-678. [PMID: 36772948 PMCID: PMC10154700 DOI: 10.1093/schbul/sbac178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
BACKGROUND AND HYPOTHESIS We used the uniquely high combined spatial and temporal resolution of magnetoencephalography to characterize working memory (WM)-related modulation of beta band activity in neuroleptic-free patients with schizophrenia in comparison to a large sample of performance-matched healthy controls. We also tested for effects of antipsychotic medication on identified differences in these same patients. STUDY DESIGN Inpatients with schizophrenia (n = 21) or psychotic disorder not otherwise specified (n = 4) completed N-back and control tasks during magnetoencephalography while on placebo and during antipsychotic medication treatment, in a blinded, randomized, counterbalanced manner. Healthy, performance-matched controls (N = 100) completed the same tasks. WM-related neural activation was estimated as beta band (14-30 Hz) desynchronization throughout the brain in successive 400 ms time windows. Voxel-wise statistical comparisons were performed between controls and patients while off-medication at each time window. Significant clusters resulting from this between-groups analysis were then used as regions-of-interest, the activations of which were compared between on- and off-medication conditions in patients. STUDY RESULTS Controls showed beta-band desynchronization (activation) of a fronto-parietal network immediately preceding correct button press responses-the time associated with WM updating and task execution. Altered activation in medication-free patients occurred largely during this time, in prefrontal, parietal, and visual cortices. Medication altered patients' neural responses such that the activation time courses in these regions-of-interest more closely resembled those of controls. CONCLUSIONS These findings demonstrate that WM-related beta band alterations in schizophrenia are time-specific and associated with neural systems targeted by antipsychotic medications. Future studies may investigate this association by examining its potential neurochemical basis.
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Affiliation(s)
- Daniel Y Rubinstein
- Section on Integrative Neuroimaging, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | - Daniel P Eisenberg
- Section on Integrative Neuroimaging, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | | | - Tom Holroyd
- MEG Core Facility, NIH, DHHS, Bethesda, MD, USA
| | - Jose A Apud
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | - Richard Coppola
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
- MEG Core Facility, NIH, DHHS, Bethesda, MD, USA
| | - Karen F Berman
- Section on Integrative Neuroimaging, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
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11
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Kumari R, Gibson H, Jarjees M, Turner C, Purcell M, Vučković A. The predictive value of cortical activity during motor imagery for subacute spinal cord injury-induced neuropathic pain. Clin Neurophysiol 2023; 148:32-43. [PMID: 36796284 DOI: 10.1016/j.clinph.2023.01.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 12/06/2022] [Accepted: 01/04/2023] [Indexed: 01/26/2023]
Abstract
OBJECTIVE The aim of this study is to explore whether cortical activation and its lateralization during motor imagery (MI) in subacute spinal cord injury (SCI) are indicative of existing or upcoming central neuropathic pain (CNP). METHODS Multichannel electroencephalogram was recorded during MI of both hands in four groups of participants: able-bodied (N = 10), SCI and CNP (N = 11), SCI who developed CNP within 6 months of EEG recording (N = 10), and SCI who remained CNP-free (N = 10). Source activations and its lateralization were derived in four frequency bands in 20 regions spanning sensorimotor cortex and pain matrix. RESULTS Statistically significant differences in lateralization were found in the theta band in premotor cortex (upcoming vs existing CNP, p = 0.036), in the alpha band at the insula (healthy vs upcoming CNP, p = 0.012), and in the higher beta band at the somatosensory association cortex (no CNP vs upcoming CNP, p = 0.042). People with upcoming CNP had stronger activation compared to those with no CNP in the higher beta band for MI of both hands. CONCLUSIONS Activation intensity and lateralization during MI in pain-related areas might hold a predictive value for CNP. SIGNIFICANCE The study increases understanding of the mechanisms underlying transition from asymptomatic to symptomatic early CNP in SCI.
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Affiliation(s)
- Radha Kumari
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
| | - Hannah Gibson
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
| | - Mohammed Jarjees
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK; Medical Instrumentation Techniques Engineering Department, Northern Technical University, Mosul 41002, Iraq
| | - Christopher Turner
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
| | - Mariel Purcell
- Queen Elizabeth National Spinal Injuries Unit, Queen Elizabeth University Hospital, Glasgow G51 4TF, UK
| | - Aleksandra Vučković
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK.
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12
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Jaiswal A, Nenonen J, Parkkonen L. On electromagnetic head digitization in MEG and EEG. Sci Rep 2023; 13:3801. [PMID: 36882438 PMCID: PMC9992397 DOI: 10.1038/s41598-023-30223-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/17/2023] [Indexed: 03/09/2023] Open
Abstract
In MEG and EEG studies, the accuracy of the head digitization impacts the co-registration between functional and structural data. The co-registration is one of the major factors that affect the spatial accuracy in MEG/EEG source imaging. Precisely digitized head-surface (scalp) points do not only improve the co-registration but can also deform a template MRI. Such an individualized-template MRI can be used for conductivity modeling in MEG/EEG source imaging if the individual's structural MRI is unavailable. Electromagnetic tracking (EMT) systems (particularly Fastrak, Polhemus Inc., Colchester, VT, USA) have been the most common solution for digitization in MEG and EEG. However, they may occasionally suffer from ambient electromagnetic interference which makes it challenging to achieve (sub-)millimeter digitization accuracy. The current study-(i) evaluated the performance of the Fastrak EMT system under different conditions in MEG/EEG digitization, and (ii) explores the usability of two alternative EMT systems (Aurora, NDI, Waterloo, ON, Canada; Fastrak with a short-range transmitter) for digitization. Tracking fluctuation, digitization accuracy, and robustness of the systems were evaluated in several test cases using test frames and human head models. The performance of the two alternative systems was compared against the Fastrak system. The results showed that the Fastrak system is accurate and robust for MEG/EEG digitization if the recommended operating conditions are met. The Fastrak with the short-range transmitter shows comparatively higher digitization error if digitization is not carried out very close to the transmitter. The study also evinces that the Aurora system can be used for MEG/EEG digitization within a constrained range; however, some modifications would be required to make the system a practical and easy-to-use digitizer. Its real-time error estimation feature can potentially improve digitization accuracy.
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Affiliation(s)
- Amit Jaiswal
- MEGIN Oy, Espoo, Finland. .,Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland.
| | | | - Lauri Parkkonen
- MEGIN Oy, Espoo, Finland.,Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
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13
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Medani T, Garcia-Prieto J, Tadel F, Antonakakis M, Erdbrügger T, Höltershinken M, Mead W, Schrader S, Joshi A, Engwer C, Wolters CH, Mosher JC, Leahy RM. Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity. Neuroimage 2023; 267:119851. [PMID: 36599389 PMCID: PMC9904282 DOI: 10.1016/j.neuroimage.2022.119851] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 11/28/2022] [Accepted: 12/31/2022] [Indexed: 01/02/2023] Open
Abstract
Human brain activity generates scalp potentials (electroencephalography - EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography - MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community.
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Affiliation(s)
- Takfarinas Medani
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States.
| | - Juan Garcia-Prieto
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States.
| | - Francois Tadel
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States
| | - Marios Antonakakis
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; School of Electrical and Computer Engineering, Technical University of Crete, Greece
| | - Tim Erdbrügger
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Malte Höltershinken
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Wayne Mead
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Sophie Schrader
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; Department of Applied Mathematics, University of Münster, Germany
| | - Anand Joshi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States
| | - Christian Engwer
- Department of Applied Mathematics, University of Münster, Germany
| | - 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
| | - John C Mosher
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Richard M Leahy
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States
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14
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Coelli S, Medina Villalon S, Bonini F, Velmurugan J, López-Madrona VJ, Carron R, Bartolomei F, Badier JM, Bénar CG. Comparison of beamformer and ICA for dynamic connectivity analysis: A simultaneous MEG-SEEG study. Neuroimage 2023; 265:119806. [PMID: 36513288 DOI: 10.1016/j.neuroimage.2022.119806] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/25/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
Magnetoencephalography (MEG) is a powerful tool for estimating brain connectivity with both good spatial and temporal resolution. It is particularly helpful in epilepsy to characterize non-invasively the epileptic networks. However, using MEG to map brain networks requires solving a difficult inverse problem that introduces uncertainty in the activity localization and connectivity measures. Our goal here was to compare independent component analysis (ICA) followed by dipole source localization and the linearly constrained minimum-variance beamformer (LCMV-BF) for characterizing regions with interictal epileptic activity and their dynamic connectivity. After a simulation study, we compared ICA and LCMV-BF results with intracerebral EEG (stereotaxic EEG, SEEG) recorded simultaneously in 8 epileptic patients, which provide a unique 'ground truth' to which non-invasive results can be confronted. We compared the signal time courses extracted applying ICA and LCMV-BF on MEG data to that of SEEG, both for the actual signals and the dynamic connectivity computed using cross-correlation (evolution of links in time). With our simulations, we illustrated the different effect of the temporal and spatial correlation among sources on the two methods. While ICA was more affected by the temporal correlation but robust against spatial configurations, LCMV-BF showed opposite behavior. Moreover, ICA seems more suited to retrieve the simulated networks. In case of real patient data, good MEG/SEEG correlation and good localization were obtained in 6 out of 8 patients. In 4 of them ICA had the best performance (higher correlation, lower localization distance). In terms of dynamic connectivity, the evolution in time of the cross-correlation links could be retrieved in 5 patients out of 6, however, with more variable results in terms of correlation and distance. In two patients LCMV-BF had better results than ICA. In one patient the two methods showed equally good outcomes, and in the remaining two patients ICA performed best. In conclusion, our results obtained by exploiting simultaneous MEG/SEEG recordings suggest that ICA and LCMV-BF have complementary qualities for retrieving the dynamics of interictal sources and their network interactions.
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Affiliation(s)
- Stefania Coelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Samuel Medina Villalon
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France; APHM, Timone Hospital, Epileptology and Cerebral Rythmology, Marseille, France
| | - Francesca Bonini
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France; APHM, Timone Hospital, Epileptology and Cerebral Rythmology, Marseille, France
| | - Jayabal Velmurugan
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Romain Carron
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France; APHM, Timone Hospital, Functional and Stereotactic Neurosurgery, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France; APHM, Timone Hospital, Epileptology and Cerebral Rythmology, Marseille, France
| | - Jean-Michel Badier
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Christian-G Bénar
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France.
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15
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Bezsudnova Y, Koponen LM, Barontini G, Jensen O, Kowalczyk AU. Optimising the sensing volume of OPM sensors for MEG source reconstruction. Neuroimage 2022; 264:119747. [PMID: 36403733 PMCID: PMC7615061 DOI: 10.1016/j.neuroimage.2022.119747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 10/17/2022] [Accepted: 11/08/2022] [Indexed: 11/19/2022] Open
Abstract
Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) has been hailed as the future of electrophysiological recordings from the human brain. In this work, we investigate how the dimensions of the sensing volume (the vapour cell) affect the performance of both a single OPM-MEG sensor and a multi-sensor OPM-MEG system. We consider a realistic noise model that accounts for background brain activity and residual noise. By using source reconstruction metrics such as localization accuracy and time-course reconstruction accuracy, we demonstrate that the best overall sensitivity and reconstruction accuracy are achieved with cells that are significantly longer and wider that those of the majority of current commercial OPM sensors. Our work provides useful tools to optimise the cell dimensions of OPM sensors in a wide range of environments.
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Affiliation(s)
- Yulia Bezsudnova
- School of Physics and Astronomy, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Lari M Koponen
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2SA, United Kingdom
| | - Giovanni Barontini
- School of Physics and Astronomy, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom; Centre for Human Brain Health, School of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2SA, United Kingdom
| | - Ole Jensen
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2SA, United Kingdom
| | - Anna U Kowalczyk
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2SA, United Kingdom.
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16
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Niso G, Krol LR, Combrisson E, Dubarry AS, Elliott MA, François C, Héjja-Brichard Y, Herbst SK, Jerbi K, Kovic V, Lehongre K, Luck SJ, Mercier M, Mosher JC, Pavlov YG, Puce A, Schettino A, Schön D, Sinnott-Armstrong W, Somon B, Šoškić A, Styles SJ, Tibon R, Vilas MG, van Vliet M, Chaumon M. Good scientific practice in EEG and MEG research: Progress and perspectives. Neuroimage 2022; 257:119056. [PMID: 35283287 PMCID: PMC11236277 DOI: 10.1016/j.neuroimage.2022.119056] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 02/25/2022] [Accepted: 03/01/2022] [Indexed: 11/22/2022] Open
Abstract
Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.
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Affiliation(s)
- Guiomar Niso
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Universidad Politecnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Laurens R Krol
- Neuroadaptive Human-Computer Interaction, Brandenburg University of Technology Cottbus-Senftenberg, Germany
| | - Etienne Combrisson
- Aix-Marseille University, Institut de Neurosciences de la Timone, France
| | | | | | | | - Yseult Héjja-Brichard
- Centre d'Ecologie Fonctionnelle et Evolutive, CNRS, EPHE, IRD, Université Montpellier, Montpellier, France
| | - Sophie K Herbst
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, NeuroSpin center, Université Paris-Saclay, Gif/Yvette, France
| | - Karim Jerbi
- Cognitive and Computational Neuroscience Laboratory, Department of Psychology, University of Montreal, Montreal, QC, Canada; Mila - Quebec Artificial Intelligence Institute, Canada
| | - Vanja Kovic
- Faculty of Philosophy, Laboratory for neurocognition and applied cognition, University of Belgrade, Serbia
| | - Katia Lehongre
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm U 1127, CNRS UMR 7225, APHP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), Paris, France
| | - Steven J Luck
- Center for Mind & Brain, University of California, Davis, CA, USA
| | - Manuel Mercier
- Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, Marseille, France
| | - John C Mosher
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yuri G Pavlov
- University of Tuebingen, Germany; Ural Federal University, Yekaterinburg, Russia
| | - Aina Puce
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Antonio Schettino
- Erasmus University Rotterdam, Rotterdam, the Netherland; Institute for Globally Distributed Open Research and Education (IGDORE), Sweden
| | - Daniele Schön
- Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, Marseille, France
| | | | | | - Anđela Šoškić
- Faculty of Philosophy, Laboratory for neurocognition and applied cognition, University of Belgrade, Serbia; Teacher Education Faculty, University of Belgrade, Serbia
| | - Suzy J Styles
- Psychology, Nanyang Technological University, Singapore; Singapore Institute for Clinical Sciences, A*STAR, Singapore
| | - Roni Tibon
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK; School of Psychology, University of Nottingham, Nottingham, UK
| | - Martina G Vilas
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main, Germany
| | | | - Maximilien Chaumon
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm U 1127, CNRS UMR 7225, APHP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), Paris, France..
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17
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Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics. Proc Natl Acad Sci U S A 2022; 119:e2201128119. [PMID: 35881787 PMCID: PMC9351497 DOI: 10.1073/pnas.2201128119] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Electrophysiological source imaging (ESI) is an indispensable tool for noninvasively studying brain function and dysfunction. The moderate to low spatial resolution of electroencephalography/magnetoencephalography recordings has been a major hindrance to their wide utility in the field. Although this challenge is mitigated significantly by ESI techniques, it is difficult for individuals without relevant training to select and optimize hyperparameters for these ESI solvers. We propose a deep learning–based source imaging methodology that incorporates current advances in biophysical computational models and neural networks into the ESI framework, and it requires minimal user intervention after the model is trained. Our work promises to enable precise and robust high-resolution spatiotemporal functional brain imaging for a variety of neuroscience research studies and clinical applications. Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning–based source imaging framework (DeepSIF) that provides robust and precise spatiotemporal estimates of underlying brain dynamics from noninvasive high-density electroencephalography (EEG) recordings. DeepSIF employs synthetic training data generated by biophysical models capable of modeling mesoscale brain dynamics. The rich characteristics of underlying brain sources are embedded in the realistic training data and implicitly learned by DeepSIF networks, avoiding complications associated with explicitly formulating and tuning priors in an optimization problem, as often is the case in conventional source imaging approaches. The performance of DeepSIF is evaluated by 1) a series of numerical experiments, 2) imaging sensory and cognitive brain responses in a total of 20 healthy subjects from three public datasets, and 3) rigorously validating DeepSIF’s capability in identifying epileptogenic regions in a cohort of 20 drug-resistant epilepsy patients by comparing DeepSIF results with invasive measurements and surgical resection outcomes. DeepSIF demonstrates robust and excellent performance, producing results that are concordant with common neuroscience knowledge about sensory and cognitive information processing as well as clinical findings about the location and extent of the epileptogenic tissue and outperforming conventional source imaging methods. The DeepSIF method, as a data-driven imaging framework, enables efficient and effective high-resolution functional imaging of spatiotemporal brain dynamics, suggesting its wide applicability and value to neuroscience research and clinical applications.
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18
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Ferrante O, Liu L, Minarik T, Gorska U, Ghafari T, Luo H, Jensen O. FLUX: A pipeline for MEG analysis. Neuroimage 2022; 253:119047. [PMID: 35276363 PMCID: PMC9127391 DOI: 10.1016/j.neuroimage.2022.119047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 01/26/2022] [Accepted: 02/28/2022] [Indexed: 11/23/2022] Open
Abstract
Magnetoencephalography (MEG) allows for quantifying modulations of human neuronal activity on a millisecond time scale while also making it possible to estimate the location of the underlying neuronal sources. The technique relies heavily on signal processing and source modelling. To this end, there are several open-source toolboxes developed by the community. While these toolboxes are powerful as they provide a wealth of options for analyses, the many options also pose a challenge for reproducible research as well as for researchers new to the field. The FLUX pipeline aims to make the analyses steps and setting explicit for standard analysis done in cognitive neuroscience. It focuses on quantifying and source localization of oscillatory brain activity, but it can also be used for event-related fields and multivariate pattern analysis. The pipeline is derived from the Cogitate consortium addressing a set of concrete cognitive neuroscience questions. Specifically, the pipeline including documented code is defined for MNE Python (a Python toolbox) and FieldTrip (a Matlab toolbox), and a data set on visuospatial attention is used to illustrate the steps. The scripts are provided as notebooks implemented in Jupyter Notebook and MATLAB Live Editor providing explanations, justifications and graphical outputs for the essential steps. Furthermore, we also provide suggestions for text and parameter settings to be used in registrations and publications to improve replicability and facilitate pre-registrations. The FLUX can be used for education either in self-studies or guided workshops. We expect that the FLUX pipeline will strengthen the field of MEG by providing some standardization on the basic analysis steps and by aligning approaches across toolboxes. Furthermore, we also aim to support new researchers entering the field by providing education and training. The FLUX pipeline is not meant to be static; it will evolve with the development of the toolboxes and with new insights. Furthermore, with the anticipated increase in MEG systems based on the Optically Pumped Magnetometers, the pipeline will also evolve to embrace these developments.
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Affiliation(s)
- Oscar Ferrante
- Centre for Human Brain Health, School of Psychology, University of Birmingham, UK
| | - Ling Liu
- School of Communication Science, Beijing Language and Culture University, Beijing, China; School of Psychological and Cognitive Sciences, Peking University, Beijing, China
| | - Tamas Minarik
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Urszula Gorska
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Tara Ghafari
- Centre for Human Brain Health, School of Psychology, University of Birmingham, UK; Department of Physiology, Medical School, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Huan Luo
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China
| | - Ole Jensen
- Centre for Human Brain Health, School of Psychology, University of Birmingham, UK.
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19
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Shah-Basak P, Sivaratnam G, Teti S, Deschamps T, Kielar A, Jokel R, Meltzer JA. Electrophysiological connectivity markers of preserved language functions in post-stroke aphasia. Neuroimage Clin 2022; 34:103036. [PMID: 35561556 PMCID: PMC9111985 DOI: 10.1016/j.nicl.2022.103036] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 04/19/2022] [Accepted: 05/04/2022] [Indexed: 11/17/2022]
Abstract
Post-stroke aphasia is a consequence of localized stroke-related damage as well as global disturbances in a highly interactive and bilaterally-distributed language network. Aphasia is increasingly accepted as a network disorder and it should be treated as such when examining the reorganization and recovery mechanisms after stroke. In the current study, we sought to investigate reorganized patterns of electrophysiological connectivity, derived from resting-state magnetoencephalography (rsMEG), in post-stroke chronic (>6 months after onset) aphasia. We implemented amplitude envelope correlations (AEC), a metric of connectivity commonly used to describe slower aspects of interregional communication in resting-state electrophysiological data. The main focus was on identifying the oscillatory frequency bands and frequency-specific spatial topology of connections associated with preserved language abilities after stroke. RsMEG was recorded for 5 min in 21 chronic stroke survivors with aphasia and in 20 matched healthy controls. Source-level MEG activity was reconstructed and summarized within 72 atlas-defined brain regions (or nodes). A 72 × 72 leakage-corrected connectivity (of AEC) matrix was obtained for frequencies from theta to low-gamma (4–50 Hz). Connectivity was compared between groups, and, the correlations between connectivity and subscale scores from the Western Aphasia Battery (WAB) were evaluated in the stroke group, using partial least squares analyses. Posthoc multiple regression analyses were also conducted on a graph theory measure of node strengths, derived from significant connectivity results, to control for node-wise properties (local spectral power and lesion sizes) and demographic and stroke-related variables. Connectivity among the left hemisphere regions, i.e. those ipsilateral to the stroke lesion, was greatly reduced in stroke survivors with aphasia compared to matched healthy controls in the alpha (8–13 Hz; p = 0.011) and beta (15–30 Hz; p = 0.001) bands. The spatial topology of hypoconnectivity in the alpha vs. beta bands was distinct, revealing a greater involvement of ventral frontal, temporal and parietal areas in alpha, and dorsal frontal and parietal areas in beta. The node strengths from alpha and beta group differences remained significant after controlling for nodal spectral power. AEC correlations with WAB subscales of object naming and fluency were significant. Greater alpha connectivity was associated with better naming performance (p = 0.045), and greater connectivity in both the alpha (p = 0.033) and beta (p = 0.007) bands was associated with better speech fluency performance. The spatial topology was distinct between these frequency bands. The node strengths remained significant after controlling for age, time post stroke onset, nodal spectral power and nodal lesion sizes. Our findings provide important insights into the electrophysiological connectivity profiles (frequency and spatial topology) potentially underpinning preserved language abilities in stroke survivors with aphasia.
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Affiliation(s)
- Priyanka Shah-Basak
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada; Canadian Partnership for Stroke Recovery, Ottawa, ON, Canada.
| | - Gayatri Sivaratnam
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Selina Teti
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Tiffany Deschamps
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Aneta Kielar
- Department of Speech, Language, and Hearing Sciences, University of Arizona, Tucson, AZ, USA
| | - Regina Jokel
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada; Canadian Partnership for Stroke Recovery, Ottawa, ON, Canada; Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada
| | - Jed A Meltzer
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada; Canadian Partnership for Stroke Recovery, Ottawa, ON, Canada; Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada
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20
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Beumer S, Boon P, Klooster DCW, van Ee R, Carrette E, Paulides MM, Mestrom RMC. Personalized tDCS for Focal Epilepsy—A Narrative Review: A Data-Driven Workflow Based on Imaging and EEG Data. Brain Sci 2022; 12:brainsci12050610. [PMID: 35624997 PMCID: PMC9139054 DOI: 10.3390/brainsci12050610] [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: 04/08/2022] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 02/01/2023] Open
Abstract
Conventional transcranial electric stimulation(tES) using standard anatomical positions for the electrodes and standard stimulation currents is frequently not sufficiently selective in targeting and reaching specific brain locations, leading to suboptimal application of electric fields. Recent advancements in in vivo electric field characterization may enable clinical researchers to derive better relationships between the electric field strength and the clinical results. Subject-specific electric field simulations could lead to improved electrode placement and more efficient treatments. Through this narrative review, we present a processing workflow to personalize tES for focal epilepsy, for which there is a clear cortical target to stimulate. The workflow utilizes clinical imaging and electroencephalography data and enables us to relate the simulated fields to clinical outcomes. We review and analyze the relevant literature for the processing steps in the workflow, which are the following: tissue segmentation, source localization, and stimulation optimization. In addition, we identify shortcomings and ongoing trends with regard to, for example, segmentation quality and tissue conductivity measurements. The presented processing steps result in personalized tES based on metrics like focality and field strength, which allow for correlation with clinical outcomes.
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Affiliation(s)
- Steven Beumer
- Department of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; (P.B.); (D.C.W.K.); (E.C.); (M.M.P.); (R.M.C.M.)
- Correspondence:
| | - Paul Boon
- Department of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; (P.B.); (D.C.W.K.); (E.C.); (M.M.P.); (R.M.C.M.)
- Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Debby C. W. Klooster
- Department of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; (P.B.); (D.C.W.K.); (E.C.); (M.M.P.); (R.M.C.M.)
- Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Raymond van Ee
- Philips Research Eindhoven, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands;
| | - Evelien Carrette
- Department of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; (P.B.); (D.C.W.K.); (E.C.); (M.M.P.); (R.M.C.M.)
- Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Maarten M. Paulides
- Department of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; (P.B.); (D.C.W.K.); (E.C.); (M.M.P.); (R.M.C.M.)
- Department of Radiation Oncology, Erasmus Medical Center Cancer Institute, Burgemeester Oudlaan 50, 3062 PA Rotterdam, The Netherlands
| | - Rob M. C. Mestrom
- Department of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; (P.B.); (D.C.W.K.); (E.C.); (M.M.P.); (R.M.C.M.)
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21
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Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions. J Neurosci Methods 2022; 367:109424. [PMID: 34826504 DOI: 10.1016/j.jneumeth.2021.109424] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/26/2021] [Accepted: 11/18/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND Electrophysiological resting state functional connectivity using high density electroencephalography (hdEEG) is gaining momentum. The increased resolution offered by hdEEG, usually either 128 or 256 channels, permits source localization of EEG signals on the cortical surface. However, the number of methodological options for the acquisition and analysis of resting state hdEEG is extremely large. These include acquisition duration, eyes open/closed, channel density, source localization methods, and functional connectivity metric. NEW METHODS We undertake an extensive examination of the test-retest reliability and methodological agreement of all these options for regional measures of functional connectivity. RESULTS Power envelope connectivity shows larger test-retest reliability than imaginary coherence across all bands. While channel density doesn't strongly impact reliability or agreement, source localization methods produce systematically different functional connectivity, highlighting an important obstacle for replicating results in the literature. Most importantly, reliability and agreement often plateaus at or after 6 minutes of acquisition, well beyond the typical duration of 3 minutes. Finally, our study demonstrates that resting EEG can be as or more reliable than resting fMRI acquired in the same individuals. CONCLUSIONS The competitive reliability and agreement of power envelope connectivity greatly increases our confidence in measuring resting state connectivity using EEG and its capacity to find individual differences.
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22
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Validating EEG, MEG and Combined MEG and EEG Beamforming for an Estimation of the Epileptogenic Zone in Focal Cortical Dysplasia. Brain Sci 2022; 12:brainsci12010114. [PMID: 35053857 PMCID: PMC8796031 DOI: 10.3390/brainsci12010114] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/04/2022] [Accepted: 01/06/2022] [Indexed: 02/04/2023] Open
Abstract
MEG and EEG source analysis is frequently used for the presurgical evaluation of pharmacoresistant epilepsy patients. The source localization of the epileptogenic zone depends, among other aspects, on the selected inverse and forward approaches and their respective parameter choices. In this validation study, we compare the standard dipole scanning method with two beamformer approaches for the inverse problem, and we investigate the influence of the covariance estimation method and the strength of regularization on the localization performance for EEG, MEG, and combined EEG and MEG. For forward modelling, we investigate the difference between calibrated six-compartment and standard three-compartment head modelling. In a retrospective study, two patients with focal epilepsy due to focal cortical dysplasia type IIb and seizure freedom following lesionectomy or radiofrequency-guided thermocoagulation (RFTC) used the distance of the localization of interictal epileptic spikes to the resection cavity resp. RFTC lesion as reference for good localization. We found that beamformer localization can be sensitive to the choice of the regularization parameter, which has to be individually optimized. Estimation of the covariance matrix with averaged spike data yielded more robust results across the modalities. MEG was the dominant modality and provided a good localization in one case, while it was EEG for the other. When combining the modalities, the good results of the dominant modality were mostly not spoiled by the weaker modality. For appropriate regularization parameter choices, the beamformer localized better than the standard dipole scan. Compared to the importance of an appropriate regularization, the sensitivity of the localization to the head modelling was smaller, due to similar skull conductivity modelling and the fixed source space without orientation constraint.
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23
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Nenonen J, Helle L, Jaiswal A, Bock E, Ille N, Bornfleth H. Sensitivity of a 29-Channel MEG Source Montage. Brain Sci 2022; 12:brainsci12010105. [PMID: 35053848 PMCID: PMC8773883 DOI: 10.3390/brainsci12010105] [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: 12/05/2021] [Revised: 01/08/2022] [Accepted: 01/11/2022] [Indexed: 12/04/2022] Open
Abstract
In this paper, we study the performance of a source montage corresponding to 29 brain regions reconstructed from whole-head magnetoencephalographic (MEG) recordings, with the aim of facilitating the review of MEG data containing epileptiform discharges. Test data were obtained by superposing simulated signals from 100-nAm dipolar sources to a resting state MEG recording from a healthy subject. Simulated sources were placed systematically to different cortical locations for defining the optimal regularization for the source montage reconstruction and for assessing the detectability of the source activity from the 29-channel MEG source montage. The signal-to-noise ratio (SNR), computed for each source from the sensor-level and source-montage signals, was used as the evaluation parameter. Without regularization, the SNR from the simulated sources was larger in the sensor-level signals than in the source montage reconstructions. Setting the regularization to 2% increased the source montage SNR to the same level as the sensor-level SNR, improving the detectability of the simulated events from the source montage reconstruction. Sources producing a SNR of at least 15 dB were visually detectable from the source-montage signals. Such sources are located closer than about 75 mm from the MEG sensors, in practice covering all areas in the grey matter. The 29-channel source montage creates more focal signals compared to the sensor space and can significantly shorten the detection time of epileptiform MEG discharges for focus localization.
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Affiliation(s)
- Jukka Nenonen
- Megin Oy, Keilasatama 5, FI-02150 Espoo, Finland; (L.H.); (A.J.); (E.B.)
- Correspondence: ; Tel.: +358-9-756-2400
| | - Liisa Helle
- Megin Oy, Keilasatama 5, FI-02150 Espoo, Finland; (L.H.); (A.J.); (E.B.)
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, FI-00076 Aalto, Finland
| | - Amit Jaiswal
- Megin Oy, Keilasatama 5, FI-02150 Espoo, Finland; (L.H.); (A.J.); (E.B.)
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, FI-00076 Aalto, Finland
| | - Elizabeth Bock
- Megin Oy, Keilasatama 5, FI-02150 Espoo, Finland; (L.H.); (A.J.); (E.B.)
| | - Nicole Ille
- BESA GmbH, 82166 Gräfelfing, Germany; (N.I.); (H.B.)
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24
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Liuzzi L, Chang KK, Zheng C, Keren H, Saha D, Nielson DM, Stringaris A. Magnetoencephalographic Correlates of Mood and Reward Dynamics in Human Adolescents. Cereb Cortex 2021; 32:3318-3330. [PMID: 34921602 PMCID: PMC9340400 DOI: 10.1093/cercor/bhab417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 10/14/2021] [Accepted: 10/15/2021] [Indexed: 11/24/2022] Open
Abstract
Despite its omnipresence in everyday interactions and its importance for mental health, mood and its neuronal underpinnings are poorly understood. Computational models can help identify parameters affecting self-reported mood during mood induction tasks. Here, we test if computationally modeled dynamics of self-reported mood during monetary gambling can be used to identify trial-by-trial variations in neuronal activity. To this end, we shifted mood in healthy (N = 24) and depressed (N = 30) adolescents by delivering individually tailored reward prediction errors while recording magnetoencephalography (MEG) data. Following a pre-registered analysis, we hypothesize that the expectation component of mood would be predictive of beta-gamma oscillatory power (25–40 Hz). We also hypothesize that trial variations in the source localized responses to reward feedback would be predicted by mood and by its reward prediction error component. Through our multilevel statistical analysis, we found confirmatory evidence that beta-gamma power is positively related to reward expectation during mood shifts, with localized sources in the posterior cingulate cortex. We also confirmed reward prediction error to be predictive of trial-level variations in the response of the paracentral lobule. To our knowledge, this is the first study to harness computational models of mood to relate mood fluctuations to variations in neural oscillations with MEG.
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Affiliation(s)
- Lucrezia Liuzzi
- Section of Clinical and Computational Psychiatry (CompΨ), Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Katharine K Chang
- Department of Psychology, University of Rochester, Rochester, NY 14627, USA
| | - Charles Zheng
- Machine Learning Team, Functional Magnetic Resonance Imaging Facility, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hanna Keren
- Section of Clinical and Computational Psychiatry (CompΨ), Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Dipta Saha
- Section of Clinical and Computational Psychiatry (CompΨ), Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Dylan M Nielson
- Section of Clinical and Computational Psychiatry (CompΨ), Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Argyris Stringaris
- Section of Clinical and Computational Psychiatry (CompΨ), Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
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25
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Reconstructing subcortical and cortical somatosensory activity via the RAMUS inverse source analysis technique using median nerve SEP data. Neuroimage 2021; 245:118726. [PMID: 34838947 DOI: 10.1016/j.neuroimage.2021.118726] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 10/22/2021] [Accepted: 11/12/2021] [Indexed: 11/23/2022] Open
Abstract
This study concerns reconstructing brain activity at various depths based on non-invasive EEG (electroencephalography) scalp measurements. We aimed at demonstrating the potential of the RAMUS (randomized multiresolution scanning) technique in localizing weakly distinguishable far-field sources in combination with coinciding cortical activity. As we have shown earlier theoretically and through simulations, RAMUS is a novel mathematical method that by employing the multigrid concept, allows marginalizing noise and depth bias effects and thus enables the recovery of both cortical and subcortical brain activity. To show this capability with experimental data, we examined the 14-30 ms post-stimulus somatosensory evoked potential (SEP) responses of human median nerve stimulation in three healthy adult subjects. We aim at reconstructing the different response components by evaluating a RAMUS-based estimate for the primary current density in the nervous tissue. We present source reconstructions obtained with RAMUS and compare them with the literature knowledge of the SEP components and the outcome of the unit-noise gain beamformer (UGNB) and standardized low-resolution brain electromagnetic tomography (sLORETA). We also analyzed the effect of the iterative alternating sequential technique, the optimization technique of RAMUS, compared to the classical minimum norm estimation (MNE) technique. Matching with our previous numerical studies, the current results suggest that RAMUS could have the potential to enhance the detection of simultaneous deep and cortical components and the distinction between the evoked sulcal and gyral activity.
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26
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Westner BU, Dalal SS, Gramfort A, Litvak V, Mosher JC, Oostenveld R, Schoffelen JM. A unified view on beamformers for M/EEG source reconstruction. Neuroimage 2021; 246:118789. [PMID: 34890794 DOI: 10.1016/j.neuroimage.2021.118789] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 10/27/2021] [Accepted: 12/06/2021] [Indexed: 11/18/2022] Open
Abstract
Beamforming is a popular method for functional source reconstruction using magnetoencephalography (MEG) and electroencephalography (EEG) data. Beamformers, which were first proposed for MEG more than two decades ago, have since been applied in hundreds of studies, demonstrating that they are a versatile and robust tool for neuroscience. However, certain characteristics of beamformers remain somewhat elusive and there currently does not exist a unified documentation of the mathematical underpinnings and computational subtleties of beamformers as implemented in the most widely used academic open source software packages for MEG analysis (Brainstorm, FieldTrip, MNE, and SPM). Here, we provide such documentation that aims at providing the mathematical background of beamforming and unifying the terminology. Beamformer implementations are compared across toolboxes and pitfalls of beamforming analyses are discussed. Specifically, we provide details on handling rank deficient covariance matrices, prewhitening, the rank reduction of forward fields, and on the combination of heterogeneous sensor types, such as magnetometers and gradiometers. The overall aim of this paper is to contribute to contemporary efforts towards higher levels of computational transparency in functional neuroimaging.
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Affiliation(s)
- Britta U Westner
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands; Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
| | - Sarang S Dalal
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | | | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
| | - John C Mosher
- Texas Institute for Restorative Neurotechnologies, McGovern Medical School, University of Texas Health Science Center at Houston, TX USA
| | - 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
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27
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Babaeeghazvini P, Rueda-Delgado LM, Gooijers J, Swinnen SP, Daffertshofer A. Brain Structural and Functional Connectivity: A Review of Combined Works of Diffusion Magnetic Resonance Imaging and Electro-Encephalography. Front Hum Neurosci 2021; 15:721206. [PMID: 34690718 PMCID: PMC8529047 DOI: 10.3389/fnhum.2021.721206] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 09/10/2021] [Indexed: 11/13/2022] Open
Abstract
Implications of structural connections within and between brain regions for their functional counterpart are timely points of discussion. White matter microstructural organization and functional activity can be assessed in unison. At first glance, however, the corresponding findings appear variable, both in the healthy brain and in numerous neuro-pathologies. To identify consistent associations between structural and functional connectivity and possible impacts for the clinic, we reviewed the literature of combined recordings of electro-encephalography (EEG) and diffusion-based magnetic resonance imaging (MRI). It appears that the strength of event-related EEG activity increases with increased integrity of structural connectivity, while latency drops. This agrees with a simple mechanistic perspective: the nature of microstructural white matter influences the transfer of activity. The EEG, however, is often assessed for its spectral content. Spectral power shows associations with structural connectivity that can be negative or positive often dependent on the frequencies under study. Functional connectivity shows even more variations, which are difficult to rank. This might be caused by the diversity of paradigms being investigated, from sleep and resting state to cognitive and motor tasks, from healthy participants to patients. More challenging, though, is the potential dependency of findings on the kind of analysis applied. While this does not diminish the principal capacity of EEG and diffusion-based MRI co-registration, it highlights the urgency to standardize especially EEG analysis.
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Affiliation(s)
- Parinaz Babaeeghazvini
- Department of Human Movements Sciences, Faculty of Behavioural and Movement Sciences, Amsterdam Movement Science Institute (AMS), Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Institute for Brain and Behaviour Amsterdam (iBBA), Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Laura M. Rueda-Delgado
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | - Jolien Gooijers
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Stephan P. Swinnen
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Andreas Daffertshofer
- Department of Human Movements Sciences, Faculty of Behavioural and Movement Sciences, Amsterdam Movement Science Institute (AMS), Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Institute for Brain and Behaviour Amsterdam (iBBA), Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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28
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Testing covariance models for MEG source reconstruction of hippocampal activity. Sci Rep 2021; 11:17615. [PMID: 34475476 PMCID: PMC8413350 DOI: 10.1038/s41598-021-96933-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/17/2021] [Indexed: 12/16/2022] Open
Abstract
Beamforming is one of the most commonly used source reconstruction methods for magneto- and electroencephalography (M/EEG). One underlying assumption, however, is that distant sources are uncorrelated and here we tested whether this is an appropriate model for the human hippocampal data. We revised the Empirical Bayesian Beamfomer (EBB) to accommodate specific a-priori correlated source models. We showed in simulation that we could use model evidence (as approximated by Free Energy) to distinguish between different correlated and uncorrelated source scenarios. Using group MEG data in which the participants performed a hippocampal-dependent task, we explored the possibility that the hippocampus or the cortex or both were correlated in their activity across hemispheres. We found that incorporating a correlated hippocampal source model significantly improved model evidence. Our findings help to explain why, up until now, the majority of MEG-reported hippocampal activity (typically making use of beamformers) has been estimated as unilateral.
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29
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van Bree S, Sohoglu E, Davis MH, Zoefel B. Sustained neural rhythms reveal endogenous oscillations supporting speech perception. PLoS Biol 2021; 19:e3001142. [PMID: 33635855 PMCID: PMC7946281 DOI: 10.1371/journal.pbio.3001142] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 03/10/2021] [Accepted: 02/08/2021] [Indexed: 12/23/2022] Open
Abstract
Rhythmic sensory or electrical stimulation will produce rhythmic brain responses. These rhythmic responses are often interpreted as endogenous neural oscillations aligned (or "entrained") to the stimulus rhythm. However, stimulus-aligned brain responses can also be explained as a sequence of evoked responses, which only appear regular due to the rhythmicity of the stimulus, without necessarily involving underlying neural oscillations. To distinguish evoked responses from true oscillatory activity, we tested whether rhythmic stimulation produces oscillatory responses which continue after the end of the stimulus. Such sustained effects provide evidence for true involvement of neural oscillations. In Experiment 1, we found that rhythmic intelligible, but not unintelligible speech produces oscillatory responses in magnetoencephalography (MEG) which outlast the stimulus at parietal sensors. In Experiment 2, we found that transcranial alternating current stimulation (tACS) leads to rhythmic fluctuations in speech perception outcomes after the end of electrical stimulation. We further report that the phase relation between electroencephalography (EEG) responses and rhythmic intelligible speech can predict the tACS phase that leads to most accurate speech perception. Together, we provide fundamental results for several lines of research-including neural entrainment and tACS-and reveal endogenous neural oscillations as a key underlying principle for speech perception.
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Affiliation(s)
- Sander van Bree
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
- Centre for Cognitive Neuroimaging, University of Glasgow, Glasgow, United Kingdom
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Ediz Sohoglu
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
- School of Psychology, University of Sussex, Brighton, United Kingdom
| | - Matthew H. Davis
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Benedikt Zoefel
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
- Centre de Recherche Cerveau et Cognition, CNRS, Toulouse, France
- Université Toulouse III Paul Sabatier, Toulouse, France
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30
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Piotrowski T, Nikadon J, Moiseev A. Localization of brain activity from EEG/MEG using MV-PURE framework. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Lucena Gómez G, Peigneux P, Wens V, Bourguignon M. Localization accuracy of a common beamformer for the comparison of two conditions. Neuroimage 2021; 230:117793. [PMID: 33497769 DOI: 10.1016/j.neuroimage.2021.117793] [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: 05/25/2020] [Revised: 11/03/2020] [Accepted: 01/15/2021] [Indexed: 11/25/2022] Open
Abstract
The linearly constrained minimum variance beamformer is frequently used to reconstruct sources underpinning neuromagnetic recordings. When reconstructions must be compared across conditions, it is considered good practice to use a single, "common" beamformer estimated from all the data at once. This is to ensure that differences between conditions are not ascribable to differences in beamformer weights. Here, we investigate the localization accuracy of such a common beamformer. Based on theoretical derivations, we first show that the common beamformer leads to localization errors in source reconstruction. We then turn to simulations in which we attempt to reconstruct a (genuine) source in a first condition, while considering a second condition in which there is an (interfering) source elsewhere in the brain. We estimate maps of mislocalization and assess statistically the difference between "standard" and "common" beamformers. We complement our findings with an application to experimental MEG data. The results show that the common beamformer may yield significant mislocalization. Specifically, the common beamformer may force the genuine source to be reconstructed closer to the interfering source than it really is. As the same applies to the reconstruction of the interfering source, both sources are pulled closer together than they are. This observation was further illustrated in experimental data. Thus, although the common beamformer allows for the comparison of conditions, in some circumstances it introduces localization inaccuracies. We recommend alternative approaches to the general problem of comparing conditions.
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Affiliation(s)
- Gustavo Lucena Gómez
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.
| | - Philippe Peigneux
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit at CRCN - Centre de Recherches Cognition et Neurosciences, and UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Magnetoencephalography unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Brussels, Belgium
| | - Mathieu Bourguignon
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; BCBL, Basque Center on Cognition, Brain and Language, 20009 San Sebastian, Spain; Laboratoire Cognition Langage et Développement, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
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