1
|
Ho SI, Lin IM, Hsieh JC, Yen CF. EEG coherences of the default mode network among patients comorbid with major depressive disorder and anxiety symptoms. J Affect Disord 2024; 361:728-738. [PMID: 38889861 DOI: 10.1016/j.jad.2024.06.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/17/2024] [Accepted: 06/14/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Higher functional connectivity within the default mode network (DMN) has been found in functional magnetic resonance imaging (fMRI) studies of major depressive disorder (MDD). We used electroencephalogram (EEG) coherence as an index of functional connectivity to examine group differences in DMN between the MDD and healthy control (HC) groups during the resting state. METHODS MDD patients with comorbid anxiety symptoms (n = 154) and healthy controls (n = 165) completed the questionnaires of depression, anxiety, and rumination. A 19-channel EEG recording was measured under resting state for all participants. EEG coherences of the delta, theta, alpha, beta, and high beta in the anterior DMN (aDMN), posterior DMN (pDMN), aDMN-pDMN, DMN-parahippocampal gyrus (PHG), and DMN-temporal gyrus were compared between the two groups. The correlations between rumination, anxiety, and DMN coherence were examined in the MDD group. RESULTS (1) No difference was found in the delta, theta, alpha, and beta within the DMN brain regions between the two groups; the MDD group showed higher high beta coherence within DMN brain regions than the HC group. (2) Rumination was negatively correlated with theta coherence of aDMN, and positively correlated with beta coherence of aDMN and with alpha coherence of pDMN and DMN-PHG. (3) Anxiety was positively correlated with high beta coherence of aDMN, pDMN, and DMN-PHG. CONCLUSIONS MDD patients with comorbid anxiety symptoms exhibited hypercoherence within the DMN brain regions. Hypercoherences were related to symptoms of rumination, and anxiety may be a biomarker for MDD patients with comorbid anxiety symptoms.
Collapse
Affiliation(s)
- Sok-In Ho
- Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, Kaohsiung City, Taiwan
| | - I-Mei Lin
- Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, Kaohsiung City, Taiwan; Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung City, Taiwan.
| | - Jen-Chuen Hsieh
- Integrated Brain Research Unit, Division of Clinical Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei City, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei City, Taiwan; Department of Biological Science and Technology, College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Cheng-Fang Yen
- Department of Psychiatry, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung City, Taiwan; Graduate Institute of Medicine, Department of Psychiatry, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City, Taiwan
| |
Collapse
|
2
|
Marino M, Mantini D. Human brain imaging with high-density electroencephalography: Techniques and applications. J Physiol 2024. [PMID: 39173191 DOI: 10.1113/jp286639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2024] [Accepted: 07/30/2024] [Indexed: 08/24/2024] Open
Abstract
Electroencephalography (EEG) is a technique for non-invasively measuring neuronal activity in the human brain using electrodes placed on the participant's scalp. With the advancement of digital technologies, EEG analysis has evolved over time from the qualitative analysis of amplitude and frequency modulations to a comprehensive analysis of the complex spatiotemporal characteristics of the recorded signals. EEG is now considered a powerful tool for measuring neural processes in the same time frame in which they happen (i.e. the subsecond range). However, it is commonly argued that EEG suffers from low spatial resolution, which makes it difficult to localize the generators of EEG activity accurately and reliably. Today, the availability of high-density EEG (hdEEG) systems, combined with methods for incorporating information on head anatomy and sophisticated source-localization algorithms, has transformed EEG into an important neuroimaging tool. hdEEG offers researchers and clinicians a rich and varied range of applications. It can be used not only for investigating neural correlates in motor and cognitive neuroscience experiments, but also for clinical diagnosis, particularly in the detection of epilepsy and the characterization of neural impairments in a wide range of neurological disorders. Notably, the integration of hdEEG systems with other physiological recordings, such as kinematic and/or electromyography data, might be especially beneficial to better understand the neuromuscular mechanisms associated with deconditioning in ageing and neuromotor disorders, by mapping the neurokinematic and neuromuscular connectivity patterns directly in the brain.
Collapse
Affiliation(s)
- Marco Marino
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
- Department of General Psychology, University of Padua, Padua, Italy
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Belgium
| |
Collapse
|
3
|
Hirata A, Niitsu M, Phang CR, Kodera S, Kida T, Rashed EA, Fukunaga M, Sadato N, Wasaka T. High-resolution EEG source localization in personalized segmentation-free head model with multi-dipole fitting. Phys Med Biol 2024; 69:055013. [PMID: 38306964 DOI: 10.1088/1361-6560/ad25c3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/02/2024] [Indexed: 02/04/2024]
Abstract
Objective. Electroencephalograms (EEGs) are often used to monitor brain activity. Several source localization methods have been proposed to estimate the location of brain activity corresponding to EEG readings. However, only a few studies evaluated source localization accuracy from measured EEG using personalized head models in a millimeter resolution. In this study, based on a volume conductor analysis of a high-resolution personalized human head model constructed from magnetic resonance images, a finite difference method was used to solve the forward problem and to reconstruct the field distribution.Approach. We used a personalized segmentation-free head model developed using machine learning techniques, in which the abrupt change of electrical conductivity occurred at the tissue interface is suppressed. Using this model, a smooth field distribution was obtained to address the forward problem. Next, multi-dipole fitting was conducted using EEG measurements for each subject (N= 10 male subjects, age: 22.5 ± 0.5), and the source location and electric field distribution were estimated.Main results.For measured somatosensory evoked potential for electrostimulation to the wrist, a multi-dipole model with lead field matrix computed with the volume conductor model was found to be superior than a single dipole model when using personalized segmentation-free models (6/10). The correlation coefficient between measured and estimated scalp potentials was 0.89 for segmentation-free head models and 0.71 for conventional segmented models. The proposed method is straightforward model development and comparable localization difference of the maximum electric field from the target wrist reported using fMR (i.e. 16.4 ± 5.2 mm) in previous study. For comparison, DUNEuro based on sLORETA was (EEG: 17.0 ± 4.0 mm). In addition, somatosensory evoked magnetic fields obtained by Magnetoencephalography was 25.3 ± 8.5 mm using three-layer sphere and sLORETA.Significance. For measured EEG signals, our procedures using personalized head models demonstrated that effective localization of the somatosensory cortex, which is located in a non-shallower cortex region. This method may be potentially applied for imaging brain activity located in other non-shallow regions.
Collapse
Affiliation(s)
- Akimasa Hirata
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Masamune Niitsu
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Chun Ren Phang
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Sachiko Kodera
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Tetsuo Kida
- Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai 480-0392, Japan
| | - Essam A Rashed
- Graduate School of Information Science, University of Hyogo, Kobe 650-0047, Japan
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Toshiaki Wasaka
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| |
Collapse
|
4
|
Schoeters R, Tarnaud T, Martens L, Tanghe E. Simulation study on high spatio-temporal resolution acousto-electrophysiological neuroimaging. J Neural Eng 2024; 20:066039. [PMID: 38109769 DOI: 10.1088/1741-2552/ad169c] [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: 05/02/2023] [Accepted: 12/18/2023] [Indexed: 12/20/2023]
Abstract
Objective.Acousto-electrophysiological neuroimaging (AENI) is a technique hypothesized to record electrophysiological activity of the brain with millimeter spatial and sub-millisecond temporal resolution. This improvement is obtained by tagging areas with focused ultrasound (fUS). Due to mechanical vibration with respect to the measuring electrodes, the electrical activity of the marked region will be modulated onto the ultrasonic frequency. The region's electrical activity can subsequently be retrieved via demodulation of the measured signal. In this study, the feasibility of this hypothesized technique is tested.Approach.This is done by calculating the forward electroencephalography response under quasi-static assumptions. The head is simplified as a set of concentric spheres. Two sizes are evaluated representing human and mouse brains. Moreover, feasibility is assessed for wet and dry transcranial, and for cortically placed electrodes. The activity sources are modeled by dipoles, with their current intensity profile drawn from a power-law power spectral density.Results.It is shown that mechanical vibration modulates the endogenous activity onto the ultrasonic frequency. The signal strength depends non-linearly on the alignment between dipole orientation, vibration direction and recording point. The strongest signal is measured when these three dependencies are perfectly aligned. The signal strengths are in the pV-range for a dipole moment of 5 nAm and ultrasonic pressures within Food and Drug Administration (FDA)-limits. The endogenous activity can then be accurately reconstructed via demodulation. Two interference types are investigated: vibrational and static. Depending on the vibrational interference, it is shown that millimeter resolution signal detection is possible also for deep brain regions. Subsequently, successful demodulation depends on the static interference, that at MHz-range has to be sub-picovolt.Significance.Our results show that mechanical vibration is a possible underlying mechanism of acousto-electrophyisological neuroimaging. This paper is a first step towards improved understanding of the conditions under which AENI is feasible.
Collapse
Affiliation(s)
- Ruben Schoeters
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium
| | - Thomas Tarnaud
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium
| | - Luc Martens
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium
| | - Emmeric Tanghe
- Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium
| |
Collapse
|
5
|
Layer N, Abdel-Latif KHA, Radecke JO, Müller V, Weglage A, Lang-Roth R, Walger M, Sandmann P. Effects of noise and noise reduction on audiovisual speech perception in cochlear implant users: An ERP study. Clin Neurophysiol 2023; 154:141-156. [PMID: 37611325 DOI: 10.1016/j.clinph.2023.07.009] [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: 11/07/2022] [Revised: 06/19/2023] [Accepted: 07/14/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVE Hearing with a cochlear implant (CI) is difficult in noisy environments, but the use of noise reduction algorithms, specifically ForwardFocus, can improve speech intelligibility. The current event-related potentials (ERP) study examined the electrophysiological correlates of this perceptual improvement. METHODS Ten bimodal CI users performed a syllable-identification task in auditory and audiovisual conditions, with syllables presented from the front and stationary noise presented from the sides. Brainstorm was used for spatio-temporal evaluation of ERPs. RESULTS CI users revealed an audiovisual benefit as reflected by shorter response times and greater activation in temporal and occipital regions at P2 latency. However, in auditory and audiovisual conditions, background noise hampered speech processing, leading to longer response times and delayed auditory-cortex-activation at N1 latency. Nevertheless, activating ForwardFocus resulted in shorter response times, reduced listening effort and enhanced superior-frontal-cortex-activation at P2 latency, particularly in audiovisual conditions. CONCLUSIONS ForwardFocus enhances speech intelligibility in audiovisual speech conditions by potentially allowing the reallocation of attentional resources to relevant auditory speech cues. SIGNIFICANCE This study shows for CI users that background noise and ForwardFocus differentially affect spatio-temporal cortical response patterns, both in auditory and audiovisual speech conditions.
Collapse
Affiliation(s)
- Natalie Layer
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Otorhinolaryngology, Head and Neck Surgery, Audiology and Pediatric Audiology, Cochlear Implant Center, Germany.
| | | | - Jan-Ole Radecke
- Dept. of Psychiatry and Psychotherapy, University of Lübeck, Germany; Center for Brain, Behaviour and Metabolism (CBBM), University of Lübeck, Germany
| | - Verena Müller
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Otorhinolaryngology, Head and Neck Surgery, Audiology and Pediatric Audiology, Cochlear Implant Center, Germany
| | - Anna Weglage
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Otorhinolaryngology, Head and Neck Surgery, Audiology and Pediatric Audiology, Cochlear Implant Center, Germany
| | - Ruth Lang-Roth
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Otorhinolaryngology, Head and Neck Surgery, Audiology and Pediatric Audiology, Cochlear Implant Center, Germany
| | - Martin Walger
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Otorhinolaryngology, Head and Neck Surgery, Audiology and Pediatric Audiology, Cochlear Implant Center, Germany; Jean-Uhrmacher-Institute for Clinical ENT Research, University of Cologne, Germany
| | - Pascale Sandmann
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Otorhinolaryngology, Head and Neck Surgery, Audiology and Pediatric Audiology, Cochlear Implant Center, Germany; Department of Otolaryngology, Head and Neck Surgery, University of Oldenburg, Oldenburg, Germany
| |
Collapse
|
6
|
Mill RD, Hamilton JL, Winfield EC, Lalta N, Chen RH, Cole MW. Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior. PLoS Biol 2022; 20:e3001686. [PMID: 35980898 PMCID: PMC9387855 DOI: 10.1371/journal.pbio.3001686] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 05/24/2022] [Indexed: 11/21/2022] Open
Abstract
How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the "where and when") and then allow for empirical testing of alternative network models of brain function that link information to behavior (the "how"). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach-dynamic activity flow modeling-then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory-motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.
Collapse
Affiliation(s)
- Ravi D. Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Julia L. Hamilton
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Emily C. Winfield
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Nicole Lalta
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Richard H. Chen
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
- Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, New Jersey, United States of America
| | - Michael W. Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| |
Collapse
|
7
|
Sasaki R, Watanabe H, Onishi H. Therapeutic benefits of noninvasive somatosensory cortex stimulation on cortical plasticity and somatosensory function: a systematic review. Eur J Neurosci 2022; 56:4669-4698. [PMID: 35804487 DOI: 10.1111/ejn.15767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/23/2022] [Accepted: 06/09/2022] [Indexed: 11/28/2022]
Abstract
Optimal limb coordination requires efficient transmission of somatosensory information to the sensorimotor cortex. The primary somatosensory cortex (S1) is frequently damaged by stroke, resulting in both somatosensory and motor impairments. Noninvasive brain stimulation (NIBS) to the primary motor cortex is thought to induce neural plasticity that facilitates neurorehabilitation. Several studies have also examined if NIBS to the S1 can enhance somatosensory processing as assessed by somatosensory-evoked potentials (SEPs) and improve behavioral task performance, but it remains uncertain if NIBS can reliably modulate S1 plasticity or even whether SEPs can reflect this plasticity. This systematic review revealed that NIBS has relatively minor effects on SEPs or somatosensory task performance, but larger early SEP changes after NIBS can still predict improved performance. Similarly, decreased paired-pulse inhibition in S1 post-NIBS is associated with improved somatosensory performance. However, several studies still debate the role of inhibitory function in somatosensory performance after NIBS in terms of the direction of the change (that, disinhibition or inhibition). Altogether, early SEP and paired-pulse inhibition (particularly inter-stimulus intervals of 30-100 ms) may become useful biomarkers for somatosensory deficits, but improved NIBS protocols are required for therapeutic applications.
Collapse
Affiliation(s)
- Ryoki Sasaki
- Institute for Human Movement and Medical Sciences, Niigata University of Health and Welfare, Niigata, Japan.,Discipline of Physiology, School of Biomedicine, The University of Adelaide, Adelaide, Australia
| | - Hiraku Watanabe
- Institute for Human Movement and Medical Sciences, Niigata University of Health and Welfare, Niigata, Japan.,Department of Physical Therapy, Niigata University of Health and Welfare, Niigata, Japan
| | - Hideaki Onishi
- Institute for Human Movement and Medical Sciences, Niigata University of Health and Welfare, Niigata, Japan.,Department of Physical Therapy, Niigata University of Health and Welfare, Niigata, Japan
| |
Collapse
|
8
|
Zhou D, Zhang G, Dang J, Unoki M, Liu X. Detection of Brain Network Communities During Natural Speech Comprehension From Functionally Aligned EEG Sources. Front Comput Neurosci 2022; 16:919215. [PMID: 35874316 PMCID: PMC9301328 DOI: 10.3389/fncom.2022.919215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022] Open
Abstract
In recent years, electroencephalograph (EEG) studies on speech comprehension have been extended from a controlled paradigm to a natural paradigm. Under the hypothesis that the brain can be approximated as a linear time-invariant system, the neural response to natural speech has been investigated extensively using temporal response functions (TRFs). However, most studies have modeled TRFs in the electrode space, which is a mixture of brain sources and thus cannot fully reveal the functional mechanism underlying speech comprehension. In this paper, we propose methods for investigating the brain networks of natural speech comprehension using TRFs on the basis of EEG source reconstruction. We first propose a functional hyper-alignment method with an additive average method to reduce EEG noise. Then, we reconstruct neural sources within the brain based on the EEG signals to estimate TRFs from speech stimuli to source areas, and then investigate the brain networks in the neural source space on the basis of the community detection method. To evaluate TRF-based brain networks, EEG data were recorded in story listening tasks with normal speech and time-reversed speech. To obtain reliable structures of brain networks, we detected TRF-based communities from multiple scales. As a result, the proposed functional hyper-alignment method could effectively reduce the noise caused by individual settings in an EEG experiment and thus improve the accuracy of source reconstruction. The detected brain networks for normal speech comprehension were clearly distinctive from those for non-semantically driven (time-reversed speech) audio processing. Our result indicates that the proposed source TRFs can reflect the cognitive processing of spoken language and that the multi-scale community detection method is powerful for investigating brain networks.
Collapse
Affiliation(s)
- Di Zhou
- School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan
| | - Gaoyan Zhang
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
| | - Jianwu Dang
- School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
| | - Masashi Unoki
- School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan
| | - Xin Liu
- School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan
| |
Collapse
|
9
|
Liu Z, Si L, Wang T, Wang G. Brain connectivity changes of propofol-induced altered states of consciousness using High-Density EEG Source Estimation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:267-271. [PMID: 36085815 DOI: 10.1109/embc48229.2022.9871256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Through source estimation, high-density electroencephalogram (EEG) signals at scalp level can be converted into signals at cerebral cortex level, which helps to measure cortical activity during anesthesia induced changes in consciousness level to explore the mechanism. In this research, the high-density EEG of propofol-induced consciousness states alterations in 20 healthy adults were converted into cortical signals of 68 regions of interest (ROI), after alpha bandpass filtering, the pairwise orthogonal power envelope connectivity (PEC) was calculated. Then, due to the number of PECs was huge, the least absolute shrinkage and selection operator (LASSO) was used to select as few PECs as possible as the indicators to distinguish baseline (BS) and moderate sedation (MD) states. The results show that most PECs that can be used as indicators are related to ROI related to default mode network (DMN). At the same time, changes of thalamocortical connectivity and frontal-parietal connectivity could be observed, similar to the neuroimaging method of directly measuring cerebral cortical activity. By extracting the PEC as a classifier to classify the BS and MD States, the accuracy could reach more than 70%. Therefore, this method can not only reflect the mechanism of cortical activity alterations induced by anesthetics, but also provide a new idea for monitoring the depth of anesthesia in the future. Clinical Relevance - This shows that the high-density EEG of scalp level can be converted into cortical signals by source estimation, which is similar to the neuroimaging method of directly measuring cortical activity.
Collapse
|
10
|
Jaatela J, Aydogan DB, Nurmi T, Vallinoja J, Piitulainen H. Identification of Proprioceptive Thalamocortical Tracts in Children: Comparison of fMRI, MEG, and Manual Seeding of Probabilistic Tractography. Cereb Cortex 2022; 32:3736-3751. [PMID: 35040948 PMCID: PMC9433422 DOI: 10.1093/cercor/bhab444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 11/05/2021] [Accepted: 11/06/2021] [Indexed: 11/16/2022] Open
Abstract
Studying white matter connections with tractography is a promising approach to understand the development of different brain processes, such as proprioception. An emerging method is to use functional brain imaging to select the cortical seed points for tractography, which is considered to improve the functional relevance and validity of the studied connections. However, it is unknown whether different functional seeding methods affect the spatial and microstructural properties of the given white matter connection. Here, we compared functional magnetic resonance imaging, magnetoencephalography, and manual seeding of thalamocortical proprioceptive tracts for finger and ankle joints separately. We showed that all three seeding approaches resulted in robust thalamocortical tracts, even though there were significant differences in localization of the respective proprioceptive seed areas in the sensorimotor cortex, and in the microstructural properties of the obtained tracts. Our study shows that the selected functional or manual seeding approach might cause systematic biases to the studied thalamocortical tracts. This result may indicate that the obtained tracts represent different portions and features of the somatosensory system. Our findings highlight the challenges of studying proprioception in the developing brain and illustrate the need for using multimodal imaging to obtain a comprehensive view of the studied brain process.
Collapse
Affiliation(s)
- Julia Jaatela
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo FI-02150, Finland
| | - Dogu Baran Aydogan
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo FI-02150, Finland
- Department of Psychiatry, Helsinki University Hospital, Helsinki FI-00029, Finland
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio FI-70211, Finland
| | - Timo Nurmi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo FI-02150, Finland
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä FI-40014, Finland
| | - Jaakko Vallinoja
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo FI-02150, Finland
| | - Harri Piitulainen
- Address correspondence to Harri Piitulainen, associate professor, Harri Piitulainen, Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. BOX 35, FI-40014, Finland.
| |
Collapse
|
11
|
Taberna GA, Samogin J, Marino M, Mantini D. Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies. Brain Sci 2021; 11:brainsci11060741. [PMID: 34204868 PMCID: PMC8226780 DOI: 10.3390/brainsci11060741] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/23/2021] [Accepted: 05/31/2021] [Indexed: 11/16/2022] Open
Abstract
Recent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurate head model, defining how neural currents propagate from the cortex to the sensors placed over the scalp. To the best of our knowledge, no study has been conducted yet to systematically test to what extent head modeling accuracy impacts on EEG-RSN reconstruction. To address this question, we used 256-channel hdEEG data collected in a group of young healthy participants at rest. We first estimated functional connectivity in EEG-RSNs by means of band-limited power envelope correlations, using neural activity estimated with an optimized analysis workflow. Then, we defined a series of head models with different levels of complexity, specifically testing the effect of different electrode positioning techniques and head tissue segmentation methods. We observed that robust EEG-RSNs can be obtained using a realistic head model, and that inaccuracies due to head tissue segmentation impact on RSN reconstruction more than those due to electrode positioning. Additionally, we found that EEG-RSN robustness to head model variations had space and frequency specificity. Overall, our results may contribute to defining a benchmark for assessing the reliability of hdEEG functional connectivity measures.
Collapse
Affiliation(s)
- Gaia Amaranta Taberna
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (G.A.T.); (J.S.); (M.M.)
| | - Jessica Samogin
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (G.A.T.); (J.S.); (M.M.)
| | - Marco Marino
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (G.A.T.); (J.S.); (M.M.)
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126 Venice, Italy
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (G.A.T.); (J.S.); (M.M.)
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126 Venice, Italy
- Correspondence: ; Tel.: +32-16-37-29-09
| |
Collapse
|
12
|
San-Martin R, Johns E, Quispe Mamani G, Tavares G, Phillips NA, Fraga FJ. A method for diagnosis support of mild cognitive impairment through EEG rhythms source location during working memory tasks. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102499] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
|
13
|
Sadat-Nejad Y, Beheshti S. Efficient high resolution sLORETA in brain source localization. J Neural Eng 2021; 18. [DOI: 10.1088/1741-2552/abcc48] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 11/19/2020] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. Estimation of the source location within the brain from electroencephalography (EEG) and magnetoencephalography measures is a challenging task. Among the existing techniques in the field, which are known as brain imaging methods, standardized low-resolution brain electromagnetic tomography (sLORETA) is the most popular method due to its simplicity and high accuracy. However, in this work we illustrate that sLORETA is still noisy and the additive noise is causing the blurry image. The existing pre-fixed/manual thresholding process after sLORETA can partially take care of denoising. However, this ad-hoc theresholding can either remove so much of the desired data or leave much of the noise in the process. Manual correction to avoid such extreme cases can be time-consuming. The objective of this paper is to automate the denoising process in the form of adaptive thresholding. Approach. The proposed method, denoted by efficient high-resolution sLORETA (EHR-sLORETA), is based on minimizing the error between the desired denoised source and the source estimates. Main results. The approach is evaluated using synthetic EEG and real EEG data. spatial dispersion (SD), and mean square error (MSE) are used as metrics to provide the quantitative performance of the method. In addition, qualitative analysis of the method is provided for real EEG data. This proposed model demonstrates advantages over the existing methods in sense of accuracy and robustness with SD and MSE comparison. Significance. EHR-sLORETA could have a significant impact on clinical studies with source estimation task, as it improves the accuracy of source estimation and eliminates the need for manual thresholding.
Collapse
|
14
|
Buril J, Burilova P, Pokorna A, Balaz M. Use of high-density EEG in patients with Parkinson's disease treated with deep brain stimulation. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2020; 164:366-370. [DOI: 10.5507/bp.2020.042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 09/15/2020] [Indexed: 12/17/2022] Open
|
15
|
Kobler RJ, Sburlea AI, Mondini V, Hirata M, Müller-Putz GR. Distance- and speed-informed kinematics decoding improves M/EEG based upper-limb movement decoder accuracy. J Neural Eng 2020; 17:056027. [PMID: 33146148 DOI: 10.1088/1741-2552/abb3b3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE One of the main goals in brain-computer interface (BCI) research is the replacement or restoration of lost function in individuals with paralysis. One line of research investigates the inference of movement kinematics from brain activity during different volitional states. A growing number of electroencephalography (EEG) and magnetoencephalography (MEG) studies suggest that information about directional (e.g. velocity) and nondirectional (e.g. speed) movement kinematics is accessible noninvasively. We sought to assess if the neural information associated with both types of kinematics can be combined to improve the decoding accuracy. APPROACH In an offline analysis, we reanalyzed the data of two previous experiments containing the recordings of 34 healthy participants (15 EEG, 19 MEG). We decoded 2D movement trajectories from low-frequency M/EEG signals in executed and observed tracking movements, and compared the accuracy of an unscented Kalman filter (UKF) that explicitly modeled the nonlinear relation between directional and nondirectional kinematics to the accuracies of linear Kalman (KF) and Wiener filters which did not combine both types of kinematics. MAIN RESULTS At the group level, posterior-parietal and parieto-occipital (executed and observed movements) and sensorimotor areas (executed movements) encoded kinematic information. Correlations between the recorded position and velocity trajectories and the UKF decoded ones were on average 0.49 during executed and 0.36 during observed movements. Compared to the other filters, the UKF could achieve the best trade-off between maximizing the signal to noise ratio and minimizing the amplitude mismatch between the recorded and decoded trajectories. SIGNIFICANCE We present direct evidence that directional and nondirectional kinematic information is simultaneously detectable in low-frequency M/EEG signals. Moreover, combining directional and nondirectional kinematic information significantly improves the decoding accuracy upon a linear KF.
Collapse
Affiliation(s)
- Reinmar J Kobler
- Institute of Neural Engineering, Graz University of Technology, Graz 8010, Styria, Austria
| | | | | | | | | |
Collapse
|
16
|
Moffet EW, Verhagen R, Jones B, Findlay G, Juan E, Bugnon T, Mensen A, Aparicio MK, Maganti R, Struck AF, Tononi G, Boly M. Local Sleep Slow-Wave Activity Colocalizes With the Ictal Symptomatogenic Zone in a Patient With Reflex Epilepsy: A High-Density EEG Study. Front Syst Neurosci 2020; 14:549309. [PMID: 33192347 PMCID: PMC7609881 DOI: 10.3389/fnsys.2020.549309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 09/17/2020] [Indexed: 11/21/2022] Open
Abstract
Background: Slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep reflects synaptic potentiation during preceding wakefulness. Epileptic activity may induce increases in state-dependent SWA in human brains, therefore, localization of SWA may prove useful in the presurgical workup of epileptic patients. We analyzed high-density electroencephalography (HDEEG) data across vigilance states from a reflex epilepsy patient with a clearly localizable ictal symptomatogenic zone to provide a proof-of-concept for the testability of this hypothesis. Methods: Overnight HDEEG recordings were obtained in the patient during REM sleep, NREM sleep, wakefulness, and during a right facial motor seizure then compared to 10 controls. After preprocessing, SWA (i.e., delta power; 1–4 Hz) was calculated at each channel. Scalp level and source reconstruction analyses were computed. We assessed for statistical differences in maximum SWA between the patient and controls within REM sleep, NREM sleep, wakefulness, and seizure. Then, we completed an identical statistical comparison after first subtracting intrasubject REM sleep SWA from that of NREM sleep, wakefulness, and seizure SWA. Results: The topographical analysis revealed greater left hemispheric SWA in the patient vs. controls in all vigilance states except REM sleep (which showed a right hemispheric maximum). Source space analysis revealed increased SWA in the left inferior frontal cortex during NREM sleep and wakefulness. Ictal data displayed poor source-space localization. Comparing each state to REM sleep enhanced localization accuracy; the most clearly localizing results were observed when subtracting REM sleep from wakefulness. Conclusion: State-dependent SWA during NREM sleep and wakefulness may help to identify aspects of the potential epileptogenic zone. Future work in larger cohorts may assess the clinical value of sleep SWA to help presurgical planning.
Collapse
Affiliation(s)
- Eric W Moffet
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Ruben Verhagen
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States.,Department of Philosophy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Benjamin Jones
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Graham Findlay
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Elsa Juan
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States.,Department of Philosophy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Tom Bugnon
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Armand Mensen
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | | | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States
| | - Giulio Tononi
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Melanie Boly
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| |
Collapse
|
17
|
Design and Characterization of an EEG-Hat for Reliable EEG Measurements. MICROMACHINES 2020; 11:mi11070635. [PMID: 32605330 PMCID: PMC7407528 DOI: 10.3390/mi11070635] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 06/24/2020] [Accepted: 06/25/2020] [Indexed: 11/22/2022]
Abstract
In this study, a new hat-type electroencephalogram (EEG) device with candle-like microneedle electrodes (CMEs), called an EEG-Hat, was designed and fabricated. CMEs are dry EEG electrodes that can measure high-quality EEG signals without skin treatment or conductive gels. One of the challenges in the measurement of high-quality EEG signals is the fixation of electrodes to the skin, i.e., the design of a good EEG headset. The CMEs were able to achieve good contact with the scalp for heads of different sizes and shapes, and the EEG-Hat has a shutter mechanism to separate the hair and ensure good contact between the CMEs and the scalp. Simultaneous measurement of EEG signals from five measurement points on the scalp was successfully conducted after a simple and brief setup process. The EEG-Hat is expected to contribute to the advancement of EEG research.
Collapse
|
18
|
Shirazi SY, Huang HJ. More Reliable EEG Electrode Digitizing Methods Can Reduce Source Estimation Uncertainty, but Current Methods Already Accurately Identify Brodmann Areas. Front Neurosci 2019; 13:1159. [PMID: 31787866 PMCID: PMC6856631 DOI: 10.3389/fnins.2019.01159] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 10/14/2019] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG) and source estimation can be used to identify brain areas activated during a task, which could offer greater insight on cortical dynamics. Source estimation requires knowledge of the locations of the EEG electrodes. This could be provided with a template or obtained by digitizing the EEG electrode locations. Operator skill and inherent uncertainties of a digitizing system likely produce a range of digitization reliabilities, which could affect source estimation and the interpretation of the estimated source locations. Here, we compared the reliabilities of five digitizing methods (ultrasound, structured-light 3D scan, infrared 3D scan, motion capture probe, and motion capture) and determined the relationship between digitization reliability and source estimation uncertainty, assuming other contributors to source estimation uncertainty were constant. We digitized a mannequin head using each method five times and quantified the reliability and validity of each method. We created five hundred sets of electrode locations based on our reliability results and applied a dipole fitting algorithm (DIPFIT) to perform source estimation. The motion capture method, which recorded the locations of markers placed directly on the electrodes had the best reliability with an average electrode variability of 0.001 cm. Then, in order of decreasing reliability were the method using a digitizing probe in the motion capture system, an infrared 3D scanner, a structured-light 3D scanner, and an ultrasound digitization system. Unsurprisingly, uncertainty of the estimated source locations increased with greater variability of EEG electrode locations and less reliable digitizing systems. If EEG electrode location variability was ∽1 cm, a single source could shift by as much as 2 cm. To help translate these distances into practical terms, we quantified Brodmann area accuracy for each digitizing method and found that the average Brodmann area accuracy for all digitizing methods was >80%. Using a template of electrode locations reduced the Brodmann area accuracy to ∽50%. Overall, more reliable digitizing methods can reduce source estimation uncertainty, but the significance of the source estimation uncertainty depends on the desired spatial resolution. For accurate Brodmann area identification, any of the digitizing methods tested can be used confidently.
Collapse
Affiliation(s)
- Seyed Yahya Shirazi
- Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, United States
| | | |
Collapse
|
19
|
Marquetand J, Vannoni S, Carboni M, Li Hegner Y, Stier C, Braun C, Focke NK. Reliability of Magnetoencephalography and High-Density Electroencephalography Resting-State Functional Connectivity Metrics. Brain Connect 2019; 9:539-553. [PMID: 31115272 DOI: 10.1089/brain.2019.0662] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Resting-state connectivity, for example, based on magnetoencephalography (MEG) or electroencephalography (EEG), is a widely used method for characterizing brain networks and a promising imaging biomarker. However, there is no established standard as to which method, modality, and analysis variant is preferable and there is only limited knowledge on the reproducibility, an important prerequisite for clinical application. We conducted an MEG-/high-density (hd)-EEG-study on 22 young healthy adults, who were measured twice in a scan/rescan design after 7 ± 2 days. Reliability of resting-state (15 min, eyes-closed) connectivity in source space was calculated via intraclass correlation coefficient (ICC) in classical frequency bands (delta-gamma). We investigated the reliability of two commonly used connectivity metrics, namely the imaginary part of coherency and the weighted phase-lag index and the influence of frequency band, vigilance, and the number of trials. We found a strong increase of reliability with more trials and relatively mild effects of vigilance. Reliability was excellent in the alpha band for MEG, as well as hd-EEG (ICC >0.85); in the theta band, reliability was good for MEG and poor for EEG. Other frequency bands showed lower reliability, with delta band being the worst. Furthermore, we investigated the spatial reliability of resting-state connectivity in a vertex-based approach, which reached fair to good reliability (ICC up to 0.67) with 5 min of data. Our results indicate that excellent reliability of global connectivity is achievable in alpha band, and vertex-based connectivity was still fair to good. Moreover, electrophysiological resting-state studies could benefit from more data than used previously. MEG and hd-EEG were similar in their overall performance but showed frequency band-specific differences.
Collapse
Affiliation(s)
- Justus Marquetand
- Department of Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Silvia Vannoni
- Department of Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,MEG-Center, University of Tübingen, Tübingen, Germany.,Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neurosciences (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Margherita Carboni
- EEG and Epilepsy, Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland.,Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Yiwen Li Hegner
- Department of Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,MEG-Center, University of Tübingen, Tübingen, Germany
| | - Christina Stier
- Department of Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,Clinical Neurophysiology, Georg-August University Göttingen, Göttingen, Germany
| | | | - Niels K Focke
- Department of Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,Clinical Neurophysiology, Georg-August University Göttingen, Göttingen, Germany
| |
Collapse
|
20
|
Kreidenhuber R, De Tiège X, Rampp S. Presurgical Functional Cortical Mapping Using Electromagnetic Source Imaging. Front Neurol 2019; 10:628. [PMID: 31249552 PMCID: PMC6584755 DOI: 10.3389/fneur.2019.00628] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 05/28/2019] [Indexed: 02/03/2023] Open
Abstract
Preoperative localization of functionally eloquent cortex (functional cortical mapping) is common clinical practice in order to avoid or reduce postoperative morbidity. This review aims at providing a general overview of magnetoencephalography (MEG) and high-density electroencephalography (hdEEG) based methods and their clinical role as compared to common alternatives for functional cortical mapping of (1) verbal language function, (2) sensorimotor cortex, (3) memory, (4) visual, and (5) auditory cortex. We highlight strengths, weaknesses and limitations of these functional cortical mapping modalities based on findings in the recent literature. We also compare their performance relative to other non-invasive functional cortical mapping methods, such as functional Magnetic Resonance Imaging (fMRI), Transcranial Magnetic Stimulation (TMS), and to invasive methods like the intracarotid Amobarbital Test (WADA-Test) or intracranial investigations.
Collapse
Affiliation(s)
- Rudolf Kreidenhuber
- Department of Neurology, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria.,Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
| | - Xavier De Tiège
- Laboratoire de Cartographie Fonctionelle du Cerveau, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Stefan Rampp
- Department of Neurosurgery, University Hospital Erlangen, Erlangen, Germany.,Department of Neurosurgery, University Hospital Halle, Halle, Germany
| |
Collapse
|
21
|
Technical Description of Long-Term High-Density EEG Monitoring Using 128-Channel Cap Applied With a Conductive Paste. J Clin Neurophysiol 2019; 36:175-180. [PMID: 30694941 DOI: 10.1097/wnp.0000000000000557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE EEG is a common diagnostic tool to localize epileptic activity with excellent temporal resolution and, however, with relatively low spatial resolution. High-density EEG recording is limited in clinical practice, mainly because of electrode placement difficulties, need of high technical skills, and advanced equipment requirement. METHODS We described the technique of long-term EEG recording using a 128-channel neoprene cap placed with a dielectric paste in 7 patients with refractory epilepsy. We captured electrographic seizures in six of seven patients. The 128-channel EEG cap was well tolerated except for a mild headache. Daily impedance checks and reapplication of the high impedance leads maintained the recording with impedances below 10 kΩ. RESULTS Successful long-term recording of high-density EEG was able to capture seizures in six of seven patients. The time needed to apply the electrodes was approximately 1 hour and approximately 30 minutes daily for maintenance. The EEG source localization was obtained in six of seven patients, concordant within the sublobar region for both standard and high-density EEG recordings. Three patients reported a mild headache not leading to discontinuation of the recording. CONCLUSIONS In general, long-term high-density scalp EEG recording with a dielectric paste is well tolerated and allows capturing both interictal and ictal data for localization. This small sample does not show a significant advantage in terms of sublobar localization when high-density EEG source is compared with standard 10 to 20 placement as long as the subtemporal areas are recorded.
Collapse
|
22
|
Michel CM, Brunet D. EEG Source Imaging: A Practical Review of the Analysis Steps. Front Neurol 2019; 10:325. [PMID: 31019487 PMCID: PMC6458265 DOI: 10.3389/fneur.2019.00325] [Citation(s) in RCA: 282] [Impact Index Per Article: 56.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Accepted: 03/15/2019] [Indexed: 11/13/2022] Open
Abstract
The electroencephalogram (EEG) is one of the oldest technologies to measure neuronal activity of the human brain. It has its undisputed value in clinical diagnosis, particularly (but not exclusively) in the identification of epilepsy and sleep disorders and in the evaluation of dysfunctions in sensory transmission pathways. With the advancement of digital technologies, the analysis of EEG has moved from pure visual inspection of amplitude and frequency modulations over time to a comprehensive exploration of the temporal and spatial characteristics of the recorded signals. Today, EEG is accepted as a powerful tool to capture brain function with the unique advantage of measuring neuronal processes in the time frame in which these processes occur, namely in the sub-second range. However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity. High-density EEG systems combined with precise information of the head anatomy and sophisticated source localization algorithms now exist that convert the EEG to a true neuroimaging modality. With these tools in hand and with the fact that EEG still remains versatile, inexpensive and portable, electrical neuroimaging has become a widely used technology to study the functions of the pathological and healthy human brain. However, several steps are needed to pass from the recording of the EEG to 3-dimensional images of neuronal activity. This review explains these different steps and illustrates them in a comprehensive analysis pipeline integrated in a stand-alone freely available academic software: Cartool. The information about how the different steps are performed in Cartool is only meant as a suggestion. Other EEG source imaging software may apply similar or different approaches to the different steps.
Collapse
Affiliation(s)
- Christoph M. Michel
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging Lausanne-Geneva (CIBM), Geneva, Switzerland
| | - Denis Brunet
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging Lausanne-Geneva (CIBM), Geneva, Switzerland
| |
Collapse
|
23
|
Electroencephalography, magnetoencephalography and source localization: their value in epilepsy. Curr Opin Neurol 2019; 31:176-183. [PMID: 29432218 DOI: 10.1097/wco.0000000000000545] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
PURPOSE OF REVIEW Source localization of cerebral activity using electroencephalography (EEG) or magnetoencephalography (MEG) can reveal noninvasively the generators of the abnormal signals recorded in epilepsy, such as interictal epileptic discharges (IEDs) and seizures. Here, we review recent progress showcasing the usefulness of these techniques in treating patients with drug-resistant epilepsy. RECENT FINDINGS The source localization of IEDs by high-density EEG and MEG has now been proved in large patient cohorts to be accurate and clinically relevant, with positive and negative predictive values rivaling those of structural MRI. Localizing seizure onsets is an emerging technique that seems to perform similarly well to the localization of interictal spikes, although there remain questions regarding the processing of signals for reliable results. The localization of somatosensory cortex using EEG/MEG is well established. The localization of language cortex is less reliable, although progress has been made regarding hemispheric lateralization. Source localization is also able to reveal how epilepsy alters the dynamics of neuronal activity in the large-scale networks that underlie cerebral function. SUMMARY Given the high performance of EEG/MEG source localization, these tools should find a place similar to that of established techniques like MRI in the assessment of patients for epilepsy surgery.
Collapse
|
24
|
Seeber M, Cantonas LM, Hoevels M, Sesia T, Visser-Vandewalle V, Michel CM. Subcortical electrophysiological activity is detectable with high-density EEG source imaging. Nat Commun 2019; 10:753. [PMID: 30765707 PMCID: PMC6376013 DOI: 10.1038/s41467-019-08725-w] [Citation(s) in RCA: 153] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 01/28/2019] [Indexed: 11/09/2022] Open
Abstract
Subcortical neuronal activity is highly relevant for mediating communication in large-scale brain networks. While electroencephalographic (EEG) recordings provide appropriate temporal resolution and coverage to study whole brain dynamics, the feasibility to detect subcortical signals is a matter of debate. Here, we investigate if scalp EEG can detect and correctly localize signals recorded with intracranial electrodes placed in the centromedial thalamus, and in the nucleus accumbens. Externalization of deep brain stimulation (DBS) electrodes, placed in these regions, provides the unique opportunity to record subcortical activity simultaneously with high-density (256 channel) scalp EEG. In three patients during rest with eyes closed, we found significant correlation between alpha envelopes derived from intracranial and EEG source reconstructed signals. Highest correlation was found for source signals in close proximity to the actual recording sites, given by the DBS electrode locations. Therefore, we present direct evidence that scalp EEG indeed can sense subcortical signals. Electroencephalography (EEG) allows the measurement of electrical signals associated with brain activity, but it is unclear if EEG can accurately measure subcortical activity. Here, the authors show that source dynamics, reconstructed from scalp EEG, correlate with activity recorded from human thalamus and nucleus accumbens.
Collapse
Affiliation(s)
- Martin Seeber
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, Campus Biotech, University of Geneva, 1201, Geneva, Switzerland
| | - Lucia-Manuela Cantonas
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, Campus Biotech, University of Geneva, 1201, Geneva, Switzerland
| | - Mauritius Hoevels
- Department of Stereotactic and Functional Neurosurgery, University of Cologne, 50937, Cologne, Germany
| | - Thibaut Sesia
- Department of Stereotactic and Functional Neurosurgery, University of Cologne, 50937, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, University of Cologne, 50937, Cologne, Germany
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, Campus Biotech, University of Geneva, 1201, Geneva, Switzerland. .,Center for Biomedical Imaging (CIBM), Lausanne and Geneva, 1015 Lausanne, Switzerland.
| |
Collapse
|
25
|
|
26
|
|
27
|
|
28
|
Stropahl M, Bauer AKR, Debener S, Bleichner MG. Source-Modeling Auditory Processes of EEG Data Using EEGLAB and Brainstorm. Front Neurosci 2018; 12:309. [PMID: 29867321 PMCID: PMC5952032 DOI: 10.3389/fnins.2018.00309] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 04/20/2018] [Indexed: 11/25/2022] Open
Abstract
Electroencephalography (EEG) source localization approaches are often used to disentangle the spatial patterns mixed up in scalp EEG recordings. However, approaches differ substantially between experiments, may be strongly parameter-dependent, and results are not necessarily meaningful. In this paper we provide a pipeline for EEG source estimation, from raw EEG data pre-processing using EEGLAB functions up to source-level analysis as implemented in Brainstorm. The pipeline is tested using a data set of 10 individuals performing an auditory attention task. The analysis approach estimates sources of 64-channel EEG data without the prerequisite of individual anatomies or individually digitized sensor positions. First, we show advanced EEG pre-processing using EEGLAB, which includes artifact attenuation using independent component analysis (ICA). ICA is a linear decomposition technique that aims to reveal the underlying statistical sources of mixed signals and is further a powerful tool to attenuate stereotypical artifacts (e.g., eye movements or heartbeat). Data submitted to ICA are pre-processed to facilitate good-quality decompositions. Aiming toward an objective approach on component identification, the semi-automatic CORRMAP algorithm is applied for the identification of components representing prominent and stereotypic artifacts. Second, we present a step-wise approach to estimate active sources of auditory cortex event-related processing, on a single subject level. The presented approach assumes that no individual anatomy is available and therefore the default anatomy ICBM152, as implemented in Brainstorm, is used for all individuals. Individual noise modeling in this dataset is based on the pre-stimulus baseline period. For EEG source modeling we use the OpenMEEG algorithm as the underlying forward model based on the symmetric Boundary Element Method (BEM). We then apply the method of dynamical statistical parametric mapping (dSPM) to obtain physiologically plausible EEG source estimates. Finally, we show how to perform group level analysis in the time domain on anatomically defined regions of interest (auditory scout). The proposed pipeline needs to be tailored to the specific datasets and paradigms. However, the straightforward combination of EEGLAB and Brainstorm analysis tools may be of interest to others performing EEG source localization.
Collapse
Affiliation(s)
- Maren Stropahl
- Neuropsychology Lab, Department of Psychology, European Medical School, University of Oldenburg, Oldenburg, Germany
| | - Anna-Katharina R Bauer
- Neuropsychology Lab, Department of Psychology, European Medical School, University of Oldenburg, Oldenburg, Germany
| | - Stefan Debener
- Neuropsychology Lab, Department of Psychology, European Medical School, University of Oldenburg, Oldenburg, Germany.,Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany
| | - Martin G Bleichner
- Neuropsychology Lab, Department of Psychology, European Medical School, University of Oldenburg, Oldenburg, Germany.,Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany
| |
Collapse
|
29
|
Presurgical electromagnetic functional brain mapping in refractory focal epilepsy. ZEITSCHRIFT FUR EPILEPTOLOGIE 2018. [DOI: 10.1007/s10309-018-0189-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
30
|
De Martin E, Duran D, Ghielmetti F, Visani E, Aquino D, Marchetti M, Sebastiano DR, Cusumano D, Bruzzone MG, Panzica F, Fariselli L. Integration of Functional Magnetic Resonance Imaging and Magnetoencephalography Functional Maps Into a CyberKnife Planning System: Feasibility Study for Motor Activity Localization and Dose Planning. World Neurosurg 2017; 108:756-762. [DOI: 10.1016/j.wneu.2017.08.187] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 08/28/2017] [Accepted: 08/30/2017] [Indexed: 12/31/2022]
|
31
|
Chowdhury RA, Pellegrino G, Aydin Ü, Lina JM, Dubeau F, Kobayashi E, Grova C. Reproducibility of EEG-MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy. Hum Brain Mapp 2017; 39:880-901. [PMID: 29164737 DOI: 10.1002/hbm.23889] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 11/03/2017] [Accepted: 11/07/2017] [Indexed: 11/06/2022] Open
Abstract
Fusion of electroencephalography (EEG) and magnetoencephalography (MEG) data using maximum entropy on the mean method (MEM-fusion) takes advantage of the complementarities between EEG and MEG to improve localization accuracy. Simulation studies demonstrated MEM-fusion to be robust especially in noisy conditions such as single spike source localizations (SSSL). Our objective was to assess the reliability of SSSL using MEM-fusion on clinical data. We proposed to cluster SSSL results to find the most reliable and consistent source map from the reconstructed sources, the so-called consensus map. Thirty-four types of interictal epileptic discharges (IEDs) were analyzed from 26 patients with well-defined epileptogenic focus. SSSLs were performed on EEG, MEG, and fusion data and consensus maps were estimated using hierarchical clustering. Qualitative (spike-to-spike reproducibility rate, SSR) and quantitative (localization error and spatial dispersion) assessments were performed using the epileptogenic focus as clinical reference. Fusion SSSL provided significantly better results than EEG or MEG alone. Fusion found at least one cluster concordant with the clinical reference in all cases. This concordant cluster was always the one involving the highest number of spikes. Fusion yielded highest reproducibility (SSR EEG = 55%, MEG = 71%, fusion = 90%) and lowest localization error. Also, using only few channels from either modality (21EEG + 272MEG or 54EEG + 25MEG) was sufficient to reach accurate fusion. MEM-fusion with consensus map approach provides an objective way of finding the most reliable and concordant generators of IEDs. We, therefore, suggest the pertinence of SSSL using MEM-fusion as a valuable clinical tool for presurgical evaluation of epilepsy.
Collapse
Affiliation(s)
- Rasheda Arman Chowdhury
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Québec, Canada
| | | | - Ümit Aydin
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montreal, Québec, Canada
| | - Jean-Marc Lina
- Ecole de Technologie Supérieure, Montréal, Québec, Canada.,Centre de Recherches Mathématiques, Université de Montréal, Montréal, Québec, Canada
| | - François Dubeau
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Eliane Kobayashi
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Québec, Canada.,Centre de Recherches Mathématiques, Université de Montréal, Montréal, Québec, Canada.,Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.,Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montreal, Québec, Canada
| |
Collapse
|
32
|
Berchio C, Piguet C, Michel CM, Cordera P, Rihs TA, Dayer AG, Aubry JM. Dysfunctional gaze processing in bipolar disorder. NEUROIMAGE-CLINICAL 2017; 16:545-556. [PMID: 28971006 PMCID: PMC5608173 DOI: 10.1016/j.nicl.2017.09.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 09/01/2017] [Accepted: 09/05/2017] [Indexed: 01/15/2023]
Abstract
Gaze conveys emotional information, and humans present sensitivity to its direction from the earliest days of life. Bipolar disorder is a disease characterized by fluctuating states of emotional and cognitive dysregulation. To explore the role of attentional control on face processing in bipolar patients (BP) we used gaze direction as an emotion modulation parameter in a two-back Working Memory (WM) task while high-density EEG data were acquired. Since gaze direction influences emotional attributions to faces with neutral expressions as well, we presented neutral faces with direct and averted gaze. Nineteen euthymic BP and a sample of age- and gender-matched controls were examined. In BP we observed diminished P200 and augmented P300 evoked responses, differentially modulated by non-repeated or repeated faces, as well as by gaze direction. BP showed a reduced P200 amplitude, significantly stronger for faces with direct gaze than averted gaze. Source localization of P200 indicated decreased activity in sensory-motor regions and frontal areas suggestive of abnormal affective processing of neutral faces. The present study provides neurophysiological evidence for abnormal gaze processing in BP and suggests dysfunctional processing of direct eye contact as a prominent characteristic of bipolar disorder. This ERP study identified abnormalities in gaze processing in bipolar patients. We observed functional anomalies in the P200 and P300 evoked responses. BP showed a strong suppression of the P200 for faces with direct gaze. Source localization indicated decreased activity in sensory-motor regions.
Collapse
Affiliation(s)
- Cristina Berchio
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland.,Department of Mental Health and Psychiatry, Service of Psychiatric Specialties, Mood Disorders Unit University Hospitals of Geneva, Switzerland
| | - Camille Piguet
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland.,Department of Mental Health and Psychiatry, Service of Psychiatric Specialties, Mood Disorders Unit University Hospitals of Geneva, Switzerland
| | - Christoph M Michel
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland.,Biomedical Imaging Center (CIBM) Lausanne, Geneva, Switzerland
| | - Paolo Cordera
- Department of Mental Health and Psychiatry, Service of Psychiatric Specialties, Mood Disorders Unit University Hospitals of Geneva, Switzerland
| | - Tonia A Rihs
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
| | - Alexandre G Dayer
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland.,Department of Mental Health and Psychiatry, Service of Psychiatric Specialties, Mood Disorders Unit University Hospitals of Geneva, Switzerland.,Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Jean-Michel Aubry
- Department of Mental Health and Psychiatry, Service of Psychiatric Specialties, Mood Disorders Unit University Hospitals of Geneva, Switzerland.,Department of Psychiatry, University of Geneva, Geneva, Switzerland
| |
Collapse
|
33
|
Hedrich T, Pellegrino G, Kobayashi E, Lina JM, Grova C. Comparison of the spatial resolution of source imaging techniques in high-density EEG and MEG. Neuroimage 2017; 157:531-544. [PMID: 28619655 DOI: 10.1016/j.neuroimage.2017.06.022] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Revised: 04/29/2017] [Accepted: 06/09/2017] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND The present study aims at evaluating and comparing electrical and magnetic distributed source imaging methods applied to high-density Electroencephalography (hdEEG) and Magnetoencephalography (MEG) data. We used resolution matrices to characterize spatial resolution properties of Minimum Norm Estimate (MNE), dynamic Statistical Parametric Mapping (dSPM), standardized Low-Resolution Electromagnetic Tomography (sLORETA) and coherent Maximum Entropy on the Mean (cMEM, an entropy-based technique). The resolution matrix provides information of the Point Spread Functions (PSF) and of the Crosstalk functions (CT), this latter being also called source leakage, as it reflects the influence of a source on its neighbors. METHODS The spatial resolution of the inverse operators was first evaluated theoretically and then with real data acquired using electrical median nerve stimulation on five healthy participants. We evaluated the Dipole Localization Error (DLE) and the Spatial Dispersion (SD) of each PSF and CT map. RESULTS cMEM showed the smallest spatial spread (SD) for both PSF and CT maps, whereas localization errors (DLE) were similar for all methods. Whereas cMEM SD values were lower in MEG compared to hdEEG, the other methods slightly favored hdEEG over MEG. In real data, cMEM provided similar localization error and significantly less spatial spread than other methods for both MEG and hdEEG. Whereas both MEG and hdEEG provided very accurate localizations, all the source imaging methods actually performed better in MEG compared to hdEEG according to all evaluation metrics, probably due to the higher signal-to-noise ratio of the data in MEG. CONCLUSION Our overall results show that all investigated methods provide similar localization errors, suggesting very accurate localization for both MEG and hdEEG when similar number of sensors are considered for both modalities. Intrinsic properties of source imaging methods as well as their behavior for well-controlled tasks, suggest an overall better performance of cMEM in regards to spatial resolution and spatial leakage for both hdEEG and MEG. This indicates that cMEM would be a good candidate for studying source localization of focal and extended generators as well as functional connectivity studies.
Collapse
Affiliation(s)
- T Hedrich
- Multimodal Functional Imaging Lab, Biomedical Engineering Dpt., McGill University, Montreal, Canada.
| | - G Pellegrino
- Multimodal Functional Imaging Lab, Biomedical Engineering Dpt., McGill University, Montreal, Canada; Neurology and Neurosurgery Department, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada; San Camillo Hospital IRCCS, Venice, Italy
| | - E Kobayashi
- Neurology and Neurosurgery Department, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - J M Lina
- Département de Génie Électrique, École de Technologie Supérieure, Canada; Centre de recherches mathémathiques, Université de Montréal, Montreal, Canada; Center for Advanced Research on Sleep Medecine (CEAMS), hôpital du Sacré-Coeur, Montreal, Canada
| | - C Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering Dpt., McGill University, Montreal, Canada; Neurology and Neurosurgery Department, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada; Physics Dpt., PERFORM Centre, Concordia University, Canada; Centre de recherches mathémathiques, Université de Montréal, Montreal, Canada
| |
Collapse
|
34
|
Magnetoencephalography for brain electrophysiology and imaging. Nat Neurosci 2017; 20:327-339. [DOI: 10.1038/nn.4504] [Citation(s) in RCA: 418] [Impact Index Per Article: 59.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Accepted: 01/17/2017] [Indexed: 12/18/2022]
|
35
|
Mill RD, Bagic A, Bostan A, Schneider W, Cole MW. Empirical validation of directed functional connectivity. Neuroimage 2017; 146:275-287. [PMID: 27856312 PMCID: PMC5321749 DOI: 10.1016/j.neuroimage.2016.11.037] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 10/18/2016] [Accepted: 11/14/2016] [Indexed: 11/15/2022] Open
Abstract
Mapping directions of influence in the human brain connectome represents the next phase in understanding its functional architecture. However, a host of methodological uncertainties have impeded the application of directed connectivity methods, which have primarily been validated via "ground truth" connectivity patterns embedded in simulated functional MRI (fMRI) and magneto-/electro-encephalography (MEG/EEG) datasets. Such simulations rely on many generative assumptions, and we hence utilized a different strategy involving empirical data in which a ground truth directed connectivity pattern could be anticipated with confidence. Specifically, we exploited the established "sensory reactivation" effect in episodic memory, in which retrieval of sensory information reactivates regions involved in perceiving that sensory modality. Subjects performed a paired associate task in separate fMRI and MEG sessions, in which a ground truth reversal in directed connectivity between auditory and visual sensory regions was instantiated across task conditions. This directed connectivity reversal was successfully recovered across different algorithms, including Granger causality and Bayes network (IMAGES) approaches, and across fMRI ("raw" and deconvolved) and source-modeled MEG. These results extend simulation studies of directed connectivity, and offer practical guidelines for the use of such methods in clarifying causal mechanisms of neural processing.
Collapse
Affiliation(s)
- Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, USA
| | - Anto Bagic
- Department of Neurology, University of Pittsburgh, USA
| | - Andreea Bostan
- Center for Neuroscience, University of Pittsburgh, USA; Center for the Neural Basis of Cognition, University of Pittsburgh, USA
| | - Walter Schneider
- Center for Neuroscience, University of Pittsburgh, USA; Center for the Neural Basis of Cognition, University of Pittsburgh, USA; Department of Psychology, University of Pittsburgh, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, USA; Center for Neuroscience, University of Pittsburgh, USA; Center for the Neural Basis of Cognition, University of Pittsburgh, USA
| |
Collapse
|
36
|
Mill RD, Ito T, Cole MW. From connectome to cognition: The search for mechanism in human functional brain networks. Neuroimage 2017; 160:124-139. [PMID: 28131891 DOI: 10.1016/j.neuroimage.2017.01.060] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 12/17/2016] [Accepted: 01/25/2017] [Indexed: 11/30/2022] Open
Abstract
Recent developments in functional connectivity research have expanded the scope of human neuroimaging, from identifying changes in regional activation amplitudes to detailed mapping of large-scale brain networks. However, linking network processes to a clear role in cognition demands advances in the theoretical frameworks, algorithms, and experimental approaches applied. This would help evolve the field from a descriptive to an explanatory state, by targeting network interactions that can mechanistically account for cognitive effects. In the present review, we provide an explicit framework to aid this search for "network mechanisms", which anchors recent methodological advances in functional connectivity estimation to a renewed emphasis on careful experimental design. We emphasize how this framework can address specific questions in network neuroscience. These span ambiguity over the cognitive relevance of resting-state networks, how to characterize task-evoked and spontaneous network dynamics, how to identify directed or "effective" connections, and how to apply multivariate pattern analysis at the network level. In parallel, we apply the framework to highlight the mechanistic interaction of network components that remain "stable" across task domains and more "flexible" components associated with on-task reconfiguration. By emphasizing the need to structure the use of diverse analytic approaches with sound experimentation, our framework promotes an explanatory mapping between the workings of the cognitive mind and the large-scale network mechanisms of the human brain.
Collapse
Affiliation(s)
- Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07120, USA.
| | - Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07120, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07120, USA
| |
Collapse
|
37
|
Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data. Neuroimage 2016; 143:175-195. [DOI: 10.1016/j.neuroimage.2016.08.044] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Revised: 08/18/2016] [Accepted: 08/20/2016] [Indexed: 11/23/2022] Open
|
38
|
Hur YJ, Kim HD. Predictive role of brain connectivity for resective surgery in Lennox–Gastaut syndrome. Clin Neurophysiol 2016; 127:2862-2868. [DOI: 10.1016/j.clinph.2016.05.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Revised: 04/14/2016] [Accepted: 05/09/2016] [Indexed: 01/05/2023]
|
39
|
Siems M, Pape AA, Hipp JF, Siegel M. Measuring the cortical correlation structure of spontaneous oscillatory activity with EEG and MEG. Neuroimage 2016; 129:345-355. [DOI: 10.1016/j.neuroimage.2016.01.055] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Revised: 01/19/2016] [Accepted: 01/23/2016] [Indexed: 10/22/2022] Open
|
40
|
A prolonged maturational time course in brain development for cortical processing of temporal modulations. Clin Neurophysiol 2015; 127:994-998. [PMID: 26480832 DOI: 10.1016/j.clinph.2015.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 08/27/2015] [Accepted: 09/01/2015] [Indexed: 11/21/2022]
|
41
|
From bird to sparrow: Learning-induced modulations in fine-grained semantic discrimination. Neuroimage 2015; 118:163-73. [DOI: 10.1016/j.neuroimage.2015.05.091] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Revised: 05/02/2015] [Accepted: 05/25/2015] [Indexed: 11/23/2022] Open
|
42
|
Klamer S, Rona S, Elshahabi A, Lerche H, Braun C, Honegger J, Erb M, Focke NK. Multimodal effective connectivity analysis reveals seizure focus and propagation in musicogenic epilepsy. Neuroimage 2015; 113:70-7. [PMID: 25797835 DOI: 10.1016/j.neuroimage.2015.03.027] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 03/08/2015] [Accepted: 03/12/2015] [Indexed: 11/20/2022] Open
Abstract
Dynamic causal modeling (DCM) is a method to non-invasively assess effective connectivity between brain regions. 'Musicogenic epilepsy' is a rare reflex epilepsy syndrome in which seizures can be elicited by musical stimuli and thus represents a unique possibility to investigate complex human brain networks and test connectivity analysis tools. We investigated effective connectivity in a case of musicogenic epilepsy using DCM for fMRI, high-density (hd-) EEG and MEG and validated results with intracranial EEG recordings. A patient with musicogenic seizures was examined using hd-EEG/fMRI and simultaneous '256-channel hd-EEG'/'whole head MEG' to characterize the epileptogenic focus and propagation effects using source analysis techniques and DCM. Results were validated with invasive EEG recordings. We recorded one seizure with hd-EEG/fMRI and four auras with hd-EEG/MEG. During the seizures, increases of activity could be observed in the right mesial temporal region as well as bilateral mesial frontal regions. Effective connectivity analysis of fMRI and hd-EEG/MEG indicated that right mesial temporal neuronal activity drives changes in the frontal areas consistently in all three modalities, which was confirmed by the results of invasive EEG recordings. Seizures thus seem to originate in the right mesial temporal lobe and propagate to mesial frontal regions. Using DCM for fMRI, hd-EEG and MEG we were able to correctly localize focus and propagation of epileptic activity and thereby characterize the underlying epileptic network in a patient with musicogenic epilepsy. The concordance between all three functional modalities validated by invasive monitoring is noteworthy, both for epileptic activity spread as well as for effective connectivity analysis in general.
Collapse
Affiliation(s)
- Silke Klamer
- Department of Neurology and Epileptology, Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany; MEG Center, University of Tuebingen, Tuebingen, Germany.
| | - Sabine Rona
- Department of Neurosurgery, University of Tuebingen, Tuebingen, Germany
| | - Adham Elshahabi
- Department of Neurology and Epileptology, Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany; MEG Center, University of Tuebingen, Tuebingen, Germany
| | - Holger Lerche
- Department of Neurology and Epileptology, Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany; Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany
| | - Christoph Braun
- MEG Center, University of Tuebingen, Tuebingen, Germany; Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany; CIMeC, Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Jürgen Honegger
- Department of Neurosurgery, University of Tuebingen, Tuebingen, Germany
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Niels K Focke
- Department of Neurology and Epileptology, Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany; Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany
| |
Collapse
|