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Taleei T, Nazem-Zadeh MR, Amiri M, Keliris GA. EEG-based functional connectivity for tactile roughness discrimination. Cogn Neurodyn 2023; 17:921-940. [PMID: 37522039 PMCID: PMC10374498 DOI: 10.1007/s11571-022-09876-1] [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: 02/03/2022] [Revised: 07/26/2022] [Accepted: 08/13/2022] [Indexed: 11/03/2022] Open
Abstract
Tactile sensation and perception involve cooperation between different parts of the brain. Roughness discrimination is an important phase of texture recognition. In this study, we investigated how different roughness levels would influence the brain network characteristics. We recorded EEG signals from nine right-handed healthy subjects who underwent touching three surfaces with different levels of roughness. The experiment was separately repeated in 108 trials for each hand for both static and dynamic touch. For estimation of the functional connectivity between brain regions, the phase lag index method was employed. Frequency-specific connectivity patterns were observed in the ipsilateral and contralateral hemispheres to the hand of interest, for delta, theta, alpha, and beta frequency bands under the study. A number of connections were identified to be in charge of discrimination between surfaces in both alpha and beta frequency bands for the left hand in static touch and for the right hand in dynamic touch. In addition, common connections were determined in both hands for all three roughness in alpha band for static touch and in theta band for dynamic touch. The common connections were identified for the smooth surface in beta band for static touch and in delta and alpha bands for dynamic touch. As observed for static touch in alpha band and for dynamic touch in theta band, the number of common connections between the two hands was decreased by increasing the surface roughness. The results of this research would extend the current knowledge about tactile information processing in the brain. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09876-1.
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Affiliation(s)
- Tahereh Taleei
- Medical Biology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mohammad-Reza Nazem-Zadeh
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mahmood Amiri
- Medical Technology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran
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2
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Bagherzadeh S, Maghooli K, Shalbaf A, Maghsoudi A. Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals. Cogn Neurodyn 2022; 16:1087-1106. [PMID: 36237402 PMCID: PMC9508317 DOI: 10.1007/s11571-021-09756-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/29/2021] [Accepted: 11/14/2021] [Indexed: 12/26/2022] Open
Abstract
Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Electroencephalogram (EEG) signal is presented. After preprocessing EEG signals, the relationships among 32 channels of EEG in the form of effective brain connectivity analysis which represents information flow between regions are computed by direct Directed Transfer Function (dDTF) method which yields a 32*32 image. Then, these constructed images from EEG signals for each subject were fed as input to four versions of pre-trained CNN models, AlexNet, ResNet-50, Inception-v3 and VGG-19 and the parameters of these models are fine-tuned, independently. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals in frequency bands. The efficiency of the proposed approach is evaluated on MAHNOB-HCI and DEAP databases. The experiments for classifying five emotional states show that the ResNet-50 applied on dDTF images in alpha band achieves best results due to specific architecture which captures the brain connectivity, efficiently. The accuracy and F1-score values for MAHNOB-HCI were obtained 99.41, 99.42 and for DEAP databases, 98.17, and 98.23. Newly proposed model is capable of effectively analyzing the brain function using information flow from multichannel EEG signals using effective connectivity measure of dDTF and ResNet-50.
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Affiliation(s)
- Sara Bagherzadeh
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arash Maghsoudi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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3
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Endemann C, Krause BM, Nourski KV, Banks MI, Veen BV. Multivariate autoregressive model estimation for high dimensional intracranial electrophysiological data. Neuroimage 2022; 254:119057. [PMID: 35354095 PMCID: PMC9360562 DOI: 10.1016/j.neuroimage.2022.119057] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 02/04/2022] [Accepted: 03/03/2022] [Indexed: 12/01/2022] Open
Abstract
Fundamental to elucidating the functional organization of the brain is the assessment of causal interactions between different brain regions. Multivariate autoregressive (MVAR) modeling techniques applied to multisite electrophysiological recordings are a promising avenue for identifying such causal links. They estimate the degree to which past activity in one or more brain regions is predictive of another region's present activity, while simultaneously accounting for the mediating effects of other regions. Including as many mediating variables as possible in the model has the benefit of drastically reducing the odds of detecting spurious causal connectivity. However, effective bounds on the number of MVAR model coefficients that can be estimated reliably from limited data make exploiting the potential of MVAR models challenging for even modest numbers of recording sites. Here, we utilize well-established dimensionality-reduction techniques to fit MVAR models to human intracranial data from ∼100 - 200 recording sites spanning dozens of anatomically and functionally distinct cortical regions. First, we show that high dimensional MVAR models can be successfully estimated from long segments of data and yield plausible connectivity profiles. Next, we use these models to generate synthetic data with known ground-truth connectivity to explore the utility of applying principal component analysis and group least absolute shrinkage and selection operator (gLASSO) to reduce the number of parameters (connections) during model fitting to shorter data segments. We show that gLASSO is highly effective for recovering ground-truth connectivity in the limited data regime, capturing important features of connectivity for high-dimensional models with as little as 10 seconds of data. The methods presented here have broad applicability to the analysis of high-dimensional time series data in neuroscience, facilitating the elucidation of the neural basis of sensation, cognition, and arousal.
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Affiliation(s)
| | - Bryan M Krause
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Kirill V Nourski
- Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, USA; Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA 52242, USA
| | - Matthew I Banks
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA; Department of Neuroscience, University of Wisconsin, Madison, WI, USA.
| | - Barry Van Veen
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, USA
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4
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Wimmer M, Kostoglou K, Müller-Putz GR. Measuring Spinal Cord Potentials and Cortico-Spinal Interactions After Wrist Movements Induced by Neuromuscular Electrical Stimulation. Front Hum Neurosci 2022; 16:858873. [PMID: 35360288 PMCID: PMC8962396 DOI: 10.3389/fnhum.2022.858873] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
Electroencephalographic (EEG) correlates of movement have been studied extensively over many years. In the present work, we focus on investigating neural correlates that originate from the spine and study their connectivity to corresponding signals from the sensorimotor cortex using multivariate autoregressive (MVAR) models. To study cortico-spinal interactions, we simultaneously measured spinal cord potentials (SCPs) and somatosensory evoked potentials (SEPs) of wrist movements elicited by neuromuscular electrical stimulation. We identified directional connections between spine and cortex during both the extension and flexion of the wrist using only non-invasive recording techniques. Our connectivity estimation results are in alignment with various studies investigating correlates of movement, i.e., we found the contralateral side of the sensorimotor cortex to be the main sink of information as well as the spine to be the main source of it. Both types of movement could also be clearly identified in the time-domain signals.
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Affiliation(s)
- Michael Wimmer
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Kyriaki Kostoglou
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Gernot R. Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- BioTechMed-Graz, Graz, Austria
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5
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Statistical Significance Assessment of Phase Synchrony in the Presence of Background Couplings: An ECoG Study. Brain Topogr 2019; 32:882-896. [PMID: 31129754 DOI: 10.1007/s10548-019-00718-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 05/13/2019] [Indexed: 01/03/2023]
Abstract
Statistical significance testing is a necessary step in connectivity analysis. Several statistical test methods have been employed to assess the significance of functional connectivity, but the performance of these methods has not been thoroughly evaluated. In addition, the effects of the intrinsic brain connectivity and background couplings on performance of statistical test methods in task-based studies have not been investigated yet. The background couplings may exist independent of cognitive state and can be observed on both pre- and post-stimulus time intervals. The background couplings may be falsely detected by a statistical test as task-related connections, which can mislead interpretations of the task-related functional networks. The aim of this study was to investigate the relative performance of four commonly used non-parametric statistical test methods-surrogate, demeaned surrogate, bootstrap resampling, and Monte Carlo permutation methods-in the presence of background couplings and noise, with different signal-to-noise ratios (SNRs). Using simulated electrocorticographic (ECoG) datasets and phase locking value (PLV) as a measure of functional connectivity, we evaluated the performances of the statistical test methods utilizing sensitivity, specificity, accuracy, and receiver operating curve (ROC) analysis. Furthermore, we calculated optimal p values for each statistical test method using the ROC analysis, and found that the optimal p values were increased by decreasing the SNR. We also found that the optimal p value of the bootstrap resampling was greater than that of other methods. Our results from the simulation datasets and a real ECoG dataset, as an illustrative case report, revealed that the bootstrap resampling is the most efficient non-parametric statistical test for identifying the significant PLV of ECoG data, especially in the presence of background couplings.
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6
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Casimo K, Madhyastha TM, Ko AL, Brown AB, Grassia F, Ojemann JG, Weaver KE. Spontaneous Variation in Electrocorticographic Resting-State Connectivity. Brain Connect 2019; 9:488-499. [PMID: 31002014 DOI: 10.1089/brain.2018.0596] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Prior studies using functional magnetic resonance imaging, electroencephalography, and magnetoencephalography have observed both structured patterns in resting-state functional connectivity and spontaneous longitudinal variation in connectivity patterns independent of a task. In this first study using electrocorticography (ECoG), we characterized spontaneous, intersession variation in resting-state functional connectivity not linked to a task. We evaluated pairwise connectivity between electrodes using three measures (phase locking value [PLV], amplitude correlation, and coherence) for six canonical frequency bands, capturing different characteristics of time-evolving signals. We grouped electrodes into 10 functional regions and used intraclass correlation (ICC) to estimate pairwise longitudinal stability. We found that stronger PLV (PLV ≥0.4) in theta through gamma bands and strong correlation in all bands (R2's ≥0.6) are linked to substantial stability (ICC ≥0.6), but that stability does not imply strong phase locking or amplitude correlation. There was no notable link between strong coherence and high ICC. All within-region PLVs are markedly stable across frequencies. In addition, we highlight interaction patterns across several regions: parahippocampal/entorhinal cortex is characterized by stable, weak functional connectivity except self-connections. Dorsolateral prefrontal cortex connectivity is weak and unstable, except self-connections. Inferior parietal lobule has little stability despite narrow connectivity bounds. We confirm prior studies linking functional connectivity strength and intersession variability, extending into higher frequencies than other modalities, with greater spatial specificity than scalp electrophysiology. We suggest further studies quantitatively compare ECoG to other modalities and/or use these findings as a baseline to capture functional connectivity and dynamics linked to perturbations with a task or disease state.
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Affiliation(s)
- Kaitlyn Casimo
- 1 Graduate Program in Neuroscience, Center for Neurotechnology, University of Washington, Seattle, Washington
| | - Tara M Madhyastha
- 2 Integrated Brain Imaging Center, Department of Radiology, University of Washington, Seattle, Washington
| | - Andrew L Ko
- 3 Department of Neurological Surgery, Graduate Program in Neuroscience, University of Washington, Seattle, Washington
| | - Alainna B Brown
- 4 Graduate Program in Neuroscience, School of Medicine, University of Washington, Seattle, Washington
| | - Fabio Grassia
- 5 Department of Neurosurgery, University of Milan, San Gerardo Hospital, Monza, Italy
| | - Jeffrey G Ojemann
- 6 Division of Neurosurgery, Seattle Children's Hospital, Seattle, Washington.,7 Department of Neurological Surgery, Graduate Program in Neuroscience, Center for Neurotechnology, University of Washington, Seattle, Washington
| | - Kurt E Weaver
- 1 Graduate Program in Neuroscience, Center for Neurotechnology, University of Washington, Seattle, Washington.,2 Integrated Brain Imaging Center, Department of Radiology, University of Washington, Seattle, Washington
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7
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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: 281] [Impact Index Per Article: 56.2] [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.
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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
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8
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Buriro AB, Shoorangiz R, Weddell SJ, Jones RD. Predicting Microsleep States Using EEG Inter-Channel Relationships. IEEE Trans Neural Syst Rehabil Eng 2018; 26:2260-2269. [DOI: 10.1109/tnsre.2018.2878587] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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9
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Moharramipour A, Mostame P, Hossein-Zadeh GA, Wheless JW, Babajani-Feremi A. Comparison of statistical tests in effective connectivity analysis of ECoG data. J Neurosci Methods 2018; 308:317-329. [DOI: 10.1016/j.jneumeth.2018.08.026] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 08/24/2018] [Accepted: 08/25/2018] [Indexed: 11/26/2022]
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10
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Young CK, Ruan M, McNaughton N. A Critical Assessment of Directed Connectivity Estimates with Artificially Imposed Causality in the Supramammillary-Septo-Hippocampal Circuit. Front Syst Neurosci 2017; 11:72. [PMID: 29033799 PMCID: PMC5627232 DOI: 10.3389/fnsys.2017.00072] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 09/19/2017] [Indexed: 11/23/2022] Open
Abstract
Algorithms for estimating directed connectivity have become indispensable to further understand the neurodynamics between functionally coupled brain areas. The evaluation of directed connectivity on the propagation of brain activity has largely been based on simulated data or toy models, where various hidden properties of neurophysiological data may not be fully recapitulated. In this study, directionality was unequivocally manipulated in the freely moving rat in a unique dataset, where normal oscillatory interactions between the supramammillary nucleus (SuM) and hippocampus (HPC) were attenuated by temporary medial septal (MS) inactivation, and replaced by electrical stimulation of the fornix to evaluate the performance of several directed connectivity assessment methods. The directed transfer function, partial directed coherence, directed coherence, pair-wise Geweke-Granger causality, phase slope index, and phase transfer entropy, all found SuM to HPC theta propagation when the MS is inactivated, and HPC activity was driven by peaks of simultaneously recorded SuM theta. As expected from theoretical expectations and simulated data, signal features including coupling strength, signal-to-noise ratio, and stationarity all weakly affected directed connectivity measures. We conclude that all the examined directed connectivity estimates correctly identify artificially imposed uni-directionality of brain oscillations in freely moving animals. Non-auto-regressive modeling based methods appear to be the most robust, and are least affected by inherent features in data such as signal-to-noise ratio and stationarity.
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Affiliation(s)
- Calvin K Young
- Department of Psychology and Brain Health Research Centre, University of Otago, Dunedin, New Zealand
| | - Ming Ruan
- Department of Psychology and Brain Health Research Centre, University of Otago, Dunedin, New Zealand.,Wuhan Asia Heart Hospital, Wuhan, China
| | - Neil McNaughton
- Department of Psychology and Brain Health Research Centre, University of Otago, Dunedin, New Zealand
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11
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Boenstrup M, Feldheim J, Heise K, Gerloff C, Hummel FC. The control of complex finger movements by directional information flow between mesial frontocentral areas and the primary motor cortex. Eur J Neurosci 2014; 40:2888-97. [DOI: 10.1111/ejn.12657] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2014] [Revised: 05/12/2014] [Accepted: 05/13/2014] [Indexed: 11/30/2022]
Affiliation(s)
- M. Boenstrup
- BrainImaging and NeuroStimulation (BINS) Laboratory; Department of Neurology; University Medical Center Hamburg-Eppendorf; 20246 Hamburg Germany
| | - J. Feldheim
- BrainImaging and NeuroStimulation (BINS) Laboratory; Department of Neurology; University Medical Center Hamburg-Eppendorf; 20246 Hamburg Germany
| | - K. Heise
- BrainImaging and NeuroStimulation (BINS) Laboratory; Department of Neurology; University Medical Center Hamburg-Eppendorf; 20246 Hamburg Germany
| | - C. Gerloff
- BrainImaging and NeuroStimulation (BINS) Laboratory; Department of Neurology; University Medical Center Hamburg-Eppendorf; 20246 Hamburg Germany
| | - F. C. Hummel
- BrainImaging and NeuroStimulation (BINS) Laboratory; Department of Neurology; University Medical Center Hamburg-Eppendorf; 20246 Hamburg Germany
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12
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Pollatos O, Yeldesbay A, Pikovsky A, Rosenblum M. How much time has passed? Ask your heart. Front Neurorobot 2014; 8:15. [PMID: 24782755 PMCID: PMC3988366 DOI: 10.3389/fnbot.2014.00015] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 03/19/2014] [Indexed: 11/13/2022] Open
Abstract
Internal signals like one's heartbeats are centrally processed via specific pathways and both their neural representations as well as their conscious perception (interoception) provide key information for many cognitive processes. Recent empirical findings propose that neural processes in the insular cortex, which are related to bodily signals, might constitute a neurophysiological mechanism for the encoding of duration. Nevertheless, the exact nature of such a proposed relationship remains unclear. We aimed to address this question by searching for the effects of cardiac rhythm on time perception by the use of a duration reproduction paradigm. Time intervals used were of 0.5, 2, 3, 7, 10, 14, 25, and 40 s length. In a framework of synchronization hypothesis, measures of phase locking between the cardiac cycle and start/stop signals of the reproduction task were calculated to quantify this relationship. The main result is that marginally significant synchronization indices (SIs) between the heart cycle and the time reproduction responses for the time intervals of 2, 3, 10, 14, and 25 s length were obtained, while results were not significant for durations of 0.5, 7, and 40 s length. On the single participant level, several subjects exhibited some synchrony between the heart cycle and the time reproduction responses, most pronounced for the time interval of 25 s (8 out of 23 participants for 20% quantile). Better time reproduction accuracy was not related with larger degree of phase locking, but with greater vagal control of the heart. A higher interoceptive sensitivity (IS) was associated with a higher synchronization index (SI) for the 2 s time interval only. We conclude that information obtained from the cardiac cycle is relevant for the encoding and reproduction of time in the time span of 2-25 s. Sympathovagal tone as well as interoceptive processes mediate the accuracy of time estimation.
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Affiliation(s)
- Olga Pollatos
- Health Psychology, Institute of Psychology, University of UlmUlm, Germany
| | - Azamat Yeldesbay
- Department of Physics and Astronomy, University of PotsdamPotsdam, Germany
| | - Arkady Pikovsky
- Department of Physics and Astronomy, University of PotsdamPotsdam, Germany
| | - Michael Rosenblum
- Department of Physics and Astronomy, University of PotsdamPotsdam, Germany
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13
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Zhang X, Lei X, Wu T, Jiang T. A review of EEG and MEG for brainnetome research. Cogn Neurodyn 2013; 8:87-98. [PMID: 24624229 DOI: 10.1007/s11571-013-9274-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Revised: 10/17/2013] [Accepted: 11/06/2013] [Indexed: 11/29/2022] Open
Abstract
The majority of brain activities are performed by functionally integrating separate regions of the brain. Therefore, the synchronous operation of the brain's multiple regions or neuronal assemblies can be represented as a network with nodes that are interconnected by links. Because of the complexity of brain interactions and their varying effects at different levels of complexity, one of the corresponding authors of this paper recently proposed the brainnetome as a new -ome to explore and integrate the brain network at different scales. Because electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive and have outstanding temporal resolution and because they are the primary clinical techniques used to capture the dynamics of neuronal connections, they lend themselves to the analysis of the neural networks comprising the brainnetome. Because of EEG/MEG's applicability to brainnetome analyses, the aim of this review is to identify the procedures that can be used to form a network using EEG/MEG data in sensor or source space and to promote EEG/MEG network analysis for either neuroscience or clinical applications. To accomplish this aim, we show the relationship of the brainnetome to brain networks at the macroscale and provide a systematic review of network construction using EEG and MEG. Some potential applications of the EEG/MEG brainnetome are to use newly developed methods to associate the properties of a brainnetome with indices of cognition or disease conditions. Associations based on EEG/MEG brainnetome analysis may improve the comprehension of the functioning of the brain in neuroscience research or the recognition of abnormal patterns in neurological disease.
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Affiliation(s)
- Xin Zhang
- Brainnetome Center, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190 China ; National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190 China
| | - Xu Lei
- Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China ; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 China
| | - Ting Wu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 China ; Department of Magnetoencephalography, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, 210029 China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190 China ; National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190 China ; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 China ; The Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072 Australia
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14
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Phase Synchronization Analysis of EEG Signals: An Evaluation Based on Surrogate Tests. IEEE Trans Biomed Eng 2012; 59:2254-63. [PMID: 22665500 DOI: 10.1109/tbme.2012.2199490] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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15
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Inferring functional neural connectivity with phase synchronization analysis: a review of methodology. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2012; 2012:239210. [PMID: 22577470 PMCID: PMC3346979 DOI: 10.1155/2012/239210] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2011] [Accepted: 01/31/2012] [Indexed: 11/18/2022]
Abstract
Functional neural connectivity is drawing increasing attention in neuroscience research. To infer functional connectivity from observed neural signals, various methods have been proposed. Among them, phase synchronization analysis is an important and effective one which examines the relationship of instantaneous phase between neural signals but neglecting the influence of their amplitudes. In this paper, we review the advances in methodologies of phase synchronization analysis. In particular, we discuss the definitions of instantaneous phase, the indexes of phase synchronization and their significance test, the issues that may affect the detection of phase synchronization and the extensions of phase synchronization analysis. In practice, phase synchronization analysis may be affected by observational noise, insufficient samples of the signals, volume conduction, and reference in recording neural signals. We make comments and suggestions on these issues so as to better apply phase synchronization analysis to inferring functional connectivity from neural signals.
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16
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Lehnertz K. Assessing directed interactions from neurophysiological signals--an overview. Physiol Meas 2011; 32:1715-24. [PMID: 22027099 DOI: 10.1088/0967-3334/32/11/r01] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The study of synchronization phenomena in coupled dynamical systems is an active field of research in many scientific disciplines including the neurosciences. Over the last decades, a number of time series analysis techniques have been proposed to capture both linear and nonlinear aspects of interactions. While most of these techniques allow one to quantify the strength of interactions, developments that resulted from advances in nonlinear dynamics and in information and synchronization theory aim at assessing directed interactions. Most of these techniques, however, assume the underlying systems to be at least approximately stationary and require a large number of data points to robustly assess directed interactions. Recent extensions allow assessing directed interactions from short and transient signals and are particularly suited for the analysis of evoked and event-related activity.
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Affiliation(s)
- Klaus Lehnertz
- Department of Epileptology, University of Bonn, Sigmund-Freud-Strasse 25, Bonn, Germany.
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Korzeniewska A, Franaszczuk PJ, Crainiceanu CM, Kuś R, Crone NE. Dynamics of large-scale cortical interactions at high gamma frequencies during word production: event related causality (ERC) analysis of human electrocorticography (ECoG). Neuroimage 2011; 56:2218-37. [PMID: 21419227 PMCID: PMC3105123 DOI: 10.1016/j.neuroimage.2011.03.030] [Citation(s) in RCA: 62] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2010] [Revised: 03/08/2011] [Accepted: 03/09/2011] [Indexed: 11/16/2022] Open
Abstract
Intracranial EEG studies in humans have shown that functional brain activation in a variety of functional-anatomic domains of human cortex is associated with an increase in power at a broad range of high gamma (>60Hz) frequencies. Although these electrophysiological responses are highly specific for the location and timing of cortical processing and in animal recordings are highly correlated with increased population firing rates, there has been little direct empirical evidence for causal interactions between different recording sites at high gamma frequencies. Such causal interactions are hypothesized to occur during cognitive tasks that activate multiple brain regions. To determine whether such causal interactions occur at high gamma frequencies and to investigate their functional significance, we used event-related causality (ERC) analysis to estimate the dynamics, directionality, and magnitude of event-related causal interactions using subdural electrocorticography (ECoG) recorded during two word production tasks: picture naming and auditory word repetition. A clinical subject who had normal hearing but was skilled in American Signed Language (ASL) provided a unique opportunity to test our hypothesis with reference to a predictable pattern of causal interactions, i.e. that language cortex interacts with different areas of sensorimotor cortex during spoken vs. signed responses. Our ERC analyses confirmed this prediction. During word production with spoken responses, perisylvian language sites had prominent causal interactions with mouth/tongue areas of motor cortex, and when responses were gestured in sign language, the most prominent interactions involved hand and arm areas of motor cortex. Furthermore, we found that the sites from which the most numerous and prominent causal interactions originated, i.e. sites with a pattern of ERC "divergence", were also sites where high gamma power increases were most prominent and where electrocortical stimulation mapping interfered with word production. These findings suggest that the number, strength and directionality of event-related causal interactions may help identify network nodes that are not only activated by a task but are critical to its performance.
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Affiliation(s)
- Anna Korzeniewska
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA.
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Rogers BP, Katwal SB, Morgan VL, Asplund CL, Gore JC. Functional MRI and multivariate autoregressive models. Magn Reson Imaging 2010; 28:1058-65. [PMID: 20444566 DOI: 10.1016/j.mri.2010.03.002] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2010] [Accepted: 03/11/2010] [Indexed: 11/24/2022]
Abstract
Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI.
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Affiliation(s)
- Baxter P Rogers
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232-2310, USA.
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Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2009:813607. [PMID: 19547657 PMCID: PMC2699435 DOI: 10.1155/2009/813607] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2009] [Accepted: 04/05/2009] [Indexed: 11/17/2022]
Abstract
Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship.
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