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Karimova ED, Ovakimian AS, Katermin NS. Live vs video interaction: sensorimotor and visual cortical oscillations during action observation. Cereb Cortex 2024; 34:bhae168. [PMID: 38679481 DOI: 10.1093/cercor/bhae168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 05/01/2024] Open
Abstract
Increasingly, in the field of communication, education, and business, people are switching to video interaction, and interlocutors frequently complain that the perception of nonverbal information and concentration suffer. We investigated this issue by analyzing electroencephalogram (EEG) oscillations of the sensorimotor (mu rhythm) and visual (alpha rhythm) cortex of the brain in an experiment with action observation live and on video. The mu rhythm reflects the activity of the mirror neuron system, and the occipital alpha rhythm shows the level of visual attention. We used 32-channel EEG recorded during live and video action observation in 83 healthy volunteers. The ICA method was used for selecting the mu- and alpha-components; the Fourier Transform was used to calculate the suppression index relative to the baseline (stationary demonstrator) of the rhythms. The main range of the mu rhythm was indeed sensitive to social movement and was highly dependent on the conditions of interaction-live or video. The upper mu-range appeared to be less sensitive to the conditions, but more sensitive to different movements. The alpha rhythm did not depend on the type of movement; however, a live performance initially caused a stronger concentration of visual attention. Thus, subtle social and nonverbal perceptions may suffer in remote video interactions.
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Affiliation(s)
- Ekaterina D Karimova
- Laboratory of Applied Physiology of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of RAS (IHNA&NPh RAS), 5A Butlerova street, 117485 Moscow, the Russian Federation
| | - Alena S Ovakimian
- Laboratory of Applied Physiology of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of RAS (IHNA&NPh RAS), 5A Butlerova street, 117485 Moscow, the Russian Federation
| | - Nikita S Katermin
- Flow cytometry data processing group, BostonGene Technologies, Hrachya Qochar Str., 2A, 0033, Yerevan, Armenia
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2
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Heinilä E, Hyvärinen A, Parkkonen L, Parviainen T. Penalized canonical correlation analysis reveals a relationship between temperament traits and brain oscillations during mind wandering. Brain Behav 2024; 14:e3428. [PMID: 38361323 PMCID: PMC10869894 DOI: 10.1002/brb3.3428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 12/13/2023] [Accepted: 01/29/2024] [Indexed: 02/17/2024] Open
Abstract
INTRODUCTION There has been a growing interest in studying brain activity under naturalistic conditions. However, the relationship between individual differences in ongoing brain activity and psychological characteristics is not well understood. We investigated this connection, focusing on the association between oscillatory activity in the brain and individually characteristic dispositional traits. Given the variability of unconstrained resting states among individuals, we devised a paradigm that could harmonize the state of mind across all participants. METHODS We constructed task contrasts that included focused attention (FA), self-centered future planning, and rumination on anxious thoughts triggered by visual imagery. Magnetoencephalography was recorded from 28 participants under these 3 conditions for a duration of 16 min. The oscillatory power in the alpha and beta bands was converted into spatial contrast maps, representing the difference in brain oscillation power between the two conditions. We performed permutation cluster tests on these spatial contrast maps. Additionally, we applied penalized canonical correlation analysis (CCA) to study the relationship between brain oscillation patterns and behavioral traits. RESULTS The data revealed that the FA condition, as compared to the other conditions, was associated with higher alpha and beta power in the temporal areas of the left hemisphere and lower alpha and beta power in the parietal areas of the right hemisphere. Interestingly, the penalized CCA indicated that behavioral inhibition was positively correlated, whereas anxiety was negatively correlated, with a pattern of high oscillatory power in the bilateral precuneus and low power in the bilateral temporal regions. This unique association was found in the anxious-thoughts condition when contrasted with the focused-attention condition. CONCLUSION Our findings suggest individual temperament traits significantly affect brain engagement in naturalistic conditions. This research underscores the importance of considering individual traits in neuroscience and offers an effective method for analyzing brain activity and psychological differences.
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Affiliation(s)
- Erkka Heinilä
- Faculty of Information TechnologyUniversity of JyväskyläJyväskyläFinland
| | - Aapo Hyvärinen
- Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland
- Université Paris‐Saclay, Inria, CEAGif‐sur‐YvetteFrance
| | - Lauri Parkkonen
- Department of Neuroscience and Biomedical EngineeringAalto University School of ScienceEspooFinland
| | - Tiina Parviainen
- Centre of Interdisciplinary Brain Research, Department of Psychology, Faculty of Education and PsychologyUniversity of JyväskyläJyväskyläFinland
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3
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Jorajuria T, Nikulin VV, Kapralov N, Gomez M, Vidaurre C. MEAN SP: How Many Channels are Needed to Predict the Performance of a SMR-Based BCI? IEEE Trans Neural Syst Rehabil Eng 2023; 31:4931-4941. [PMID: 38051627 DOI: 10.1109/tnsre.2023.3339612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Predicting whether a particular individual would reach an adequate control of a Brain-Computer Interface (BCI) has many practical advantages. On the one hand, participants with low predicted performance could be trained with specifically designed sessions and avoid frustrating experiments; on the other hand, planning time and resources would be more efficient; and finally, the variables related to an accurate prediction could be manipulated to improve the prospective BCI performance. To this end, several predictors have been proposed in the literature, most of them based on the power estimation of EEG signals at the specific frequency bands. Many of these studies evaluate their predictors in relatively small datasets and/or using a relatively high number of channels. In this manuscript, we propose a novel predictor called [Formula: see text] to predict the performance of participants using BCIs that are based on the modulation of sensorimotor rhythms. This novel predictor has been positively evaluated using only 2, 3, 4 or 5 channels. [Formula: see text] has shown to perform as well as or better than other state-of-the-art predictors. The best sets of different number of channels are also provided, which have been tested in two different settings to prove their robustness. The proposed predictor has been successfully evaluated using two large-scale datasets containing 150 and 80 participants, respectively. We also discuss predictor thresholds for users to expect good performance in feedback experiments and show the advantages in comparison to a competing algorithm.
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Santos JL, Petsidou E, Saraogi P, Bartsch U, Gerber AP, Seibt J. Effect of Acute Enriched Environment Exposure on Brain Oscillations and Activation of the Translation Initiation Factor 4E-BPs at Synapses across Wakefulness and Sleep in Rats. Cells 2023; 12:2320. [PMID: 37759542 PMCID: PMC10528220 DOI: 10.3390/cells12182320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/15/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
Brain plasticity is induced by learning during wakefulness and is consolidated during sleep. But the molecular mechanisms involved are poorly understood and their relation to experience-dependent changes in brain activity remains to be clarified. Localised mRNA translation is important for the structural changes at synapses supporting brain plasticity consolidation. The translation mTOR pathway, via phosphorylation of 4E-BPs, is known to be activate during sleep and contributes to brain plasticity, but whether this activation is specific to synapses is not known. We investigated this question using acute exposure of rats to an enriched environment (EE). We measured brain activity with EEGs and 4E-BP phosphorylation at cortical and cerebellar synapses with Western blot analyses. Sleep significantly increased the conversion of 4E-BPs to their hyperphosphorylated forms at synapses, especially after EE exposure. EE exposure increased oscillations in the alpha band during active exploration and in the theta-to-beta (4-30 Hz) range, as well as spindle density, during NREM sleep. Theta activity during exploration and NREM spindle frequency predicted changes in 4E-BP hyperphosphorylation at synapses. Hence, our results suggest a functional link between EEG and molecular markers of plasticity across wakefulness and sleep.
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Affiliation(s)
- José Lucas Santos
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XP, UK; (J.L.S.); (U.B.)
- Department of Microbial Sciences, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK;
- Department of Physiology, Development and Neuroscience, University of Cambridge, Physiological Laboratory, Downing Street, Cambridge CB2 3EG, UK
| | - Evlalia Petsidou
- Undergraduate Programme in Biological Science, University of Surrey, Guildford GU2 7XH, UK
- Postgraduate Programme in Neuroscience (MSc), Cyprus Institute of Neurology and Genetics, Iroon Avenue 6, Egkomi 2371, Cyprus
| | - Pallavi Saraogi
- Undergraduate Programme in Biological Science, University of Surrey, Guildford GU2 7XH, UK
| | - Ullrich Bartsch
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XP, UK; (J.L.S.); (U.B.)
- UK Dementia Research Institute, Care Research & Technology Centre at Imperial College London and University of Surrey, Guildford GU2 7XH, UK
| | - André P. Gerber
- Department of Microbial Sciences, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK;
| | - Julie Seibt
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XP, UK; (J.L.S.); (U.B.)
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Viswanathan V, Bharadwaj HM, Heinz MG, Shinn-Cunningham BG. Induced alpha and beta electroencephalographic rhythms covary with single-trial speech intelligibility in competition. Sci Rep 2023; 13:10216. [PMID: 37353552 PMCID: PMC10290148 DOI: 10.1038/s41598-023-37173-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 06/17/2023] [Indexed: 06/25/2023] Open
Abstract
Neurophysiological studies suggest that intrinsic brain oscillations influence sensory processing, especially of rhythmic stimuli like speech. Prior work suggests that brain rhythms may mediate perceptual grouping and selective attention to speech amidst competing sound, as well as more linguistic aspects of speech processing like predictive coding. However, we know of no prior studies that have directly tested, at the single-trial level, whether brain oscillations relate to speech-in-noise outcomes. Here, we combined electroencephalography while simultaneously measuring intelligibility of spoken sentences amidst two different interfering sounds: multi-talker babble or speech-shaped noise. We find that induced parieto-occipital alpha (7-15 Hz; thought to modulate attentional focus) and frontal beta (13-30 Hz; associated with maintenance of the current sensorimotor state and predictive coding) oscillations covary with trial-wise percent-correct scores; importantly, alpha and beta power provide significant independent contributions to predicting single-trial behavioral outcomes. These results can inform models of speech processing and guide noninvasive measures to index different neural processes that together support complex listening.
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Affiliation(s)
- Vibha Viswanathan
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
| | - Hari M Bharadwaj
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Michael G Heinz
- Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, IN, 47907, USA
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Viswanathan V, Bharadwaj HM, Heinz MG, Shinn-Cunningham BG. Induced Alpha And Beta Electroencephalographic Rhythms Covary With Single-Trial Speech Intelligibility In Competition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2022.12.31.522365. [PMID: 36712081 PMCID: PMC9884507 DOI: 10.1101/2022.12.31.522365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Neurophysiological studies suggest that intrinsic brain oscillations influence sensory processing, especially of rhythmic stimuli like speech. Prior work suggests that brain rhythms may mediate perceptual grouping and selective attention to speech amidst competing sound, as well as more linguistic aspects of speech processing like predictive coding. However, we know of no prior studies that have directly tested, at the single-trial level, whether brain oscillations relate to speech-in-noise outcomes. Here, we combined electroencephalography while simultaneously measuring intelligibility of spoken sentences amidst two different interfering sounds: multi-talker babble or speech-shaped noise. We find that induced parieto-occipital alpha (7-15 Hz; thought to modulate attentional focus) and frontal beta (13-30 Hz; associated with maintenance of the current sensorimotor state and predictive coding) oscillations covary with trial-wise percent-correct scores; importantly, alpha and beta power provide significant independent contributions to predicting single-trial behavioral outcomes. These results can inform models of speech processing and guide noninvasive measures to index different neural processes that together support complex listening.
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Affiliation(s)
- Vibha Viswanathan
- Neuroscience Institute, Carnegie Mellon University, Pitttsburgh, PA 15213
| | - Hari M. Bharadwaj
- Department of Communication Science and Disorders, University of Pittsburgh, Pitttsburgh, PA 15260
| | - Michael G. Heinz
- Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, IN 47907
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Caravaglios G, Muscoso EG, Blandino V, Di Maria G, Gangitano M, Graziano F, Guajana F, Piccoli T. EEG Resting-State Functional Networks in Amnestic Mild Cognitive Impairment. Clin EEG Neurosci 2023; 54:36-50. [PMID: 35758261 DOI: 10.1177/15500594221110036] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background. Alzheimer's cognitive-behavioral syndrome is the result of impaired connectivity between nerve cells, due to misfolded proteins, which accumulate and disrupt specific brain networks. Electroencephalography, because of its excellent temporal resolution, is an optimal approach for assessing the communication between functionally related brain regions. Objective. To detect and compare EEG resting-state networks (RSNs) in patients with amnesic mild cognitive impairment (aMCI), and healthy elderly (HE). Methods. We recruited 125 aMCI patients and 70 healthy elderly subjects. One hundred and twenty seconds of artifact-free EEG data were selected and compared between patients with aMCI and HE. We applied standard low-resolution brain electromagnetic tomography (sLORETA)-independent component analysis (ICA) to assess resting-state networks. Each network consisted of a set of images, one for each frequency (delta, theta, alpha1/2, beta1/2). Results. The functional ICA analysis revealed 17 networks common to groups. The statistical procedure demonstrated that aMCI used some networks differently than HE. The most relevant findings were as follows. Amnesic-MCI had: i) increased delta/beta activity in the superior frontal gyrus and decreased alpha1 activity in the paracentral lobule (ie, default mode network); ii) greater delta/theta/alpha/beta in the superior frontal gyrus (i.e, attention network); iii) lower alpha in the left superior parietal lobe, as well as a lower delta/theta and beta, respectively in post-central, and in superior frontal gyrus(ie, attention network). Conclusions. Our study confirms sLORETA-ICA method is effective in detecting functional resting-state networks, as well as between-groups connectivity differences. The findings provide support to the Alzheimer's network disconnection hypothesis.
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Affiliation(s)
- G Caravaglios
- U.O.C. Neurologia, A.O. Cannizzaro per l'emergenza, Catania, Italy
| | - E G Muscoso
- U.O.C. Neurologia, A.O. Cannizzaro per l'emergenza, Catania, Italy
| | - V Blandino
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), 18998University of Palermo, Palermo, Italy
| | - G Di Maria
- U.O.C. Neurologia, A.O. Cannizzaro per l'emergenza, Catania, Italy
| | - M Gangitano
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), 18998University of Palermo, Palermo, Italy
| | - F Graziano
- U.O.C. Neurologia, A.O. Cannizzaro per l'emergenza, Catania, Italy
| | - F Guajana
- U.O.C. Neurologia, A.O. Cannizzaro per l'emergenza, Catania, Italy
| | - T Piccoli
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), 18998University of Palermo, Palermo, Italy
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Kostyalik D, Kelemen K, Lendvai B, Hernádi I, Román V, Lévay G. Response-related sensorimotor rhythms under scopolamine and MK-801 exposures in the touchscreen visual discrimination test in rats. Sci Rep 2022; 12:8168. [PMID: 35581280 PMCID: PMC9114334 DOI: 10.1038/s41598-022-12146-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 04/21/2022] [Indexed: 11/10/2022] Open
Abstract
The human mu rhythm has been suggested to represent an important function in information processing. Rodent homologue rhythms have been assumed though no study has investigated them from the cognitive aspect yet. As voluntary goal-directed movements induce the desynchronization of mu rhythm, we aimed at exploring whether the response-related brain activity during the touchscreen visual discrimination (VD) task is suitable to detect sensorimotor rhythms and their change under cognitive impairment. Different doses of scopolamine or MK-801 were injected subcutaneously to rats, and epidural electroencephalogram (EEG) was recorded during task performance. Arciform ~ 10 Hz oscillations appeared during visual processing, then two characteristic alpha/beta desynchronization-resynchronization patterns emerged mainly above the sensorimotor areas, serving presumably different motor functions. Beyond causing cognitive impairment, both drugs supressed the touch-related upper alpha (10–15 Hz) reactivity for desynchronization. Reaction time predominantly correlated positively with movement-related alpha and beta power both in normal and impaired conditions. These results support the existence of a mu homologue rodent rhythm whose upper alpha component appeared to be modulated by cholinergic and glutamatergic mechanisms and its power change might indicate a potential EEG correlate of processing speed. The VD task can be utilized for the investigation of sensorimotor rhythms in rats.
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Affiliation(s)
- Diána Kostyalik
- Cognitive Pharmacology Laboratory, Department of Pharmacology and Drug Safety, Gedeon Richter Plc., Gyömrői út 19-21, Budapest, 1103, Hungary
| | - Kristóf Kelemen
- Cognitive Pharmacology Laboratory, Department of Pharmacology and Drug Safety, Gedeon Richter Plc., Gyömrői út 19-21, Budapest, 1103, Hungary
| | - Balázs Lendvai
- Department of Pharmacology and Drug Safety, Gedeon Richter Plc., Budapest, 1103, Hungary
| | - István Hernádi
- Department of Pharmacology and Drug Safety, Gedeon Richter Plc., Budapest, 1103, Hungary.,Department of Experimental Zoology and Neurobiology, Faculty of Sciences, University of Pécs, Pécs, 7622, Hungary.,Institute of Physiology, Medical School, University of Pécs, Pécs, 7622, Hungary.,Grastyán Translational Research Center, University of Pécs, Pécs, 7622, Hungary.,Szentágothai Research Center, University of Pécs, Pécs, 7622, Hungary
| | - Viktor Román
- Department of Pharmacology and Drug Safety, Gedeon Richter Plc., Budapest, 1103, Hungary
| | - György Lévay
- Cognitive Pharmacology Laboratory, Department of Pharmacology and Drug Safety, Gedeon Richter Plc., Gyömrői út 19-21, Budapest, 1103, Hungary. .,Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, 1085, Hungary.
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Jenson D, Saltuklaroglu T. Sensorimotor contributions to working memory differ between the discrimination of Same and Different syllable pairs. Neuropsychologia 2021; 159:107947. [PMID: 34216594 DOI: 10.1016/j.neuropsychologia.2021.107947] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 02/01/2021] [Accepted: 06/27/2021] [Indexed: 10/21/2022]
Abstract
Sensorimotor activity during speech perception is both pervasive and highly variable, changing as a function of the cognitive demands imposed by the task. The purpose of the current study was to evaluate whether the discrimination of Same (matched) and Different (unmatched) syllable pairs elicit different patterns of sensorimotor activity as stimuli are processed in working memory. Raw EEG data recorded from 42 participants were decomposed with independent component analysis to identify bilateral sensorimotor mu rhythms from 36 subjects. Time frequency decomposition of mu rhythms revealed concurrent event related desynchronization (ERD) in alpha and beta frequency bands across the peri- and post-stimulus time periods, which were interpreted as evidence of sensorimotor contributions to working memory encoding and maintenance. Left hemisphere alpha/beta ERD was stronger in Different trials than Same trials during the post-stimulus period, while right hemisphere alpha/beta ERD was stronger in Same trials than Different trials. A between-hemispheres contrast revealed no differences during Same trials, while post-stimulus alpha/beta ERD was stronger in the left hemisphere than the right during Different trials. Results were interpreted to suggest that predictive coding mechanisms lead to repetition suppression effects in Same trials. Mismatches arising from predictive coding mechanisms in Different trials shift subsequent working memory processing to the speech-dominant left hemisphere. Findings clarify how sensorimotor activity differentially supports working memory encoding and maintenance stages during speech discrimination tasks and have potential to inform sensorimotor models of speech perception and working memory.
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Affiliation(s)
- David Jenson
- Washington State University, Elson S. Floyd College of Medicine, Department of Speech and Hearing Sciences, Spokane, WA, USA.
| | - Tim Saltuklaroglu
- University of Tennessee Health Science Center, College of Health Professions, Department of Audiology and Speech-Pathology, Knoxville, TN, USA
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Thammasan N, Miyakoshi M. Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG. SENSORS 2020; 20:s20247040. [PMID: 33316928 PMCID: PMC7763560 DOI: 10.3390/s20247040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 11/28/2020] [Accepted: 12/03/2020] [Indexed: 01/26/2023]
Abstract
Magneto-/Electro-encephalography (M/EEG) commonly uses (fast) Fourier transformation to compute power spectral density (PSD). However, the resulting PSD plot lacks temporal information, making interpretation sometimes equivocal. For example, consider two different PSDs: a central parietal EEG PSD with twin peaks at 10 Hz and 20 Hz and a central parietal PSD with twin peaks at 10 Hz and 50 Hz. We can assume the first PSD shows a mu rhythm and the second harmonic; however, the latter PSD likely shows an alpha peak and an independent line noise. Without prior knowledge, however, the PSD alone cannot distinguish between the two cases. To address this limitation of PSD, we propose using cross-frequency power-power coupling (PPC) as a post-processing of independent component (IC) analysis (ICA) to distinguish brain components from muscle and environmental artifact sources. We conclude that post-ICA PPC analysis could serve as a new data-driven EEG classifier in M/EEG studies. For the reader's convenience, we offer a brief literature overview on the disparate use of PPC. The proposed cross-frequency power-power coupling analysis toolbox (PowPowCAT) is a free, open-source toolbox, which works as an EEGLAB extension.
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Affiliation(s)
- Nattapong Thammasan
- Human Media Interaction, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands;
| | - Makoto Miyakoshi
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA
- Correspondence: ; Tel.: +1-858-822-7534
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Tacchino G, Coelli S, Reali P, Galli M, Bianchi AM. Bicoherence Interpretation in EEG Requires Signal to Noise Ratio Quantification: An Application to Sensorimotor Rhythms. IEEE Trans Biomed Eng 2020; 67:2696-2704. [DOI: 10.1109/tbme.2020.2969278] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Jenson D, Bowers AL, Hudock D, Saltuklaroglu T. The Application of EEG Mu Rhythm Measures to Neurophysiological Research in Stuttering. Front Hum Neurosci 2020; 13:458. [PMID: 31998103 PMCID: PMC6965028 DOI: 10.3389/fnhum.2019.00458] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 12/13/2019] [Indexed: 11/29/2022] Open
Abstract
Deficits in basal ganglia-based inhibitory and timing circuits along with sensorimotor internal modeling mechanisms are thought to underlie stuttering. However, much remains to be learned regarding the precise manner how these deficits contribute to disrupting both speech and cognitive functions in those who stutter. Herein, we examine the suitability of electroencephalographic (EEG) mu rhythms for addressing these deficits. We review some previous findings of mu rhythm activity differentiating stuttering from non-stuttering individuals and present some new preliminary findings capturing stuttering-related deficits in working memory. Mu rhythms are characterized by spectral peaks in alpha (8-13 Hz) and beta (14-25 Hz) frequency bands (mu-alpha and mu-beta). They emanate from premotor/motor regions and are influenced by basal ganglia and sensorimotor function. More specifically, alpha peaks (mu-alpha) are sensitive to basal ganglia-based inhibitory signals and sensory-to-motor feedback. Beta peaks (mu-beta) are sensitive to changes in timing and capture motor-to-sensory (i.e., forward model) projections. Observing simultaneous changes in mu-alpha and mu-beta across the time-course of specific events provides a rich window for observing neurophysiological deficits associated with stuttering in both speech and cognitive tasks and can provide a better understanding of the functional relationship between these stuttering symptoms. We review how independent component analysis (ICA) can extract mu rhythms from raw EEG signals in speech production tasks, such that changes in alpha and beta power are mapped to myogenic activity from articulators. We review findings from speech production and auditory discrimination tasks demonstrating that mu-alpha and mu-beta are highly sensitive to capturing sensorimotor and basal ganglia deficits associated with stuttering with high temporal precision. Novel findings from a non-word repetition (working memory) task are also included. They show reduced mu-alpha suppression in a stuttering group compared to a typically fluent group. Finally, we review current limitations and directions for future research.
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Affiliation(s)
- David Jenson
- Department of Speech and Hearing Sciences, Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, United States
| | - Andrew L. Bowers
- Epley Center for Health Professions, Communication Sciences and Disorders, University of Arkansas, Fayetteville, AR, United States
| | - Daniel Hudock
- Department of Communication Sciences and Disorders, Idaho State University, Pocatello, ID, United States
| | - Tim Saltuklaroglu
- College of Health Professions, Department of Audiology and Speech-Pathology, University of Tennessee Health Science Center, Knoxville, TN, United States
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Stolk A, Brinkman L, Vansteensel MJ, Aarnoutse E, Leijten FSS, Dijkerman CH, Knight RT, de Lange FP, Toni I. Electrocorticographic dissociation of alpha and beta rhythmic activity in the human sensorimotor system. eLife 2019; 8:e48065. [PMID: 31596233 PMCID: PMC6785220 DOI: 10.7554/elife.48065] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 09/10/2019] [Indexed: 11/13/2022] Open
Abstract
This study uses electrocorticography in humans to assess how alpha- and beta-band rhythms modulate excitability of the sensorimotor cortex during psychophysically-controlled movement imagery. Both rhythms displayed effector-specific modulations, tracked spectral markers of action potentials in the local neuronal population, and showed spatially systematic phase relationships (traveling waves). Yet, alpha- and beta-band rhythms differed in their anatomical and functional properties, were weakly correlated, and traveled along opposite directions across the sensorimotor cortex. Increased alpha-band power in the somatosensory cortex ipsilateral to the selected arm was associated with spatially-unspecific inhibition. Decreased beta-band power over contralateral motor cortex was associated with a focal shift from relative inhibition to excitation. These observations indicate the relevance of both inhibition and disinhibition mechanisms for precise spatiotemporal coordination of movement-related neuronal populations, and illustrate how those mechanisms are implemented through the substantially different neurophysiological properties of sensorimotor alpha- and beta-band rhythms.
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Affiliation(s)
- Arjen Stolk
- Helen Wills Neuroscience InstituteUniversity of California, BerkeleyBerkeleyUnited States
- Donders Institute for Brain, Cognition, and BehaviourRadboud UniversityNijmegenNetherlands
| | - Loek Brinkman
- Department of Neurology and Neurosurgery, UMC Utrecht Brain CenterUMC UtrechtUtrechtNetherlands
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, UMC Utrecht Brain CenterUMC UtrechtUtrechtNetherlands
| | - Erik Aarnoutse
- Department of Neurology and Neurosurgery, UMC Utrecht Brain CenterUMC UtrechtUtrechtNetherlands
| | - Frans SS Leijten
- Department of Neurology and Neurosurgery, UMC Utrecht Brain CenterUMC UtrechtUtrechtNetherlands
| | - Chris H Dijkerman
- Helmholtz Institute, Experimental PsychologyUtrecht UniversityUtrechtNetherlands
| | - Robert T Knight
- Helen Wills Neuroscience InstituteUniversity of California, BerkeleyBerkeleyUnited States
| | - Floris P de Lange
- Donders Institute for Brain, Cognition, and BehaviourRadboud UniversityNijmegenNetherlands
| | - Ivan Toni
- Donders Institute for Brain, Cognition, and BehaviourRadboud UniversityNijmegenNetherlands
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14
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Beese C, Vassileiou B, Friederici AD, Meyer L. Age Differences in Encoding-Related Alpha Power Reflect Sentence Comprehension Difficulties. Front Aging Neurosci 2019; 11:183. [PMID: 31379561 PMCID: PMC6654000 DOI: 10.3389/fnagi.2019.00183] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 07/04/2019] [Indexed: 12/29/2022] Open
Abstract
When sentence processing taxes verbal working memory, comprehension difficulties arise. This is specifically the case when processing resources decline with advancing adult age. Such decline likely affects the encoding of sentences into working memory, which constitutes the basis for successful comprehension. To assess age differences in encoding-related electrophysiological activity, we recorded the electroencephalogram from three age groups (24, 43, and 65 years). Using an auditory sentence comprehension task, age differences in encoding-related oscillatory power were examined with respect to the accuracy of the given response. That is, the difference in oscillatory power between correctly and incorrectly encoded sentences, yielding subsequent memory effects (SME), was compared across age groups. Across age groups, we observed an age-related SME inversion in the alpha band from a power decrease in younger adults to a power increase in older adults. We suggest that this SME inversion underlies age-related comprehension difficulties. With alpha being commonly linked to inhibitory processes, this shift may reflect a change in the cortical inhibition-disinhibition balance. A cortical disinhibition may imply enriched sentence encoding in younger adults. In contrast, resource limitations in older adults may necessitate an increase in cortical inhibition during sentence encoding to avoid an information overload. Overall, our findings tentatively suggest that age-related comprehension difficulties are associated with alterations to the electrophysiological dynamics subserving general higher cognitive functions.
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Affiliation(s)
- Caroline Beese
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Benedict Vassileiou
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Angela D. Friederici
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Lars Meyer
- Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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15
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Wilson R, Mullinger KJ, Francis ST, Mayhew SD. The relationship between negative BOLD responses and ERS and ERD of alpha/beta oscillations in visual and motor cortex. Neuroimage 2019; 199:635-650. [PMID: 31189075 DOI: 10.1016/j.neuroimage.2019.06.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 04/10/2019] [Accepted: 06/03/2019] [Indexed: 01/06/2023] Open
Abstract
Previous work has investigated the electrophysiological origins of the intra-modal (within the stimulated sensory cortex) negative BOLD fMRI response (NBR, decrease from baseline) but little attention has been paid to the origin of cross-modal NBRs, those in a different sensory cortex. In the current study we use simultaneous EEG-fMRI recordings to assess the neural correlates of both intra- and cross-modal responses to left-hemifield visual stimuli and right-hand motor tasks, and evaluate the balance of activation and deactivation between the visual and motor systems. Within- and between-subject covariations of EEG and fMRI responses to both tasks are assessed to determine how patterns of event-related desynchronization/synchronisation (ERD/ERS) of alpha/beta frequency oscillations relate to the NBR in the two sensory cortices. We show that both visual and motor tasks induce intra-modal NBR and cross-modal NBR (e.g. visual stimuli evoked NBRs in both visual and motor cortices). In the EEG data, bilateral intra-modal alpha/beta ERD were consistently observed to both tasks, whilst the cross-modal EEG response varied across subjects between alpha/beta ERD and ERS. Both the mean cross-modal EEG and fMRI response amplitudes showed a small increase in magnitude with increasing task intensity. In response to the visual stimuli, subjects displaying cross-modal ERS of motor beta power displayed a significantly larger magnitude of cross-modal NBR in motor cortex. However, in contrast to the motor stimuli, larger cross-modal ERD of visual alpha power was associated with larger cross-modal visual NBR. Single-trial correlation analysis provided further evidence of relationship between EEG signals and the NBR, motor cortex beta responses to motor tasks were significantly negatively correlated with cross-modal visual cortex NBR amplitude, and positively correlated with intra-modal motor cortex PBR. This study provides a new body of evidence that the coupling between BOLD and low-frequency (alpha/beta) sensory cortex EEG responses extends to cross-modal NBR.
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Affiliation(s)
- Ross Wilson
- Centre for Human Brain Health (CHBH), University of Birmingham, Birmingham, UK
| | - Karen J Mullinger
- Centre for Human Brain Health (CHBH), University of Birmingham, Birmingham, UK; SPMIC, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Susan T Francis
- SPMIC, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Stephen D Mayhew
- Centre for Human Brain Health (CHBH), University of Birmingham, Birmingham, UK.
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16
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Schaworonkow N, Nikulin VV. Spatial neuronal synchronization and the waveform of oscillations: Implications for EEG and MEG. PLoS Comput Biol 2019; 15:e1007055. [PMID: 31086368 PMCID: PMC6534335 DOI: 10.1371/journal.pcbi.1007055] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 05/24/2019] [Accepted: 04/26/2019] [Indexed: 11/24/2022] Open
Abstract
Neuronal oscillations are ubiquitous in the human brain and are implicated in virtually all brain functions. Although they can be described by a prominent peak in the power spectrum, their waveform is not necessarily sinusoidal and shows rather complex morphology. Both frequency and temporal descriptions of such non-sinusoidal neuronal oscillations can be utilized. However, in non-invasive EEG/MEG recordings the waveform of oscillations often takes a sinusoidal shape which in turn leads to a rather oversimplified view on oscillatory processes. In this study, we show in simulations how spatial synchronization can mask non-sinusoidal features of the underlying rhythmic neuronal processes. Consequently, the degree of non-sinusoidality can serve as a measure of spatial synchronization. To confirm this empirically, we show that a mixture of EEG components is indeed associated with more sinusoidal oscillations compared to the waveform of oscillations in each constituent component. Using simulations, we also show that the spatial mixing of the non-sinusoidal neuronal signals strongly affects the amplitude ratio of the spectral harmonics constituting the waveform. Finally, our simulations show how spatial mixing can affect the strength and even the direction of the amplitude coupling between constituent neuronal harmonics at different frequencies. Validating these simulations, we also demonstrate these effects in real EEG recordings. Our findings have far reaching implications for the neurophysiological interpretation of spectral profiles, cross-frequency interactions, as well as for the unequivocal determination of oscillatory phase.
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Affiliation(s)
- Natalie Schaworonkow
- Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, Frankfurt am Main, Germany
- Department of Neurology & Stroke, and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Vadim V. Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russian Federation
- Neurophysics Group, Department of Neurology, Charité-University Medicine Berlin – Campus Benjamin Franklin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
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17
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Berntsen MB, Cooper NR, Hughes G, Romei V. Prefrontal transcranial alternating current stimulation improves motor sequence reproduction. Behav Brain Res 2019; 361:39-49. [PMID: 30578806 DOI: 10.1016/j.bbr.2018.12.035] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 12/01/2018] [Accepted: 12/18/2018] [Indexed: 11/18/2022]
Abstract
Cortical activity in frontal, parietal, and motor regions during sequence observation correlates with performance on sequence reproduction. Increased cortical activity observed during observation has therefore been suggested to represent increased learning. Causal relationships have been demonstrated between M1 and motor sequence reproduction and between parietal cortex and bimanual learning. However, similar effects have not been reported for frontal regions despite a number of reports implicating its involvement in encoding of motor sequences. Investigating causal relations between cortical activity and reproduction of motor sequences in parietal, frontal and primary motor regions can disentangle whether specific regions during simple observation can be selectively ascribed to encoding or reproduction or both. We designed a sensorimotor paradigm that included a strong motor sequence component, and tested the impact of individually adjusted transcranial alternating current stimulation (IAF-tACS) to prefrontal, parietal, and primary motor regions on electroencephalographic motor rhythms (alpha and beta bandwidths) during motor sequence observation and the ability to reproduce the observed sequences. Independently of the stimulated region, IAF-tACS led to a reduction in suppression in the lower beta-range relative to sham. Prefrontal IAF-tACS however, led to significant improvement in motor sequence reproduction, pinpointing the crucial role of prefrontal regions in motor sequence reproduction.
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Affiliation(s)
- Monica B Berntsen
- Centre for Brain Science, Department of Psychology, University of Essex, CO4 3SQ, United Kingdom.
| | - Nicholas R Cooper
- Centre for Brain Science, Department of Psychology, University of Essex, CO4 3SQ, United Kingdom.
| | - Gethin Hughes
- Centre for Brain Science, Department of Psychology, University of Essex, CO4 3SQ, United Kingdom
| | - Vincenzo Romei
- Centre for Brain Science, Department of Psychology, University of Essex, CO4 3SQ, United Kingdom; Dipartimento di Psicologia and Centro Studi e Ricerche in Neuroscienze Cognitive, Campus di Cesena, Universitá di Bologna, 47521 Cesena, Italy
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18
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Saltuklaroglu T, Bowers A, Harkrider AW, Casenhiser D, Reilly KJ, Jenson DE, Thornton D. EEG mu rhythms: Rich sources of sensorimotor information in speech processing. BRAIN AND LANGUAGE 2018; 187:41-61. [PMID: 30509381 DOI: 10.1016/j.bandl.2018.09.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 09/27/2017] [Accepted: 09/23/2018] [Indexed: 06/09/2023]
Affiliation(s)
- Tim Saltuklaroglu
- Department of Audiology and Speech-Language Pathology, University of Tennessee Health Sciences, Knoxville, TN 37996, USA.
| | - Andrew Bowers
- University of Arkansas, Epley Center for Health Professions, 606 N. Razorback Road, Fayetteville, AR 72701, USA
| | - Ashley W Harkrider
- Department of Audiology and Speech-Language Pathology, University of Tennessee Health Sciences, Knoxville, TN 37996, USA
| | - Devin Casenhiser
- Department of Audiology and Speech-Language Pathology, University of Tennessee Health Sciences, Knoxville, TN 37996, USA
| | - Kevin J Reilly
- Department of Audiology and Speech-Language Pathology, University of Tennessee Health Sciences, Knoxville, TN 37996, USA
| | - David E Jenson
- Department of Speech and Hearing Sciences, Elson S. Floyd College of Medicine, Spokane, WA 99210-1495, USA
| | - David Thornton
- Department of Hearing, Speech, and Language Sciences, Gallaudet University, 800 Florida Avenue NE, Washington, DC 20002, USA
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19
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Thornton D, Harkrider AW, Jenson D, Saltuklaroglu T. Sensorimotor activity measured via oscillations of EEG mu rhythms in speech and non-speech discrimination tasks with and without segmentation demands. BRAIN AND LANGUAGE 2018; 187:62-73. [PMID: 28431691 DOI: 10.1016/j.bandl.2017.03.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Revised: 01/24/2017] [Accepted: 03/31/2017] [Indexed: 06/07/2023]
Abstract
Better understanding of the role of sensorimotor processing in speech and non-speech segmentation can be achieved with more temporally precise measures. Twenty adults made same/different discriminations of speech and non-speech stimuli pairs, with and without segmentation demands. Independent component analysis of 64-channel EEG data revealed clear sensorimotor mu components, with characteristic alpha and beta peaks, localized to premotor regions in 70% of participants.Time-frequency analyses of mu components from accurate trials showed that (1) segmentation tasks elicited greater event-related synchronization immediately following offset of the first stimulus, suggestive of inhibitory activity; (2) strong late event-related desynchronization in all conditions, suggesting that working memory/covert replay contributed substantially to sensorimotor activity in all conditions; (3) stronger beta desynchronization in speech versus non-speech stimuli during stimulus presentation, suggesting stronger auditory-motor transforms for speech versus non-speech stimuli. Findings support the continued use of oscillatory approaches for helping understand segmentation and other cognitive tasks.
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Affiliation(s)
- David Thornton
- University of Tennessee Health Science Center, United States.
| | | | - David Jenson
- University of Tennessee Health Science Center, United States
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20
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Volk D, Dubinin I, Myasnikova A, Gutkin B, Nikulin VV. Generalized Cross-Frequency Decomposition: A Method for the Extraction of Neuronal Components Coupled at Different Frequencies. Front Neuroinform 2018; 12:72. [PMID: 30405385 PMCID: PMC6200871 DOI: 10.3389/fninf.2018.00072] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 09/26/2018] [Indexed: 11/15/2022] Open
Abstract
Perceptual, motor and cognitive processes are based on rich interactions between remote regions in the human brain. Such interactions can be carried out through phase synchronization of oscillatory signals. Neuronal synchronization has been primarily studied within the same frequency range, e.g., within alpha or beta frequency bands. Yet, recent research shows that neuronal populations can also demonstrate phase synchronization between different frequency ranges. An extraction of such cross-frequency interactions in EEG/MEG recordings remains, however, methodologically challenging. Here we present a new method for the robust extraction of cross-frequency phase-to-phase synchronized components. Generalized Cross-Frequency Decomposition (GCFD) reconstructs the time courses of synchronized neuronal components, their spatial filters and patterns. Our method extends the previous state of the art, Cross-Frequency Decomposition (CFD), to the whole range of frequencies: it works for any f1 and f2 whenever f1:f2 is a rational number. GCFD gives a compact description of non-linearly interacting neuronal sources on the basis of their cross-frequency phase coupling. We successfully validated the new method in simulations and tested it with real EEG recordings including resting state data and steady state visually evoked potentials (SSVEP).
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Affiliation(s)
- Denis Volk
- Interdisciplinary Scientific Center J.-V. Poncelet (CNRS UMI 2615), Moscow, Russia
| | - Igor Dubinin
- Institute for Cognitive Neuroscience of the National Research University Higher School of Economics, Moscow, Russia.,Moscow Institute of Physics and Technology, Moscow, Russia
| | - Alexandra Myasnikova
- Institute for Cognitive Neuroscience of the National Research University Higher School of Economics, Moscow, Russia
| | - Boris Gutkin
- Institute for Cognitive Neuroscience of the National Research University Higher School of Economics, Moscow, Russia.,Group for Neural Theory, Laboratoire des Neurosciences Cognitives et Computationelles INSERM U960, Department of Cognitive Studies, Ecole Normale Superieure PSL University, Paris, France
| | - Vadim V Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Neurophysics Group, Department of Neurology, Charité-Universittsmedizin Berlin, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany.,Center for Bioelectric Interfaces of the Institute for Cognitive Neuroscience of the National Research University Higher School of Economics, Moscow, Russia
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21
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Klimesch W. The frequency architecture of brain and brain body oscillations: an analysis. Eur J Neurosci 2018; 48:2431-2453. [PMID: 30281858 PMCID: PMC6668003 DOI: 10.1111/ejn.14192] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 08/19/2018] [Accepted: 09/13/2018] [Indexed: 01/04/2023]
Abstract
Research on brain oscillations has brought up a picture of coupled oscillators. Some of the most important questions that will be analyzed are, how many frequencies are there, what are the coupling principles, what their functional meaning is, and whether body oscillations follow similar coupling principles. It is argued that physiologically, two basic coupling principles govern brain as well as body oscillations: (i) amplitude (envelope) modulation between any frequencies m and n, where the phase of the slower frequency m modulates the envelope of the faster frequency n, and (ii) phase coupling between m and n, where the frequency of n is a harmonic multiple of m. An analysis of the center frequency of traditional frequency bands and their coupling principles suggest a binary hierarchy of frequencies. This principle leads to the foundation of the binary hierarchy brain body oscillation theory. Its central hypotheses are that the frequencies of body oscillations can be predicted from brain oscillations and that brain and body oscillations are aligned to each other. The empirical evaluation of the predicted frequencies for body oscillations is discussed on the basis of findings for heart rate, heart rate variability, breathing frequencies, fluctuations in the BOLD signal, and other body oscillations. The conclusion is that brain and many body oscillations can be described by a single system, where the cross talk - reflecting communication - within and between brain and body oscillations is governed by m : n phase to envelope and phase to phase coupling.
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Affiliation(s)
- Wolfgang Klimesch
- Centre of Cognitive NeuroscienceUniversity of SalzburgSalzburgAustria
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22
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Causal Shannon-Fisher Characterization of Motor/Imagery Movements in EEG. ENTROPY 2018; 20:e20090660. [PMID: 33265749 PMCID: PMC7513182 DOI: 10.3390/e20090660] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 08/30/2018] [Accepted: 08/30/2018] [Indexed: 11/30/2022]
Abstract
The electroencephalogram (EEG) is an electrophysiological monitoring method that allows us to glimpse the electrical activity of the brain. Neural oscillations patterns are perhaps the best salient feature of EEG as they are rhythmic activities of the brain that can be generated by interactions across neurons. Large-scale oscillations can be measured by EEG as the different oscillation patterns reflected within the different frequency bands, and can provide us with new insights into brain functions. In order to understand how information about the rhythmic activity of the brain during visuomotor/imagined cognitive tasks is encoded in the brain we precisely quantify the different features of the oscillatory patterns considering the Shannon–Fisher plane H×F. This allows us to distinguish the dynamics of rhythmic activities of the brain showing that the Beta band facilitate information transmission during visuomotor/imagined tasks.
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23
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Kittilstved T, Reilly KJ, Harkrider AW, Casenhiser D, Thornton D, Jenson DE, Hedinger T, Bowers AL, Saltuklaroglu T. The Effects of Fluency Enhancing Conditions on Sensorimotor Control of Speech in Typically Fluent Speakers: An EEG Mu Rhythm Study. Front Hum Neurosci 2018; 12:126. [PMID: 29670516 PMCID: PMC5893846 DOI: 10.3389/fnhum.2018.00126] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 03/16/2018] [Indexed: 01/04/2023] Open
Abstract
Objective: To determine whether changes in sensorimotor control resulting from speaking conditions that induce fluency in people who stutter (PWS) can be measured using electroencephalographic (EEG) mu rhythms in neurotypical speakers. Methods: Non-stuttering (NS) adults spoke in one control condition (solo speaking) and four experimental conditions (choral speech, delayed auditory feedback (DAF), prolonged speech and pseudostuttering). Independent component analysis (ICA) was used to identify sensorimotor μ components from EEG recordings. Time-frequency analyses measured μ-alpha (8–13 Hz) and μ-beta (15–25 Hz) event-related synchronization (ERS) and desynchronization (ERD) during each speech condition. Results: 19/24 participants contributed μ components. Relative to the control condition, the choral and DAF conditions elicited increases in μ-alpha ERD in the right hemisphere. In the pseudostuttering condition, increases in μ-beta ERD were observed in the left hemisphere. No differences were present between the prolonged speech and control conditions. Conclusions: Differences observed in the experimental conditions are thought to reflect sensorimotor control changes. Increases in right hemisphere μ-alpha ERD likely reflect increased reliance on auditory information, including auditory feedback, during the choral and DAF conditions. In the left hemisphere, increases in μ-beta ERD during pseudostuttering may have resulted from the different movement characteristics of this task compared with the solo speaking task. Relationships to findings in stuttering are discussed. Significance: Changes in sensorimotor control related feedforward and feedback control in fluency-enhancing speech manipulations can be measured using time-frequency decompositions of EEG μ rhythms in neurotypical speakers. This quiet, non-invasive, and temporally sensitive technique may be applied to learn more about normal sensorimotor control and fluency enhancement in PWS.
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Affiliation(s)
- Tiffani Kittilstved
- Department of Audiology and Speech Pathology, The University of Tennessee Health Science Center, Knoxville, TN, United States
| | - Kevin J Reilly
- Department of Audiology and Speech Pathology, The University of Tennessee Health Science Center, Knoxville, TN, United States
| | - Ashley W Harkrider
- Department of Audiology and Speech Pathology, The University of Tennessee Health Science Center, Knoxville, TN, United States
| | - Devin Casenhiser
- Department of Audiology and Speech Pathology, The University of Tennessee Health Science Center, Knoxville, TN, United States
| | - David Thornton
- Department of Audiology and Speech Pathology, The University of Tennessee Health Science Center, Knoxville, TN, United States
| | - David E Jenson
- Department of Audiology and Speech Pathology, The University of Tennessee Health Science Center, Knoxville, TN, United States
| | - Tricia Hedinger
- Department of Audiology and Speech Pathology, The University of Tennessee Health Science Center, Knoxville, TN, United States
| | - Andrew L Bowers
- Department of Communication Disorders, The University of Arkansas, Fayetteville, AR, United States
| | - Tim Saltuklaroglu
- Department of Audiology and Speech Pathology, The University of Tennessee Health Science Center, Knoxville, TN, United States
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24
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Stamoulis C, Vanderwert RE, Zeanah CH, Fox NA, Nelson CA. Neuronal networks in the developing brain are adversely modulated by early psychosocial neglect. J Neurophysiol 2017; 118:2275-2288. [PMID: 28679837 DOI: 10.1152/jn.00014.2017] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 05/30/2017] [Accepted: 07/04/2017] [Indexed: 12/19/2022] Open
Abstract
The brain's neural circuitry plays a ubiquitous role across domains in cognitive processing and undergoes extensive reorganization during the course of development in part as a result of experience. In this study we investigated the effects of profound early psychosocial neglect associated with institutional rearing on the development of task-independent brain networks, estimated from longitudinally acquired electroencephalographic (EEG) data from <30 to 96 mo, in three cohorts of children from the Bucharest Early Intervention Project (BEIP), including abandoned children reared in institutions who were randomly assigned either to a foster care intervention or to remain in care as usual and never-institutionalized children. Two aberrantly connected brain networks were identified in children that had been reared in institutions: 1) a hyperconnected parieto-occipital network, which included cortical hubs and connections that may partially overlap with default-mode network, and 2) a hypoconnected network between left temporal and distributed bilateral regions, both of which were aberrantly connected across neural oscillations. This study provides the first evidence of the adverse effects of early psychosocial neglect on the wiring of the developing brain. Given these networks' potentially significant role in various cognitive processes, including memory, learning, social communication, and language, these findings suggest that institutionalization in early life may profoundly impact the neural correlates underlying multiple cognitive domains, in ways that may not be fully reversible in the short term.NEW & NOTEWORTHY This paper provides first evidence that early psychosocial neglect associated with institutional rearing profoundly affects the development of the brain's neural circuitry. Using longitudinally acquired electrophysiological data from the Bucharest Early Intervention Project (BEIP), the paper identifies multiple task-independent networks that are abnormally connected (hyper- or hypoconnected) in children reared in institutions compared with never-institutionalized children. These networks involve spatially distributed brain areas and their abnormal connections may adversely impact neural information processing across cognitive domains.
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Affiliation(s)
- Catherine Stamoulis
- Harvard Medical School, Boston, Massachusetts; .,Division of Adolescent Medicine, Boston Children's Hospital, Boston, Massachusetts.,Department of Neurology, Boston Children's Hospital, Boston, Massachusetts
| | | | - Charles H Zeanah
- Department of Psychiatry and Behavioral Sciences, Tulane University, New Orleans, Louisiana
| | - Nathan A Fox
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, Maryland
| | - Charles A Nelson
- Harvard Medical School, Boston, Massachusetts.,Division of Developmental Medicine, Boston Children's Hospital, Boston, Massachusetts.,Graduate School of Education, Harvard University, Cambridge, Massachusetts; and
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25
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Saltuklaroglu T, Harkrider AW, Thornton D, Jenson D, Kittilstved T. EEG Mu (µ) rhythm spectra and oscillatory activity differentiate stuttering from non-stuttering adults. Neuroimage 2017; 153:232-245. [PMID: 28400266 PMCID: PMC5569894 DOI: 10.1016/j.neuroimage.2017.04.022] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 01/24/2017] [Accepted: 04/08/2017] [Indexed: 10/19/2022] Open
Abstract
Stuttering is linked to sensorimotor deficits related to internal modeling mechanisms. This study compared spectral power and oscillatory activity of EEG mu (μ) rhythms between persons who stutter (PWS) and controls in listening and auditory discrimination tasks. EEG data were analyzed from passive listening in noise and accurate (same/different) discrimination of tones or syllables in quiet and noisy backgrounds. Independent component analysis identified left and/or right μ rhythms with characteristic alpha (α) and beta (β) peaks localized to premotor/motor regions in 23 of 27 people who stutter (PWS) and 24 of 27 controls. PWS produced μ spectra with reduced β amplitudes across conditions, suggesting reduced forward modeling capacity. Group time-frequency differences were associated with noisy conditions only. PWS showed increased μ-β desynchronization when listening to noise and early in discrimination events, suggesting evidence of heightened motor activity that might be related to forward modeling deficits. PWS also showed reduced μ-α synchronization in discrimination conditions, indicating reduced sensory gating. Together these findings indicate spectral and oscillatory analyses of μ rhythms are sensitive to stuttering. More specifically, they can reveal stuttering-related sensorimotor processing differences in listening and auditory discrimination that also may be influenced by basal ganglia deficits.
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Affiliation(s)
- Tim Saltuklaroglu
- University of Tennessee Health Science Center, Department of Audiology and Speech Pathology, 578 South Stadium Hall, Knoxville, TN 37996, USA
| | - Ashley W Harkrider
- University of Tennessee Health Science Center, Department of Audiology and Speech Pathology, 578 South Stadium Hall, Knoxville, TN 37996, USA.
| | - David Thornton
- University of Tennessee Health Science Center, Department of Audiology and Speech Pathology, 578 South Stadium Hall, Knoxville, TN 37996, USA
| | - David Jenson
- University of Tennessee Health Science Center, Department of Audiology and Speech Pathology, 578 South Stadium Hall, Knoxville, TN 37996, USA
| | - Tiffani Kittilstved
- University of Tennessee Health Science Center, Department of Audiology and Speech Pathology, 578 South Stadium Hall, Knoxville, TN 37996, USA
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26
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Hsu WY. Enhancing the performance of motor imagery EEG classification using phase features. Clin EEG Neurosci 2015; 46:113-8. [PMID: 25404753 DOI: 10.1177/1550059414555123] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2014] [Accepted: 09/19/2014] [Indexed: 11/17/2022]
Abstract
An electroencephalogram recognition system considering phase features is proposed to enhance the performance of motor imagery classification in this study. It mainly consists of feature extraction, feature selection and classification. Surface Laplacian filter is used for background removal. Several potential features, including phase features, are then extracted to enhance the classification accuracy. Next, genetic algorithm is used to select sub-features from feature combination. Finally, selected features are classified by extreme learning machine. Compared with "without phase features" and linear discriminant analysis on motor imagery data from 2 data sets, the results denote that the proposed system achieves enhanced performance, which is suitable for the brain-computer interface applications.
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Affiliation(s)
- Wei-Yen Hsu
- Department of Information Management, National Chung Cheng University, Chiayi County, Taiwan Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi County, Taiwan
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Distinct roles for alpha- and beta-band oscillations during mental simulation of goal-directed actions. J Neurosci 2015; 34:14783-92. [PMID: 25355230 DOI: 10.1523/jneurosci.2039-14.2014] [Citation(s) in RCA: 125] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Rhythmic neural activity within the alpha (8-12 Hz) and beta (15-25 Hz) frequency bands is modulated during actual and imagined movements. Changes in these rhythms provide a mechanism to select relevant neuronal populations, although the relative contributions of these rhythms remain unclear. Here we use MEG to investigate changes in oscillatory power while healthy human participants imagined grasping a cylinder oriented at different angles. This paradigm allowed us to study the neural signals involved in the simulation of a movement in the absence of signals related to motor execution and sensory reafference. Movement selection demands were manipulated by exploiting the fact that some object orientations evoke consistent grasping movements, whereas others are compatible with both overhand and underhand grasping. By modulating task demands, we show a functional dissociation of the alpha- and beta-band rhythms. As movement selection demands increased, alpha-band oscillatory power increased in the sensorimotor cortex ipsilateral to the arm used for imagery, whereas beta-band power concurrently decreased in the contralateral sensorimotor cortex. The same pattern emerged when motor imagery trials were compared with a control condition, providing converging evidence for the functional dissociation of the two rhythms. These observations call for a re-evaluation of the role of sensorimotor rhythms. We propose that neural oscillations in the alpha-band mediate the allocation of computational resources by disengaging task-irrelevant cortical regions. In contrast, the reduction of neural oscillations in the beta-band is directly related to the disinhibition of neuronal populations involved in the computations of movement parameters.
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Abstract
In this study, an electroencephalogram (EEG) analysis system combined with feature selection, is proposed to enhance the classification of motor imagery (MI) data. It principally comprises feature extraction, feature selection, and classification. First, several features, including adaptive autoregressive (AAR) parameters, spectral power, asymmetry ratio, coherence and phase-locking value are extracted for subsequent classification. A genetic algorithm is then used to select features from the combination of the aforementioned features. Finally, the selected features are classified by support vector machine (SVM). Compared with "without feature selection" and back-propagation neural network (BPNN) on MI data from 2 data sets, the proposed system achieves better classification accuracy and is suitable for the applications of brain-computer interface (BCI).
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Affiliation(s)
- Wei-Yen Hsu
- Department of Information Management, National Chung Cheng University, Chiayi County, Taiwan
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29
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Haegens S, Cousijn H, Wallis G, Harrison PJ, Nobre AC. Inter- and intra-individual variability in alpha peak frequency. Neuroimage 2014; 92:46-55. [PMID: 24508648 PMCID: PMC4013551 DOI: 10.1016/j.neuroimage.2014.01.049] [Citation(s) in RCA: 330] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Revised: 01/19/2014] [Accepted: 01/27/2014] [Indexed: 11/19/2022] Open
Abstract
Converging electrophysiological evidence suggests that the alpha rhythm plays an important and active role in cognitive processing. Here, we systematically studied variability in posterior alpha peak frequency both between and within subjects. We recorded brain activity using MEG in 51 healthy human subjects under three experimental conditions - rest, passive visual stimulation and an N-back working memory paradigm, using source reconstruction methods to separate alpha activity from parietal and occipital sources. We asked how alpha peak frequency differed within subjects across cognitive conditions and regions of interest, and looked at the distribution of alpha peak frequency between subjects. In both regions we observed an increase of alpha peak frequency from resting state and passive visual stimulation conditions to the N-back paradigm, with a significantly higher alpha peak frequency in the 2-back compared to the 0-back condition. There was a trend for a greater increase in alpha peak frequency during the N-back task in the occipital vs. parietal cortex. The average alpha peak frequency across all subjects, conditions, and regions of interest was 10.3 Hz with a within-subject SD of 0.9 Hz and a between-subject SD of 2.8 Hz. We also measured beta peak frequencies, and except in the parietal cortex during rest, found no indication of a strictly harmonic relationship with alpha peak frequencies. We conclude that alpha peak frequency in posterior regions increases with increasing cognitive demands, and that the alpha rhythm operates across a wider frequency range than the 8-12 Hz band many studies tend to include in their analysis. Thus, using a fixed and limited alpha frequency band might bias results against certain subjects and conditions.
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Affiliation(s)
- Saskia Haegens
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, USA; Cognitive Neuroscience and Schizophrenia Program, Nathan Kline Institute, Orangeburg, USA.
| | - Helena Cousijn
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK; Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK
| | - George Wallis
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK; Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Paul J Harrison
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Anna C Nobre
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK; Department of Experimental Psychology, University of Oxford, Oxford, UK
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Dähne S, Nikulin VV, Ramírez D, Schreier PJ, Müller KR, Haufe S. Finding brain oscillations with power dependencies in neuroimaging data. Neuroimage 2014; 96:334-48. [PMID: 24721331 DOI: 10.1016/j.neuroimage.2014.03.075] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2014] [Accepted: 03/27/2014] [Indexed: 11/29/2022] Open
Abstract
Phase synchronization among neuronal oscillations within the same frequency band has been hypothesized to be a major mechanism for communication between different brain areas. On the other hand, cross-frequency communications are more flexible allowing interactions between oscillations with different frequencies. Among such cross-frequency interactions amplitude-to-amplitude interactions are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetoencephalography (EEG/MEG) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low. In addition to using cSPoC for the analysis of cross-frequency interactions in the same subject, we show that it can also be utilized for studying amplitude dynamics of neuronal oscillations across subjects. We assess the performance of cSPoC in simulations as well as in three distinctively different analysis scenarios of real EEG data, each involving several subjects. In the simulations, cSPoC outperforms unsupervised state-of-the-art approaches. In the analysis of real EEG recordings, we demonstrate excellent unsupervised discovery of meaningful power-to-power couplings, within as well as across subjects and frequency bands.
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Affiliation(s)
- Sven Dähne
- Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
| | - Vadim V Nikulin
- Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité University Medicine Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany; Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia.
| | - David Ramírez
- Signal and System Theory Group, Department of Electrical Engineering and Information Technology, Universität Paderborn, Paderborn, Germany
| | - Peter J Schreier
- Signal and System Theory Group, Department of Electrical Engineering and Information Technology, Universität Paderborn, Paderborn, Germany
| | - Klaus-Robert Müller
- Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany; Bernstein Focus Neurotechnology, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul Republic of Korea.
| | - Stefan Haufe
- Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany; Bernstein Focus Neurotechnology, Berlin, Germany; Neural Engineering Group, Department of Biomedical Engineering, The City College of New York, New York City, USA.
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31
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Hsu WY. Embedded grey relation theory in Hopfield neural network: application to motor imagery EEG recognition. Clin EEG Neurosci 2013; 44:257-64. [PMID: 23536381 DOI: 10.1177/1550059413477090] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this study, grey-based Hopfield neural network (GHNN), is proposed for the unsupervised analysis of motor imagery (MI) electroencephalogram (EEG) data. Combined with segment selection and feature extraction, GHNN is used for the recognition of left and right MI data. A Gaussian-like filter is proposed to reduce noise, to further enhance performance of active segment selection. Features are extracted by coherence from wavelet data, and then discriminated by GHNN, which is an unsupervised approach suitable for the online classification of nonstationary biomedical signals. Compared to EEG data without segment selection, several usual features, and classifiers, the proposed system is potentially an analytic approach in brain-computer interface (BCI) applications.
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Affiliation(s)
- Wei-Yen Hsu
- Department of Information Management, National Chung Cheng University, Taiwan
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32
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HSU WEIYEN. Application of quantum-behaved particle swarm optimization to motor imagery EEG classification. Int J Neural Syst 2013; 23:1350026. [PMID: 24156669 DOI: 10.1142/s0129065713500263] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.
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Affiliation(s)
- WEI-YEN HSU
- Department of Information Management, Advanced Institute of Manufacturing with High-tech Innovations, National Chung Cheng University, No. 168, Sec. 1, University Rd., Min-Hsiung Township, Chia-yi County 621, Taiwan
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HSU WEIYEN. SINGLE-TRIAL MOTOR IMAGERY CLASSIFICATION USING ASYMMETRY RATIO, PHASE RELATION, WAVELET-BASED FRACTAL, AND THEIR SELECTED COMBINATION. Int J Neural Syst 2013; 23:1350007. [PMID: 23578057 DOI: 10.1142/s012906571350007x] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
An electroencephalogram (EEG) analysis system is proposed for single-trial classification of motor imagery (MI) data in this study. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system mainly consists of enhanced active segment selection, feature extraction, feature selection and classification. In addition to the original use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. We then extract several features, including spectral power and asymmetry ratio, coherence and phase-locking value, and multiresolution fractal feature vector, for subsequent classification. Next, genetic algorithm (GA) is used to select features from the combination of above-mentioned features. Finally, support vector machine (SVM) is used for classification. Compared with "without enhanced active segment selection," several potential features and linear discriminant analysis (LDA) on MI data from two data sets for 10 subjects, the results indicate that the proposed method achieves 86.7% average classification accuracy, which is promising in BCI applications.
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Affiliation(s)
- WEI-YEN HSU
- Department of Information Management, National Chung Cheng University, No. 168, Sec. 1, University Rd., Min-Hsiung Township, Chia-yi County 621, Taiwan
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34
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van Albada SJ, Robinson PA. Relationships between Electroencephalographic Spectral Peaks Across Frequency Bands. Front Hum Neurosci 2013; 7:56. [PMID: 23483663 PMCID: PMC3586764 DOI: 10.3389/fnhum.2013.00056] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2012] [Accepted: 02/11/2013] [Indexed: 11/18/2022] Open
Abstract
The degree to which electroencephalographic spectral peaks are independent, and the relationships between their frequencies have been debated. A novel fitting method was used to determine peak parameters in the range 2-35 Hz from a large sample of eyes-closed spectra, and their interrelationships were investigated. Findings were compared with a mean-field model of thalamocortical activity, which predicts near-harmonic relationships between peaks. The subject set consisted of 1424 healthy subjects from the Brain Resource International Database. Peaks in the theta range occurred on average near half the alpha peak frequency, while peaks in the beta range tended to occur near twice and three times the alpha peak frequency on an individual-subject basis. Moreover, for the majority of subjects, alpha peak frequencies were significantly positively correlated with frequencies of peaks in the theta and low and high beta ranges. Such a harmonic progression agrees semiquantitatively with theoretical predictions from the mean-field model. These findings indicate a common or analogous source for different rhythms, and help to define appropriate individual frequency bands for peak identification.
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Affiliation(s)
- S. J. van Albada
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre and Jülich-Aachen Research AllianceJülich, Germany
- School of Physics, The University of SydneySydney, NSW, Australia
- Brain Dynamics Center, Sydney Medical School – Western, University of SydneySydney, NSW, Australia
| | - P. A. Robinson
- School of Physics, The University of SydneySydney, NSW, Australia
- Brain Dynamics Center, Sydney Medical School – Western, University of SydneySydney, NSW, Australia
- Center for Integrated Research and Understanding of SleepGlebe, NSW, Australia
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35
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Johnson ML, Bodenhamer-Davis E, Bailey LJ, Gates MS. Spectral Dynamics and Therapeutic Implications of the Theta/Alpha Crossover in Alpha-Theta Neurofeedback. ACTA ACUST UNITED AC 2013. [DOI: 10.1080/10874208.2013.758968] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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36
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Zhou Z, Wan B. Wavelet packet-based independent component analysis for feature extraction from motor imagery EEG of complex movements. Clin Neurophysiol 2012; 123:1779-88. [DOI: 10.1016/j.clinph.2012.02.071] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2009] [Revised: 02/15/2012] [Accepted: 02/16/2012] [Indexed: 11/16/2022]
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37
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Tangermann M, Müller KR, Aertsen A, Birbaumer N, Braun C, Brunner C, Leeb R, Mehring C, Miller KJ, Müller-Putz GR, Nolte G, Pfurtscheller G, Preissl H, Schalk G, Schlögl A, Vidaurre C, Waldert S, Blankertz B. Review of the BCI Competition IV. Front Neurosci 2012; 6:55. [PMID: 22811657 PMCID: PMC3396284 DOI: 10.3389/fnins.2012.00055] [Citation(s) in RCA: 347] [Impact Index Per Article: 28.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2011] [Accepted: 03/30/2012] [Indexed: 11/13/2022] Open
Abstract
The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.
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Affiliation(s)
- Michael Tangermann
- Machine Learning Laboratory, Berlin Institute of Technology Berlin, Germany
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38
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Nikulin VV, Nolte G, Curio G. Cross-frequency decomposition: a novel technique for studying interactions between neuronal oscillations with different frequencies. Clin Neurophysiol 2012; 123:1353-60. [PMID: 22217959 DOI: 10.1016/j.clinph.2011.12.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2011] [Revised: 12/05/2011] [Accepted: 12/06/2011] [Indexed: 10/14/2022]
Abstract
OBJECTIVE We present a novel method for the extraction of neuronal components showing cross-frequency phase synchronization. METHODS In general the method can be applied for the detection of phase interactions between components with frequencies f1 and f2, where f2 ≈ rf1 and r is some integer. We refer to the method as cross-frequency decomposition (CFD), which consists of the following steps: (a) extraction of f1-oscillations with the spatio-spectral decomposition algorithm (SSD); (b) frequency modification of the f1-oscillations obtained with SSD; and (c) finding f2-oscillations synchronous with f1-oscillations using least-squares estimation. RESULTS Our simulations showed that CFD was capable of recovering interacting components even when the signal-to-noise ratio was as low as 0.01. An application of CFD to the real EEG data demonstrated that cross-frequency phase synchronization between alpha and beta oscillations can originate from the same or remote neuronal populations. CONCLUSIONS CFD allows a compact representation of the sets of interacting components. The application of CFD to EEG data allows differentiating cross-frequency synchronization arising due to genuine neurophysiological interactions from interactions occurring due to quasi-sinusoidal waveform of neuronal oscillations. SIGNIFICANCE CFD is a method capable of extracting cross-frequency coupled neuronal oscillations even in the presence of strong noise.
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Affiliation(s)
- Vadim V Nikulin
- Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité - University Medicine Berlin, D-12200 Berlin, Germany.
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39
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Nikulin VV, Linkenkaer-Hansen K, Nolte G, Curio G. Non-zero mean and asymmetry of neuronal oscillations have different implications for evoked responses. Clin Neurophysiol 2010; 121:186-93. [DOI: 10.1016/j.clinph.2009.09.028] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2009] [Revised: 09/02/2009] [Accepted: 09/04/2009] [Indexed: 10/20/2022]
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40
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Kramer MA, Roopun AK, Carracedo LM, Traub RD, Whittington MA, Kopell NJ. Rhythm generation through period concatenation in rat somatosensory cortex. PLoS Comput Biol 2008; 4:e1000169. [PMID: 18773075 PMCID: PMC2518953 DOI: 10.1371/journal.pcbi.1000169] [Citation(s) in RCA: 85] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2008] [Accepted: 07/29/2008] [Indexed: 11/18/2022] Open
Abstract
Rhythmic voltage oscillations resulting from the summed activity of neuronal populations occur in many nervous systems. Contemporary observations suggest that coexistent oscillations interact and, in time, may switch in dominance. We recently reported an example of these interactions recorded from in vitro preparations of rat somatosensory cortex. We found that following an initial interval of coexistent gamma ( approximately 25 ms period) and beta2 ( approximately 40 ms period) rhythms in the superficial and deep cortical layers, respectively, a transition to a synchronous beta1 ( approximately 65 ms period) rhythm in all cortical layers occurred. We proposed that the switch to beta1 activity resulted from the novel mechanism of period concatenation of the faster rhythms: gamma period (25 ms)+beta2 period (40 ms) = beta1 period (65 ms). In this article, we investigate in greater detail the fundamental mechanisms of the beta1 rhythm. To do so we describe additional in vitro experiments that constrain a biologically realistic, yet simplified, computational model of the activity. We use the model to suggest that the dynamic building blocks (or motifs) of the gamma and beta2 rhythms combine to produce a beta1 oscillation that exhibits cross-frequency interactions. Through the combined approach of in vitro experiments and mathematical modeling we isolate the specific components that promote or destroy each rhythm. We propose that mechanisms vital to establishing the beta1 oscillation include strengthened connections between a population of deep layer intrinsically bursting cells and a transition from antidromic to orthodromic spike generation in these cells. We conclude that neural activity in the superficial and deep cortical layers may temporally combine to generate a slower oscillation.
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Affiliation(s)
- Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America.
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41
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Miranda de Sá AMFL, Infantosi AFC. Evaluating the relationship of non-phase locked activities in the electroencephalogram during intermittent stimulation: a partial coherence-based approach. Med Biol Eng Comput 2007; 45:635-42. [PMID: 17611790 DOI: 10.1007/s11517-007-0191-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2006] [Accepted: 04/30/2007] [Indexed: 10/23/2022]
Abstract
Partial coherence estimate between two signals removing the contribution of a periodic, deterministic one is proposed for measuring the coherence between two ongoing eletroencephalografic (EEG) activities collected at distinct cortical regions under sensory stimulation. The estimator expression was derived and shown to be independent of the stimulating signal. Simulations were used for obtaining the critical values for this coherence estimate. The technique was also evaluated throughout simulations and next applied to the EEG from 12 subjects under intermittent photic stimulation at 4 and 6 Hz. In both simulation and EEG data, major differences between partial and simple coherences occurred at the stimulation frequency and harmonics, except for those falling within the alpha band. These findings suggest that the technique is highly selective in removing the contribution of the periodic source. They also indicate high coherence values of the ongoing EEG within the alpha band.
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Abstract
The present review summarizes the research in EEG performed by our group during the last 5 years. Our studies have been focussed on two areas: studies of variability and correlations in the oscillations during resting conditions of normal subjects, and the abnormalities related to type 1 diabetes. Recordings in normal subjects showed that also under standardized conditions with regular cycles of closed and open eyes, there is a temporal variability of the spectral components in EEG that necessitates samples>124 s in order to achieve estimates of alpha power with a coefficient of variation<0.1 in all recording channels (brain regions). The temporal variability in alpha and beta power demonstrates long-range temporal correlations, i.e., periods of large power (alpha or beta) are more likely to be followed by large power, and vice versa. The long-range temporal correlations were reproducible, especially during the closed-eyes condition, stronger in males than females, and not age dependent. In patients with type 1 diabetes, the alpha and beta power components were both decreased with similar nadirs in the posterior temporal regions, and the slow spectral components were increased in the frontal regions. The cognitive function was presently not studied but in a group of adolescents with diabetes we found a correlation between the presence of slow activity and the number of hypoglycaemic episodes. The loss of alpha and beta power was highly correlated which initiated a study of the normal alpha-beta correlation. A significant 1:2 phase synchronization was present between alpha and beta oscillations with a phase lag of about pi/2 in all electrode derivations. The strong frequency relationship between the resting beta and alpha oscillations suggested that they are generated by a common mechanism.
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Affiliation(s)
- Tom Brismar
- Karolinska Institutet, Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden.
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43
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Nikulin VV, Brismar T. Phase synchronization between alpha and beta oscillations in the human electroencephalogram. Neuroscience 2006; 137:647-57. [PMID: 16338092 DOI: 10.1016/j.neuroscience.2005.10.031] [Citation(s) in RCA: 86] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2005] [Revised: 10/13/2005] [Accepted: 10/17/2005] [Indexed: 11/19/2022]
Abstract
Coordination of neuronal oscillations generated at different frequencies has been hypothesized to be an important feature of integrative brain functions. The present study aimed at the evaluation of the cross-frequency phase synchronization between electroencephalographic alpha and beta oscillations. The amplitude and phase information were extracted from electroencephalograms recorded in 176 healthy human subjects using an analytic signal approach based on the Hilbert transform. The results reliably demonstrated the presence of phase synchronization between alpha and beta oscillations, with a maximum in the occipito-parietal areas. The phase difference between alpha and beta oscillations showed characteristic peaks at about 2 and -1 radians, which were common for many subjects and electrodes. A specific phase difference might reflect similarity in the organization and interconnections of the networks generating alpha and beta oscillations across the entire cortex. Beta oscillations, which are phase-locked to alpha oscillations--alpha-synchronous beta oscillations--were largest in the occipito-parietal area with a second smaller maximum in the frontal area, thus demonstrating a topography, which was different from the conventional alpha and beta oscillations. The strength of the alpha-synchronous beta oscillations was not exclusively defined by the amplitude of the alpha rhythm indicating that they represent a distinct feature of the spontaneous electroencephalogram, which allows for a refined discrimination of the dynamics of beta oscillations.
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Affiliation(s)
- V V Nikulin
- Department of Clinical Neuroscience, Karolinska Institutet, Clinical Neurophysiology, Karolinska Hospital R2:01, S-17176, Stockholm, Sweden.
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