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Davis JJJ, Schübeler F, Kozma R. Information-Theoretical Analysis of the Cycle of Creation of Knowledge and Meaning in Brains under Multiple Cognitive Modalities. SENSORS (BASEL, SWITZERLAND) 2024; 24:1605. [PMID: 38475141 DOI: 10.3390/s24051605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 02/21/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
It is of great interest to develop advanced sensory technologies allowing non-invasive monitoring of neural correlates of cognitive processing in people performing everyday tasks. A lot of progress has been reported in recent years in this research area using scalp EEG arrays, but the high level of noise in the electrode signals poses a lot of challenges. This study presents results of detailed statistical analysis of experimental data on the cycle of creation of knowledge and meaning in human brains under multiple cognitive modalities. We measure brain dynamics using a HydroCel Geodesic Sensor Net, 128-electrode dense-array electroencephalography (EEG). We compute a pragmatic information (PI) index derived from analytic amplitude and phase, by Hilbert transforming the EEG signals of 20 participants in six modalities, which combine various audiovisual stimuli, leading to different mental states, including relaxed and cognitively engaged conditions. We derive several relevant measures to classify different brain states based on the PI indices. We demonstrate significant differences between engaged brain states that require sensory information processing to create meaning and knowledge for intentional action, and relaxed-meditative brain states with less demand on psychophysiological resources. We also point out that different kinds of meanings may lead to different brain dynamics and behavioral responses.
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Affiliation(s)
- Joshua J J Davis
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Physics & Ian Kirk's Lab., Centre for Brain Research, The University of Auckland, Auckland 1142, New Zealand
| | | | - Robert Kozma
- Department of Mathematics, University of Memphis, Memphis, TN 38152, USA
- School of Informatics, Obuda University, H-1034 Budapest, Hungary
- Kozmos Research Laboratories, Boston, MA 02215, USA
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Parise AG, Oliveira TFDC, Debono MW, Souza GM. The Electrome of a Parasitic Plant in a Putative State of Attention Increases the Energy of Low Band Frequency Waves: A Comparative Study with Neural Systems. PLANTS (BASEL, SWITZERLAND) 2023; 12:2005. [PMID: 37653922 PMCID: PMC10224360 DOI: 10.3390/plants12102005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 09/02/2023]
Abstract
Selective attention is an important cognitive phenomenon that allows organisms to flexibly engage with certain environmental cues or activities while ignoring others, permitting optimal behaviour. It has been proposed that selective attention can be present in many different animal species and, more recently, in plants. The phenomenon of attention in plants would be reflected in its electrophysiological activity, possibly being observable through electrophytographic (EPG) techniques. Former EPG time series obtained from the parasitic plant Cuscuta racemosa in a putative state of attention towards two different potential hosts, the suitable bean (Phaseolus vulgaris) and the unsuitable wheat (Triticum aestivum), were revisited. Here, we investigated the potential existence of different band frequencies (including low, delta, theta, mu, alpha, beta, and gamma waves) using a protocol adapted from neuroscientific research. Average band power (ABP) was used to analyse the energy distribution of each band frequency in the EPG signals, and time dispersion analysis of features (TDAF) was used to explore the variations in the energy of each band. Our findings indicated that most band waves were centred in the lower frequencies. We also observed that C. racemosa invested more energy in these low-frequency waves when suitable hosts were present. However, we also noted peaks of energy investment in all the band frequencies, which may be linked to extremely low oscillatory electrical signals in the entire tissue. Overall, the presence of suitable hosts induced a higher energy power, which supports the hypothesis of attention in plants. We further discuss and compare our results with generic neural systems.
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Affiliation(s)
| | - Thiago Francisco de Carvalho Oliveira
- Laboratory of Plant Cognition and Electrophysiology (LACEV), Department of Botany, Institute of Biology, Federal University of Pelotas, Capão do Leão 96160-000, RS, Brazil; (T.F.d.C.O.)
| | | | - Gustavo Maia Souza
- Laboratory of Plant Cognition and Electrophysiology (LACEV), Department of Botany, Institute of Biology, Federal University of Pelotas, Capão do Leão 96160-000, RS, Brazil; (T.F.d.C.O.)
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Xu Z, Karwowski W, Çakıt E, Reineman-Jones L, Murata A, Aljuaid A, Sapkota N, Hancock P. Nonlinear dynamics of EEG responses to unmanned vehicle visual detection with different levels of task difficulty. APPLIED ERGONOMICS 2023; 111:104045. [PMID: 37178489 DOI: 10.1016/j.apergo.2023.104045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 05/05/2023] [Accepted: 05/08/2023] [Indexed: 05/15/2023]
Abstract
The main objective of this study was to examine the presence of chaos in the EEG recordings of brain activity under simulated unmanned ground vehicle visual detection scenarios with different levels of task difficulty. One hundred and fifty people participated in the experiment and completed four visual detection task scenarios: (1) change detection, (2) a threat detection task, (3) a dual-task with different change detection task rates, and (4) a dual-task with different threat detection task rates. We used the largest Lyapunov exponent and correlation dimension of the EEG data and performed 0-1 tests on the EEG data. The results revealed a change in the level of nonlinearity in the EEG data corresponding to different levels of cognitive task difficulty. The differences in EEG nonlinearity measures among the studied levels of task difficulty, as well as between a single task scenario and a dual-task scenario, have also been assessed. The results increase our understanding of the nature of unmanned systems' operational requirements.
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Affiliation(s)
- Ziqing Xu
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, 32816-2993, USA
| | - Waldemar Karwowski
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, 32816-2993, USA
| | - Erman Çakıt
- Department of Industrial Engineering, Gazi University, 06570, Ankara, Turkey.
| | - Lauren Reineman-Jones
- Autonomous Mobility Simulation and Training Lab, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Atsuo Murata
- Department of Intelligent Mechanical Systems, Graduate School of Natural Science and Technology, Okayama University, Okayama, 700-8530, Japan
| | - Awad Aljuaid
- Department of Industrial Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
| | - Nabin Sapkota
- Department of Engineering Technology, Northwestern State University of Louisiana, Natchitoches, 71497, USA
| | - Peter Hancock
- Department of Psychology, University of Central Florida, Orlando, FL, 32816-2993, USA
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A Pilot Investigation of Visual Pathways in Patients with Mild Traumatic Brain Injury. Neurol Int 2023; 15:534-548. [PMID: 36976675 PMCID: PMC10054811 DOI: 10.3390/neurolint15010032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/02/2023] [Accepted: 03/09/2023] [Indexed: 03/22/2023] Open
Abstract
In this study, we examined visual processing within primary visual areas (V1) in normal and visually impaired individuals who exhibit significant visual symptomology due to sports-related mild traumatic brain injury (mTBI). Five spatial frequency stimuli were applied to the right, left and both eyes in order to assess the visual processing of patients with sports-related mild traumatic brain injuries who exhibited visual abnormalities, i.e., photophobia, blurriness, etc., and controls. The measurement of the left/right eye and binocular integration was accomplished via the quantification of the spectral power and visual event-related potentials. The principal results have shown that the power spectral density (PSD) measurements display a distinct loss in the alpha band-width range, which corresponded to more instances of medium-sized receptive field loss. Medium-size receptive field loss may correspond to parvocellular (p-cell) processing deprecation. Our major conclusion provides a new measurement, using PSD analysis to assess mTBI conditions from primary V1 areas. The statistical analysis demonstrated significant differences between the mTBI and control cohort in the Visual Evoked Potentials (VEP) amplitude responses and PSD measurements. Additionally, the PSD measurements were able to assess the improvement in the mTBI primary visual areas over time through rehabilitation.
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Xie Y, Oniga S. Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:1932. [PMID: 36850530 PMCID: PMC9961359 DOI: 10.3390/s23041932] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
In brain-computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are commonly used to detect participant intent. Many factors, including low signal-to-noise ratios and few high-quality samples, make MI classification difficult. In order for BCI systems to function, MI-EEG signals must be studied. In pattern recognition and other fields, deep learning approaches have recently been successfully applied. In contrast, few effective deep learning algorithms have been applied to BCI systems, especially MI-based systems. In this paper, we address these problems from two aspects based on the characteristics of EEG signals: first, we proposed a combined time-frequency domain data enhancement method. This method guarantees that the size of the training data is effectively increased while maintaining the intrinsic composition of the data. Second, our design consists of a parallel CNN that takes both raw EEG images and images transformed through continuous wavelet transform (CWT) as inputs. We conducted classification experiments on a public data set to verify the effectiveness of the algorithm. According to experimental results based on the BCI Competition IV Dataset2a, the average classification accuracy is 97.61%. A comparison of the proposed algorithm with other algorithms shows that it performs better in classification. The algorithm can be used to improve the classification performance of MI-based BCIs and BCI systems created for people with disabilities.
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Affiliation(s)
- Yu Xie
- Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary
| | - Stefan Oniga
- Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary
- North University Center of Baia Mare, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
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Poorganji M, Zomorrodi R, Zrenner C, Bansal A, Hawco C, Hill AT, Hadas I, Rajji TK, Chen R, Zrenner B, Voineskos D, Blumberger DM, Daskalakis ZJ. Pre-Stimulus Power but Not Phase Predicts Prefrontal Cortical Excitability in TMS-EEG. BIOSENSORS 2023; 13:220. [PMID: 36831986 PMCID: PMC9953459 DOI: 10.3390/bios13020220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/10/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
The cortical response to transcranial magnetic stimulation (TMS) has notable inter-trial variability. One source of this variability can be the influence of the phase and power of pre-stimulus neuronal oscillations on single-trial TMS responses. Here, we investigate the effect of brain oscillatory activity on TMS response in 49 distinct healthy participants (64 datasets) who had received single-pulse TMS over the left dorsolateral prefrontal cortex. Across all frequency bands of theta (4-7 Hz), alpha (8-13 Hz), and beta (14-30 Hz), there was no significant effect of pre-TMS phase on single-trial cortical evoked activity. After high-powered oscillations, whether followed by a TMS pulse or not, the subsequent activity was larger than after low-powered oscillations. We further defined a measure, corrected_effect, to enable us to investigate brain responses to the TMS pulse disentangled from the power of ongoing (spontaneous) oscillations. The corrected_effect was significantly different from zero (meaningful added effect of TMS) only in theta and beta bands. Our results suggest that brain state prior to stimulation might play some role in shaping the subsequent TMS-EEG response. Specifically, our findings indicate that the power of ongoing oscillatory activity, but not phase, can influence brain responses to TMS. Aligning the TMS pulse with specific power thresholds of an EEG signal might therefore reduce variability in neurophysiological measurements and also has the potential to facilitate more robust therapeutic effects of stimulation.
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Affiliation(s)
- Mohsen Poorganji
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Reza Zomorrodi
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
| | - Christoph Zrenner
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Institute for Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Aiyush Bansal
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
| | - Colin Hawco
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Aron T. Hill
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC 3125, Australia
| | - Itay Hadas
- Department of Psychiatry, School of Medicine, University of California San Diego, La Jolla, CA 92093-0603, USA
| | - Tarek K. Rajji
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Robert Chen
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Brigitte Zrenner
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Daphne Voineskos
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Daniel M. Blumberger
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Zafiris J. Daskalakis
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, School of Medicine, University of California San Diego, La Jolla, CA 92093-0603, USA
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Maria Pani S, Saba L, Fraschini M. Clinical applications of EEG power spectra aperiodic component analysis: a mini-review. Clin Neurophysiol 2022; 143:1-13. [DOI: 10.1016/j.clinph.2022.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/09/2022] [Accepted: 08/11/2022] [Indexed: 11/03/2022]
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H Myers M, Hossain G. Dual EEG alignment between participants during shared intentionality experiments. Brain Res 2022; 1790:147986. [PMID: 35714711 DOI: 10.1016/j.brainres.2022.147986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/08/2022] [Accepted: 06/10/2022] [Indexed: 11/18/2022]
Abstract
Electroencephalograph (EEG) analysis from human subjects have demonstrated that beta oscillations carried perceptual information across the cortex featuring amplitude and phase modulation occurrences when subjects are engaged in task-oriented activities. A hypothesis was tested that synchronized patterns could be found in the scalp EEG of two human subjects engaged in similar intentional activity. Signals were recorded from scalp electrodes and band-pass filtered. The Hilbert transform decomposes the EEG signals into the analytic phase and amplitude. With these components of the EEG signal, a systematic search of the alpha, beta, delta, gamma, and theta spectrum is executed to locate temporal patterns. The amplitude and phase modulation were classified with respect to task intervals. Temporal patterns were found in the alpha-beta range (15-30 Hz). Our results suggest that the scalp EEG can yield information about the timing of episodically synchronized brain activity in higher cognitive function between two individuals engaged in similar task-oriented activities.
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Affiliation(s)
- Mark H Myers
- Department of Anatomy and Neurobiology, University of Tennessee Health Sciences Center, Memphis, TN, United States.
| | - Gahangir Hossain
- Department of Computer and Information Systems, West Texas A&M University, Canyon, TX, United States
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Hammer J, Schirrmeister RT, Hartmann K, Marusic P, Schulze-Bonhage A, Ball T. Interpretable functional specialization emerges in deep convolutional networks trained on brain signals. J Neural Eng 2022; 19. [PMID: 35421857 DOI: 10.1088/1741-2552/ac6770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 04/14/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Functional specialization is fundamental to neural information processing. Here, we study whether and how functional specialization emerges in artificial deep convolutional neural networks (CNNs) during a brain-computer interfacing (BCI) task. APPROACH We trained CNNs to predict hand movement speed from intracranial EEG (iEEG) and delineated how units across the different CNN hidden layers learned to represent the iEEG signal. MAIN RESULTS We show that distinct, functionally interpretable neural populations emerged as a result of the training process. While some units became sensitive to either iEEG amplitude or phase, others showed bimodal behavior with significant sensitivity to both features. Pruning of highly-sensitive units resulted in a steep drop of decoding accuracy not observed for pruning of less sensitive units, highlighting the functional relevance of the amplitude- and phase-specialized populations. SIGNIFICANCE We anticipate that emergent functional specialization as uncovered here will become a key concept in research towards interpretable deep learning for neuroscience and BCI applications.
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Affiliation(s)
- Jiri Hammer
- Neuromedical AI Lab, Department of Neurosurgery, University of Freiburg, Engelbergerstraße 21, Freiburg, 79106, GERMANY
| | | | - Kay Hartmann
- Neuromedical AI Lab, Department of Neurosurgery, University of Freiburg, Engelbergerstraße 21, Freiburg, 79106, GERMANY
| | - Petr Marusic
- Department of Neurology, Motol University Hospital, V Úvalu 84, Prague, 150 06, CZECH REPUBLIC
| | - Andreas Schulze-Bonhage
- Epilepsy Center, University Clinics, Albert-Ludwigs-Universitaet Freiburg, Albert-Ludwigs-University,, 79095 Freiburg, Germany, Freiburg, 79095, GERMANY
| | - Tonio Ball
- Epilepsy Center, University Clinics, Albert-Ludwigs-Universitaet Freiburg, Albert-Ludwigs-University,, 79095 Freiburg, Germany, Freiburg, 79106, GERMANY
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Long-Range Respiratory and Theta Oscillation Networks Depend on Spatial Sensory Context. J Neurosci 2021; 41:9957-9970. [PMID: 34667070 DOI: 10.1523/jneurosci.0719-21.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 09/13/2021] [Accepted: 10/12/2021] [Indexed: 11/21/2022] Open
Abstract
Neural oscillations can couple networks of brain regions, especially at lower frequencies. The nasal respiratory rhythm, which elicits robust olfactory bulb oscillations, has been linked to episodic memory, locomotion, and exploration, along with widespread oscillatory coherence. The piriform cortex is implicated in propagating the olfactory-bulb-driven respiratory rhythm, but this has not been tested explicitly in the context of both hippocampal theta and nasal respiratory rhythm during exploratory behaviors. We investigated systemwide interactions during foraging behavior, which engages respiratory and theta rhythms. Local field potentials from the olfactory bulb, piriform cortex, dentate gyrus, and CA1 of hippocampus, primary visual cortex, and nasal respiration were recorded simultaneously from male rats. We compared interactions among these areas while rats foraged using either visual or olfactory spatial cues. We found high coherence during foraging compared with home cage activity in two frequency bands that matched slow and fast respiratory rates. Piriform cortex and hippocampus maintained strong coupling at theta frequency during periods of slow respiration, whereas other pairs showed coupling only at the fast respiratory frequency. Directional analysis shows that the modality of spatial cues was matched to larger influences in the network by the respective primary sensory area. Respiratory and theta rhythms also coupled to faster oscillations in primary sensory and hippocampal areas. These data provide the first evidence of widespread interactions among nasal respiration, olfactory bulb, piriform cortex, and hippocampus in awake freely moving rats, and support the piriform cortex as an integrator of respiratory and theta activity.SIGNIFICANCE STATEMENT Recent studies have shown widespread interactions between the nasally driven respiratory rhythm and neural oscillations in hippocampus and neocortex. With this study, we address how the respiratory rhythm interacts with ongoing slow brain rhythms across olfactory, hippocampal, and visual systems in freely moving rats. Patterns of network connectivity change with behavioral state, with stronger interactions at fast and slow respiratory frequencies during foraging as compared with home cage activity. Routing of interactions between sensory cortices depends on the modality of spatial cues present during foraging. Functional connectivity and cross-frequency coupling analyses suggest strong bidirectional interactions between olfactory and hippocampal systems related to respiration and point to the piriform cortex as a key area for mediating respiratory and theta rhythms.
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Panachakel JT, G RA. Classification of Phonological Categories in Imagined Speech using Phase Synchronization Measure. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2226-2229. [PMID: 34891729 DOI: 10.1109/embc46164.2021.9630699] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Phonological categories in articulated speech are defined based on the place and manner of articulation. In this work, we investigate whether the phonological categories of the prompts imagined during speech imagery lead to differences in phase synchronization in various cortical regions that can be discriminated from the EEG captured during the imagination. Nasal and bilabial consonant are the two phonological categories considered due to their differences in both place and manner of articulation. Mean phase coherence (MPC) is used for measuring the phase synchronization and shallow neural network (NN) is used as the classifier. As a benchmark, we have also designed another NN based on statistical parameters extracted from imagined speech EEG. The NN trained on MPC values in the beta band gives classification results superior to NN trained on alpha band MPC values, gamma band MPC values and statistical parameters extracted from the EEG.Clinical relevance: Brain-computer interface (BCI) is a promising tool for aiding differently-abled people and for neurorehabilitation. One of the challenges in designing speech imagery based BCI is the identification of speech prompts that can lead to distinct neural activations. We have shown that nasal and blilabial consonants lead to dissimilar activations. Hence prompts orthogonal in these phonological categories are good choices as speech imagery prompts.
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Panachakel JT, Sharma K, A S A, A G R. Can we identify the category of imagined phoneme from EEG? ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:459-462. [PMID: 34891332 DOI: 10.1109/embc46164.2021.9630604] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Phonemes are classified into different categories based on the place and manner of articulation. We investigate the differences between the neural correlates of imagined nasal and bilabial consonants (distinct phonological categories). Mean phase coherence is used as a metric for measuring the phase synchronisation between pairs of electrodes in six cortical regions (auditory, motor, prefrontal, sensorimotor, so-matosensory and premotor) during the imagery of nasal and bilabial consonants. Statistically significant difference at 95% confidence interval is observed in beta and lower-gamma bands in various cortical regions. Our observations are inline with the directions into velocities of articulators and dual stream prediction models and support the hypothesis that phonological categories not only exist in articulated speech but can also be distinguished from the EEG of imagined speech.
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Gordon PC, Dörre S, Belardinelli P, Stenroos M, Zrenner B, Ziemann U, Zrenner C. Prefrontal Theta-Phase Synchronized Brain Stimulation With Real-Time EEG-Triggered TMS. Front Hum Neurosci 2021; 15:691821. [PMID: 34234662 PMCID: PMC8255809 DOI: 10.3389/fnhum.2021.691821] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 05/27/2021] [Indexed: 11/30/2022] Open
Abstract
Background Theta-band neuronal oscillations in the prefrontal cortex are associated with several cognitive functions. Oscillatory phase is an important correlate of excitability and phase synchrony mediates information transfer between neuronal populations oscillating at that frequency. The ability to extract and exploit the prefrontal theta rhythm in real time in humans would facilitate insight into neurophysiological mechanisms of cognitive processes involving the prefrontal cortex, and development of brain-state-dependent stimulation for therapeutic applications. Objectives We investigate individual source-space beamforming-based estimation of the prefrontal theta oscillation as a method to target specific phases of the ongoing theta oscillations in the human dorsomedial prefrontal cortex (DMPFC) with real-time EEG-triggered transcranial magnetic stimulation (TMS). Different spatial filters for extracting the prefrontal theta oscillation from EEG signals are compared and additional signal quality criteria are assessed to take into account the dynamics of this cortical oscillation. Methods Twenty two healthy participants were recruited for anatomical MRI scans and EEG recordings with 18 composing the final analysis. We calculated individual spatial filters based on EEG beamforming in source space. The extracted EEG signal was then used to simulate real-time phase-detection and quantify the accuracy as compared to post-hoc phase estimates. Different spatial filters and triggering parameters were compared. Finally, we validated the feasibility of this approach by actual real-time triggering of TMS pulses at different phases of the prefrontal theta oscillation. Results Higher phase-detection accuracy was achieved using individualized source-based spatial filters, as compared to an average or standard Laplacian filter, and also by detecting and avoiding periods of low theta amplitude and periods containing a phase reset. Using optimized parameters, prefrontal theta-phase synchronized TMS of DMPFC was achieved with an accuracy of ±55°. Conclusion This study demonstrates the feasibility of triggering TMS pulses during different phases of the ongoing prefrontal theta oscillation in real time. This method is relevant for brain state-dependent stimulation in human studies of cognition. It will also enable new personalized therapeutic repetitive TMS protocols for more effective treatment of neuropsychiatric disorders.
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Affiliation(s)
- Pedro Caldana Gordon
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Sara Dörre
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Paolo Belardinelli
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,CIMeC, Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Brigitte Zrenner
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Ulf Ziemann
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Christoph Zrenner
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
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14
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Stylianou O, Racz FS, Eke A, Mukli P. Scale-Free Coupled Dynamics in Brain Networks Captured by Bivariate Focus-Based Multifractal Analysis. Front Physiol 2021; 11:615961. [PMID: 33613302 PMCID: PMC7887319 DOI: 10.3389/fphys.2020.615961] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 12/29/2020] [Indexed: 12/12/2022] Open
Abstract
While most connectivity studies investigate functional connectivity (FC) in a scale-dependent manner, coupled neural processes may also exhibit broadband dynamics, manifesting as power-law scaling of their measures of interdependence. Here we introduce the bivariate focus-based multifractal (BFMF) analysis as a robust tool for capturing such scale-free relations and use resting-state electroencephalography (EEG) recordings of 12 subjects to demonstrate its performance in reconstructing physiological networks. BFMF was employed to characterize broadband FC between 62 cortical regions in a pairwise manner, with all investigated connections being tested for true bivariate multifractality. EEG channels were also grouped to represent the activity of six resting-state networks (RSNs) in the brain, thus allowing for the analysis of within- and between- RSNs connectivity, separately. Most connections featured true bivariate multifractality, which could be attributed to the genuine scale-free coupling of neural dynamics. Bivariate multifractality showed a characteristic topology over the cortex that was highly concordant among subjects. Long-term autocorrelation was higher in within-RSNs, while the degree of multifractality was generally found stronger in between-RSNs connections. These results offer statistical evidence of the bivariate multifractal nature of functional coupling in the brain and validate BFMF as a robust method to capture such scale-independent coupled dynamics.
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Affiliation(s)
- Orestis Stylianou
- Department of Physiology, Semmelweis University, Budapest, Hungary.,Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
| | | | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary.,Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States
| | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary.,Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
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15
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von Wegner F, Bauer S, Rosenow F, Triesch J, Laufs H. EEG microstate periodicity explained by rotating phase patterns of resting-state alpha oscillations. Neuroimage 2020; 224:117372. [PMID: 32979526 DOI: 10.1016/j.neuroimage.2020.117372] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 08/08/2020] [Accepted: 09/11/2020] [Indexed: 02/07/2023] Open
Abstract
Spatio-temporal patterns in electroencephalography (EEG) can be described by microstate analysis, a discrete approximation of the continuous electric field patterns produced by the cerebral cortex. Resting-state EEG microstates are largely determined by alpha frequencies (8-12 Hz) and we recently demonstrated that microstates occur periodically with twice the alpha frequency. To understand the origin of microstate periodicity, we analyzed the analytic amplitude and the analytic phase of resting-state alpha oscillations independently. In continuous EEG data we found rotating phase patterns organized around a small number of phase singularities which varied in number and location. The spatial rotation of phase patterns occurred with the underlying alpha frequency. Phase rotors coincided with periodic microstate motifs involving the four canonical microstate maps. The analytic amplitude showed no oscillatory behaviour and was almost static across time intervals of 1-2 alpha cycles, resulting in the global pattern of a standing wave. In n=23 healthy adults, time-lagged mutual information analysis of microstate sequences derived from amplitude and phase signals of awake eyes-closed EEG records showed that only the phase component contributed to the periodicity of microstate sequences. Phase sequences showed mutual information peaks at multiples of 50 ms and the group average had a main peak at 100 ms (10 Hz), whereas amplitude sequences had a slow and monotonous information decay. This result was confirmed by an independent approach combining temporal principal component analysis (tPCA) and autocorrelation analysis. We reproduced our observations in a generic model of EEG oscillations composed of coupled non-linear oscillators (Stuart-Landau model). Phase-amplitude dynamics similar to experimental EEG occurred when the oscillators underwent a supercritical Hopf bifurcation, a common feature of many computational models of the alpha rhythm. These findings explain our previous description of periodic microstate recurrence and its relation to the time scale of alpha oscillations. Moreover, our results corroborate the predictions of computational models and connect experimentally observed EEG patterns to properties of critical oscillator networks.
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Affiliation(s)
- F von Wegner
- School of Medical Sciences, University of New South Wales, Wallace Wurth Building, Kensington, NSW 2052, Australia; Epilepsy Center Frankfurt Rhine-Main, Center of Neurology and Neurosurgery, University Hospital Frankfurt and Center for Personalized Translational Epilepsy Research (CePTER), Goethe University Frankfurt, Frankfurt am Main, Germany.
| | - S Bauer
- Epilepsy Center Frankfurt Rhine-Main, Center of Neurology and Neurosurgery, University Hospital Frankfurt and Center for Personalized Translational Epilepsy Research (CePTER), Goethe University Frankfurt, Frankfurt am Main, Germany
| | - F Rosenow
- Epilepsy Center Frankfurt Rhine-Main, Center of Neurology and Neurosurgery, University Hospital Frankfurt and Center for Personalized Translational Epilepsy Research (CePTER), Goethe University Frankfurt, Frankfurt am Main, Germany
| | - J Triesch
- Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, Germany
| | - H Laufs
- Department of Neurology, Christian-Albrechts University Kiel, Arnold-Heller-Strasse 3, Kiel 24105, Germany
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16
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Zrenner C, Galevska D, Nieminen JO, Baur D, Stefanou MI, Ziemann U. The shaky ground truth of real-time phase estimation. Neuroimage 2020; 214:116761. [PMID: 32198050 PMCID: PMC7284312 DOI: 10.1016/j.neuroimage.2020.116761] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 03/09/2020] [Accepted: 03/16/2020] [Indexed: 01/02/2023] Open
Abstract
Instantaneous phase of brain oscillations in electroencephalography (EEG) is a measure of brain state that is relevant to neuronal processing and modulates evoked responses. However, determining phase at the time of a stimulus with standard signal processing methods is not possible due to the stimulus artifact masking the future part of the signal. Here, we quantify the degree to which signal-to-noise ratio and instantaneous amplitude of the signal affect the variance of phase estimation error and the precision with which "ground truth" phase is even defined, using both the variance of equivalent estimators and realistic simulated EEG data with known synthetic phase. Necessary experimental conditions are specified in which pre-stimulus phase estimation is meaningfully possible based on instantaneous amplitude and signal-to-noise ratio of the oscillation of interest. An open source toolbox is made available for causal (using pre-stimulus signal only) phase estimation along with a EEG dataset consisting of recordings from 140 participants and a best practices workflow for algorithm optimization and benchmarking. As an illustration, post-hoc sorting of open-loop transcranial magnetic stimulation (TMS) trials according to pre-stimulus sensorimotor μ-rhythm phase is performed to demonstrate modulation of corticospinal excitability, as indexed by the amplitude of motor evoked potentials.
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Affiliation(s)
- Christoph Zrenner
- Department of Neurology & Stroke, And Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Dragana Galevska
- Department of Neurology & Stroke, And Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Jaakko O Nieminen
- Department of Neurology & Stroke, And Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - David Baur
- Department of Neurology & Stroke, And Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Maria-Ioanna Stefanou
- Department of Neurology & Stroke, And Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Ulf Ziemann
- Department of Neurology & Stroke, And Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
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17
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Fingelkurts AA, Fingelkurts AA, Neves CFH. From spatio-temporal brain-mind dynamics to Spatiotemporal Neuroscience: Comment on "Is temporo-spatial dynamics the "common currency" of brain and mind? In Quest of "Spatiotemporal Neuroscience"" by Georg Northoff, Soren Wainio-Theberge, Kathinka Evers. Phys Life Rev 2019; 33:61-63. [PMID: 31585791 DOI: 10.1016/j.plrev.2019.09.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 09/24/2019] [Indexed: 11/29/2022]
Affiliation(s)
| | - Alexander A Fingelkurts
- BM-Science - Brain and Mind Technologies Research Centre, Espoo, Finland. http://www.bm-science.com/team/fingelkurts.html
| | - Carlos F H Neves
- BM-Science - Brain and Mind Technologies Research Centre, Espoo, Finland
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18
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Heck DH, Kozma R, Kay LM. The rhythm of memory: how breathing shapes memory function. J Neurophysiol 2019; 122:563-571. [PMID: 31215344 DOI: 10.1152/jn.00200.2019] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
The mammalian olfactory bulb displays a prominent respiratory rhythm, which is linked to the sniff cycle and is driven by sensory input from olfactory receptors in the nasal sensory epithelium. In rats and mice, respiratory frequencies occupy the same band as the hippocampal θ-rhythm, which has been shown to be a key player in memory processes. Hippocampal and olfactory bulb rhythms were previously found to be uncorrelated except in specific odor-contingency learning circumstances. However, many recent electrophysiological studies in both rodents and humans reveal a surprising cycle-by-cycle influence of nasal respiration on neuronal activity throughout much of the cerebral cortex beyond the olfactory system, including the prefrontal cortex, hippocampus, and subcortical structures. In addition, respiratory phase has been shown to influence higher-frequency oscillations associated with cognitive functions, including attention and memory, such as the power of γ-rhythms and the timing of hippocampal sharp wave ripples. These new findings support respiration's role in cognitive function, which is supported by studies in human subjects, in which nasal respiration has been linked to memory processes. Here, we review recent reports from human and rodent experiments that link respiration to the modulation of memory function and the neurophysiological processes involved in memory in rodents and humans. We argue that respiratory influence on the neuronal activity of two key memory structures, the hippocampus and prefrontal cortex, provides a potential neuronal mechanism behind respiratory modulation of memory.
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Affiliation(s)
- Detlef H Heck
- Department of Anatomy and Neurobiology, University of Tennessee Health Science Center
| | - Robert Kozma
- Department of Mathematical Sciences, University of Memphis, Memphis, Tennessee.,Department of Computer Sciences, University of Massachusetts Amherst, Massachusetts
| | - Leslie M Kay
- Department of Psychology and Institute for Mind and Biology, The University of Chicago, Chicago, Illinois
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19
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Cortico-Striatal Cross-Frequency Coupling and Gamma Genesis Disruptions in Huntington's Disease Mouse and Computational Models. eNeuro 2018; 5:eN-NWR-0210-18. [PMID: 30627632 PMCID: PMC6325534 DOI: 10.1523/eneuro.0210-18.2018] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 10/19/2018] [Accepted: 11/12/2018] [Indexed: 12/16/2022] Open
Abstract
Abnormal gamma band power across cortex and striatum is an important phenotype of Huntington's disease (HD) in both patients and animal models, but neither the origin nor the functional relevance of this phenotype is well understood. Here, we analyzed local field potential (LFP) activity in freely behaving, symptomatic R6/2 and Q175 mouse models and corresponding wild-type (WT) controls. We focused on periods of quiet rest, which show strong γ activity in HD mice. Simultaneous recording from motor cortex and its target area in dorsal striatum in the R6/2 model revealed exaggerated functional coupling over that observed in WT between the phase of delta frequencies (1-4 Hz) in cortex and striatum and striatal amplitude modulation of low γ frequencies (25-55 Hz; i.e., phase-amplitude coupling, PAC), but no evidence that abnormal cortical activity alone can account for the increase in striatal γ power. Both HD mouse models had stronger coupling of γ amplitude to δ phase and more unimodal phase distributions than their WT counterparts. To assess the possible role of striatal fast-spiking interneurons (FSIs) in these phenomena, we developed a computational model based on additional striatal recordings from Q175 mice. Changes in peak γ frequency and power ratio were readily reproduced by our computational model, accounting for several experimental findings reported in the literature. Our results suggest that HD is characterized by both a reorganization of cortico-striatal drive and specific population changes related to intrastriatal synaptic coupling.
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20
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Townsend RG, Gong P. Detection and analysis of spatiotemporal patterns in brain activity. PLoS Comput Biol 2018; 14:e1006643. [PMID: 30507937 PMCID: PMC6292652 DOI: 10.1371/journal.pcbi.1006643] [Citation(s) in RCA: 32] [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: 03/19/2018] [Revised: 12/13/2018] [Accepted: 11/14/2018] [Indexed: 12/31/2022] Open
Abstract
There is growing evidence that population-level brain activity is often organized into propagating waves that are structured in both space and time. Such spatiotemporal patterns have been linked to brain function and observed across multiple recording methodologies and scales. The ability to detect and analyze these patterns is thus essential for understanding the working mechanisms of neural circuits. Here we present a mathematical and computational framework for the identification and analysis of multiple classes of wave patterns in neural population-level recordings. By drawing a conceptual link between spatiotemporal patterns found in the brain and coherent structures such as vortices found in turbulent flows, we introduce velocity vector fields to characterize neural population activity. These vector fields are calculated for both phase and amplitude of oscillatory neural signals by adapting optical flow estimation methods from the field of computer vision. Based on these velocity vector fields, we then introduce order parameters and critical point analysis to detect and characterize a diverse range of propagating wave patterns, including planar waves, sources, sinks, spiral waves, and saddle patterns. We also introduce a novel vector field decomposition method that extracts the dominant spatiotemporal structures in a recording. This enables neural data to be represented by the activity of a small number of independent spatiotemporal modes, providing an alternative to existing dimensionality reduction techniques which separate space and time components. We demonstrate the capabilities of the framework and toolbox with simulated data, local field potentials from marmoset visual cortex and optical voltage recordings from whole mouse cortex, and we show that pattern dynamics are non-random and are modulated by the presence of visual stimuli. These methods are implemented in a MATLAB toolbox, which is freely available under an open-source licensing agreement. Structured activity such as propagating wave patterns at the level of neural circuits can arise from highly variable firing activity of individual neurons. This property makes the brain, a quintessential example of a complex system, analogous to other complex physical systems such as turbulent fluids, in which structured patterns like vortices similarly emerge from molecules that behave irregularly. In this study, by uniquely adapting techniques for the identification of coherent structures in fluid turbulence, we develop new analytical and computational methods for the reliable detection of a diverse range of propagating wave patterns in large-scale neural recordings, for comprehensive analysis and visualization of these patterns, and for analysis of their dominant spatiotemporal modes. We demonstrate that these methods can be used to uncover the essential spatiotemporal properties of neural population activity recorded by different modalities, thus offering new insights into understanding the working mechanisms of neural systems.
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Affiliation(s)
- Rory G. Townsend
- School of Physics, The University of Sydney, NSW, Australia
- ARC Centre of Excellence for Integrative Brain Function, The University of Sydney, NSW, Australia
| | - Pulin Gong
- School of Physics, The University of Sydney, NSW, Australia
- ARC Centre of Excellence for Integrative Brain Function, The University of Sydney, NSW, Australia
- * E-mail:
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21
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Muthukumaraswamy SD, Liley DT. 1/f electrophysiological spectra in resting and drug-induced states can be explained by the dynamics of multiple oscillatory relaxation processes. Neuroimage 2018; 179:582-595. [PMID: 29959047 DOI: 10.1016/j.neuroimage.2018.06.068] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 06/20/2018] [Accepted: 06/25/2018] [Indexed: 02/01/2023] Open
Abstract
Neurophysiological recordings are dominated by arhythmical activity whose spectra can be characterised by power-law functions, and on this basis are often referred to as reflecting scale-free brain dynamics (1/fβ). Relatively little is known regarding the neural generators and temporal dynamics of this arhythmical behaviour compared to rhythmical behaviour. Here we used Irregularly Resampled AutoSpectral Analysis (IRASA) to quantify β, in both the high (5-100 Hz, βhf) and low frequency bands (0.1-2.5 Hz, βlf) in MEG/EEG/ECoG recordings and to separate arhythmical from rhythmical modes of activity, such as, alpha rhythms. In MEG/EEG/ECoG data, we demonstrate that oscillatory alpha power dynamically correlates over time with βhf and similarly, participants with higher rhythmical alpha power have higher βhf. In a series of pharmaco-MEG investigations using the GABA reuptake inhibitor tiagabine, the glutamatergic AMPA receptor antagonist perampanel, the NMDA receptor antagonist ketamine and the mixed partial serotonergic agonist LSD, a variety of effects on both βhf and βlf were observed. Additionally, strong modulations of βhf were seen in monkey ECoG data during general anaesthesia using propofol and ketamine. We develop and test a unifying model which can explain, the 1/f nature of electrophysiological spectra, their dynamic interaction with oscillatory rhythms as well as the sensitivity of 1/f activity to drug interventions by considering electrophysiological spectra as being generated by a collection of stochastically perturbed damped oscillators having a distribution of relaxation rates.
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Affiliation(s)
- Suresh D Muthukumaraswamy
- School of Pharmacy and Centre for Brain Research, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand.
| | - David Tj Liley
- Centre for Human Psychopharmacology, School of Health Sciences, Swinburne University of Technology, Melbourne, Australia
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22
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Myers MH, Kozma R. Mesoscopic neuron population modeling of normal/epileptic brain dynamics. Cogn Neurodyn 2017; 12:211-223. [PMID: 29564029 DOI: 10.1007/s11571-017-9468-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Revised: 11/13/2017] [Accepted: 12/19/2017] [Indexed: 10/18/2022] Open
Abstract
Simulations of EEG data provide the understanding of how the limbic system exhibits normal and abnormal states of the electrical activity of the brain. While brain activity exhibits a type of homeostasis of excitatory and inhibitory mesoscopic neuron behavior, abnormal neural firings found in the seizure state exhibits brain instability due to runaway oscillatory entrained neural behavior. We utilize a model of mesoscopic brain activity, the KIV model, where each network represents the areas of the limbic system, i.e., hippocampus, sensory cortex, and the amygdala. Our model initially demonstrates oscillatory entrained neural behavior as the epileptogenesis, and then by increasing the external weights that join the three networks that represent the areas of the limbic system, seizure activity entrains the entire system. By introducing an external signal into the model, simulating external electrical titration therapy, the modeled seizure behavior can be 'rebalanced' back to its normal state.
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Affiliation(s)
- Mark H Myers
- 1Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN USA
| | - Robert Kozma
- 2Department of Mathematics, University of Memphis, Memphis, TN USA
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23
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Myers MH, Jolly E, Li Y, de Jongh Curry A, Parfenova H. Power Spectral Density Analysis of Electrocorticogram Recordings during Cerebral Hypothermia in Neonatal Seizures. Ann Neurosci 2017; 24:12-19. [PMID: 28596673 PMCID: PMC5460947 DOI: 10.1159/000464418] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 09/11/2016] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Neonatal seizures (NS) are the most common form of neurological dysfunction observed in newborns. PURPOSE The purpose of this study in newborn piglets was to determine the effect of cerebral hypothermia (CH) on neural activity during pharmacologically induced NS. We hypothesized that the neuroprotective effects of CH would preserve higher frequencies observed in electrocorticogram (ECoG) recordings. METHODS Power spectral density was employed to determine the levels of brain activity in ECoGs to quantitatively assess the power of each frequency observed in neurological brain states of delta, theta, alpha, and beta-gamma frequencies. RESULT The most significant reduction of power occurs in the lower frequency band of delta-theta-alpha of CH cohorts, while t score probabilities imply that high-frequency brain activity in the beta-gamma range is preserved in the CH population. CONCLUSION While the overall power density decreases over time in both groups, the decrease is to a lesser degree in the CH population.
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Affiliation(s)
- Mark H. Myers
- Department of Anatomy and Neurobiology, University of Tennessee Health Science Center Memphis, Memphis, TN, USA
| | - Elliott Jolly
- Department of Biomedical Engineering, University of Memphis, Memphis, TN, USA
| | - Yaqin Li
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Amy de Jongh Curry
- Department of Biomedical Engineering, University of Memphis, Memphis, TN, USA
| | - Helena Parfenova
- Department of Physiology, University of Tennessee Health Science Center, Memphis, TN, USA
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24
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Kozma R, Freeman WJ. Cinematic Operation of the Cerebral Cortex Interpreted via Critical Transitions in Self-Organized Dynamic Systems. Front Syst Neurosci 2017; 11:10. [PMID: 28352218 PMCID: PMC5348494 DOI: 10.3389/fnsys.2017.00010] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Accepted: 02/16/2017] [Indexed: 11/10/2022] Open
Abstract
Measurements of local field potentials over the cortical surface and the scalp of animals and human subjects reveal intermittent bursts of beta and gamma oscillations. During the bursts, narrow-band metastable amplitude modulation (AM) patters emerge for a fraction of a second and ultimately dissolve to the broad-band random background activity. The burst process depends on previously learnt conditioned stimuli (CS), thus different AM patterns may emerge in response to different CS. This observation leads to our cinematic theory of cognition when perception happens in discrete steps manifested in the sequence of AM patterns. Our article summarizes findings in the past decades on experimental evidence of cinematic theory of cognition and relevant mathematical models. We treat cortices as dissipative systems that self-organize themselves near a critical level of activity that is a non-equilibrium metastable state. Criticality is arguably a key aspect of brains in their rapid adaptation, reconfiguration, high storage capacity, and sensitive response to external stimuli. Self-organized criticality (SOC) became an important concept to describe neural systems. We argue that transitions from one AM pattern to the other require the concept of phase transitions, extending beyond the dynamics described by SOC. We employ random graph theory (RGT) and percolation dynamics as fundamental mathematical approaches to model fluctuations in the cortical tissue. Our results indicate that perceptions are formed through a phase transition from a disorganized (high entropy) to a well-organized (low entropy) state, which explains the swiftness of the emergence of the perceptual experience in response to learned stimuli.
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Affiliation(s)
- Robert Kozma
- College of Information and Computer Sciences, University of MassachusettsAmherst, MA, USA; Department of Mathematical Sciences, University of MemphisMemphis, TN, USA
| | - Walter J Freeman
- Department of Molecular and Cell Biology, University of California at Berkeley Berkeley, CA, USA
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25
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Seraj E, Sameni R. Robust electroencephalogram phase estimation with applications in brain-computer interface systems. Physiol Meas 2017; 38:501-523. [DOI: 10.1088/1361-6579/aa5bba] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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26
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Heck DH, McAfee SS, Liu Y, Babajani-Feremi A, Rezaie R, Freeman WJ, Wheless JW, Papanicolaou AC, Ruszinkó M, Sokolov Y, Kozma R. Breathing as a Fundamental Rhythm of Brain Function. Front Neural Circuits 2017; 10:115. [PMID: 28127277 PMCID: PMC5226946 DOI: 10.3389/fncir.2016.00115] [Citation(s) in RCA: 99] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 12/26/2016] [Indexed: 11/17/2022] Open
Abstract
Ongoing fluctuations of neuronal activity have long been considered intrinsic noise that introduces unavoidable and unwanted variability into neuronal processing, which the brain eliminates by averaging across population activity (Georgopoulos et al., 1986; Lee et al., 1988; Shadlen and Newsome, 1994; Maynard et al., 1999). It is now understood, that the seemingly random fluctuations of cortical activity form highly structured patterns, including oscillations at various frequencies, that modulate evoked neuronal responses (Arieli et al., 1996; Poulet and Petersen, 2008; He, 2013) and affect sensory perception (Linkenkaer-Hansen et al., 2004; Boly et al., 2007; Sadaghiani et al., 2009; Vinnik et al., 2012; Palva et al., 2013). Ongoing cortical activity is driven by proprioceptive and interoceptive inputs. In addition, it is partially intrinsically generated in which case it may be related to mental processes (Fox and Raichle, 2007; Deco et al., 2011). Here we argue that respiration, via multiple sensory pathways, contributes a rhythmic component to the ongoing cortical activity. We suggest that this rhythmic activity modulates the temporal organization of cortical neurodynamics, thereby linking higher cortical functions to the process of breathing.
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Affiliation(s)
- Detlef H Heck
- Department of Anatomy and Neurobiology, University of Tennessee Health Science Center Memphis, TN, USA
| | - Samuel S McAfee
- Department of Anatomy and Neurobiology, University of Tennessee Health Science Center Memphis, TN, USA
| | - Yu Liu
- Department of Anatomy and Neurobiology, University of Tennessee Health Science Center Memphis, TN, USA
| | - Abbas Babajani-Feremi
- Department of Anatomy and Neurobiology, University of Tennessee Health Science CenterMemphis, TN, USA; Department of Pediatrics, Division of Pediatric Neurology, University of Tennessee Health Science Center and Le Bonheur Children's Hospital Neuroscience InstituteMemphis, TN, USA
| | - Roozbeh Rezaie
- Department of Pediatrics, Division of Pediatric Neurology, University of Tennessee Health Science Center and Le Bonheur Children's Hospital Neuroscience Institute Memphis, TN, USA
| | - Walter J Freeman
- Department of Molecular and Cell Biology, Division of Neurobiology, University of California at Berkeley Berkeley, CA, USA
| | - James W Wheless
- Department of Pediatrics, Division of Pediatric Neurology, University of Tennessee Health Science Center and Le Bonheur Children's Hospital Neuroscience Institute Memphis, TN, USA
| | - Andrew C Papanicolaou
- Department of Anatomy and Neurobiology, University of Tennessee Health Science CenterMemphis, TN, USA; Department of Pediatrics, Division of Pediatric Neurology, University of Tennessee Health Science Center and Le Bonheur Children's Hospital Neuroscience InstituteMemphis, TN, USA
| | - Miklós Ruszinkó
- Rényi Institute of Mathematics, Hungarian Academy of Sciences Budapest, Hungary
| | - Yury Sokolov
- Department of Mathematical Sciences, University of Memphis Memphis, TN, USA
| | - Robert Kozma
- Department of Mathematical Sciences, University of MemphisMemphis, TN, USA; Department Computer Sciences, University of Massachusetts AmherstAmherst, MA, USA
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27
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Kozma R. Reflections on a giant of brain science: How lucky we are having Walter J. Freeman as our beacon in cognitive neurodynamics research. Cogn Neurodyn 2016; 10:457-469. [PMID: 27891195 PMCID: PMC5106457 DOI: 10.1007/s11571-016-9403-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Revised: 08/19/2016] [Accepted: 08/19/2016] [Indexed: 10/21/2022] Open
Abstract
Walter J. Freeman was a giant of the field of neuroscience whose visionary work contributed various experimental and theoretical breakthroughs to brain research in the past 60 years. He has pioneered a number of Electroencephalogram and Electrocorticogram tools and approaches that shaped the field, while "Freeman Neurodynamics" is a theoretical concept that is widely known, used, and respected among neuroscientists all over the world. His recent death is a profound loss to neuroscience and biomedical engineering. Many of his revolutionary ideas on brain dynamics have been ahead of their time by decades. We summarize his following groundbreaking achievements: (1) Mass Action in the Nervous System, from microscopic (single cell) recordings, through mesoscopic populations, to large-scale collective brain patterns underlying cognition; (2) Freeman-Kachalsky model of multi-scale, modular brain dynamics; (3) cinematic theory of cognitive dynamics; (4) phase transitions in cortical dynamics modeled with random graphs and quantum field theory; (5) philosophical aspects of intentionality, consciousness, and the unity of brain-mind-body. His work has been admired by many of his neuroscientist colleagues and followers. At the same time, his multidisciplinary approach combining advanced concepts of control theory and the mathematics of nonlinear systems and chaos, poses significant challenges to those who wish to thoroughly understand his message. The goal of this commemorative paper is to review key aspects of Freeman's neurodynamics and to provide some handles to gain better understanding about Freeman's extraordinary intellectual achievement.
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Affiliation(s)
- Robert Kozma
- Department of Mathematics, University of Memphis, Memphis, TN 38152 USA
- Department of Computer Science, University of Massachusetts, Amherst, MA 01003 USA
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28
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Abstract
This work explores a feature of brain dynamics, metastability, by which transients are observed in functional brain data. Metastability is a balance between static (stable) and dynamic (unstable) tendencies in electrophysiological brain activity. Furthermore, metastability is a theoretical mechanism underlying the rapid synchronization of cell assemblies that serve as neural substrates for cognitive states, and it has been associated with cognitive flexibility. While much previous research has sought to characterize metastability in the adult human brain, few studies have examined metastability in early development, in part because of the challenges of acquiring adequate, noise free continuous data in young children. To accomplish this endeavor, we studied a new method for characterizing the stability of EEG frequency in early childhood, as inspired by prior approaches for describing cortical phase resets in the scalp EEG of healthy adults. Specifically, we quantified the variance of the rate of change of the signal phase (i.e., frequency) as a proxy for phase resets (signal instability), given that phase resets occur almost simultaneously across large portions of the scalp. We tested our method in a cohort of 39 preschool age children (age =53 ± 13.6 months). We found that our outcome variable of interest, frequency variance, was a promising marker of signal stability, as it increased with the number of phase resets in surrogate (artificial) signals. In our cohort of children, frequency variance decreased cross-sectionally with age (r = -0.47, p = 0.0028). EEG signal stability, as quantified by frequency variance, increases with age in preschool age children. Future studies will relate this biomarker with the development of executive function and cognitive flexibility in children, with the overarching goal of understanding metastability in atypical development.
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Rey HG, Ahmadi M, Quian Quiroga R. Single trial analysis of field potentials in perception, learning and memory. Curr Opin Neurobiol 2015; 31:148-55. [DOI: 10.1016/j.conb.2014.10.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Revised: 10/17/2014] [Accepted: 10/19/2014] [Indexed: 11/30/2022]
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30
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Mechanism and significance of global coherence in scalp EEG. Curr Opin Neurobiol 2015; 31:199-205. [PMID: 25506772 DOI: 10.1016/j.conb.2014.11.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2014] [Revised: 11/13/2014] [Accepted: 11/22/2014] [Indexed: 11/21/2022]
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31
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Transcranial focused ultrasound modulates intrinsic and evoked EEG dynamics. Brain Stimul 2014; 7:900-8. [PMID: 25265863 DOI: 10.1016/j.brs.2014.08.008] [Citation(s) in RCA: 124] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Revised: 08/26/2014] [Accepted: 08/27/2014] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND The integration of EEG recordings and transcranial neuromodulation has provided a useful construct for noninvasively investigating the modification of human brain circuit activity. Recent evidence has demonstrated that focused ultrasound can be targeted through the human skull to affect the amplitude of somatosensory evoked potentials and its associated spectral content. OBJECTIVE/HYPOTHESIS The present study tests whether focused ultrasound transmitted through the human skull and targeted to somatosensory cortex can affect the phase and phase rate of cortical oscillatory dynamics. METHODS A computational model was developed to gain insight regarding the insertion behavior of ultrasound induced pressure waves in the human head. The instantaneous phase and phase rate of EEG recordings before, during, and after transmission of transcranial focused ultrasound (tFUS) to human somatosensory cortex were examined to explore its effects on phase dynamics. RESULTS Computational modeling results show the skull effectively reinforces the focusing of tFUS due to curvature of material interfaces. Neurophysiological recordings show that tFUS alters the phase distribution of intrinsic brain activity for beta frequencies, but not gamma. This modulation was accompanied by a change in phase rate of both beta and gamma frequencies. Additionally, tFUS modulated phase distributions in the beta band of early sensory-evoked activity but did not affect late sensory-evoked activity, lending support to the spatial specificity of tFUS for neuromodulation. This spatial specificity was confirmed through an additional experiment where the ultrasound transducer was moved 1 cm laterally from the original cortical target. CONCLUSIONS Focused ultrasonic energy can alter EEG oscillatory dynamics through local mechanical perturbation of discrete cortical circuits.
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Vitiello G. The use of many-body physics and thermodynamics to describe the dynamics of rhythmic generators in sensory cortices engaged in memory and learning. Curr Opin Neurobiol 2014; 31:7-12. [PMID: 25079054 DOI: 10.1016/j.conb.2014.07.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Accepted: 07/08/2014] [Indexed: 10/25/2022]
Abstract
The problem of the transition from the molecular and cellular level to the macroscopic level of observed assemblies of myriads of neurons is the subject addressed in this report. The great amount of detailed information available at molecular and cellular level seems not sufficient to account for the high effectiveness and reliability observed in the brain macroscopic functioning. It is suggested that the dissipative many-body model and thermodynamics might offer the dynamical frame underlying the rich phenomenology observed at microscopic and macroscopic level and help in the understanding on how to fill the gap between the bio-molecular and cellular level and the one of brain macroscopic functioning.
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Affiliation(s)
- Giuseppe Vitiello
- Dipartimento di Fisica "E.R. Caianiello", Università di Salerno, Fisciano, I-84084 Salerno, Italy; Istituto Nazionale di Fisica Nucleare, Gruppo Collegato di Salerno, Fisciano, I-84084 Salerno, Italy.
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33
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On the Quantization of Time-Varying Phase Synchrony Patterns into Distinct Functional Connectivity Microstates (FCμstates) in a Multi-trial Visual ERP Paradigm. Brain Topogr 2013; 26:397-409. [DOI: 10.1007/s10548-013-0276-z] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2012] [Accepted: 02/08/2013] [Indexed: 11/26/2022]
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34
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Fingelkurts AA, Fingelkurts AA. Operational Architectonics Methodology for EEG Analysis: Theory and Results. MODERN ELECTROENCEPHALOGRAPHIC ASSESSMENT TECHNIQUES 2013. [DOI: 10.1007/7657_2013_60] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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35
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Wu J, Zhang J, Liu C, Liu D, Ding X, Zhou C. Graph theoretical analysis of EEG functional connectivity during music perception. Brain Res 2012; 1483:71-81. [PMID: 22982591 DOI: 10.1016/j.brainres.2012.09.014] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2012] [Revised: 08/03/2012] [Accepted: 09/10/2012] [Indexed: 12/22/2022]
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36
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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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37
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Buzsáki G, Anastassiou CA, Koch C. The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes. Nat Rev Neurosci 2012; 13:407-20. [PMID: 22595786 DOI: 10.1038/nrn3241] [Citation(s) in RCA: 2325] [Impact Index Per Article: 193.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources--including Na(+) and Ca(2+) spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations--can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.
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Affiliation(s)
- György Buzsáki
- Center for Molecular and Behavioural Neuroscience, Rutgers, The State University of New Jersey, 197 University Avenue, Newark, New Jersey 07102, USA.
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38
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Scale-free modulation of resting-state neuronal oscillations reflects prolonged brain maturation in humans. J Neurosci 2011; 31:13128-36. [PMID: 21917796 DOI: 10.1523/jneurosci.1678-11.2011] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Human neuronal circuits undergo life-long functional reorganization with profound effects on cognition and behavior. Well documented prolonged development of anatomical brain structures includes white and gray matter changes that continue into the third decade of life. We investigated resting-state EEG oscillations in 1433 subjects from 5 to 71 years. Neuronal oscillations exhibit scale-free amplitude modulation as reflected in power-law decay of autocorrelations--also known as long-range temporal correlations (LRTC)--which was assessed by detrended fluctuation analysis. We observed pronounced increases in LRTC from childhood to adolescence, during adolescence, and even into early adulthood (∼25 years of age) after which the temporal structure stabilized. A principal component analysis of the spatial distribution of LRTC revealed increasingly uniform scores across the scalp. Together, these findings indicate that the scale-free modulation of resting-state oscillations reflects brain maturation, and suggests that scaling analysis may prove useful as a biomarker of pathophysiology in neurodevelopmental disorders such as attention deficit hyperactivity disorder and schizophrenia.
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WRIGHT JJ. CORTICAL PHASE TRANSITIONS: PROPERTIES DEMONSTRATED IN CONTINUUM SIMULATIONS AT MESOSCOPIC AND MACROSCOPIC SCALES. ACTA ACUST UNITED AC 2011. [DOI: 10.1142/s1793005709001210] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Continuum simulations of cortical dynamics permit consistent simulations to be performed at different spatial scales, using scale-adjusted parameter values. Properties of the simulations described here accord with Freeman's experimental and theoretical findings on gamma synchrony, phase transition, phase cones, and null spikes. State equations include effects of retrograde action potential propagation into dendritic trees, and kinetics of AMPA, GABA, and NMDA receptors. Realistic field potentials and pulse rates, gamma resonance and oscillation, and 1/f2 background activity are obtained. Zero-lag synchrony and traveling waves occur as complementary aspects of cortical transmission, and lead/lag relations between excitatory and inhibitory cell populations vary systematically around transition to autonomous gamma oscillation. Autonomous gamma is initiated by focal excitation of excitatory cells and suppressed by laterally spreading trans-cortical excitation. By implication, patches of cortex excited to gamma oscillation can mutually synchronize into larger fields, self-organized into sequences by mutual negative feedback relations, while the sequence of synchronous fields is regulated both by cortical/subcortical interactions and by traveling waves in the cortex — the latter observable as phase cones. At a critical level of cortical excitation, just before transition to autonomous gamma, patches of cortex exhibit selective sensitivity to action potential pulse trains modulated in the gamma band, while autonomous gamma releases pulse trains modulated in the same band, implying coupling of input and output modes. Transition between input and output modes may be heralded by phase slips and null spikes. Synaptic segregation by retrograde action potential propagation implies state-specific synaptic information storage.
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Affiliation(s)
- J. J. WRIGHT
- Liggins Institute, and Department of Psychological Medicine, Faculty of Medicine and Health Sciences, University of Auckland, Grafton Road, Auckland, New Zealand
- Brain Dynamics Centre, University of Sydney, Acacia House, Westmead Hospital, Hawkesbury Road, Westmead NSW 2145, Australia
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40
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Abstract
I show that a functional representation of self-similarity (as the one occurring in fractals) is provided by squeezed coherent states. In this way, the dissipative model of brain is shown to account for the self-similarity in brain background activity suggested by power-law distributions of power spectral densities of electrocorticograms. I also briefly discuss the action-perception cycle in the dissipative model with reference to intentionality in terms of trajectories in the memory state space.
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Affiliation(s)
- GIUSEPPE VITIELLO
- Dipartimento di Matematica e Informatica and INFN, Università di Salerno, I-84100 Salerno, Italy
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41
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Yazdan-Shahmorad A, Lehmkuhle MJ, Gage GJ, Marzullo TC, Parikh H, Miriani RM, Kipke DR. Estimation of electrode location in a rat motor cortex by laminar analysis of electrophysiology and intracortical electrical stimulation. J Neural Eng 2011; 8:046018. [PMID: 21690656 DOI: 10.1088/1741-2560/8/4/046018] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
While the development of microelectrode arrays has enabled access to disparate regions of a cortex for neurorehabilitation, neuroprosthetic and basic neuroscience research, accurate interpretation of the signals and manipulation of the cortical neurons depend upon the anatomical placement of the electrode arrays in a layered cortex. Toward this end, this report compares two in vivo methods for identifying the placement of electrodes in a linear array spaced 100 µm apart based on in situ laminar analysis of (1) ketamine-xylazine-induced field potential oscillations in a rat motor cortex and (2) an intracortical electrical stimulation-induced movement threshold. The first method is based on finding the polarity reversal in laminar oscillations which is reported to appear at the transition between layers IV and V in laminar 'high voltage spindles' of the rat cortical column. Analysis of histological images in our dataset indicates that polarity reversal is detected 150.1 ± 104.2 µm below the start of layer V. The second method compares the intracortical microstimulation currents that elicit a physical movement for anodic versus cathodic stimulation. It is based on the hypothesis that neural elements perpendicular to the electrode surface are preferentially excited by anodic stimulation while cathodic stimulation excites those with a direction component parallel to its surface. With this method, we expect to see a change in the stimulation currents that elicits a movement at the beginning of layer V when comparing anodic versus cathodic stimulation as the upper cortical layers contain neuronal structures that are primarily parallel to the cortical surface and lower layers contain structures that are primarily perpendicular. Using this method, there was a 78.7 ± 68 µm offset in the estimate of the depth of the start of layer V. The polarity reversal method estimates the beginning of layer V within ±90 µm with 95% confidence and the intracortical stimulation method estimates it within ±69.3 µm. We propose that these methods can be used to estimate the in situ location of laminar electrodes implanted in the rat motor cortex.
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Affiliation(s)
- A Yazdan-Shahmorad
- Biomedical Engineering Department, University of Michigan, Ann Arbor, MI, USA.
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42
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Noninvasive Epileptic Seizure Localization from Stochastic Behavior of Short Duration Interictal High Density Scalp EEG Data. Brain Topogr 2011; 25:106-15. [DOI: 10.1007/s10548-011-0188-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2010] [Accepted: 05/12/2011] [Indexed: 10/18/2022]
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43
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Krusienski DJ, Grosse-Wentrup M, Galán F, Coyle D, Miller KJ, Forney E, Anderson CW. Critical issues in state-of-the-art brain-computer interface signal processing. J Neural Eng 2011; 8:025002. [PMID: 21436519 PMCID: PMC3412170 DOI: 10.1088/1741-2560/8/2/025002] [Citation(s) in RCA: 72] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
This paper reviews several critical issues facing signal processing for brain-computer interfaces (BCIs) and suggests several recent approaches that should be further examined. The topics were selected based on discussions held during the 4th International BCI Meeting at a workshop organized to review and evaluate the current state of, and issues relevant to, feature extraction and translation of field potentials for BCIs. The topics presented in this paper include the relationship between electroencephalography and electrocorticography, novel features for performance prediction, time-embedded signal representations, phase information, signal non-stationarity, and unsupervised adaptation.
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Affiliation(s)
- Dean J Krusienski
- Department of Electrical & Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA.
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44
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Andres DS, Cerquetti DF, Merello M. Turbulence in Globus pallidum neurons in patients with Parkinson's disease: Exponential decay of the power spectrum. J Neurosci Methods 2011; 197:14-20. [DOI: 10.1016/j.jneumeth.2011.01.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2010] [Revised: 12/18/2010] [Accepted: 01/20/2011] [Indexed: 10/18/2022]
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45
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Single pulse TMS-induced modulations of resting brain neurodynamics encoded in EEG phase. Brain Topogr 2011; 24:105-13. [PMID: 21203817 DOI: 10.1007/s10548-010-0169-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2010] [Accepted: 12/16/2010] [Indexed: 10/18/2022]
Abstract
Integration of electroencephalographic (EEG) recordings and transcranial magnetic stimulation (TMS) provides a useful framework for quantifying stimulation-induced modulations of neural dynamics. Amplitude and frequency modulations by different TMS protocols have been previously investigated, but the study of stimulation-induced effects on EEG phase has been more limited. We examined changes in resting brain dynamics following single TMS pulses, focusing on measures in the phase domain, to assess their sensitivity to stimulation effects. We observed a significant, approximately global increase in EEG relative phase following prolonged (>20 min) single-pulse TMS. In addition, we estimated higher rates of phase fluctuation from the slope of estimated phase curves, and higher numbers of phase resetting intervals following TMS over motor cortex, particularly in frontal and centro-parietal/parietal channels. Phase changes were only significantly different from their pre-TMS values at the end of the stimulation session, which suggests that prolonged single-pulse TMS may result in cumulative changes in neural activity reflected in the phase of the EEG. This is a novel result, as prior studies have reported only transient stimulation-related effects in the amplitude and frequency domains following single-pulse TMS.
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46
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Panagiotides H, Freeman WJ, Holmes MD, Pantazis D. Behavioral states may be associated with distinct spatial patterns in electrocorticogram. Cogn Neurodyn 2010; 5:55-66. [PMID: 21464836 PMCID: PMC3045495 DOI: 10.1007/s11571-010-9139-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2009] [Revised: 10/13/2010] [Accepted: 10/25/2010] [Indexed: 11/30/2022] Open
Abstract
To determine if behavioral states are associated with unique spatial electrocorticographic (ECoG) patterns, we obtained recordings with a microgrid electrode array applied to the cortical surface of a human subject. The array was constructed with the intent of extracting maximal spatial information by optimizing interelectrode distances. A 34-year-old patient with intractable epilepsy underwent intracranial ECoG monitoring after standard methods failed to reveal localization of seizures. During the 8-day period of invasive recording, in addition to standard clinical electrodes a square 1 × 1 cm microgrid array with 64 electrodes (1.25 mm separation) was placed on the right inferior temporal gyrus. Careful review of video recordings identified four extended naturalistic behaviors: reading, conversing on the telephone, looking at photographs, and face-to-face interactions. ECoG activity recorded with the microgrid that corresponded to these behaviors was collected and ECoG spatial patterns were analyzed. During periods of ECoG selected for analysis, no electrographic seizures or epileptiform patterns were present. Moments of maximal spatial variance are shown to cluster by behavior. Comparisons between conditions using a permutation test reveal significantly different spatial patterns for each behavior. We conclude that ECoG recordings obtained on the cortical surface with optimal high spatial frequency resolution reveal distinct local spatial patterns that reflect different behavioral states, and we predict that similar patterns will be found in many if not most cortical areas on which a microgrid is placed.
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Affiliation(s)
- Heracles Panagiotides
- Department of Neurological Surgery, University of Washington, 1959 Pacific Ave, Seattle, WA USA
| | - Walter J. Freeman
- Department of Molecular & Cell, Division of Neurobiology, University of California, Donner 101, Berkeley, CA USA
| | - Mark D. Holmes
- Regional Epilepsy Center, Department of Neurology, University of Washington, Harborview Medical Center, 325 Ninth Ave, Seattle, WA USA
| | - Dimitrios Pantazis
- Signal & Image Processing Institute, Department of Electrical Engineering, University of Southern California, 3740 McClintock Ave, Los Angeles, CA 90089-2564 USA
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Whitmer D, Worrell G, Stead M, Lee IK, Makeig S. Utility of independent component analysis for interpretation of intracranial EEG. Front Hum Neurosci 2010; 4:184. [PMID: 21152349 PMCID: PMC2998050 DOI: 10.3389/fnhum.2010.00184] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2009] [Accepted: 09/09/2010] [Indexed: 11/18/2022] Open
Abstract
Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG) recordings is typically performed in the electrode channel domain without explicit separation of the sources that generate the signals. However, intracranial EEG signals, like scalp EEG signals, could be linear mixtures of local activity and volume-conducted activity arising in multiple source areas. Independent component analysis (ICA) has recently been applied to scalp EEG data, and shown to separate the signal mixtures into independently generated brain and non-brain source signals. Here, we applied ICA to unmix source signals from intracranial EEG recordings from four epilepsy patients during a visually cued finger movement task in the presence of background pathological brain activity. This ICA decomposition demonstrated that the iEEG recordings were not maximally independent, but rather are linear mixtures of activity from multiple sources. Many of the independent component (IC) projections to the iEEG recording grid were consistent with sources from single brain regions, including components exhibiting classic movement-related dynamics. Notably, the largest IC projection to each channel accounted for no more than 20–80% of the channel signal variance, implying that in general intracranial recordings cannot be accurately interpreted as recordings of independent brain sources. These results suggest that ICA can be used to identify and monitor major field sources of local and distributed functional networks generating iEEG data. ICA decomposition methods are useful for improving the fidelity of source signals of interest, likely including distinguishing the sources of pathological brain activity.
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Affiliation(s)
- Diane Whitmer
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego La Jolla, CA, USA
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48
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Fingelkurts AA, Fingelkurts AA. Topographic mapping of rapid transitions in EEG multiple frequencies: EEG frequency domain of operational synchrony. Neurosci Res 2010; 68:207-24. [DOI: 10.1016/j.neures.2010.07.2031] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2010] [Revised: 07/13/2010] [Accepted: 07/14/2010] [Indexed: 10/19/2022]
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49
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Attractor dynamics and thermodynamic analogies in the cerebral cortex: synchronous oscillation, the background EEG, and the regulation of attention. Bull Math Biol 2010; 73:436-57. [PMID: 20821066 DOI: 10.1007/s11538-010-9562-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2010] [Accepted: 06/17/2010] [Indexed: 10/19/2022]
Abstract
Ongoing changes in attention and cognition depend upon cortical/subcortical interactions, which select sequences of different spatial patterns of activation in the cortex. It is proposed that each pattern of cortical activation permits evolution of electrocortical wave activity toward statistically stationary states, analogous to thermodynamic equilibrium. In each steady-state, neurons fire with an intrinsic Poisson spike probability and also with a bursting pattern related to network oscillations. Excitatory cell dendrites act as a regenerative reservoir in which pulse generation is balanced against dissipations. Equilibria exhibit contrasting limits. One limit, at high cortical activation, generates widespread zero-lag synchrony among excitatory cells, with partial suppression of noise. Excitatory and inhibitory cells approach zero-lag local correlation, with 1/4 cycle lag-correlation at greater distances of separation. The high-activation limit defines a correlated system of attractor basins, capable of co-ordinating synaptic modifications and intracortical signal generation. Suppression of noise would enhance convergence about attractor basins in the manner of simulated annealing, while, conversely, the persistence of some noise prevents network paralysis by phase locking. At the opposite limit-that of low activation-spikes and waves have low cross- and auto-correlation, but have wide-spectrum sensitivity to inputs. It is hypothesised that cortical regions, transiently at equilibrium near these extremes, engage in interaction with each other and with subcortical systems, to generate ongoing sequences of attention and cognition. This account is compatible with classical and recently observed experimental phenomena. The principle features inferred from a simplified linear mathematical account are reproduced in a more physiologically realistic and non-linear numerical simulation.
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50
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Ruiz Y, Pockett S, Freeman WJ, Gonzalez E, Li G. A method to study global spatial patterns related to sensory perception in scalp EEG. J Neurosci Methods 2010; 191:110-8. [DOI: 10.1016/j.jneumeth.2010.05.021] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2010] [Accepted: 05/27/2010] [Indexed: 10/19/2022]
Affiliation(s)
- Yusely Ruiz
- Center for Studies on Electronic and Information Technologies, Universidad Central Marta Abreu de Las Villas, Santa Clara, VC, CP 54830, Cuba.
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