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Polyakov D, Robinson PA, Muller EJ, Shriki O. Recruiting neural field theory for data augmentation in a motor imagery brain-computer interface. Front Robot AI 2024; 11:1362735. [PMID: 38694882 PMCID: PMC11061403 DOI: 10.3389/frobt.2024.1362735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/20/2024] [Indexed: 05/04/2024] Open
Abstract
We introduce a novel approach to training data augmentation in brain-computer interfaces (BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs often suffer from limited accuracy due to a limited amount of training data. To address this, we leveraged a corticothalamic NFT model to generate artificial EEG time series as supplemental training data. We employed the BCI competition IV '2a' dataset to evaluate this augmentation technique. For each individual, we fitted the model to common spatial patterns of each motor imagery class, jittered the fitted parameters, and generated time series for data augmentation. Our method led to significant accuracy improvements of over 2% in classifying the "total power" feature, but not in the case of the "Higuchi fractal dimension" feature. This suggests that the fit NFT model may more favorably represent one feature than the other. These findings pave the way for further exploration of NFT-based data augmentation, highlighting the benefits of biophysically accurate artificial data.
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Affiliation(s)
- Daniel Polyakov
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Be’er Sheva, Israel
- Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, Israel
| | | | - Eli J. Muller
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Oren Shriki
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Be’er Sheva, Israel
- Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, Israel
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2
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Babaie-Janvier T, Gabay NC, McInnes A, Robinson PA. Neural field theory of adaptive effects on auditory evoked responses and mismatch negativity in multifrequency stimulus sequences. Front Hum Neurosci 2024; 17:1282924. [PMID: 38234595 PMCID: PMC10791997 DOI: 10.3389/fnhum.2023.1282924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 10/27/2023] [Indexed: 01/19/2024] Open
Abstract
Physiologically based neural field theory (NFT) of the corticothalamic system, including adaptation, is used to calculate the responses evoked by trains of auditory stimuli that differ in frequency. In oddball paradigms, fully distinguishable frequencies lead to different standard (common stimulus) and deviant (rare stimulus) responses; the signal obtained by subtracting the standard response from the deviant is termed the mismatch negativity (MMN). In this analysis, deviant responses are found to correspond to unadapted cortex, whereas the part of auditory cortex that processes the standard stimuli adapts over several stimulus presentations until the final standard response form is achieved. No higher-order memory processes are invoked. In multifrequency experiments, the deviant response approaches the standard one as the deviant frequency approaches that of the standard and analytic criteria for this effect to be obtained. It is shown that these criteria can also be used to understand adaptation in random tone sequences. A method of probing MMNs and adaptation in random tone sequences is suggested to makes more use of such data.
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Affiliation(s)
- Tahereh Babaie-Janvier
- School of Physics, The University of Sydney, Sydney, NSW, Australia
- Center of Excellence for Integrative Brain Function, The University of Sydney, Sydney, NSW, Australia
| | - Natasha C. Gabay
- School of Physics, The University of Sydney, Sydney, NSW, Australia
- Center of Excellence for Integrative Brain Function, The University of Sydney, Sydney, NSW, Australia
| | | | - Peter A. Robinson
- School of Physics, The University of Sydney, Sydney, NSW, Australia
- Center of Excellence for Integrative Brain Function, The University of Sydney, Sydney, NSW, Australia
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3
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Assadzadeh S, Annen J, Sanz L, Barra A, Bonin E, Thibaut A, Boly M, Laureys S, Gosseries O, Robinson PA. Method for quantifying arousal and consciousness in healthy states and severe brain injury via EEG-based measures of corticothalamic physiology. J Neurosci Methods 2023; 398:109958. [PMID: 37661056 DOI: 10.1016/j.jneumeth.2023.109958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/09/2023] [Accepted: 08/27/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND Characterization of normal arousal states has been achieved by fitting predictions of corticothalamic neural field theory (NFT) to electroencephalographic (EEG) spectra to yield relevant physiological parameters. NEW METHOD A prior fitting method is extended to distinguish conscious and unconscious states in healthy and brain injured subjects by identifying additional parameters and clusters in parameter space. RESULTS Fits of NFT predictions to EEG spectra are used to estimate neurophysiological parameters in healthy and brain injured subjects. Spectra are used from healthy subjects in wake and sleep and from patients with unresponsive wakefulness syndrome, in a minimally conscious state (MCS), and emerged from MCS. Subjects cluster into three groups in parameter space: conscious healthy (wake and REM), sleep, and brain injured. These are distinguished by the difference X-Y between corticocortical (X) and corticothalamic (Y) feedbacks, and by mean neural response rates α and β to incoming spikes. X-Y tracks consciousness in healthy individuals, with smaller values in wake/REM than sleep, but cannot distinguish between brain injuries. Parameters α and β differentiate deep sleep from wake/REM and brain injury. COMPARISON WITH EXISTING METHODS Other methods typically rely on laborious clinical assessment, manual EEG scoring, or evaluation of measures like Φ from integrated information theory, for which no efficient method exists. In contrast, the present method can be automated on a personal computer. CONCLUSION The method provides a means to quantify consciousness and arousal in healthy and brain injured subjects, but does not distinguish subtypes of brain injury.
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Affiliation(s)
- S Assadzadeh
- School of Physics, The University of Sydney, NSW 2006, Australia; Center for Integrative Brain Function, The University of Sydney, NSW 2006, Australia
| | - J Annen
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - L Sanz
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - A Barra
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - E Bonin
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - A Thibaut
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - M Boly
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA; Department of Neurology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - S Laureys
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, U Laval, Canada; International Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China
| | - O Gosseries
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - P A Robinson
- School of Physics, The University of Sydney, NSW 2006, Australia; Center for Integrative Brain Function, The University of Sydney, NSW 2006, Australia.
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4
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Robinson PA, Gabay NC, Babaie-Janvier T. Neural Field Theory of Evoked Response Sequences and Mismatch Negativity With Adaptation. Front Hum Neurosci 2021; 15:655505. [PMID: 34483860 PMCID: PMC8415526 DOI: 10.3389/fnhum.2021.655505] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 07/20/2021] [Indexed: 12/02/2022] Open
Abstract
Physiologically based neural field theory of the corticothalamic system is used to calculate the responses evoked by trains of auditory stimuli that correspond to different cortical locations via the tonotopic map. The results are shown to account for standard and deviant evoked responses to frequent and rare stimuli, respectively, in the auditory oddball paradigms widely used in human cognitive studies, and the so-called mismatch negativity between them. It also reproduces a wide range of other effects and variants, including the mechanism by which a change in standard responses relative to deviants can develop through adaptation, different responses when two deviants are presented in a row or a standard is presented after two deviants, relaxation of standard responses back to deviant form after a stimulus-free period, and more complex sequences. Some cases are identified in which adaptation does not account for the whole difference between standard and deviant responses. The results thus provide a systematic means to determine how much of the response is due to adaptation in the system comprising the primary auditory cortex and medial geniculate nucleus, and how much requires involvement of higher-level processing.
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Affiliation(s)
- Peter A Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Natasha C Gabay
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Tara Babaie-Janvier
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
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5
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El-Zghir RK, Gabay NC, Robinson PA. Modal-Polar Representation of Evoked Response Potentials in Multiple Arousal States. Front Hum Neurosci 2021; 15:642479. [PMID: 34163339 PMCID: PMC8215109 DOI: 10.3389/fnhum.2021.642479] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 05/10/2021] [Indexed: 11/13/2022] Open
Abstract
An expansion of the corticothalamic transfer function into eigenmodes and resonant poles is used to derive a simple formula for evoked response potentials (ERPs) in various states of arousal. The transfer function corresponds to the cortical response to an external stimulus, which encodes all the information and properties of the linear system. This approach links experimental observations of resonances and characteristic timescales in brain activity with physically based neural field theory (NFT). The present work greatly simplifies the formula of the analytical ERP, and separates its spatial part (eigenmodes) from the temporal part (poles). Within this framework, calculations involve contour integrations that yield an explicit expression for ERPs. The dominant global mode is considered explicitly in more detail to study how the ERP varies with time in this mode and to illustrate the method. For each arousal state in sleep and wake, the resonances of the system are determined and it is found that five poles are sufficient to study the main dynamics of the system in waking eyes-open and eyes-closed states. Similarly, it is shown that six poles suffice to reproduce ERPs in rapid-eye movement sleep, sleep state 1, and sleep state 2 states, whereas just four poles suffice to reproduce the dynamics in slow wave sleep. Thus, six poles are sufficient to preserve the main global ERP dynamics of the system for all states of arousal. These six poles correspond to the dominant resonances of the system at slow-wave, alpha, and beta frequencies. These results provide the basis for simplified analytic treatment of brain dynamics and link observations more closely to theory.
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Affiliation(s)
- Rawan K. El-Zghir
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Natasha C. Gabay
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Peter A. Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
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6
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Brain dynamics and structure-function relationships via spectral factorization and the transfer function. Neuroimage 2021; 235:117989. [PMID: 33819612 DOI: 10.1016/j.neuroimage.2021.117989] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 02/15/2021] [Accepted: 03/12/2021] [Indexed: 01/07/2023] Open
Abstract
It is shown how the brain's linear transfer function provides a means to systematically analyze brain connectivity and dynamics, and to infer connectivity, eigenmodes, and activity measures such as spectra, evoked responses, coherence, and causality, all of which are widely used in brain monitoring. In particular, the Wilson spectral factorization algorithm is outlined and used to efficiently obtain linear transfer functions from experimental two-point correlation functions. The algorithm is tested on a series of brain-like structures of increasing complexity which include time delays, asymmetry, two-dimensionality, and complex network connectivity. These tests are used to verify the algorithm is suitable for application to brain dynamics, specify sampling requirements for experimental time series, and to verify that its runtime is short enough to obtain accurate results for systems of similar size to current experiments. The results can equally well be applied to inference of the transfer function in complex linear systems other than brains.
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7
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Mukta KN, Robinson PA, Pagès JC, Gabay NC, Gao X. Evoked response activity eigenmode analysis in a convoluted cortex via neural field theory. Phys Rev E 2020; 102:062303. [PMID: 33466049 DOI: 10.1103/physreve.102.062303] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 07/15/2020] [Indexed: 11/07/2022]
Abstract
Neural field theory of the corticothalamic system is used to explore evoked response potentials (ERPs) caused by spatially localized impulse stimuli on the convoluted cortex and on a spherical cortex. Eigenfunctions are calculated analytically on the spherical cortex and numerically on the convoluted cortex via eigenfunction expansions. Eigenmodes on a convoluted cortex are similar to those of the spherical cortex, and a few such modes are found to be sufficient to reproduce the main ERP features. It is found that the ERP peak is stronger in spherical cortex than convoluted cortex, but in both cases the peak decreases monotonically with increasing distance from the stimulus point. In the convoluted case, cortical folding causes ERPs to differ between locations at the same distance from the stimulus point and spherical symmetries are only approximately preserved.
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Affiliation(s)
- K N Mukta
- School of Physics, University of Sydney, New South Wales 2006, Australia
- Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - P A Robinson
- School of Physics, University of Sydney, New South Wales 2006, Australia
- Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - J C Pagès
- School of Physics, University of Sydney, New South Wales 2006, Australia
- Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
- School of Physics, University of Zurich, Zürich, Canton of Zürich, Switzerland
| | - N C Gabay
- School of Physics, University of Sydney, New South Wales 2006, Australia
- Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - Xiao Gao
- School of Physics, University of Sydney, New South Wales 2006, Australia
- Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
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8
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Babaie-Janvier T, Robinson PA. Neural Field Theory of Evoked Response Potentials With Attentional Gain Dynamics. Front Hum Neurosci 2020; 14:293. [PMID: 32848668 PMCID: PMC7426978 DOI: 10.3389/fnhum.2020.00293] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 06/29/2020] [Indexed: 11/30/2022] Open
Abstract
A generalized neural field model of large-scale activity in the corticothalamic system is used to predict standard evoked potentials. This model embodies local feedbacks that modulate the gains of neural activity as part of the response to incoming stimuli and thus enables both activity changes and effective connectivity changes to be calculated as parts of a generalized evoked response, and their relative contributions to be determined. The results show that incorporation of gain modulations enables a compact and physically justifiable description of the differences in gain between background-EEG and standard-ERP conditions, with the latter able to be initiated from the background state, rather than requiring distinct parameters as in earlier work. In particular, top-down gains are found to be reduced during an ERP, consistent with recent theoretical suggestions that the role of internal models is diminished in favor of external inputs when the latter change suddenly. The static-gain and modulated-gain system transfer functions are analyzed via control theory in terms of system resonances that were recently shown to implement data filtering whose gain adjustments can be interpreted as attention. These filters are shown to govern early and late features in standard evoked responses and their gain parameters are shown to be dynamically adjusted in a way that implements a form of attention. The results show that dynamically modulated resonant filters responsible for the low-frequency oscillations in an evoked potential response have different parameters than those responsible for low-frequency resting EEG responses, while both responses share similar mid- and high-frequency resonant filters. These results provide a biophysical mechanism by which oscillatory activity in the theta, alpha, and beta frequency ranges of an evoked response are modulated as reflections of attention; notably theta is enhanced and alpha suppressed during the latter parts of the ERP. Furthermore, the model enables the part of the ERP response induced by gain modulations to be estimated and interpreted in terms of attention.
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Affiliation(s)
- Tara Babaie-Janvier
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Peter A Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
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9
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Babaie-Janvier T, Robinson PA. Neural Field Theory of Corticothalamic Attention With Control System Analysis. Front Neurosci 2019; 13:1240. [PMID: 31849576 PMCID: PMC6892952 DOI: 10.3389/fnins.2019.01240] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 11/04/2019] [Indexed: 11/25/2022] Open
Abstract
Neural field theory is used to analyze attention by extending an existing model of the large-scale activity in the corticothalamic system to incorporate local feedbacks that modulate the gains of neural connectivity as part of the response to incoming stimuli. Treatment of both activity changes and connectivity changes as part of a generalized response enables generalized linear transfer functions of the combined response to be derived. These are then analyzed and interpreted via control theory in terms of stimulus-driven changes in system resonances that were recently shown to implement data filtering and prediction of the inputs. Using simple visual stimuli as a test case, it is shown that the gain response can implement attention by evaluating two main features of the stimuli: the magnitude and the rate of change, by increasing the weight placed on the rate of change in response to sudden changes, while reducing the contribution of stimuli value in tandem. These changes of filter parameters are shown to improve the prediction of the upcoming stimuli based on its recent time course. This outcome is analogous to controller-parameter tuning for performance enhancement in engineering control theory.
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Affiliation(s)
- Tara Babaie-Janvier
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Peter A Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
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10
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Roque L, Karawani H, Gordon-Salant S, Anderson S. Effects of Age, Cognition, and Neural Encoding on the Perception of Temporal Speech Cues. Front Neurosci 2019; 13:749. [PMID: 31379494 PMCID: PMC6659127 DOI: 10.3389/fnins.2019.00749] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 07/05/2019] [Indexed: 12/11/2022] Open
Abstract
Older adults commonly report difficulty understanding speech, particularly in adverse listening environments. These communication difficulties may exist in the absence of peripheral hearing loss. Older adults, both with normal hearing and with hearing loss, demonstrate temporal processing deficits that affect speech perception. The purpose of the present study is to investigate aging, cognition, and neural processing factors that may lead to deficits on perceptual tasks that rely on phoneme identification based on a temporal cue - vowel duration. A better understanding of the neural and cognitive impairments underlying temporal processing deficits could lead to more focused aural rehabilitation for improved speech understanding for older adults. This investigation was conducted in younger (YNH) and older normal-hearing (ONH) participants who completed three measures of cognitive functioning known to decline with age: working memory, processing speed, and inhibitory control. To evaluate perceptual and neural processing of auditory temporal contrasts, identification functions for the contrasting word-pair WHEAT and WEED were obtained on a nine-step continuum of vowel duration, and frequency-following responses (FFRs) and cortical auditory-evoked potentials (CAEPs) were recorded to the two endpoints of the continuum. Multiple linear regression analyses were conducted to determine the cognitive, peripheral, and/or central mechanisms that may contribute to perceptual performance. YNH participants demonstrated higher cognitive functioning on all three measures compared to ONH participants. The slope of the identification function was steeper in YNH than in ONH participants, suggesting a clearer distinction between the contrasting words in the YNH participants. FFRs revealed better response waveform morphology and more robust phase-locking in YNH compared to ONH participants. ONH participants also exhibited earlier latencies for CAEP components compared to the YNH participants. Linear regression analyses revealed that cortical processing significantly contributed to the variance in perceptual performance in the WHEAT/WEED identification functions. These results suggest that reduced neural precision contributes to age-related speech perception difficulties that arise from temporal processing deficits.
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Affiliation(s)
- Lindsey Roque
- Department of Hearing and Speech Sciences, University of Maryland, College Park, College Park, MD, United States
| | - Hanin Karawani
- Department of Hearing and Speech Sciences, University of Maryland, College Park, College Park, MD, United States.,Department of Communication Sciences and Disorders, University of Haifa, Haifa, Israel
| | - Sandra Gordon-Salant
- Department of Hearing and Speech Sciences, University of Maryland, College Park, College Park, MD, United States
| | - Samira Anderson
- Department of Hearing and Speech Sciences, University of Maryland, College Park, College Park, MD, United States
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11
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Mukta KN, Gao X, Robinson PA. Neural field theory of evoked response potentials in a spherical brain geometry. Phys Rev E 2019; 99:062304. [PMID: 31330724 DOI: 10.1103/physreve.99.062304] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Indexed: 11/07/2022]
Abstract
Evoked response potentials (ERPs) are calculated in spherical and planar geometries using neural field theory of the corticothalamic system. The ERP is modeled as an impulse response and the resulting modal effects of spherical corticothalamic dynamics are explored, showing that results for spherical and planar geometries converge in the limit of large brain size. Cortical modal effects can lead to a double-peak structure in the ERP time series. It is found that the main difference between infinite planar geometry and spherical geometry is that the ERP peak is sharper and stronger in the spherical geometry. It is also found that the magnitude of the response decreases with increasing spatial width of the stimulus at the cortex. The peak is slightly delayed at large angles from the stimulus point, corresponding to group velocities of 6-10 m s^{-1}. Strong modal effects are found in the spherical geometry, with the lowest few modes sufficing to describe the main features of ERPs, except very near to spatially narrow stimuli.
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Affiliation(s)
- K N Mukta
- School of Physics, University of Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - Xiao Gao
- School of Physics, University of Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - P A Robinson
- School of Physics, University of Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
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Zobaer MS, Robinson PA, Kerr CC. Physiology-based ERPs in normal and abnormal states. BIOLOGICAL CYBERNETICS 2018; 112:465-482. [PMID: 30019237 DOI: 10.1007/s00422-018-0766-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 06/03/2018] [Indexed: 06/08/2023]
Abstract
Evoked response potentials (ERPs) and other transients are modeled as impulse responses using physiology-based neural field theory (NFT) of the corticothalamic system of neural activity in the human brain that incorporates synaptic and dendritic dynamics, firing response, axonal propagation, and corticocortical and corticothalamic pathways. The properties of model-predicted ERPs are explored throughout the stability zone of the corticothalamic system, and predicted time series and wavelet spectra are also analyzed. This provides a unified treatment of predicted ERPs for both normal and abnormal states within the brain's stability zone, including likely parameters to represent abnormal states of reduced arousal.
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Affiliation(s)
- M S Zobaer
- School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia.
- Center for Integrative Brain Function, The University of Sydney, Sydney, NSW, 2006, Australia.
- Center for Research Excellence, Neurosleep, 431 Glebe Point Rd, Glebe, NSW, 2037, Australia.
- Department of Physics, Bangladesh University of Textiles, Dhaka, 1208, Bangladesh.
| | - P A Robinson
- School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia
- Center for Integrative Brain Function, The University of Sydney, Sydney, NSW, 2006, Australia
- Center for Research Excellence, Neurosleep, 431 Glebe Point Rd, Glebe, NSW, 2037, Australia
| | - C C Kerr
- School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia
- Center for Integrative Brain Function, The University of Sydney, Sydney, NSW, 2006, Australia
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, 450 Clarkson Ave, Brooklyn, NY, USA
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13
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Babaie Janvier T, Robinson PA. Neural Field Theory of Corticothalamic Prediction With Control Systems Analysis. Front Hum Neurosci 2018; 12:334. [PMID: 30250428 PMCID: PMC6139319 DOI: 10.3389/fnhum.2018.00334] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 08/02/2018] [Indexed: 11/13/2022] Open
Abstract
Neural field theory is used to model and analyze realistic corticothalamic responses to simple visual stimuli. This yields system transfer functions that embody key features in common with those of engineering control systems, which enables interpretation of brain dynamics in terms of data filters. In particular, these features assist in finding internal signals that represent input stimuli and their changes, which are exactly the types of quantities used in control systems to enable prediction of future input signals, and adjustment of gains which is argued to be the analog of attention in control theory. Corticothalamic dynamics are shown to be analogous to the classical proportional-integral-derivative (PID) filters that are widely used in engineering.
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Affiliation(s)
- Tahereh Babaie Janvier
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Peter A Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
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14
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Sanz-Leon P, Robinson PA, Knock SA, Drysdale PM, Abeysuriya RG, Fung FK, Rennie CJ, Zhao X. NFTsim: Theory and Simulation of Multiscale Neural Field Dynamics. PLoS Comput Biol 2018; 14:e1006387. [PMID: 30133448 PMCID: PMC6122812 DOI: 10.1371/journal.pcbi.1006387] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 09/04/2018] [Accepted: 07/22/2018] [Indexed: 01/02/2023] Open
Abstract
A user ready, portable, documented software package, NFTsim, is presented to facilitate numerical simulations of a wide range of brain systems using continuum neural field modeling. NFTsim enables users to simulate key aspects of brain activity at multiple scales. At the microscopic scale, it incorporates characteristics of local interactions between cells, neurotransmitter effects, synaptodendritic delays and feedbacks. At the mesoscopic scale, it incorporates information about medium to large scale axonal ranges of fibers, which are essential to model dissipative wave transmission and to produce synchronous oscillations and associated cross-correlation patterns as observed in local field potential recordings of active tissue. At the scale of the whole brain, NFTsim allows for the inclusion of long range pathways, such as thalamocortical projections, when generating macroscopic activity fields. The multiscale nature of the neural activity produced by NFTsim has the potential to enable the modeling of resulting quantities measurable via various neuroimaging techniques. In this work, we give a comprehensive description of the design and implementation of the software. Due to its modularity and flexibility, NFTsim enables the systematic study of an unlimited number of neural systems with multiple neural populations under a unified framework and allows for direct comparison with analytic and experimental predictions. The code is written in C++ and bundled with Matlab routines for a rapid quantitative analysis and visualization of the outputs. The output of NFTsim is stored in plain text file enabling users to select from a broad range of tools for offline analysis. This software enables a wide and convenient use of powerful physiologically-based neural field approaches to brain modeling. NFTsim is distributed under the Apache 2.0 license.
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Affiliation(s)
- Paula Sanz-Leon
- School of Physics, University of Sydney, Sydney, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, Australia
| | - Peter A. Robinson
- School of Physics, University of Sydney, Sydney, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, Australia
| | - Stuart A. Knock
- School of Physics, University of Sydney, Sydney, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, Australia
| | | | - Romesh G. Abeysuriya
- School of Physics, University of Sydney, Sydney, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, Australia
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Felix K. Fung
- School of Physics, University of Sydney, Sydney, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, Australia
- Downstate Medical Center, State University of New York, Brooklyn, New York, United States of America
| | | | - Xuelong Zhao
- School of Physics, University of Sydney, Sydney, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, Australia
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Mukta KN, MacLaurin JN, Robinson PA. Theory of corticothalamic brain activity in a spherical geometry: Spectra, coherence, and correlation. Phys Rev E 2017; 96:052410. [PMID: 29347754 DOI: 10.1103/physreve.96.052410] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2017] [Indexed: 11/07/2022]
Abstract
Corticothalamic neural field theory is applied to a spherical geometry to better model neural activity in the human brain and is also compared with planar approximations. The frequency power spectrum, correlation, and coherence functions are computed analytically and numerically. The effects of cortical boundary conditions and resulting modal aspects of spherical corticothalamic dynamics are explored, showing that the results of spherical and finite planar geometries converge to those for the infinite planar geometry in the limit of large brain size. Estimates are made of the point at which modal series can be truncated and it is found that for physiologically plausible parameters only the lowest few spatial eigenmodes are needed for an accurate representation of macroscopic brain activity. A difference between the geometries is that there is a low-frequency 1/f spectrum in the infinite planar geometry, whereas in the spherical geometry it is 1/f^{2}. Another difference is that the alpha peak in the spherical geometry is sharper and stronger than in the planar geometry. Cortical modal effects can lead to a double alpha peak structure in the power spectrum, although the main determinant of the alpha peak is corticothalamic feedback. In the spherical geometry, the cross spectrum between two points is found to only depend on their relative distance apart. At small spatial separations the low-frequency cross spectrum is stronger than for an infinite planar geometry and the alpha peak is sharper and stronger due to the partitioning of the energy into discrete modes. In the spherical geometry, the coherence function between points decays monotonically as their separation increases at a fixed frequency, but persists further at resonant frequencies. The correlation between two points is found to be positive, regardless of the time lag and spatial separation, but decays monotonically as the separation increases at fixed time lag. At fixed distance the correlation has peaks at multiples of the period of the dominant frequency of system activity.
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Affiliation(s)
- K N Mukta
- School of Physics, University of Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - J N MacLaurin
- School of Physics, University of Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - P A Robinson
- School of Physics, University of Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
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16
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Zobaer MS, Anderson RM, Kerr CC, Robinson PA, Wong KKH, D'Rozario AL. K-complexes, spindles, and ERPs as impulse responses: unification via neural field theory. BIOLOGICAL CYBERNETICS 2017; 111:149-164. [PMID: 28251306 DOI: 10.1007/s00422-017-0713-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2016] [Accepted: 02/12/2017] [Indexed: 06/06/2023]
Abstract
To interrelate K-complexes, spindles, evoked response potentials (ERPs), and spontaneous electroencephalography (EEG) using neural field theory (NFT), physiology-based NFT of the corticothalamic system is used to model cortical excitatory and inhibitory populations and thalamic relay and reticular nuclei. The impulse response function of the model is used to predict the responses to impulses, which are compared with transient waveforms in sleep studies. Fits to empirical data then allow underlying brain physiology to be inferred and compared with other waves. Spontaneous K-complexes, spindles, and other transient waveforms can be reproduced using NFT by treating them as evoked responses to impulsive stimuli with brain parameters appropriate to spontaneous EEG in sleep stage 2. Using this approach, spontaneous K-complexes and sleep spindles can be analyzed using the same single theory as previously been used to account for waking ERPs and other EEG phenomena. As a result, NFT can explain a wide variety of transient waveforms that have only been phenomenologically classified to date. This enables noninvasive fitting to be used to infer underlying physiological parameters. This physiology-based model reproduces the time series of different transient EEG waveforms; it has previously reproduced experimental EEG spectra, and waking ERPs, and many other observations, thereby unifying transient sleep waveforms with these phenomena.
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Affiliation(s)
- M S Zobaer
- School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia.
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, 2006, Australia.
- Center for Research Excellence, Neurosleep, 431 Glebe Point Rd, Glebe, NSW, 2037, Australia.
- Department of Physics, Bangladesh University of Textiles, Dhaka, 1208, Bangladesh.
| | - R M Anderson
- School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - C C Kerr
- School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, 2006, Australia
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, 450 Clarkson Ave, Brooklyn, NY, USA
| | - P A Robinson
- School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, 2006, Australia
- Center for Research Excellence, Neurosleep, 431 Glebe Point Rd, Glebe, NSW, 2037, Australia
| | - K K H Wong
- CIRUS, Woolcock Institute of Medical Research, The University of Sydney, Sydney, NSW, Australia
- Respiratory and Sleep Disorders Department, Royal Prince Alfred Hospital and Sydney Local Health District, Sydney, NSW, Australia
- Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
| | - A L D'Rozario
- CIRUS, Woolcock Institute of Medical Research, The University of Sydney, Sydney, NSW, Australia
- Respiratory and Sleep Disorders Department, Royal Prince Alfred Hospital and Sydney Local Health District, Sydney, NSW, Australia
- School of Psychology, Faculty of Science, Brain and Mind Centre and Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
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Schellenberger Costa M, Weigenand A, Ngo HVV, Marshall L, Born J, Martinetz T, Claussen JC. A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation. PLoS Comput Biol 2016; 12:e1005022. [PMID: 27584827 PMCID: PMC5008627 DOI: 10.1371/journal.pcbi.1005022] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Accepted: 06/17/2016] [Indexed: 11/18/2022] Open
Abstract
Few models exist that accurately reproduce the complex rhythms of the thalamocortical system that are apparent in measured scalp EEG and at the same time, are suitable for large-scale simulations of brain activity. Here, we present a neural mass model of the thalamocortical system during natural non-REM sleep, which is able to generate fast sleep spindles (12–15 Hz), slow oscillations (<1 Hz) and K-complexes, as well as their distinct temporal relations, and response to auditory stimuli. We show that with the inclusion of detailed calcium currents, the thalamic neural mass model is able to generate different firing modes, and validate the model with EEG-data from a recent sleep study in humans, where closed-loop auditory stimulation was applied. The model output relates directly to the EEG, which makes it a useful basis to develop new stimulation protocols. Sleep plays a pivotal role for the consolidation of memory. While REM sleep had originally been the focus of research due to its similarity with wakefulness, more recent studies suggest that different sleep stages are responsible for the consolidation of different types of memory. To better understand the changes in neuronal dynamics between the different sleep stages, neural mass models are a valuable tool, as they relate directly to the large-scale dynamics measured by an EEG. Here, we present a model of the sleeping thalamocortical system. We first show that the isolated thalamic submodule is able to generate different oscillatory behavior found in vivo. Furthermore, the full thalamocortical model reproduces the EEG of sleep stages N2 and N3 and preserves the temporal relationship between cortical K-complexes/slow oscillations and thalamic sleep spindles. A comparison with event related potential data from a recent sleep study in humans demonstrates its possible application in predicting the outcome of external stimulation on EEG rhythms. Our study shows, that a neural mass model incorporating few key mechanisms is sufficient to reproduce the complex brain dynamics observed during sleep.
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Affiliation(s)
- Michael Schellenberger Costa
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- * E-mail:
| | - Arne Weigenand
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Graduate School for Computing in Medicine and Life Science, University of Lübeck, Lübeck, Germany
| | - Hong-Viet V. Ngo
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Lisa Marshall
- Graduate School for Computing in Medicine and Life Science, University of Lübeck, Lübeck, Germany
- Institute of Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Lübeck, Germany
| | - Jan Born
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
| | - Thomas Martinetz
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Graduate School for Computing in Medicine and Life Science, University of Lübeck, Lübeck, Germany
| | - Jens Christian Claussen
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Computational Systems Biology, Jacobs University Bremen, Bremen, Germany
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Griffiths JD. Causal influence in neural systems: Reconciling mechanistic-reductionist and statistical perspectives: Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino & S.L. Bressler. Phys Life Rev 2015; 15:130-2. [PMID: 26596992 DOI: 10.1016/j.plrev.2015.11.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Accepted: 11/04/2015] [Indexed: 11/16/2022]
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Saggar M, Zanesco AP, King BG, Bridwell DA, MacLean KA, Aichele SR, Jacobs TL, Wallace BA, Saron CD, Miikkulainen R. Mean-field thalamocortical modeling of longitudinal EEG acquired during intensive meditation training. Neuroimage 2015; 114:88-104. [PMID: 25862265 DOI: 10.1016/j.neuroimage.2015.03.073] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Revised: 12/10/2014] [Accepted: 03/27/2015] [Indexed: 12/18/2022] Open
Abstract
Meditation training has been shown to enhance attention and improve emotion regulation. However, the brain processes associated with such training are poorly understood and a computational modeling framework is lacking. Modeling approaches that can realistically simulate neurophysiological data while conforming to basic anatomical and physiological constraints can provide a unique opportunity to generate concrete and testable hypotheses about the mechanisms supporting complex cognitive tasks such as meditation. Here we applied the mean-field computational modeling approach using the scalp-recorded electroencephalogram (EEG) collected at three assessment points from meditating participants during two separate 3-month-long shamatha meditation retreats. We modeled cortical, corticothalamic, and intrathalamic interactions to generate a simulation of EEG signals recorded across the scalp. We also present two novel extensions to the mean-field approach that allow for: (a) non-parametric analysis of changes in model parameter values across all channels and assessments; and (b) examination of variation in modeled thalamic reticular nucleus (TRN) connectivity over the retreat period. After successfully fitting whole-brain EEG data across three assessment points within each retreat, two model parameters were found to replicably change across both meditation retreats. First, after training, we observed an increased temporal delay between modeled cortical and thalamic cells. This increase provides a putative neural mechanism for a previously observed reduction in individual alpha frequency in these same participants. Second, we found decreased inhibitory connection strength between the TRN and secondary relay nuclei (SRN) of the modeled thalamus after training. This reduction in inhibitory strength was found to be associated with increased dynamical stability of the model. Altogether, this paper presents the first computational approach, taking core aspects of physiology and anatomy into account, to formally model brain processes associated with intensive meditation training. The observed changes in model parameters inform theoretical accounts of attention training through meditation, and may motivate future study on the use of meditation in a variety of clinical populations.
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Affiliation(s)
- Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Department of Computer Science, University of Texas at Austin, TX, USA.
| | - Anthony P Zanesco
- Department of Psychology, University of California, Davis, CA, USA; Center for Mind and Brain, University of California, Davis, CA, USA
| | - Brandon G King
- Department of Psychology, University of California, Davis, CA, USA; Center for Mind and Brain, University of California, Davis, CA, USA
| | | | - Katherine A MacLean
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Stephen R Aichele
- Department of Psychology, University of California, Davis, CA, USA; Center for Mind and Brain, University of California, Davis, CA, USA
| | - Tonya L Jacobs
- Center for Mind and Brain, University of California, Davis, CA, USA
| | - B Alan Wallace
- Santa Barbara Institute for Consciousness Studies, Santa Barbara, CA, USA
| | - Clifford D Saron
- Center for Mind and Brain, University of California, Davis, CA, USA; The M.I.N.D. Institute, University of California, Davis, Sacramento, CA, USA
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Roberts J, Robinson P. Quantitative theory of driven nonlinear brain dynamics. Neuroimage 2012; 62:1947-55. [DOI: 10.1016/j.neuroimage.2012.05.054] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2011] [Revised: 04/05/2012] [Accepted: 05/21/2012] [Indexed: 11/16/2022] Open
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Roberts JA, Robinson PA. Corticothalamic dynamics: structure of parameter space, spectra, instabilities, and reduced model. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2012; 85:011910. [PMID: 22400594 DOI: 10.1103/physreve.85.011910] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2011] [Revised: 12/18/2011] [Indexed: 05/31/2023]
Abstract
Linear instabilities are analyzed in a physiologically based mean-field corticothalamic model and a reduced-parameter model derived from it. In both models, the stable zone corresponding to normal arousal states is bounded by a series of surfaces demarcating the onsets of instabilities. The stable zone is found to depend on delay and rate parameters, whose values have a simple relationship to the number of instabilities and dominant frequencies on the stable zone's boundary. The dominant frequencies of linear activity inside the stable zone are found to lie in clearly delineated regions, each corresponding to an instability surface on its boundary and having approximately the same dominant frequency. These regions are ordered in parameter space according to their dominant frequencies, and an instability associated with the intrathalamic loop is shown to have the highest frequency that can become unstable. This reveals an important role for the thalamus in controlling the stability and bandwidth of dynamics in the corticothalamic system as a whole. The reduced model is found to agree well with the full model in a wide region of parameter space and, thus, is a useful guide to the full model's dynamics.
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Affiliation(s)
- J A Roberts
- School of Physics, University of Sydney, New South Wales 2006, Australia.
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Kerr CC, Kemp AH, Rennie CJ, Robinson PA. Thalamocortical changes in major depression probed by deconvolution and physiology-based modeling. Neuroimage 2011; 54:2672-82. [PMID: 21073966 DOI: 10.1016/j.neuroimage.2010.11.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2010] [Revised: 10/22/2010] [Accepted: 11/01/2010] [Indexed: 10/18/2022] Open
Abstract
Auditory event-related potentials (ERPs) have been extensively studied in patients with depression, but most studies have focused on purely phenomenological analysis methods, such as component scoring. In contrast, this study applies two recently developed physiology-based methods-fitting using a thalamocortical model of neuronal activity and waveform deconvolution - to data from a selective-attention task in four subject groups (49 patients with melancholic depression, 34 patients with non-melancholic depression, 111 participants with subclinical depressed mood, and 98 healthy controls), to yield insight into physiological differences in attentional processing between participants with major depression and controls. This approach found evidence that: participants with depressed mood, regardless of clinical status, shift from excitation in the thalamocortical system towards inhibition; that clinically depressed participants have decreased relative response amplitude between target and standard waveforms; and that patients with melancholic depression also have increased thalamocortical delays. These findings suggest possible physiological mechanisms underlying different depression subtypes, and may eventually prove useful in motivating new physiology-based diagnostic methods.
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Affiliation(s)
- Cliff C Kerr
- School of Physics, University of Sydney, New South Wales, Australia.
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Capilla A, Pazo-Alvarez P, Darriba A, Campo P, Gross J. Steady-state visual evoked potentials can be explained by temporal superposition of transient event-related responses. PLoS One 2011; 6:e14543. [PMID: 21267081 PMCID: PMC3022588 DOI: 10.1371/journal.pone.0014543] [Citation(s) in RCA: 128] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2010] [Accepted: 12/10/2010] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND One common criterion for classifying electrophysiological brain responses is based on the distinction between transient (i.e. event-related potentials, ERPs) and steady-state responses (SSRs). The generation of SSRs is usually attributed to the entrainment of a neural rhythm driven by the stimulus train. However, a more parsimonious account suggests that SSRs might result from the linear addition of the transient responses elicited by each stimulus. This study aimed to investigate this possibility. METHODOLOGY/PRINCIPAL FINDINGS We recorded brain potentials elicited by a checkerboard stimulus reversing at different rates. We modeled SSRs by sequentially shifting and linearly adding rate-specific ERPs. Our results show a strong resemblance between recorded and synthetic SSRs, supporting the superposition hypothesis. Furthermore, we did not find evidence of entrainment of a neural oscillation at the stimulation frequency. CONCLUSIONS/SIGNIFICANCE This study provides evidence that visual SSRs can be explained as a superposition of transient ERPs. These findings have critical implications in our current understanding of brain oscillations. Contrary to the idea that neural networks can be tuned to a wide range of frequencies, our findings rather suggest that the oscillatory response of a given neural network is constrained within its natural frequency range.
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Affiliation(s)
- Almudena Capilla
- Centre for Cognitive Neuroimaging, Institute for Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.
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Model-based analysis and quantification of age trends in auditory evoked potentials. Clin Neurophysiol 2011; 122:134-47. [DOI: 10.1016/j.clinph.2010.05.030] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2009] [Revised: 04/07/2010] [Accepted: 05/15/2010] [Indexed: 11/24/2022]
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Kerr CC, van Albada SJ, Rennie CJ, Robinson PA. Age trends in auditory oddball evoked potentials via component scoring and deconvolution. Clin Neurophysiol 2010; 121:962-76. [DOI: 10.1016/j.clinph.2009.11.077] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2009] [Revised: 10/16/2009] [Accepted: 11/18/2009] [Indexed: 11/29/2022]
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Dugué P, Le Bouquin-Jeannès R, Edeline JM, Faucon G. A physiologically based model for temporal envelope encoding in human primary auditory cortex. Hear Res 2010; 268:133-44. [PMID: 20685388 DOI: 10.1016/j.heares.2010.05.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2009] [Revised: 05/17/2010] [Accepted: 05/21/2010] [Indexed: 10/19/2022]
Abstract
Communication sounds exhibit temporal envelope fluctuations in the low frequency range (<70 Hz) and human speech has prominent 2-16 Hz modulations with a maximum at 3-4 Hz. Here, we propose a new phenomenological model of the human auditory pathway (from cochlea to primary auditory cortex) to simulate responses to amplitude-modulated white noise. To validate the model, performance was estimated by quantifying temporal modulation transfer functions (TMTFs). Previous models considered either the lower stages of the auditory system (up to the inferior colliculus) or only the thalamocortical loop. The present model, divided in two stages, is based on anatomical and physiological findings and includes the entire auditory pathway. The first stage, from the outer ear to the colliculus, incorporates inhibitory interneurons in the cochlear nucleus to increase performance at high stimuli levels. The second stage takes into account the anatomical connections of the thalamocortical system and includes the fast and slow excitatory and inhibitory currents. After optimizing the parameters of the model to reproduce the diversity of TMTFs obtained from human subjects, a patient-specific model was derived and the parameters were optimized to effectively reproduce both spontaneous activity and the oscillatory part of the evoked response.
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van Albada SJ, Kerr CC, Chiang AKI, Rennie CJ, Robinson PA. Neurophysiological changes with age probed by inverse modeling of EEG spectra. Clin Neurophysiol 2009; 121:21-38. [PMID: 19854102 DOI: 10.1016/j.clinph.2009.09.021] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2009] [Revised: 08/19/2009] [Accepted: 09/22/2009] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To investigate age-associated changes in physiologically-based EEG spectral parameters in the healthy population. METHODS Eyes-closed EEG spectra of 1498 healthy subjects aged 6-86 years were fitted to a mean-field model of thalamocortical dynamics in a cross-sectional study. Parameters were synaptodendritic rates, cortical wave decay rates, connection strengths (gains), axonal delays for thalamocortical loops, and power normalizations. Age trends were approximated using smooth asymptotically linear functions with a single turning point. We also considered sex differences and relationships between model parameters and traditional quantitative EEG measures. RESULTS The cross-sectional data suggest that changes tend to be most rapid in childhood, generally leveling off at age 15-20 years. Most gains decrease in magnitude with age, as does power normalization. Axonal and dendritic delays decrease in childhood and then increase. Axonal delays and gains show small but significant sex differences. CONCLUSIONS Mean-field brain modeling allows interpretation of age-associated EEG trends in terms of physiological processes, including the growth and regression of white matter, influencing axonal delays, and the establishment and pruning of synaptic connections, influencing gains. SIGNIFICANCE This study demonstrates the feasibility of inverse modeling of EEG spectra as a noninvasive method for investigating large-scale corticothalamic dynamics, and provides a basis for future comparisons.
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Affiliation(s)
- S J van Albada
- School of Physics, The University of Sydney, NSW 2006, Australia.
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van Albada SJ, Robinson PA. Mean-field modeling of the basal ganglia-thalamocortical system. I Firing rates in healthy and parkinsonian states. J Theor Biol 2008; 257:642-63. [PMID: 19168074 DOI: 10.1016/j.jtbi.2008.12.018] [Citation(s) in RCA: 76] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2008] [Revised: 12/08/2008] [Accepted: 12/08/2008] [Indexed: 01/02/2023]
Abstract
Parkinsonism leads to various electrophysiological changes in the basal ganglia-thalamocortical system (BGTCS), often including elevated discharge rates of the subthalamic nucleus (STN) and the output nuclei, and reduced activity of the globus pallidus external (GPe) segment. These rate changes have been explained qualitatively in terms of the direct/indirect pathway model, involving projections of distinct striatal populations to the output nuclei and GPe. Although these populations partly overlap, evidence suggests dopamine depletion differentially affects cortico-striato-pallidal connection strengths to the two pallidal segments. Dopamine loss may also decrease the striatal signal-to-noise ratio, reducing both corticostriatal coupling and striatal firing thresholds. Additionally, nigrostriatal degeneration may cause secondary changes including weakened lateral inhibition in the GPe, and mesocortical dopamine loss may decrease intracortical excitation and especially inhibition. Here a mean-field model of the BGTCS is presented with structure and parameter estimates closely based on physiology and anatomy. Changes in model rates due to the possible effects of dopamine loss listed above are compared with experiment. Our results suggest that a stronger indirect pathway, possibly combined with a weakened direct pathway, is compatible with empirical evidence. However, altered corticostriatal connection strengths are probably not solely responsible for substantially increased STN activity often found. A lower STN firing threshold, weaker intracortical inhibition, and stronger striato-GPe inhibition help explain the relatively large increase in STN rate. Reduced GPe-GPe inhibition and a lower GPe firing threshold can account for the comparatively small decrease in GPe rate frequently observed. Changes in cortex, GPe, and STN help normalize the cortical rate, also in accord with experiments. The model integrates the basal ganglia into a unified framework along with an existing thalamocortical model that already accounts for a wide range of electrophysiological phenomena. A companion paper discusses the dynamics and oscillations of this combined system.
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Affiliation(s)
- S J van Albada
- School of Physics, The University of Sydney, New South Wales 2006, Australia.
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CLEARWATER JM, KERR CC, RENNIE CJ, ROBINSON PA. NEURAL MECHANISMS OF ERP CHANGE: COMBINING INSIGHTS FROM ELECTROPHYSIOLOGY AND MATHEMATICAL MODELING. J Integr Neurosci 2008; 7:529-50. [DOI: 10.1142/s0219635208002003] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2008] [Revised: 11/10/2008] [Indexed: 11/18/2022] Open
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Roberts JA, Robinson PA. Modeling distributed axonal delays in mean-field brain dynamics. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2008; 78:051901. [PMID: 19113149 DOI: 10.1103/physreve.78.051901] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2008] [Revised: 09/02/2008] [Indexed: 05/27/2023]
Abstract
The range of conduction delays between connected neuronal populations is often modeled as a single discrete delay, assumed to be an effective value averaging over all fiber velocities. This paper shows the effects of distributed delays on signal propagation. A distribution acts as a linear filter, imposing an upper frequency cutoff that is inversely proportional to the delay width. Distributed thalamocortical and corticothalamic delays are incorporated into a physiologically based mean-field model of the cortex and thalamus to illustrate their effects on the electroencephalogram (EEG). The power spectrum is acutely sensitive to the width of the thalamocortical delay distribution, and more so than the corticothalamic distribution, because all input signals must travel along the thalamocortical pathway. This imposes a cutoff frequency above which the spectrum is overly damped. The positions of spectral peaks in the resting EEG depend primarily on the distribution mean, with only weak dependences on distribution width. Increasing distribution width increases the stability of fixed point solutions. A single discrete delay successfully approximates a distribution for frequencies below a cutoff that is inversely proportional to the delay width, provided that other model parameters are moderately adjusted. A pair of discrete delays together having the same mean, variance, and skewness as the distribution approximates the distribution over the same frequency range without needing parameter adjustment. Delay distributions with large fractional widths are well approximated by low-order differential equations.
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Affiliation(s)
- J A Roberts
- School of Physics, University of Sydney, and Brain Dynamics Centre, Westmead Millenium Institute, Westmead Hospital, Westmead, New South Wales 2145, Australia.
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Moran RJ, Stephan KE, Seidenbecher T, Pape HC, Dolan RJ, Friston KJ. Dynamic causal models of steady-state responses. Neuroimage 2008; 44:796-811. [PMID: 19000769 PMCID: PMC2644453 DOI: 10.1016/j.neuroimage.2008.09.048] [Citation(s) in RCA: 109] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2008] [Revised: 09/01/2008] [Accepted: 09/28/2008] [Indexed: 10/25/2022] Open
Abstract
In this paper, we describe a dynamic causal model (DCM) of steady-state responses in electrophysiological data that are summarised in terms of their cross-spectral density. These spectral data-features are generated by a biologically plausible, neural-mass model of coupled electromagnetic sources; where each source comprises three sub-populations. Under linearity and stationarity assumptions, the model's biophysical parameters (e.g., post-synaptic receptor density and time constants) prescribe the cross-spectral density of responses measured directly (e.g., local field potentials) or indirectly through some lead-field (e.g., electroencephalographic and magnetoencephalographic data). Inversion of the ensuing DCM provides conditional probabilities on the synaptic parameters of intrinsic and extrinsic connections in the underlying neuronal network. This means we can make inferences about synaptic physiology, as well as changes induced by pharmacological or behavioural manipulations, using the cross-spectral density of invasive or non-invasive electrophysiological recordings. In this paper, we focus on the form of the model, its inversion and validation using synthetic and real data. We conclude with an illustrative application to multi-channel local field potential data acquired during a learning experiment in mice.
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Affiliation(s)
- R J Moran
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK.
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Clearwater JM, Rennie CJ, Robinson PA. Mean field model of acetylcholine mediated dynamics in the thalamocortical system. J Theor Biol 2008; 255:287-98. [PMID: 18775441 DOI: 10.1016/j.jtbi.2008.08.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2008] [Revised: 07/01/2008] [Accepted: 08/08/2008] [Indexed: 12/31/2022]
Abstract
A recent continuum model of the large scale electrical activity of the thalamocortical system is generalized to include cholinergic modulation. The model is examined analytically and numerically to determine the effect of acetylcholine (ACh) on its steady states, linear stability, spectrum, and temporal responses. Changing the ACh concentration moves the system between zones of one, three, and five steady states, showing that neuromodulation of synaptic strength is a possible mechanism by which multiple steady states emerge in the brain. The lowest firing rate steady state is always stable, and subsequent fixed points alternate between stable and unstable. Increasing ACh concentration changes the form of the spectrum. Increasing the tonic level of ACh concentration increases the magnitudes of the N100 and P200 in the evoked response potential (ERP), without changing the timing of these peaks. Driving the system with a pulse of cholinergic activity results in a transient increase in the firing rate of cortical neurons that lasts over 10s. Step-like increases in cortical ACh concentration cause increases in the firing rate of cortical neurons, with rapid responses due to fast acting nicotinic receptors and slower responses due to muscarinic receptor suppression of intracortical connections.
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Affiliation(s)
- J M Clearwater
- School of Physics, University of Sydney, New South Wales 2006, Australia.
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