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Chen Y, You W, Hu Y, Chu H, Chen X, Shi W, Gao X. EEG measurement for the effect of perceptual eye position and eye position training on comitant strabismus. Cereb Cortex 2023; 33:10194-10206. [PMID: 37522301 PMCID: PMC10502583 DOI: 10.1093/cercor/bhad275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 08/01/2023] Open
Abstract
One of the clinical features of comitant strabismus is that the deviation angles in the first and second eye positions are equal. However, there has been no report of consistency in the electroencephalography (EEG) signals between the 2 positions. In order to address this issue, we developed a new paradigm based on perceptual eye position. We collected steady-state visual evoked potentials (SSVEPs) signals and resting-state EEG data before and after the eye position training. We found that SSVEP signals could characterize the suppression effect and eye position effect of comitant strabismus, that is, the SSVEP response of the dominant eye was stronger than that of the strabismus eye in the first eye position but not in the second eye position. Perceptual eye position training could modulate the frequency band activities in the occipital and surrounding areas. The changes in the visual function of comitant strabismus after training could also be characterized by SSVEP. There was a correlation between intermodulation frequency, power of parietal electrodes, and perceptual eye position, indicating that EEG might be a potential indicator for evaluating strabismus visual function.
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Affiliation(s)
- Yuzhen Chen
- Shenzhen International Graduate School, Tsinghua University, Nanshan District, Shenzhen 518055, China
| | - Weicong You
- Shenzhen International Graduate School, Tsinghua University, Nanshan District, Shenzhen 518055, China
| | - Yijun Hu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing 100084, China
| | - Hang Chu
- The National Engineering Research Center for Healthcare Devices, Tianhe District, Guangzhou 510500, China
| | - Xiaogang Chen
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Nankai District, Tianjin 300192, China
| | - Wei Shi
- Department of Ophthalmology, Beijing Children’s Hospital, Capital Medical University, Xicheng District, Beijing 100045, China
| | - Xiaorong Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing 100084, China
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2
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Archery under the (electroencephalography-)hood: Theta-lateralization as a marker for motor learning. Neuroscience 2022; 499:23-39. [PMID: 35870564 DOI: 10.1016/j.neuroscience.2022.07.019] [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: 05/09/2022] [Revised: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 11/22/2022]
Abstract
An intrinsic characteristic of the motor system is the preference of one side of the body. Lateralization is found in motor behavior and in the structural and functional correlates of cortical motor networks. While genetic factors have been elucidated as mechanisms leading to such asymmetries, findings in motor learning and experience from clinical experience demonstrate considerable additional plasticity during the lifespan. If and how functional lateralization develops in short timeframes during training of motor skills involving both sides of the body is still largely unclear. In the present exploratory study, we investigate lateralization of theta-, alpha- and beta-band oscillations during training of an ecologically valid skill - archery. We relate lateralization shift to performance improvement and elucidate the underlying cortical areas. To this end, healthy participants without any previous experience in archery underwent intensive training with 100 shots on each of three days. 64-channel electroencephalography was recorded simultaneously during the individual shots. We found that a central-parietal theta lateralization shift to the left immediately before the shot was associated with performance improvement. Lateralization of alpha or beta did not yield a significant association. Importantly, areas of maximum activation were not identical with areas showing the strongest associations with performance improvement. These data suggest that learning a complex bimanual motor skill is associated with a shift of theta-band oscillations to the left in central-parietal areas. The relationship with performance improvement may reflect increased cortical efficiency of task-relevant processing.
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3
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Chen Y, Shi W, Liu Q, Chu H, Chen X, Yan L, Wu J, Li L, Gao X. EEG Measurement for Suppression in Refractive Amblyopia and Push-pull Perception Efficacy. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1321-1330. [PMID: 35576430 DOI: 10.1109/tnsre.2022.3175177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In order to evaluate refractive amblyopia suppression and understand the neural mechanism of amblyopia suppression and push-pull perception training, we recorded the EEG of refractive amblyopia children before, during, and after push-pull perception training. We compared the brain activity in different states through the steady-state visual evoked potentials (SSVEPs) response and power topography and compared them with normal children. We found that amblyopic and fellow eyes have different performances in fundamental and harmonic frequency responses. They also show different characteristics when be masked. Push-pull perception training improved the SSVEP performance of amblyopia children by reducing the SSVEP response difference between eyes and improving the intermodulation frequency response. The result of topography showed that push-pull perception reduced the alpha power of occipital and temporal lobes, which was conducive to improving binocular function. The changes of intermodulation response and occipital alpha power were significantly correlated with the clinical indicator. Thus, EEG is a potential method to measure amblyopia suppression and the efficacy of push-pull perception.
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4
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Pino O, Romano G. Engagement and Arousal effects in predicting the increase of cognitive functioning following a neuromodulation program. ACTA BIO-MEDICA : ATENEI PARMENSIS 2022; 93:e2022248. [PMID: 35775751 PMCID: PMC9335441 DOI: 10.23750/abm.v93i3.13145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND AND AIM Research in the field of Brain-Computer Interfaces (BCIs) has increased exponentially over the past few years, demonstrating their effectiveness and application in several areas. The main purpose of the present paper was to explore the relevance of user engagement during interaction with a BCI prototype (Neuro-Upper, NU), which aimed at brainwave synchronization through audio-visual entrainment, in the improvement of cognitive performance. METHODS This paper presents findings on data collected from a sample of 18 subjects with clinical disorders who completed about 55 consecutive sessions of 30 min of audio-visual stimulation. The relationship between engagement and improvement of cognitive function (measured through the Intelligence Quotient - IQ) during NU neuromodulation was evaluated through the Index of Cognitive Engagement (ICE) measured by the Pope ratio (Beta / (Alpha + Theta), and Arousal [(High Beta + Low Beta) / (High Alpha + Low Alpha)]. RESULTS A significant correlation between engagement and IQ improvement, but no correlation between arousal and IQ improvement emerged, as expected. CONCLUSIONS Future research aiming at clarifying the role of arousal in psychological disorders and related symptoms will be essential.
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Affiliation(s)
- Olimpia Pino
- University of Parma, Department of Medicine & Surgery, Neuroscience Unit.
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5
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Stephen JM, Hill DE, Candelaria-Cook FT. Examining the effects of prenatal alcohol exposure on corticothalamic connectivity: A multimodal neuroimaging study in children. Dev Cogn Neurosci 2021; 52:101019. [PMID: 34666262 PMCID: PMC8524752 DOI: 10.1016/j.dcn.2021.101019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 09/22/2021] [Accepted: 10/06/2021] [Indexed: 02/01/2023] Open
Abstract
Children with a fetal alcohol spectrum disorder (FASD) experience a range of cognitive and behavioral effects. Prior studies have demonstrated white matter changes in children with FASD relative to typically developing controls (TDC) and these changes relate to behavior. Our prior MEG study (Candelaria-Cook et al. 2020) demonstrated reduced alpha oscillations during rest in FASD relative to TDC and alpha power is correlated with behavior. However, little is known about how brain structure influences brain function. We hypothesized that alpha power was related to corticothalamic connectivity. Children 8–13 years of age (TDC: N = 25, FASD: N = 24) underwent rest MEG with eyes open or closed and MRI to collect structural and diffusion tensor imaging data. MEG spectral analysis was performed for sensor and source data. We estimated mean fractional anisotropy in regions of interest (ROIs) that included the corticothalamic tracts. The FASD group had reduced mean FA in three of the corticothalamic ROIs. FA in these tracts was significantly correlated with alpha power at the sensor and source level. The results support the hypothesis that integrity of the corticothalamic tracts influences cortical alpha power. Further research is needed to understand how brain structure and function influence behavior.
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Affiliation(s)
- J M Stephen
- The Mind Research Network and Lovelace Biomedical Research Institute, 1101 Yale Blvd NE, Albuquerque, NM 87106, United States; Psychiatry Department, University of New Mexico Health Sciences Center, Albuquerque, NM, United States.
| | - D E Hill
- The Mind Research Network and Lovelace Biomedical Research Institute, 1101 Yale Blvd NE, Albuquerque, NM 87106, United States; Psychiatry Department, University of New Mexico Health Sciences Center, Albuquerque, NM, United States
| | - F T Candelaria-Cook
- The Mind Research Network and Lovelace Biomedical Research Institute, 1101 Yale Blvd NE, Albuquerque, NM 87106, United States; Psychiatry Department, University of New Mexico Health Sciences Center, Albuquerque, NM, United States
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6
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Lundqvist M, Wutz A. New methods for oscillation analyses push new theories of discrete cognition. Psychophysiology 2021; 59:e13827. [PMID: 33942323 DOI: 10.1111/psyp.13827] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/18/2021] [Accepted: 03/23/2021] [Indexed: 11/28/2022]
Abstract
Classical ways of analyzing neural time series data has led to static views on cognition, in which the cognitive processes are linked to sustained neural activity and interpreted as stationary states. The core analytical focus was on slow power modulations of neural oscillations averaged across many experimental trials. Whereas this custom analytical approach reduces the complexity and increases the signal-to-noise ratio, it may disregard or even remove important aspects of the underlying neural dynamics. Novel analysis methods investigate the instantaneous frequency and phase of neural oscillations and relate them to the precisely controlled timing of brief successive sensory stimuli. This enables to capture how cognitive processes unfold in discrete windows within and across oscillatory cycles. Moreover, several recent studies analyze the oscillatory power modulations on single experimental trials. They suggest that the power modulations are packed into discrete bursts of activity, which occur at different rates and times, and with different durations from trial-to-trial. Here, we review the current work that made use of these methodological advances for neural oscillations. These novel analysis perspectives emphasize that cognitive processes occur in discrete time windows, instead of sustained, stationary states. Evidence for discretization was observed for the entire range of cognitive functions from perception and attention to working memory, goal-directed thought and motor actions, as well as throughout the entire cortical hierarchy and in subcortical regions. These empirical observations create demand for new psychological theories and computational models of cognition in the brain, which integrate its discrete temporal dynamics.
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Affiliation(s)
- Mikael Lundqvist
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.,Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andreas Wutz
- Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.,Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
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7
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An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity. eNeuro 2020; 7:ENEURO.0374-19.2020. [PMID: 32127347 PMCID: PMC7189483 DOI: 10.1523/eneuro.0374-19.2020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 01/17/2020] [Accepted: 01/27/2020] [Indexed: 12/21/2022] Open
Abstract
Working memory (WM) is a key component of human memory and cognition. Computational models have been used to study the underlying neural mechanisms, but neglected the important role of short-term memory (STM) and long-term memory (LTM) interactions for WM. Here, we investigate these using a novel multiarea spiking neural network model of prefrontal cortex (PFC) and two parietotemporal cortical areas based on macaque data. We propose a WM indexing theory that explains how PFC could associate, maintain, and update multimodal LTM representations. Our simulations demonstrate how simultaneous, brief multimodal memory cues could build a temporary joint memory representation as an “index” in PFC by means of fast Hebbian synaptic plasticity. This index can then reactivate spontaneously and thereby also the associated LTM representations. Cueing one LTM item rapidly pattern completes the associated uncued item via PFC. The PFC–STM network updates flexibly as new stimuli arrive, thereby gradually overwriting older representations.
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8
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Valenza G, Greco A, Bianchi M, Nardelli M, Rossi S, Scilingo EP. EEG oscillations during caress-like affective haptic elicitation. Psychophysiology 2018; 55:e13199. [DOI: 10.1111/psyp.13199] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2017] [Revised: 04/09/2018] [Accepted: 04/12/2018] [Indexed: 01/26/2023]
Affiliation(s)
- Gaetano Valenza
- Department of Information Engineering and the Bioengineering and Robotics Research Center “E. Piaggio,” School of Engineering; University of Pisa; Pisa Italy
| | - Alberto Greco
- Department of Information Engineering and the Bioengineering and Robotics Research Center “E. Piaggio,” School of Engineering; University of Pisa; Pisa Italy
| | - Matteo Bianchi
- Department of Information Engineering and the Bioengineering and Robotics Research Center “E. Piaggio,” School of Engineering; University of Pisa; Pisa Italy
| | - Mimma Nardelli
- Department of Information Engineering and the Bioengineering and Robotics Research Center “E. Piaggio,” School of Engineering; University of Pisa; Pisa Italy
| | - Simone Rossi
- Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Unit; University of Siena; Siena Italy
| | - Enzo Pasquale Scilingo
- Department of Information Engineering and the Bioengineering and Robotics Research Center “E. Piaggio,” School of Engineering; University of Pisa; Pisa Italy
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9
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Prestimulus EEG Power Predicts Conscious Awareness But Not Objective Visual Performance. eNeuro 2017; 4:eN-NWR-0182-17. [PMID: 29255794 PMCID: PMC5732016 DOI: 10.1523/eneuro.0182-17.2017] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 11/02/2017] [Accepted: 11/03/2017] [Indexed: 01/01/2023] Open
Abstract
Prestimulus oscillatory neural activity has been linked to perceptual outcomes during performance of psychophysical detection and discrimination tasks. Specifically, the power and phase of low frequency oscillations have been found to predict whether an upcoming weak visual target will be detected or not. However, the mechanisms by which baseline oscillatory activity influences perception remain unclear. Recent studies suggest that the frequently reported negative relationship between α power and stimulus detection may be explained by changes in detection criterion (i.e., increased target present responses regardless of whether the target was present/absent) driven by the state of neural excitability, rather than changes in visual sensitivity (i.e., more veridical percepts). Here, we recorded EEG while human participants performed a luminance discrimination task on perithreshold stimuli in combination with single-trial ratings of perceptual awareness. Our aim was to investigate whether the power and/or phase of prestimulus oscillatory activity predict discrimination accuracy and/or perceptual awareness on a trial-by-trial basis. Prestimulus power (3-28 Hz) was inversely related to perceptual awareness ratings (i.e., higher ratings in states of low prestimulus power/high excitability) but did not predict discrimination accuracy. In contrast, prestimulus oscillatory phase did not predict awareness ratings or accuracy in any frequency band. These results provide evidence that prestimulus α power influences the level of subjective awareness of threshold visual stimuli but does not influence visual sensitivity when a decision has to be made regarding stimulus features. Hence, we find a clear dissociation between the influence of ongoing neural activity on conscious awareness and objective performance.
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10
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Clayton MS, Yeung N, Cohen Kadosh R. The many characters of visual alpha oscillations. Eur J Neurosci 2017; 48:2498-2508. [DOI: 10.1111/ejn.13747] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 09/27/2017] [Accepted: 10/09/2017] [Indexed: 11/26/2022]
Affiliation(s)
| | - Nick Yeung
- Department of Experimental Psychology; University of Oxford; Oxford UK
| | - Roi Cohen Kadosh
- Department of Experimental Psychology; University of Oxford; Oxford UK
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11
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Rickard RE, Young AMJ, Gerdjikov TV. Cortical Local Field Potential Power Is Associated with Behavioral Detection of Near-threshold Stimuli in the Rat Whisker System: Dissociation between Orbitofrontal and Somatosensory Cortices. J Cogn Neurosci 2017; 30:42-49. [PMID: 28891783 DOI: 10.1162/jocn_a_01187] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
There is growing evidence that ongoing brain oscillations may represent a key regulator of attentional processes and as such may contribute to behavioral performance in psychophysical tasks. OFC appears to be involved in the top-down modulation of sensory processing; however, the specific contribution of ongoing OFC oscillations to perception has not been characterized. Here we used the rat whiskers as a model system to further characterize the relationship between cortical state and tactile detection. Head-fixed rats were trained to report the presence of a vibrotactile stimulus (frequency = 60 Hz, duration = 2 sec, deflection amplitude = 0.01-0.5 mm) applied to a single vibrissa. We calculated power spectra of local field potentials preceding the onset of near-threshold stimuli from microelectrodes chronically implanted in OFC and somatosensory cortex. We found a dissociation between slow oscillation power in the two regions in relation to detection probability: Higher OFC but not somatosensory delta power was associated with increased detection probability. Furthermore, coherence between OFC and barrel cortex was reduced preceding successful detection. Consistent with the role of OFC in attention, our results identify a cortical network whose activity is differentially modulated before successful tactile detection.
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12
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Samaha J, Iemi L, Postle BR. Prestimulus alpha-band power biases visual discrimination confidence, but not accuracy. Conscious Cogn 2017; 54:47-55. [PMID: 28222937 DOI: 10.1016/j.concog.2017.02.005] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 01/30/2017] [Accepted: 02/06/2017] [Indexed: 10/20/2022]
Abstract
The magnitude of power in the alpha-band (8-13Hz) of the electroencephalogram (EEG) prior to the onset of a near threshold visual stimulus predicts performance. Together with other findings, this has been interpreted as evidence that alpha-band dynamics reflect cortical excitability. We reasoned, however, that non-specific changes in excitability would be expected to influence signal and noise in the same way, leaving actual discriminability unchanged. Indeed, using a two-choice orientation discrimination task, we found that discrimination accuracy was unaffected by fluctuations in prestimulus alpha power. Decision confidence, on the other hand, was strongly negatively correlated with prestimulus alpha power. This finding constitutes a clear dissociation between objective and subjective measures of visual perception as a function of prestimulus cortical excitability. This dissociation is predicted by a model where the balance of evidence supporting each choice drives objective performance but only the magnitude of evidence supporting the selected choice drives subjective reports, suggesting that human perceptual confidence can be suboptimal with respect to tracking objective accuracy.
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Affiliation(s)
- Jason Samaha
- Department of Psychology, The University of Wisconsin-Madison, Madison, WI, USA.
| | - Luca Iemi
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Germany
| | - Bradley R Postle
- Department of Psychology, The University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, The University of Wisconsin-Madison, Madison, WI, USA
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13
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Auditory cortical delta-entrainment interacts with oscillatory power in multiple fronto-parietal networks. Neuroimage 2016; 147:32-42. [PMID: 27903440 PMCID: PMC5315055 DOI: 10.1016/j.neuroimage.2016.11.062] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 11/25/2016] [Accepted: 11/25/2016] [Indexed: 01/28/2023] Open
Abstract
The timing of slow auditory cortical activity aligns to the rhythmic fluctuations in speech. This entrainment is considered to be a marker of the prosodic and syllabic encoding of speech, and has been shown to correlate with intelligibility. Yet, whether and how auditory cortical entrainment is influenced by the activity in other speech–relevant areas remains unknown. Using source-localized MEG data, we quantified the dependency of auditory entrainment on the state of oscillatory activity in fronto-parietal regions. We found that delta band entrainment interacted with the oscillatory activity in three distinct networks. First, entrainment in the left anterior superior temporal gyrus (STG) was modulated by beta power in orbitofrontal areas, possibly reflecting predictive top-down modulations of auditory encoding. Second, entrainment in the left Heschl's Gyrus and anterior STG was dependent on alpha power in central areas, in line with the importance of motor structures for phonological analysis. And third, entrainment in the right posterior STG modulated theta power in parietal areas, consistent with the engagement of semantic memory. These results illustrate the topographical network interactions of auditory delta entrainment and reveal distinct cross-frequency mechanisms by which entrainment can interact with different cognitive processes underlying speech perception. We study auditory cortical speech entrainment from a network perspective. Found three distinct networks interacting with delta-entrainment in auditory cortex. Entrainment is modulated by frontal beta power, possibly indexing predictions. Central alpha power interacts with entrainment, suggesting motor involvement. Parietal theta is modulated by entrainment, suggesting working memory compensation.
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14
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Tošić T, Sellers KK, Fröhlich F, Fedotenkova M, Beim Graben P, Hutt A. Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots. Front Syst Neurosci 2016; 9:184. [PMID: 26834580 PMCID: PMC4712310 DOI: 10.3389/fnsys.2015.00184] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 12/18/2015] [Indexed: 01/27/2023] Open
Abstract
For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain.
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Affiliation(s)
- Tamara Tošić
- Team Neurosys, InriaVillers-lès-Nancy, France; Loria, Centre National de la Recherche Scientifique, UMR no 7503Villers-lès-Nancy, France; Université de Lorraine, Loria, UMR no 7503Villers-lès-Nancy, France
| | - Kristin K Sellers
- Department of Psychiatry, University of North Carolina at Chapel HillChapel Hill, NC, USA; Neurobiology Curriculum, University of North Carolina at Chapel HillChapel Hill, NC, USA
| | - Flavio Fröhlich
- Department of Psychiatry, University of North Carolina at Chapel HillChapel Hill, NC, USA; Neurobiology Curriculum, University of North Carolina at Chapel HillChapel Hill, NC, USA; Department of Cell Biology and Physiology, University of North Carolina at Chapel HillChapel Hill, NC, USA; Department of Biomedical Engineering, University of North Carolina at Chapel HillChapel Hill, NC, USA; Neuroscience Center, University of North Carolina at Chapel HillChapel Hill, NC, USA
| | - Mariia Fedotenkova
- Team Neurosys, InriaVillers-lès-Nancy, France; Loria, Centre National de la Recherche Scientifique, UMR no 7503Villers-lès-Nancy, France; Université de Lorraine, Loria, UMR no 7503Villers-lès-Nancy, France
| | - Peter Beim Graben
- Department of German Studies and LinguisticsBerlin, Germany; Bernstein Center for Computational NeuroscienceBerlin, Germany
| | - Axel Hutt
- Team Neurosys, InriaVillers-lès-Nancy, France; Loria, Centre National de la Recherche Scientifique, UMR no 7503Villers-lès-Nancy, France; Université de Lorraine, Loria, UMR no 7503Villers-lès-Nancy, France
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15
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Mayer A, Schwiedrzik CM, Wibral M, Singer W, Melloni L. Expecting to See a Letter: Alpha Oscillations as Carriers of Top-Down Sensory Predictions. Cereb Cortex 2015; 26:3146-60. [PMID: 26142463 DOI: 10.1093/cercor/bhv146] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Predictions strongly influence perception. However, the neurophysiological processes that implement predictions remain underexplored. It has been proposed that high- and low-frequency neuronal oscillations act as carriers of sensory evidence and top-down predictions, respectively (von Stein and Sarnthein 2000; Bastos et al. 2012). However, evidence for the latter hypothesis remains scarce. In particular, it remains to be shown whether slow prestimulus alpha oscillations in task-relevant brain regions are stronger in the presence of predictions, whether they influence early categorization processes, and whether this interplay indeed boosts perception. Here, we directly address these questions by manipulating subjects' prior expectations about the identity of visually presented letters while collecting magnetoencephalographic recordings. We find that predictions lead to increased prestimulus alpha oscillations in a multisensory network representing grapheme/phoneme associations. Furthermore, alpha power interacts with stimulus degradation and top-down expectations to predict visibility ratings, and correlates with the amplitude of early sensory components (P1/N1m complex), suggesting a role in the selective amplification of predicted information. Our results thus indicate that low-frequency alpha oscillations can serve as a mechanism to carry and test sensory predictions about letters.
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Affiliation(s)
- Anna Mayer
- Department of Neurophysiology, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
| | | | - Michael Wibral
- MEG Unit, Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
| | - Wolf Singer
- Department of Neurophysiology, Max Planck Institute for Brain Research, Frankfurt am Main, Germany Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany
| | - Lucia Melloni
- Department of Neurophysiology, Max Planck Institute for Brain Research, Frankfurt am Main, Germany Columbia University Medical Center, New York, NY, USA NYU Langone Medical Center, New York, NY, USA
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16
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Vogginger B, Schüffny R, Lansner A, Cederström L, Partzsch J, Höppner S. Reducing the computational footprint for real-time BCPNN learning. Front Neurosci 2015; 9:2. [PMID: 25657618 PMCID: PMC4302947 DOI: 10.3389/fnins.2015.00002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2014] [Accepted: 01/03/2015] [Indexed: 11/26/2022] Open
Abstract
The implementation of synaptic plasticity in neural simulation or neuromorphic hardware is usually very resource-intensive, often requiring a compromise between efficiency and flexibility. A versatile, but computationally-expensive plasticity mechanism is provided by the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm. Building upon Bayesian statistics, and having clear links to biological plasticity processes, the BCPNN learning rule has been applied in many fields, ranging from data classification, associative memory, reward-based learning, probabilistic inference to cortical attractor memory networks. In the spike-based version of this learning rule the pre-, postsynaptic and coincident activity is traced in three low-pass-filtering stages, requiring a total of eight state variables, whose dynamics are typically simulated with the fixed step size Euler method. We derive analytic solutions allowing an efficient event-driven implementation of this learning rule. Further speedup is achieved by first rewriting the model which reduces the number of basic arithmetic operations per update to one half, and second by using look-up tables for the frequently calculated exponential decay. Ultimately, in a typical use case, the simulation using our approach is more than one order of magnitude faster than with the fixed step size Euler method. Aiming for a small memory footprint per BCPNN synapse, we also evaluate the use of fixed-point numbers for the state variables, and assess the number of bits required to achieve same or better accuracy than with the conventional explicit Euler method. All of this will allow a real-time simulation of a reduced cortex model based on BCPNN in high performance computing. More important, with the analytic solution at hand and due to the reduced memory bandwidth, the learning rule can be efficiently implemented in dedicated or existing digital neuromorphic hardware.
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Affiliation(s)
- Bernhard Vogginger
- Department of Electrical Engineering and Information Technology, Technische Universität Dresden Germany
| | - René Schüffny
- Department of Electrical Engineering and Information Technology, Technische Universität Dresden Germany
| | - Anders Lansner
- Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology (KTH) Stockholm, Sweden ; Department of Numerical Analysis and Computer Science, Stockholm University Stockholm, Sweden
| | - Love Cederström
- Department of Electrical Engineering and Information Technology, Technische Universität Dresden Germany
| | - Johannes Partzsch
- Department of Electrical Engineering and Information Technology, Technische Universität Dresden Germany
| | - Sebastian Höppner
- Department of Electrical Engineering and Information Technology, Technische Universität Dresden Germany
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17
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Sigala R, Haufe S, Roy D, Dinse HR, Ritter P. The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models. Front Comput Neurosci 2014; 8:36. [PMID: 24772077 PMCID: PMC3983484 DOI: 10.3389/fncom.2014.00036] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2013] [Accepted: 03/11/2014] [Indexed: 12/15/2022] Open
Abstract
During the past two decades growing evidence indicates that brain oscillations in the alpha band (~10 Hz) not only reflect an "idle" state of cortical activity, but also take a more active role in the generation of complex cognitive functions. A recent study shows that more than 60% of the observed inter-subject variability in perceptual learning can be ascribed to ongoing alpha activity. This evidence indicates a significant role of alpha oscillations for perceptual learning and hence motivates to explore the potential underlying mechanisms. Hence, it is the purpose of this review to highlight existent evidence that ascribes intrinsic alpha oscillations a role in shaping our ability to learn. In the review, we disentangle the alpha rhythm into different neural signatures that control information processing within individual functional building blocks of perceptual learning. We further highlight computational studies that shed light on potential mechanisms regarding how alpha oscillations may modulate information transfer and connectivity changes relevant for learning. To enable testing of those model based hypotheses, we emphasize the need for multidisciplinary approaches combining assessment of behavior and multi-scale neuronal activity, active modulation of ongoing brain states and computational modeling to reveal the mathematical principles of the complex neuronal interactions. In particular we highlight the relevance of multi-scale modeling frameworks such as the one currently being developed by "The Virtual Brain" project.
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Affiliation(s)
- Rodrigo Sigala
- Department Neurology, Charité—University MedicineBerlin, Germany
- Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational NeuroscienceBerlin, Germany
| | - Sebastian Haufe
- Department Neurology, Charité—University MedicineBerlin, Germany
- Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational NeuroscienceBerlin, Germany
| | - Dipanjan Roy
- Department Neurology, Charité—University MedicineBerlin, Germany
- Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational NeuroscienceBerlin, Germany
| | - Hubert R. Dinse
- Neural Plasticity Lab, Institute for Neuroinformatics, Ruhr-University BochumBochum, Germany
| | - Petra Ritter
- Department Neurology, Charité—University MedicineBerlin, Germany
- Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational NeuroscienceBerlin, Germany
- Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain SciencesLeipzig, Germany
- Berlin School of Mind and Brain, Mind and Brain Institute, Humboldt UniversityBerlin, Germany
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18
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Ai L, Ro T. The phase of prestimulus alpha oscillations affects tactile perception. J Neurophysiol 2014; 111:1300-7. [DOI: 10.1152/jn.00125.2013] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Previous studies have shown that neural oscillations in the 8- to 12-Hz range influence sensory perception. In the current study, we examined whether both the power and phase of these mu/alpha oscillations predict successful conscious tactile perception. Near-threshold tactile stimuli were applied to the left hand while electroencephalographic (EEG) activity was recorded over the contralateral right somatosensory cortex. We found a significant inverted U-shaped relationship between prestimulus mu/alpha power and detection rate, suggesting that there is an intermediate level of alpha power that is optimal for tactile perception. We also found a significant difference in phase angle concentration at stimulus onset that predicted whether the upcoming tactile stimulus was perceived or missed. As has been shown in the visual system, these findings suggest that these mu/alpha oscillations measured over somatosensory areas exert a strong inhibitory control on tactile perception and that pulsed inhibition by these oscillations shapes the state of brain activity necessary for conscious perception. They further suggest that these common phasic processing mechanisms across different sensory modalities and brain regions may reflect a common underlying encoding principle in perceptual processing that leads to momentary windows of perceptual awareness.
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Affiliation(s)
- Lei Ai
- Program in Behavioral and Cognitive Neuroscience, The Graduate Center of the City University of New York, New York, New York; and
- Department of Psychology, The City College of the City University of New York, New York, New York
| | - Tony Ro
- Program in Behavioral and Cognitive Neuroscience, The Graduate Center of the City University of New York, New York, New York; and
- Department of Psychology, The City College of the City University of New York, New York, New York
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