scholarly journals Desynchronization between speech rhythms and neural oscillations: a possible cause of phonological problems in dyslexia

ANALES RANM ◽  
2018 ◽  
Vol 135 (135(02)) ◽  
pp. 47-51
Author(s):  
Mikel Lizarazu ◽  
Marie Lallier ◽  
Nicola Molinaro
Keyword(s):  
Auditory Perception ◽  
Neural Mechanisms ◽  
Theoretical Research ◽  
Neural Oscillations ◽  
Neuronal Oscillations ◽  
Research Framework ◽  
Frequency Bands ◽  
Neural Bases ◽  
Possible Cause

The main objective of our studies is to understand the neural bases underlying phonological difficulties in dyslexia. First, we will review the theoretical research framework generated around the phonological theory of dyslexia. Second, we will review what are the neural mechanisms involved in the segmentation of speech in control readers. In this section we will demonstrate that the synchronization between speech rhythms and neural oscillations at different frequency bands plays a key role in the segmentation of speech. Next, we will present different studies that suggest that dyslexic readers present a desynchronization between speech rhythms and neuronal oscillations in auditory regions. This lack of synchronization could cause the auditory perception problems and the phonological difficulties that we observe in readers with dyslexia. Finally, we will present recent studies from our laboratory that support the theory of neuronal desynchronization in dyslexia and show that these problems are also present in children with dyslexia.

Recommendations on Promoting the Landscape Architecture Esthetics Research in China

2013 ◽  
Vol 295-298 ◽  
pp. 701-707
Author(s):  
Yan Long ◽  
Rong Ke Jin
Keyword(s):  
Landscape Architecture ◽  
Academic Research ◽  
Current Situation ◽  
Theoretical Research ◽  
Related Literature ◽  
Research Framework ◽  
Research Objects

This article reviews the landscape esthetics research in China in the last thirty years by collecting and listing the domestic journals concerning landscape esthetics and related literature since 1980. It analyzes and surmises the disadvantages and characteristics in the current situation of research on landscape esthetics in China. In order to solve the problems of vague subject in the research of landscape esthetics in China, as well as the complexity and variation in the scale of research objects, the authors propose that the concept of “landscape esthetics” should be renamed to “landscape architecture esthetics” in domestic academic research, recommending the improvement of theoretical research framework on landscape esthetics. Finally, it ends with an attempt to prospect the research on landscape esthetics in China.

scholarly journals Identifying robust and sensitive frequency bands for interrogating neural oscillations

NeuroImage ◽  
2010 ◽  
Vol 51 (4) ◽  
pp. 1319-1333 ◽  
Author(s):  
Alexander J. Shackman ◽  
Brenton W. McMenamin ◽  
Jeffrey S. Maxwell ◽  
Lawrence L. Greischar ◽  
Richard J. Davidson
Keyword(s):  
Neural Oscillations ◽  
Frequency Bands

scholarly journals A Theoretical Research Framework of Future Sustainable Urban Freight Transport for Smart Cities

2020 ◽  
Vol 12 (5) ◽  
pp. 1975 ◽  
Author(s):  
Zhangyuan He ◽  
Hans-Dietrich Haasis
Keyword(s):  
Literature Review ◽  
Urban Development ◽  
Research Methods ◽  
Freight Transport ◽  
Theoretical Research ◽  
Research Framework ◽  
Urban Freight ◽  
Considerable Research ◽  
Urban Freight Transport

This paper aims to construct a theoretical research framework for sustainable urban freight transport (SUFT) from the perspectives of future urban development and distribution innovations, and appropriate research methods are discussed, as well. Urban freight transport plays a critical role in the promotion of sustainable and livable cities. According to the literature review, considerable research on SUFT has focused on resolving some specific problems with a short-term perspective. The existence of an urban freight transport strategy is noted, which should be embedded in an overall sustainable development strategy with a long-term perspective (approximately 20–30 years). Nevertheless, considerable research has paid scant attention to the long-term planning of SUFT. Given this, this paper contributes to the closure of this gap. First, this paper presents a systematic literature review (SLR) to highlight published papers involving foresight research within the past 16 years (2003–2018). This step contributes to the understanding of research methods that can be used in foresight research. Subsequently, this paper discusses the impacts of both urban development and distribution innovations on future SUFT, and these effects are used to select the appropriate methods to construct the theoretical research framework. Finally, the theoretical research framework of long-term planning for SUFT is developed on the basis of two future perspectives: the trends of urban development and the application of urban distribution innovations. This framework is intended to provide an approach to designing sustainable urban logistics, taking into account urban development and distribution innovations.

scholarly journals Neural oscillations in human auditory cortex revealed by fast fMRI during auditory perception

NeuroImage ◽  
2020 ◽  
Vol 207 ◽  
pp. 116401 ◽  
Author(s):  
Sascha Frühholz ◽  
Wiebke Trost ◽  
Didier Grandjean ◽  
Pascal Belin
Keyword(s):  
Auditory Cortex ◽  
Auditory Perception ◽  
Neural Oscillations ◽  
Human Auditory Cortex

scholarly journals Identification of selection and inhibition components in a Go/NoGo task from EEG spectra using a machine learning model

2019 ◽  
Author(s):  
Bambi L. DeLaRosa ◽  
Jeffrey S. Spence ◽  
Michael A. Motes ◽  
Wing To ◽  
Sven Vanneste ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Mechanisms ◽  
Frequency Bands ◽  
Time Frequency ◽  
Eeg Spectra ◽  
Therapeutic Uses ◽  
Machine Learning Model ◽  
Superior Parietal Cortex ◽  
Task Conditions ◽  
Traditional Approaches

AbstractPrior Go/NoGo studies have localized specific regions and EEG spectra for which traditional approaches have distinguished between Go and NoGo conditions. A more detailed characterization of the spatial distribution and timing of the synchronization of frequency bands would contribute substantially to the clarification of neural mechanisms that underlie performance of the Go/NoGo task. The present study used a machine learning approach to learn the features that distinguish between ERSPs involved in selection and inhibition in a Go/NoGo task. A neural network classifier was used to predict task conditions for each subject to characterize ERSPs associated with Go versus NoGo trials. The final model accurately identified individual task conditions at an overall rate of 92%, estimated by 5-fold cross-validation. The detailed accounting of EEG time-frequency patterns localized to brain sources (i.e., thalamus, preSMA, orbitofrontal cortex, and superior parietal cortex) provides elaboration on previous findings from fMRI and EEG studies and more information about EEG power changes in multiple frequency bands (i.e., primarily theta power increase, alpha decreases, and beta increases and decreases) within these regions underlying the selection and inhibition processes engaged in the Go and NoGo trials. This extends previous findings, providing more information about neural mechanisms underlying selection and inhibition processes engaged in the Go and NoGo trials, respectively, and may offer insight into therapeutic uses of neuromodulation in neural dysfunction.

scholarly journals The Oscillatory ReConstruction Algorithm (ORCA) adaptively identifies frequency bands to improve spectral decomposition in human and rodent neural recordings

2019 ◽  
Author(s):  
Andrew J Watrous ◽  
Robert Buchanan
Keyword(s):  
Spectral Decomposition ◽  
Spectral Characteristics ◽  
Reconstruction Algorithm ◽  
Brain Regions ◽  
Neural Oscillations ◽  
Neural Signals ◽  
Frequency Bands ◽  
Eyes Closed ◽  
Spectral Estimates ◽  
Band Limited

AbstractNeural oscillations are routinely analyzed using methods that measure activity in canonical frequency bands (e.g. alpha, 8-12 Hz), though the frequency of neural signals is not fixed and varies within and across individuals based on numerous factors including neuroanatomy, behavioral demands, and species. Further, band-limited activity is an often assumed, typically unmeasured model of neural activity and band definitions vary considerably across studies. These factors together mask individual differences and can lead to noisy spectral estimates and interpretational problems when linking electrophysiology to behavior. We developed the Oscillatory ReConstruction Algorithm (“ORCA”), an unsupervised method to measure the spectral characteristics of neural signals in adaptively identified bands which incorporates two new methods for frequency band identification. ORCA uses the instantaneous power, phase, and frequency of activity in each band to reconstruct the signal and directly quantify spectral decomposition performance using each of four different models. To reduce researcher bias, ORCA provides spectral estimates derived from the best model and requires minimal hyperparameterization. Analyzing human scalp EEG data during eyes open and eyes-closed “resting” conditions, we first identify variability in the frequency content of neural signals across subjects and electrodes. We demonstrate that ORCA significantly improves spectral decomposition compared to conventional methods and captures the well-known increase in low-frequency activity during eyes closure in electrode- and subject-specific frequency bands. We further illustrate the utility of our method in rodent CA1 recordings. ORCA is a novel analytic tool that will allow researchers to investigate how non-stationary neural oscillations vary across behaviors, brain regions, individuals, and species.

scholarly journals Synchronization between keyboard typing and neural oscillations

2020 ◽  
Author(s):  
J. Duprez ◽  
M. Stokkermans ◽  
L. Drijvers ◽  
M.X Cohen
Keyword(s):  
Cognitive Control ◽  
Estimation Method ◽  
Neural Oscillations ◽  
Neuronal Oscillations ◽  
Oscillatory Dynamics ◽  
Brain Rhythm ◽  
Filtering Technique ◽  
Keyboard Typing ◽  
Brain Behavior ◽  
Rhythmic Behaviors

AbstractRhythmic neural activity synchronizes with certain rhythmic behaviors, such as breathing, sniffing, saccades, and speech. The extent to which neural oscillations synchronize with higher-level and more complex behaviors is largely unknown. Here we investigated electrophysiological synchronization with keyboard typing, which is an omnipresent behavior daily engaged by an uncountably large number of people. Keyboard typing is rhythmic with frequency characteristics roughly the same as neural oscillatory dynamics associated with cognitive control, notably through midfrontal theta (4 -7 Hz) oscillations. We tested the hypothesis that synchronization occurs between typing and midfrontal theta, and breaks down when errors are committed. Thirty healthy participants typed words and sentences on a keyboard without visual feedback, while EEG was recorded. Typing rhythmicity was investigated by inter-keystroke interval analyses and by a kernel density estimation method. We used a multivariate spatial filtering technique to investigate frequency-specific synchronization between typing and neuronal oscillations. Our results demonstrate theta rhythmicity in typing (around 6.5 Hz) through the two different behavioral analyses. Synchronization between typing and neuronal oscillations occurred at frequencies ranging from 4 to 15 Hz, but to a larger extent for lower frequencies. However, peak synchronization frequency was idiosyncratic across subjects, therefore not specific to theta nor to midfrontal regions, and correlated somewhat with peak typing frequency. Errors and trials associated with stronger cognitive control were not associated with changes in synchronization at any frequency. As a whole, this study shows that brain-behavior synchronization does occur during keyboard typing but is not specific to midfrontal theta.Significance statementEvery day, millions of people type on keyboards. Keyboard typing is a rhythmic behavior, with inter-keystroke-intervals of around 135 ms (~7 Hz), which is roughly the same frequency as the brain rhythm implicated in cognitive control (“theta” band, ~6 Hz). Here we investigated the hypothesis that the EEG signature of cognitive control is synchronized with keyboard typing. By recording EEG during typing in 30 healthy subjects we showed that keyboard typing indeed follows theta rhythmicity, and that synchronization between typing and neural oscillations occurs. However, synchronization was not limited to theta but occurred at frequencies ranging from 4 to 15 Hz, and in several regions. Brain-behavior synchronization during typing thus seems more nuanced and complex than we originally hypothesized.

073 Whole-brain network analysis of neural oscillations during light sleep

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A30-A31
Author(s):  
Joline Fan ◽  
Kiwamu Kudo ◽  
Kamalini Ranasinghe ◽  
Hirofumi Morise ◽  
Anne Findlay ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Information Flow ◽  
Phase Transfer ◽  
Sleep Onset ◽  
Adaptive Beamforming ◽  
Neural Oscillations ◽  
High Temporal Resolution ◽  
Whole Brain ◽  
Frequency Bands ◽  
Brain Functional Connectivity

Abstract Introduction Sleep is a highly stereotyped phenomenon that is ubiquitous across species. Although behaviorally appearing as a homogeneous process, sleep has been recognized as cortically heterogenous and locally dynamic. PET/fMRI studies have provided key insights into regional activation and deactivation with sleep onset, but they lack the high temporal resolution and electrophysiology for understanding neural interactions. Using simultaneous electrocorticography (EEG) and magnetoencephalography (MEG) imaging, we systematically characterize whole-brain neural oscillations and identify frequency specific, cortically-based patterns associated with sleep onset. Methods In this study, 14 healthy subjects underwent simultaneous EEG and MEG imaging. Sleep states were determined by scalp EEG. Eight 15s artifact-free epochs, e.g. 120s sensor time series, were selected to represent each behavioral state: N1, N2 and wake. Atlas-based source reconstruction was performed using adaptive beamforming methods. Functional connectivity measures were computed using imaginary coherence and across regions of interests (ROIs, segmentation of 210 cortical regions with Brainnetome Atlas) in multiple frequency bands, including delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), sigma (12-15Hz), beta (15-30Hz), and gamma (30-50Hz). Directional phase transfer entropy (PTE) was also evaluated to determine the direction of information flow with transition to sleep. Results We show that the transition to sleep is encoded in a spatially and temporally specific dynamic pattern of whole-brain functional connectivity. With sleep onset, there is increased functional connectivity diffusely within the delta frequency, while spatially specific profiles in other frequency bands, e.g. increased fronto-temporal connectivity in the alpha frequency band and fronto-occipital connectivity in the theta band. In addition, rather than a decoupling of anterior-posterior regions with transition to sleep, there is a spectral shift to delta frequencies observed in the synchrony and information flow of neural activity. Conclusion Sleep onset is cortically heterogeneous, composed of spatially and temporally specific patterns of whole-brain functional connectivity, which may play an essential role in the transition to sleep. Support (if any) Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the NIH under Award Number (5TL1TR001871-05 to JMF). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

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