A Tool for Neurodesign: Interpreting Neurophysiological Data from Designers’ Perspective

2021 ◽  
Vol 15 (1) ◽  
pp. 55-61
Author(s):  
Alessio Paoletti ◽  
Lorenzo Imbesi
Keyword(s):  
Neurophysiological Data

Development of Reading Remediation for Dyslexic Individuals

2016 ◽  
Vol 224 (4) ◽  
pp. 240-246 ◽  
Author(s):  
Mélanie Bédard ◽  
Line Laplante ◽  
Julien Mercier
Keyword(s):  
20Th Century ◽  
Scientific Literature ◽  
Behavioral Data ◽  
Educational Neuroscience ◽  
Reading Remediation ◽  
Functional Brain ◽  
Neurophysiological Data ◽  
Brain Correlates ◽  
Research Questions ◽  
The Brain

Abstract. Dyslexia is a phenomenon for which the brain correlates have been studied since the beginning of the 20th century. Simultaneously, the field of education has also been studying dyslexia and its remediation, mainly through behavioral data. The last two decades have seen a growing interest in integrating neuroscience and education. This article provides a quick overview of pertinent scientific literature involving neurophysiological data on functional brain differences in dyslexia and discusses their very limited influence on the development of reading remediation for dyslexic individuals. Nevertheless, it appears that if certain conditions are met – related to the key elements of educational neuroscience and to the nature of the research questions – conceivable benefits can be expected from the integration of neurophysiological data with educational research. When neurophysiological data can be employed to overcome the limits of using behavioral data alone, researchers can both unravel phenomenon otherwise impossible to document and raise new questions.

Reproducibility and efficiency in handling complex neurophysiological data

Neuroforum ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michael Denker ◽  
Sonja Grün ◽  
Thomas Wachtler ◽  
Hansjörg Scherberger
Keyword(s):  
Best Practices ◽  
Data Management ◽  
Data Integrity ◽  
Behavioral Neuroscience ◽  
Data Set ◽  
Data Publication ◽  
Electrophysiological Data ◽  
Data Formats ◽  
Neurophysiological Data ◽  
Community Effort

Abstract Preparing a neurophysiological data set with the aim of sharing and publishing is hard. Many of the available tools and services to provide a smooth workflow for data publication are still in their maturing stages and not well integrated. Also, best practices and concrete examples of how to create a rigorous and complete package of an electrophysiology experiment are still lacking. Given the heterogeneity of the field, such unifying guidelines and processes can only be formulated together as a community effort. One of the goals of the NFDI-Neuro consortium initiative is to build such a community for systems and behavioral neuroscience. NFDI-Neuro aims to address the needs of the community to make data management easier and to tackle these challenges in collaboration with various international initiatives (e.g., INCF, EBRAINS). This will give scientists the opportunity to spend more time analyzing the wealth of electrophysiological data they leverage, rather than dealing with data formats and data integrity.

scholarly journals Neurophysiological and behavioural effects of conventional and high definition tDCS

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fabio Masina ◽  
Giorgio Arcara ◽  
Eleonora Galletti ◽  
Isabella Cinque ◽  
Luciano Gamberini ◽  
...  
Keyword(s):  
Response Times ◽  
Power Reduction ◽  
Alpha Power ◽  
High Definition ◽  
Mixed Effect ◽  
State Dependency ◽  
Mixed Effect Models ◽  
Beta Power ◽  
Neurophysiological Data ◽  
Spectrum Density

AbstractHigh-definition transcranial direct current stimulation (HD-tDCS) seems to overcome a drawback of traditional bipolar tDCS: the wide-spread diffusion of the electric field. Nevertheless, most of the differences that characterise the two techniques are based on mathematical simulations and not on real, behavioural and neurophysiological, data. The study aims to compare a widespread tDCS montage (i.e., a Conventional bipolar montage with extracephalic return electrode) and HD-tDCS, investigating differences both at a behavioural level, in terms of dexterity performance, and a neurophysiological level, as modifications of alpha and beta power as measured with EEG. Thirty participants took part in three sessions, one for each montage: Conventional tDCS, HD-tDCS, and sham. In all the conditions, the anode was placed over C4, while the cathode/s placed according to the montage. At baseline, during, and after each stimulation condition, dexterity was assessed with a Finger Tapping Task. In addition, resting-state EEG was recorded at baseline and after the stimulation. Power spectrum density was calculated, selecting two frequency bands: alpha (8–12 Hz) and beta (18–22 Hz). Linear mixed effect models (LMMs) were used to analyse the modulation induced by tDCS. To evaluate differences among the montages and consider state-dependency phenomenon, the post-stimulation measurements were covariate-adjusted for baseline levels. We observed that HD-tDCS induced an alpha power reduction in participants with lower alpha at baseline. Conversely, Conventional tDCS induced a beta power reduction in participants with higher beta at baseline. Furthermore, data showed a trend towards a behavioural effect of HD-tDCS in participants with lower beta at baseline showing faster response times. Conventional and HD-tDCS distinctively modulated cortical activity. The study highlights the importance of considering state-dependency to determine the effects of tDCS on individuals.

scholarly journals Pseudosparse neural coding in the visual system of primates

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sidney R. Lehky ◽  
Keiji Tanaka ◽  
Anne B. Sereno
Keyword(s):  
Neural Coding ◽  
Macaque Monkey ◽  
Implicit Assumption ◽  
Data Set ◽  
Efficient Coding ◽  
Population Responses ◽  
Lateral Intraparietal Cortex ◽  
Neural Populations ◽  
Neurophysiological Data ◽  
The Mean

AbstractWhen measuring sparseness in neural populations as an indicator of efficient coding, an implicit assumption is that each stimulus activates a different random set of neurons. In other words, population responses to different stimuli are, on average, uncorrelated. Here we examine neurophysiological data from four lobes of macaque monkey cortex, including V1, V2, MT, anterior inferotemporal cortex, lateral intraparietal cortex, the frontal eye fields, and perirhinal cortex, to determine how correlated population responses are. We call the mean correlation the pseudosparseness index, because high pseudosparseness can mimic statistical properties of sparseness without being authentically sparse. In every data set we find high levels of pseudosparseness ranging from 0.59–0.98, substantially greater than the value of 0.00 for authentic sparseness. This was true for synthetic and natural stimuli, as well as for single-electrode and multielectrode data. A model indicates that a key variable producing high pseudosparseness is the standard deviation of spontaneous activity across the population. Consistently high values of pseudosparseness in the data demand reconsideration of the sparse coding literature as well as consideration of the degree to which authentic sparseness provides a useful framework for understanding neural coding in the cortex.

Representation of Spectral and Temporal Envelope of Twitter Vocalizations in Common Marmoset Primary Auditory Cortex

2002 ◽  
Vol 87 (4) ◽  
pp. 1723-1737 ◽  
Author(s):  
Srikantan S. Nagarajan ◽  
Steven W. Cheung ◽  
Purvis Bedenbaugh ◽  
Ralph E. Beitel ◽  
Christoph E. Schreiner ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Social Contact ◽  
Primary Auditory Cortex ◽  
Common Marmoset ◽  
Spectral Envelope ◽  
Response Patterns ◽  
Temporal Envelope ◽  
Degraded Speech ◽  
Neurophysiological Data ◽  
Narrowband Channels

Cortical sensitivity in representations of behaviorally relevant complex input signals was examined in recordings from primary auditory cortical neurons (AI) in adult, barbiturate-anesthetized common marmoset monkeys ( Callithrix jacchus). We studied the robustness of distributed responses to natural and degraded forms of twitter calls, social contact vocalizations comprising several quasi-periodic phrases of frequency and AM. We recorded neuronal responses to a monkey's own twitter call (MOC), degraded forms of their twitter call, and sinusoidal amplitude modulated (SAM) tones with modulation rates similar to those of twitter calls. In spectral envelope degradation, calls with narrowband channels of varying bandwidths had the same temporal envelope as a natural call. However, the carrier phase was randomized within each narrowband channel. In temporal envelope degradation, the temporal envelope within narrowband channels was filtered while the carrier frequencies and phases remained unchanged. In a third form of degradation, noise was added to the natural calls. Spatiotemporal discharge patterns in AI both within and across frequency bands encoded spectrotemporal acoustic features in the call although the encoded response is an abstract version of the call. The average temporal response pattern in AI, however, was significantly correlated with the average temporal envelope for each phrase of a call. Response entrainment to MOC was significantly correlated with entrainment to SAM stimuli at comparable modulation frequencies. Sensitivity of the response patterns to MOC was substantially greater for temporal envelope than for spectral envelope degradations. The distributed responses in AI were robust to additive continuous noise at signal-to-noise ratios ≥10 dB. Neurophysiological data reflecting response sensitivity in AI to these forms of degradation closely parallel human psychophysical results on the intelligibility of degraded speech in quiet and noisy conditions.

A distributed computing system for multivariate time series analyses of multichannel neurophysiological data

2006 ◽  
Vol 152 (1-2) ◽  
pp. 190-201 ◽  
Author(s):  
Andy Müller ◽  
Hannes Osterhage ◽  
Robert Sowa ◽  
Ralph G. Andrzejak ◽  
Florian Mormann ◽  
...  
Keyword(s):  
Time Series ◽  
Distributed Computing ◽  
Multivariate Time Series ◽  
Computing System ◽  
Neurophysiological Data ◽  
Time Series Analyses

scholarly journals Recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Siddharth Panwar ◽  
Shiv Dutt Joshi ◽  
Anubha Gupta ◽  
Sandhya Kunnatur ◽  
Puneet Agarwal
Keyword(s):  
Functional Connectivity ◽  
Order Statistics ◽  
Correlation Structure ◽  
Connectivity Pattern ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Scale Invariant ◽  
Dynamic Functional Connectivity ◽  
Neurophysiological Data ◽  
Show Evidence

AbstractTime-varying neurophysiological activity has been classically explored using correlation based sliding window analysis. However, this method employs only lower order statistics to track dynamic functional connectivity of the brain. We introduce recursive dynamic functional connectivity (rdFC) that incorporates higher order statistics to generate a multi-order connectivity pattern by analyzing neurophysiological data at multiple time scales. The technique builds a hierarchical graph between various temporal scales as opposed to traditional approaches that analyze each scale independently. We examined more than a million rdFC patterns obtained from morphologically diverse EEGs of 2378 subjects of varied age and neurological health. Spatiotemporal evaluation of these patterns revealed three dominant connectivity patterns that represent a universal underlying correlation structure seen across subjects and scalp locations. The three patterns are both mathematically equivalent and observed with equal prevalence in the data. The patterns were observed across a range of distances on the scalp indicating that they represent a spatially scale-invariant correlation structure. Moreover, the number of patterns representing the correlation structure has been shown to be linked with the number of nodes used to generate them. We also show evidence that temporal changes in the rdFC patterns are linked with seizure dynamics.

Spark-based Platform for Neurophysiological Data Storage and Processing: a Proof of Concept

2021 ◽  
Author(s):  
Jiangmao Zheng ◽  
Jian Zhao ◽  
Ju Li ◽  
Changan Zhan ◽  
Tao Wang
Keyword(s):  
Data Storage ◽  
Proof Of Concept ◽  
Neurophysiological Data
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