Literature DB >> 33562848

Cortical Activity Linked to Clocking in Deaf Adults: fNIRS Insights with Static and Animated Stimuli Presentation.

Sébastien Laurent1, Laurence Paire-Ficout1, Jean-Michel Boucheix2, Stéphane Argon2, Antonio R Hidalgo-Muñoz3.   

Abstract

The question of the possible impact of deafness on temporal processing remains unanswered. Different findings, based on behavioral measures, show contradictory results. The goal of the present study is to analyze the brain activity underlying time estimation by using functional near infrared spectroscopy (fNIRS) techniques, which allow examination of the frontal, central and occipital cortical areas. A total of 37 participants (19 deaf) were recruited. The experimental task involved processing a road scene to determine whether the driver had time to safely execute a driving task, such as overtaking. The road scenes were presented in animated format, or in sequences of 3 static images showing the beginning, mid-point, and end of a situation. The latter presentation required a clocking mechanism to estimate the time between the samples to evaluate vehicle speed. The results show greater frontal region activity in deaf people, which suggests that more cognitive effort is needed to process these scenes. The central region, which is involved in clocking according to several studies, is particularly activated by the static presentation in deaf people during the estimation of time lapses. Exploration of the occipital region yielded no conclusive results. Our results on the frontal and central regions encourage further study of the neural basis of time processing and its links with auditory capacity.

Entities:  

Keywords:  animation; clocking; deafness; fNIRS; motion prediction; temporal skill; time estimation

Year:  2021        PMID: 33562848      PMCID: PMC7914875          DOI: 10.3390/brainsci11020196

Source DB:  PubMed          Journal:  Brain Sci        ISSN: 2076-3425


  49 in total

1.  Modality differences in short-term memory for rhythms.

Authors:  G L Collier; G Logan
Journal:  Mem Cognit       Date:  2000-06

2.  Wavelet-based motion artifact removal for functional near-infrared spectroscopy.

Authors:  Behnam Molavi; Guy A Dumont
Journal:  Physiol Meas       Date:  2012-01-25       Impact factor: 2.833

3.  How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation.

Authors:  F Scholkmann; S Spichtig; T Muehlemann; M Wolf
Journal:  Physiol Meas       Date:  2010-03-22       Impact factor: 2.833

4.  Hearing what the eyes see: auditory encoding of visual temporal sequences.

Authors:  Sharon E Guttman; Lee A Gilroy; Randolph Blake
Journal:  Psychol Sci       Date:  2005-03

5.  Mayer waves reduce the accuracy of estimated hemodynamic response functions in functional near-infrared spectroscopy.

Authors:  Meryem A Yücel; Juliette Selb; Christopher M Aasted; Pei-Yi Lin; David Borsook; Lino Becerra; David A Boas
Journal:  Biomed Opt Express       Date:  2016-07-22       Impact factor: 3.732

6.  Neuroimaging with near-infrared spectroscopy demonstrates speech-evoked activity in the auditory cortex of deaf children following cochlear implantation.

Authors:  Alexander B G Sevy; Heather Bortfeld; Theodore J Huppert; Michael S Beauchamp; Ross E Tonini; John S Oghalai
Journal:  Hear Res       Date:  2010-10-01       Impact factor: 3.208

7.  Near-infrared spectroscopy based neurofeedback training increases specific motor imagery related cortical activation compared to sham feedback.

Authors:  S E Kober; G Wood; J Kurzmann; E V C Friedrich; M Stangl; T Wippel; A Väljamäe; C Neuper
Journal:  Biol Psychol       Date:  2013-05-25       Impact factor: 3.251

8.  Neurofeedback-based functional near-infrared spectroscopy upregulates motor cortex activity in imagined motor tasks.

Authors:  Pawan Lapborisuth; Xian Zhang; Adam Noah; Joy Hirsch
Journal:  Neurophotonics       Date:  2017-06-23       Impact factor: 3.593

9.  Spatial Cues Influence Time Estimations in Deaf Individuals.

Authors:  Maria Bianca Amadeo; Claudio Campus; Francesco Pavani; Monica Gori
Journal:  iScience       Date:  2019-07-31

10.  Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective.

Authors:  Alexander von Lühmann; Antonio Ortega-Martinez; David A Boas; Meryem Ayşe Yücel
Journal:  Front Hum Neurosci       Date:  2020-02-18       Impact factor: 3.169

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