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A high-density diffuse optical tomography dataset of naturalistic viewing
Traditional laboratory tasks offer tight experimental control but lack the richness of our everyday human experience. As a result many cognitive neuroscientists have been motivated to adopt experimental paradigms that are more natural, such as stories and movies. Here we describe data collected from...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659362/ https://www.ncbi.nlm.nih.gov/pubmed/37986896 http://dx.doi.org/10.1101/2023.11.07.565473 |
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author | Sherafati, Arefeh Bajracharya, Aahana Jones, Michael S. Speh, Emma Munsi, Monalisa Lin, Chen-Hao P. Fishell, Andrew K. Hershey, Tamara Eggebrecht, Adam T. Culver, Joseph P. Peelle, Jonathan E. |
author_facet | Sherafati, Arefeh Bajracharya, Aahana Jones, Michael S. Speh, Emma Munsi, Monalisa Lin, Chen-Hao P. Fishell, Andrew K. Hershey, Tamara Eggebrecht, Adam T. Culver, Joseph P. Peelle, Jonathan E. |
author_sort | Sherafati, Arefeh |
collection | PubMed |
description | Traditional laboratory tasks offer tight experimental control but lack the richness of our everyday human experience. As a result many cognitive neuroscientists have been motivated to adopt experimental paradigms that are more natural, such as stories and movies. Here we describe data collected from 58 healthy adult participants (aged 18–76 years) who viewed 10 minutes of a movie (The Good, the Bad, and the Ugly, 1966). Most (36) participants viewed the clip more than once, resulting in 106 sessions of data. Cortical responses were mapped using high-density diffuse optical tomography (first-through fourth nearest neighbor separations of 1.3, 3.0, 3.9, and 4.7 cm), covering large portions of superficial occipital, temporal, parietal, and frontal lobes. Consistency of measured activity across subjects was quantified using intersubject correlation analysis. Data are provided in both channel format (SNIRF) and projected to standard space (NIfTI), using an atlas-based light model. These data are suitable for methods exploration as well as investigating a wide variety of cognitive phenomena. |
format | Online Article Text |
id | pubmed-10659362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-106593622023-11-20 A high-density diffuse optical tomography dataset of naturalistic viewing Sherafati, Arefeh Bajracharya, Aahana Jones, Michael S. Speh, Emma Munsi, Monalisa Lin, Chen-Hao P. Fishell, Andrew K. Hershey, Tamara Eggebrecht, Adam T. Culver, Joseph P. Peelle, Jonathan E. bioRxiv Article Traditional laboratory tasks offer tight experimental control but lack the richness of our everyday human experience. As a result many cognitive neuroscientists have been motivated to adopt experimental paradigms that are more natural, such as stories and movies. Here we describe data collected from 58 healthy adult participants (aged 18–76 years) who viewed 10 minutes of a movie (The Good, the Bad, and the Ugly, 1966). Most (36) participants viewed the clip more than once, resulting in 106 sessions of data. Cortical responses were mapped using high-density diffuse optical tomography (first-through fourth nearest neighbor separations of 1.3, 3.0, 3.9, and 4.7 cm), covering large portions of superficial occipital, temporal, parietal, and frontal lobes. Consistency of measured activity across subjects was quantified using intersubject correlation analysis. Data are provided in both channel format (SNIRF) and projected to standard space (NIfTI), using an atlas-based light model. These data are suitable for methods exploration as well as investigating a wide variety of cognitive phenomena. Cold Spring Harbor Laboratory 2023-11-11 /pmc/articles/PMC10659362/ /pubmed/37986896 http://dx.doi.org/10.1101/2023.11.07.565473 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Sherafati, Arefeh Bajracharya, Aahana Jones, Michael S. Speh, Emma Munsi, Monalisa Lin, Chen-Hao P. Fishell, Andrew K. Hershey, Tamara Eggebrecht, Adam T. Culver, Joseph P. Peelle, Jonathan E. A high-density diffuse optical tomography dataset of naturalistic viewing |
title | A high-density diffuse optical tomography dataset of naturalistic viewing |
title_full | A high-density diffuse optical tomography dataset of naturalistic viewing |
title_fullStr | A high-density diffuse optical tomography dataset of naturalistic viewing |
title_full_unstemmed | A high-density diffuse optical tomography dataset of naturalistic viewing |
title_short | A high-density diffuse optical tomography dataset of naturalistic viewing |
title_sort | high-density diffuse optical tomography dataset of naturalistic viewing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659362/ https://www.ncbi.nlm.nih.gov/pubmed/37986896 http://dx.doi.org/10.1101/2023.11.07.565473 |
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