University of Illinois Urbana-Champaign

Comparing analytic approaches to infant functional near-infrared spectroscopy data

Liu, Yiyu

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https://hdl.handle.net/2142/115629

Description

Title
Comparing analytic approaches to infant functional near-infrared spectroscopy data
Author(s)
Liu, Yiyu
Issue Date
2022-04-29
Director of Research (if dissertation) or Advisor (if thesis)
Hyde, Daniel C.
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • fCOI
  • fixed-array analysis
  • infant fNIRs
  • cognition
  • temporal lobe
Abstract
Near-infrared spectroscopy (NIRS) is increasingly used to study brain function in infants, but the development and standardization of analysis techniques for use with infant NIRS data has not paced other technical advances. Here we quantify and compare the effects of different methods of analysis of infant NIRS data. Specifically, we analyzed two independent NIRS datasets involving 6-9-month-old infants contrasting results from more traditional, fixed array analyses with several functional channel of interest (fCOI) analysis approaches. In addition, we tested the effects of varying the number and anatomical location of potential data channels to be included in the fCOI definition. Over two studies we find that fCOI approaches are more sensitive than fixed array analyses, especially when channels of interests were defined within-subjects. Applying anatomical restriction and/or including multiple channels in the fCOI definition does not decrease and in some cases increases sensitivity of fCOI methods. Based on these results, we recommend that researchers consider employing fCOI approaches to the anlaysis of infant NIRS data and provide some guidelines for choosing between particular fCOI approaches and settings for the study of infant brain function and development.
Graduation Semester
2022-05
Type of Resource
Thesis
Copyright and License Information
Copyright 2022 Yiyu Liu

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