Using functional near-infrared spectroscopy to study the early developing brain: future directions and new challenges

被引:9
|
作者
Gervain, Judit [1 ,2 ,3 ]
Minagawa, Yasuyo [4 ]
Emberson, Lauren [5 ]
Lloyd-Fox, Sarah [6 ]
机构
[1] Univ Padua, Dept Dev & Social Psychol, Padua, Italy
[2] Univ Padua, Padova Neurosci Ctr, Padua, Italy
[3] Univ Paris Cite, Integrat Neurosci & Cognit Ctr, CNRS, Paris, France
[4] Keio Univ, Fac Letters, Dept Psychol, Yokohama, Japan
[5] Univ British Columbia, Dept Psychol, Vancouver, BC, Canada
[6] Univ Cambridge, Dept Psychol, Cambridge, England
基金
欧洲研究理事会; 加拿大自然科学与工程研究理事会;
关键词
functional near-infrared spectroscopy; developmental neuroscience; infants; children; brain imaging; HEMODYNAMIC-RESPONSE; SENSORY PREDICTION; HEMOGLOBIN PHASE; TOP-DOWN; INFANTS; PRETERM; TERM;
D O I
10.1117/1.NPh.10.2.023519
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Significance: Functional near-infrared spectroscopy (fNIRS) is a frequently used neuroimaging tool to explore the developing brain, particularly in infancy, with studies spanning from birth to toddlerhood (0 to 2 years). We provide an overview of the challenges and opportunities that the developmental fNIRS field faces, after almost 25 years of research. Aim: We discuss the most recent advances in fNIRS brain imaging with infants and outlines the trends and perspectives that will likely influence progress in the field in the near future. Approach: We discuss recent progress and future challenges in various areas and applications of developmental fNIRS from methodological and technological innovations to data processing and statistical approaches. Results and Conclusions: The major trends identified include uses of fNIRS "in the wild," such as global health contexts, home and community testing, and hyperscanning; advances in hardware, such as wearable technology; assessment of individual variation and developmental trajectories particularly while embedded in studies examining other environmental, health, and context specific factors and longitudinal designs; statistical advances including resting-state network and connectivity, machine learning and reproducibility, and collaborative studies. Standardization and larger studies have been, and will likely continue to be, a major goal in the field, and new data analysis techniques, statistical methods, and collaborative cross-site projects are emerging. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
引用
收藏
页数:15
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