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Neuroimage. 2018 Jul 15;175:413-424. doi: 10.1016/j.neuroimage.2018.04.022. Epub 2018 Apr 12.

Dynamic causal modelling on infant fNIRS data: A validation study on a simultaneously recorded fNIRS-fMRI dataset.

Author information

1
Centre for Brain and Cognitive Development, Birkbeck College, University of London, United Kingdom. Electronic address: c.bulga01@mail.bbk.ac.uk.
2
Centre for Brain and Cognitive Development, Birkbeck College, University of London, United Kingdom.
3
Centre for Medical Image Computing, University College London, United Kingdom.
4
Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom.
5
Department of Psychology, University of Copenhagen, Denmark.
6
Department of Developmental Psychology, University of Padova, Italy.
7
School of Psychology, University of East Anglia, Norwich, United Kingdom.
8
Bioimaging Research Team, Korea Basic Science Institute, South Korea.
9
Institute of Cognitive Neuroscience, University College London, United Kingdom.

Abstract

Tracking the connectivity of the developing brain from infancy through childhood is an area of increasing research interest, and fNIRS provides an ideal method for studying the infant brain as it is compact, safe and robust to motion. However, data analysis methods for fNIRS are still underdeveloped compared to those available for fMRI. Dynamic causal modelling (DCM) is an advanced connectivity technique developed for fMRI data, that aims to estimate the coupling between brain regions and how this might be modulated by changes in experimental conditions. DCM has recently been applied to adult fNIRS, but not to infants. The present paper provides a proof-of-principle for the application of this method to infant fNIRS data and a demonstration of the robustness of this method using a simultaneously recorded fMRI-fNIRS single case study, thereby allowing the use of this technique in future infant studies. fMRI and fNIRS were simultaneously recorded from a 6-month-old sleeping infant, who was presented with auditory stimuli in a block design. Both fMRI and fNIRS data were preprocessed using SPM, and analysed using a general linear model approach. The main challenges that adapting DCM for fNIRS infant data posed included: (i) the import of the structural image of the participant for spatial pre-processing, (ii) the spatial registration of the optodes on the structural image of the infant, (iii) calculation of an accurate 3-layer segmentation of the structural image, (iv) creation of a high-density mesh as well as (v) the estimation of the NIRS optical sensitivity functions. To assess our results, we compared the values obtained for variational Free Energy (F), Bayesian Model Selection (BMS) and Bayesian Model Average (BMA) with the same set of possible models applied to both the fMRI and fNIRS datasets. We found high correspondence in F, BMS, and BMA between fMRI and fNIRS data, therefore showing for the first time high reliability of DCM applied to infant fNIRS data. This work opens new avenues for future research on effective connectivity in infancy by contributing a data analysis pipeline and guidance for applying DCM to infant fNIRS data.

KEYWORDS:

DCM; Effective connectivity; Infants; Simultaneous fMRI-fNIRS recording

PMID:
29655936
PMCID:
PMC5971219
DOI:
10.1016/j.neuroimage.2018.04.022
[Indexed for MEDLINE]
Free PMC Article

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