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Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

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Published on: May 27, 2020

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn

Sean P Fitzgibbon¹, Samuel J Harrison², Mark Jenkinson¹

¹Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.

|September 1, 2020

Summary

The developing Human Connectome Project created an automated pipeline to process neonatal resting-state fMRI data. This pipeline significantly reduces motion artifacts, improving signal quality for better brain network analysis.

Keywords:

Connectome Developing Human Connectome Project Functional MRI Neonate Pipeline Quality control

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Area of Science:

Neuroimaging
Developmental Neuroscience
Computational Neuroscience

Background:

The developing Human Connectome Project (dHCP) aims to map brain development in early life.
Neonatal neuroimaging presents unique challenges, including motion and low signal-to-noise ratio (SNR).
Existing preprocessing pipelines often struggle with the complexities of neonatal resting-state fMRI data.

Purpose of the Study:

To present an automated, robust, and quality-assured pipeline for minimally preprocessing neonatal resting-state fMRI data.
To address specific challenges in neonatal data, such as head motion and image distortion.
To improve the reliability and quality of functional brain network analysis in neonates.

Main Methods:

Development of an automated preprocessing pipeline integrating slice-to-volume motion correction and distortion correction.
Implementation of a robust multimodal registration approach.
Application of Independent Component Analysis (ICA)-based denoising and an automated quality control (QC) framework.
Evaluation on a large cohort of dHCP subjects.

Main Results:

The pipeline demonstrates robust performance with low failure rates and high quality assurance.
Significant reduction in motion-related artifacts and distortions was achieved.
Substantial improvements in SNR and detection of high-quality resting-state networks (RSNs) were observed.
The processing refinements enhance the analysis of neonatal brain connectivity.

Conclusions:

The developed automated pipeline effectively addresses challenges in neonatal fMRI preprocessing.
The pipeline enhances data quality, leading to more reliable identification of brain networks in neonates.
This work provides a valuable tool for advancing research in early brain development and connectomics.