Literature DB >> 35834105

RS-FetMRI: a MATLAB-SPM Based Tool for Pre-processing Fetal Resting-State fMRI Data.

Nicolò Pecco1, Matteo Canini2, Kelsey H H Mosser2, Martina Caglioni3, Paola Scifo4, Antonella Castellano2,5, Paolo Cavoretto3,5, Massimo Candiani3,5, Cristina Baldoli2, Andrea Falini2,5, Pasquale Anthony Della Rosa6.   

Abstract

Resting-state functional magnetic resonance imaging (rs-fMRI) most recently has proved to open a measureless window on functional neurodevelopment in utero. Fetal brain activation and connectivity maps can be heavily influenced by 1) fetal-specific motion effects on the time-series and 2) the accuracy of time-series spatial normalization to a standardized gestational-week (GW) specific fetal template space.Due to the absence of a standardized and generalizable image processing protocol, the objective of the present work was to implement a validated fetal rs-fMRI preprocessing pipeline (RS-FetMRI) divided into 6 inter-dependent preprocessing modules (i.e., M1 to M6) and designed to work entirely as an extension for Statistical Parametric Mapping (SPM).RS-FetMRI pipeline output analyses on rs-fMRI time-series sampled from a cohort of fetuses acquired on both 1.5 T and 3 T MRI scanning systems showed increased efficacy of estimation of the degree of movement coupled with an efficient motion censoring procedure, resulting in increased number of motion-uncorrupted volumes and temporal continuity in fetal rs-fMRI time-series data. Moreover, a "structural-free" SPM-based spatial normalization procedure granted a high degree of spatial overlap with high reproducibility and a significant improvement in whole-brain and parcellation-specific Temporal Signal-to-Noise Ratio (TSNR) mirrored by functional connectivity analysis.To our knowledge, the RS-FetMRI pipeline is the first semi-automatic and easy-to-use standardized fetal rs-fMRI preprocessing pipeline completely integrated in MATLAB-SPM able to remove entry barriers for new research groups into the field of fetal rs-fMRI, for both research or clinical purposes, and ultimately to make future fetal brain connectivity investigations more suitable for comparison and cross-validation.
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Fetal; Motion estimation; Pipeline; Pre-processing; Resting-state Fmri; Spatial normalization

Year:  2022        PMID: 35834105     DOI: 10.1007/s12021-022-09592-5

Source DB:  PubMed          Journal:  Neuroinformatics        ISSN: 1539-2791


  27 in total

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Journal:  Neuroimage       Date:  2000-06       Impact factor: 6.556

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Journal:  Neuroimage       Date:  2005-04-01       Impact factor: 6.556

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Authors:  Yashar Behzadi; Khaled Restom; Joy Liau; Thomas T Liu
Journal:  Neuroimage       Date:  2007-05-03       Impact factor: 6.556

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Review 6.  Developmental Pathoconnectomics and Advanced Fetal MRI.

Authors:  András Jakab
Journal:  Top Magn Reson Imaging       Date:  2019-10

7.  Noise removal in resting-state and task fMRI: functional connectivity and activation maps.

Authors:  Bianca De Blasi; Lorenzo Caciagli; Silvia Francesca Storti; Marian Galovic; Matthias Koepp; Gloria Menegaz; Anna Barnes; Ilaria Boscolo Galazzo
Journal:  J Neural Eng       Date:  2020-08-19       Impact factor: 5.379

8.  Resting State fMRI in the moving fetus: a robust framework for motion, bias field and spin history correction.

Authors:  Giulio Ferrazzi; Maria Kuklisova Murgasova; Tomoki Arichi; Christina Malamateniou; Matthew J Fox; Antonios Makropoulos; Joanna Allsop; Mary Rutherford; Shaihan Malik; Paul Aljabar; Joseph V Hajnal
Journal:  Neuroimage       Date:  2014-07-06       Impact factor: 6.556

9.  Fetal functional imaging portrays heterogeneous development of emerging human brain networks.

Authors:  András Jakab; Ernst Schwartz; Gregor Kasprian; Gerlinde M Gruber; Daniela Prayer; Veronika Schöpf; Georg Langs
Journal:  Front Hum Neurosci       Date:  2014-10-22       Impact factor: 3.169

10.  Functional brain connectivity in ex utero premature infants compared to in utero fetuses.

Authors:  Josepheen De Asis-Cruz; Kushal Kapse; Sudeepta K Basu; Mariam Said; Dustin Scheinost; Jonathan Murnick; Taeun Chang; Adre du Plessis; Catherine Limperopoulos
Journal:  Neuroimage       Date:  2020-06-11       Impact factor: 6.556

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