Comparing consistency of R2* and T2*-weighted BOLD analysis of resting state fetal fMRI

Seshamani, Sharmishtaa; Blazejewska, Anna I.; Gatenby, J. Christopher; Mckown, Susan; Caucutt, Jason; Dighe, Manjiri; Studholme, Colin

doi:10.1117/12.2082236

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Ouyang et al, 2015;M. Ouyang et al, 2018;Qiu et al, 2015;Song et al, 2017;Takahashi et al, 2012), and comparing different MRI signals (Seshamani et al, 2015). These strategies have made it possible to use sparse acquisition sequences, which alleviate movement concerns (Serag et al, 2017;Seshamani et al, 2014), and enable more sophisticated analytic approaches, such as morphometric (Gholipour et al, 2017;Kuklisova-Murgasova et al, 2011;Serag et al, 2012;Shi et al, 2010;Studholme, 2011) cortical folding (Wright et al, 2014), and cytoarchitectural examinations (Miller et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Automated Brain Masking of Fetal Functional MRI with Open Data

et al. 2021

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Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.

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Section: Introductionmentioning

confidence: 99%

Automated Brain Masking of Fetal Functional MRI with Open Data

et al. 2021

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“…Image acquisition improvements have occurred mainly with respect to fetal structural MRI, particularly in anatomical (primarily T2 HASTE; Half-Fourier Acquisition Single-shot Turbo spin Echo imaging) and diffusion tensor imaging (DTI) [5,6]. Advances have been made in inter-slice motion correction and volume reconstruction [7,8], mapping structural connectivity [9,10,11,12,13], comparing different MRI signals [14]. These strategies have made it possible to use sparse acquisition sequences, which alleviate movement concerns [15,16], and enable more sophisticated analytic approaches, such as morphometric [7,17,18,19], cortical folding [20], and cytoarchitectural examinations [21].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, papers focusing on image post-processing improvements are markedly few and have also primarily focused on structural imaging. Papers suggesting possible solutions for the analysis of fetal functional MRI data are rare [14,22]. In this work, we focus on methods for improving image post-processing for fetal functional MRI data.…”

Section: Introductionmentioning

confidence: 99%

Automated Brain Masking of Fetal Functional MRI

Rutherford

Sturmfels

Angstadt

et al. 2019

Preprint

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Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite rapid and widespread growth of this approach, at present we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses (gestational age 24-39 weeks, M=30.9, SD=4.2), we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open source workflow for fetal functional MRI data preprocessing.

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Detecting default mode networks in utero by integrated 4D fMRI reconstruction and analysis

Seshamani

Blazejewska

Mckown

et al. 2016

Human Brain Mapping

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Recently, there has been considerable interest, especially for in-utero imaging, in the detection of functional connectivity in subjects whose motion cannot be controlled while in the MRI scanner. These cases require two advances over current studies: 1) multi-echo acquisitions and 2) post processing and reconstruction that can deal with significant between slice motion during multi-slice protocols to allow for the ability to detect temporal correlations introduced by spatial scattering of slices into account. This paper focusses on the estimation of a spatially and temporally regular time series from motion scattered slices of multi-echo fMRI datasets using a full 4D iterative image reconstruction framework. The framework which includes quantitative MRI methods for artifact correction, is evaluated using adult studies with and without motion to both refine parameter settings and evaluate the analysis pipeline. ICA analysis is then applied to the 4D image reconstruction of both adult and in-utero fetal studies where resting state activity is perturbed by motion. Results indicate quantitative improvements in reconstruction quality when compared to the conventional 3D reconstruction approach (using simulated adult data), and demonstrate the ability to detect the default mode network in moving adults and fetuses with single-subject and group analysis.

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Comparing consistency of R2* and T2*-weighted BOLD analysis of resting state fetal fMRI

Cited by 4 publications

References 8 publications

Automated Brain Masking of Fetal Functional MRI with Open Data

Automated Brain Masking of Fetal Functional MRI with Open Data

Automated Brain Masking of Fetal Functional MRI

Detecting default mode networks in utero by integrated 4D fMRI reconstruction and analysis

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