Estimation of iron levels via T2* mapping increases with age in the fetal brain
Poster No:
1318
Submission Type:
Abstract Submission
Authors:
Mark Duffy1, Amyn Majbri2, Ellyn Kennelly3, Tanya Bhatia2, Lanxin Ji1, Moriah Thomason4
Institutions:
1NYU Langone Health, New York, NY, 2New York University Medical Center, New York, NY, 3Wayne State University, Detroit, MI, 4NYU Langone Medical Center, New York, NY
First Author:
Co-Author(s):
Introduction:
Iron is highly expressed in the brain, playing an essential role in neurodevelopmental processes including DNA and neurotransmitter synthesis, myelination and mitochondrial function (1). Studies in children and adults have shown iron deficiency to have a significant effect on neurological mechanisms (2, 3). Recent studies have shown brain iron in subcortical regions, such as the thalamus, caudate, putamen and striatum, to be linked with cognitive function (4, 5). T2* mapping is an established method for attaining an indirect measure of brain iron levels (6, 7). Approximation of infant brain iron levels using T2* mapping has shown iron to increase across the entire brain directly after birth (8). However, the study of brain iron levels in the fetus and their implications is an area relatively unexplored. This study aims to assess the relationship between fetal brain iron levels in subcortical regions and age via a T2*-based measure.
Methods:
Multi-echo (ME) fMRIs for 41 fetuses gestational aged 30.69 ± 4.26 weeks were attained from the Perinatal Imaging of Neural Connectivity (PINC) project. Imaging data was obtained on a Siemens Magnetom Verio syngo MRI system with a 550g abdominal 4-channel Siemens Flex Coil. Two sets of ME-fMRI data were attained via 12-min ME-fMRI (360 volumes), with the following scanning parameters: dataset a) TR = 2000ms; TE = 18, 31.07, 44.14ms (3 echoes); flip angle: 83 degrees; voxel size: 3.5 x 3.5 x 3.5 mm3; N=10 runs, dataset b) TR = 2000ms; TE = 18, 34, 50ms (3 echoes); flip-angle: 83 degrees; voxel-size: 3.487 x 3.487 x 3.5 mm3, N=130 runs.
Pre-processing of fMRI data was performed using SPM and FSL software. A measure for motion, DVARS, was estimated for each volume. Ten consecutive volumes with the lowest DVARS were identified and averaged for T2* estimation (9,10). T2* maps were generated through the fitting of a logarithmic curve across echoes for each voxel. Voxels of T2* value outside the accepted range (0-200ms) were replaced by zero in FSL.
T2* maps were then normalized to a 32-week template, by applying the transformation matrix estimated from the fMRI data using SPM. The reciprocal of T2*, R2*, was then estimated in 12 bilateral sub-cortical regions of interest (ROIs); anterior and posterior thalamus, putamen and caudate. The ROIs are defined by a data-driven parcellation approach. Mean values of the non-zero voxels within ROIs were calculated in FSL. Pearson's correlation analysis was performed via Python and SciPy/Pandas libraries to assess the relationship between T2* voxel values for ROIs and fetal age (weeks).
Pre-processing of fMRI data was performed using SPM and FSL software. A measure for motion, DVARS, was estimated for each volume. Ten consecutive volumes with the lowest DVARS were identified and averaged for T2* estimation (9,10). T2* maps were generated through the fitting of a logarithmic curve across echoes for each voxel. Voxels of T2* value outside the accepted range (0-200ms) were replaced by zero in FSL.
T2* maps were then normalized to a 32-week template, by applying the transformation matrix estimated from the fMRI data using SPM. The reciprocal of T2*, R2*, was then estimated in 12 bilateral sub-cortical regions of interest (ROIs); anterior and posterior thalamus, putamen and caudate. The ROIs are defined by a data-driven parcellation approach. Mean values of the non-zero voxels within ROIs were calculated in FSL. Pearson's correlation analysis was performed via Python and SciPy/Pandas libraries to assess the relationship between T2* voxel values for ROIs and fetal age (weeks).
Results:
Significant positive correlations were observed between mean voxel values in specific ROIs and increase in age. The left thalamic regions, caudate and putamen increased significantly with age (caudate: R=0.3965 p=0.0084; putamen: R=0.5886 p=0.00005; anterior thalamus: R=0.4485 p=0.0033; posterior thalamus: R=0.4063 p=0.0084). Similar trends were also seen in the right anterior thalamus and right putamen (thalamus: R=0.1431; putamen: R=0.1511).
·Fig.1. T2* decay curve across 3 echo times (TEs) (A-D). Example T2* map and distribution (E-F).
·Fig.2. Iron levels increase with age (A-E).
Conclusions:
This study is the first to demonstrate the simulation of iron in brain development before birth. Our results indicate that fetal brain iron levels increase with age in subcortical regions that are high in iron concentration and key in cognitive processes. Although significance was not determined in all ROIs, positive trends across almost all regions support positive correlation. T2* mapping from multi-echo fMRI data represents a valid method for iron estimation in fetal brains. Future studies with larger datasets are required to further establish the relationship between iron and fetal brain development. To expand analysis, iron may be estimated across the whole brain over longer age ranges, and be linked with behavioral development after birth.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Modeling and Analysis Methods:
Motion Correction and Preprocessing
Other Methods
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Development
FUNCTIONAL MRI
Spatial Normalization
Sub-Cortical
Thalamus
Other - fetus
1|2Indicates the priority used for review
Provide references using author date format
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