DOI: 10.5061/dryad.cvdncjt6n
title: Towards a more informative representation of the fetal-neonatal brain connectome using Variational Autoencoder

description: Recent advances in functional magnetic resonance imaging (fMRI) have
 helped elucidate previously inaccessible trajectories of early-life
 prenatal and neonatal brain development. To date, the interpretation of
 fetal-neonatal fMRI data has relied on linear analytic models, akin to
 adult neuroimaging data. However, unlike the adult brain, the fetal and
 newborn brain develops extraordinarily rapidly, far outpacing any other
 brain development period across the lifespan. Consequently, conventional
 linear computational models may not adequately capture these accelerated
 and complex neurodevelopmental trajectories during this critical period of
 brain development along the prenatal-neonatal continuum. To obtain a
 nuanced understanding of fetal-neonatal brain development, including
 non-linear growth, for the first time, we developed quantitative,
 systems-wide representations of brain activity in a large sample
 (&gt;500) of fetuses, preterm, and full-term neonates using an
 unsupervised deep generative model called Variational Autoencoder (VAE), a
 model previously shown to be superior to linear models in representing
 complex resting state data in healthy adults. Here, we demonstrated that
 non-linear brain features, i.e., latent variables, derived with the VAE
 pretrained on rsfMRI of human adults, carried important individual neural
 signatures, leading to improved representation of prenatal-neonatal brain
 maturational patterns and more accurate and stable age prediction in the
 neonate cohort compared to linear models. Using the VAE decoder,
 we also revealed distinct functional brain networks spanning the sensory
 and default mode networks. Using the VAE, we are able to reliably capture
 and quantify complex, non-linear fetal-neonatal functional neural
 connectivity. This will lay the critical foundation for detailed mapping
 of healthy and aberrant functional brain signatures that have their
 origins in fetal life.
description: This dataset includes 270 processed resting state fMRI scans from
 95 healthy fetuses and 160 full-term born healthy infants. Scans were
 collected as a part of an ongoing longitudinal study examining
 prenatal-neonatal brain development at Children’s National Hospital in
 Washington DC. Fetuses (49 female) from healthy pregnancies were scanned
 between 19.14–39.71 gestational weeks (mean and SD; 33.65±4.01).
 Postmenstrual age of full-term born infants at scan ranges between
 37.71–47.43 weeks (41.67±1.84). Maternal exclusion criteria were
 psychiatric disorders, metabolic disorders, genetic disorders, complicated
 pregnancies, multiple pregnancies, alcohol, and tobacco use, maternal
 medications, and contraindications to MRI. All experiments were conducted
 under the regulations and guidelines approved by the Institutional Review
 Board (IRB) of Children’s National; written informed consent was obtained
 from each pregnant woman who participated in the study.
 For fetal scans, structural and functional resting-state MR
 images were acquired using a 1.5 Tesla GE MRI scanner with 8-channel
 receiver coil. The structural MR images for the fetal brain were acquired
 using single-shot fast spin-echo T2-weighted images by following settings:
 TR=1100ms, TE=160ms, flip angle=90 degrees, and voxel size= 0.8 0.8 2mm.
 Functional data were acquired using echo planar images (EPI) with
 TR=3000ms, TE=60ms, flip angle=90 degrees, field of view= 33cm, and voxel
 size = 2.58 2.58 3mm, and total scan volume=144 (=7.2min). The structural
 and functional MR images of full-term infant brain MRI studies were
 acquired using 3T GE scanner. T2-weighted fast spin echo MRI was obtained
 using the following parameters: TR=2500ms; TE=64.49ms, voxel size= 0.625 1
 0.625mm. The parameters of fMRI MRI scans were set to TR=2000ms, TE=35ms,
 voxel size=3.125 3.125 3mm, flip angle= 60 degrees, field of view=100mm,
 and total scan volume=200~300 (=6.7~10min). Functional
 MR images were preprocessed as followings: slice time correction,
 bias-field correction, motion-correction, spatial smoothing at
 full-width-half-maximum=4.5mm, and intensity scaling. We excluded volumes
 having excessive head motion (frame-wise motion &gt;1mm or rotational
 motion &gt;1.5 degrees). Neonatal MRI scans with &lt; 4 mins were
 excluded in the analysis. The analyzed data length of fetus and neonate
 group was 4-7 (mean S.D.=5.4 0.9) and 4-8.9 (5.5 0.8) mins, respectively.
 Finally, preprocessed rsfMRI scans at the volumetric brain space were
 projected to the standard cortical space using HCP workbench command
 <em>-volume-to-surface-mapping</em>.
description: This dataset contains three types of latent variables from
 preprocessed fMRI data using different types of models (VAE, ICA, and
 cortical parcel). The details of VAE can be found at Kim, Jung-Hoon, et
 al. "Representation learning of resting state fMRI with variational
 autoencoder." NeuroImage 241 (2021): 118423. Actual implementation of
 code can be found at GitHub -
 libilab/rsfMRI-VAE: Pytorch implementation of 'Representation
 Learning of Resting State fMRI with Variational
 Autoencoder'. Details regarding ICA map and cortical parcel
 can be found at www.humanconnectome.org. The format of the dataset is TXT file. Columns and rows of each text file stand for latent variables (for example, 512 for VAE) and time points (variable over different subjects). Any type of program will be compatible with this dataset but we recommend using MATLAB.  For the latent variable from VAE, there are 512 latent variables: first 256 and later 256 stand for the mean and log variance of 256 latent variables, respectively. For ICA, there are different number of latent variables (50, 100, 200, and 300) based on which ICA maps were used. For cortical parcel, there are 360 latent variables.  If you have any questions, please contact the first author (Junghoon Kim, PhD) or the corresponding author.