Biology subjects

Elison, J. T.

Publications and source records attributed to Elison, J. T..

3 recordsLinked to original sources

bioRxiv2023

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans

ObjectivesBrain segmentation of infant magnetic resonance (MR) images is vitally important for studying typical and atypical brain development. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model for robust and generalizable brain segmentation leveraging data augmentation and a large sample size of manually annotated images. Experimental DesignIncluded in model training and testing were MR brain images from 90 participants with an age range of 0-8 months (median age 4.6 months). Using the BOBs repository of manually annotated real images along with synthetic segmentation images produced using SynthSeg, the model was trained using a 10-fold procedure. Model performance of segmentations was assessed by comparing BIBSNet, joint label fusion (JLF) inferred segmentation to ground truth segmentations using Dice Similarity Coefficient (DSC). Additionally, MR data along with the FreeSurfer compatible segmentations were processed with the DCAN labs infant-ABCD-BIDS processing pipeline from ground truth, JLF, and BIBSNet to further assess model performance on derivative data, including cortical thickness, resting state connectivity and brain region volumes. Principal ObservationsBIBSNet segmentations outperforms JLF across all regions based on DSC comparisons. Additionally, with processed derived metrics, BIBSNet segmentations outperforms JLF segmentations across nearly all metrics. ConclusionsBIBSNet segmentation shows marked improvement over JLF across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF, produces FreeSurfer-compatible segmentation labels, and can be easily included in other processing pipelines. BIBSNet provides a viable alternative for segmenting the brain in the earliest stages of development.

neuroscience↗

bioRxiv2022

Jointly analyzing the association of human milk nutrients with cognition and temperament traits during the first 6 months of life

Early dietary exposure via human milk (HM) components offers a window of opportunity to support cognitive and temperamental development. While several studies have focused on associations of few pre-selected HM components with cognition and temperament, it is highly plausible that HM components synergistically and jointly support cognitive and behavioral development in early life. We aimed to discern the combined associations of a wide array of HM nutrients with cognition and temperament during the first six months of life and explore if there were persistent effects up to 18 months old, when HM is the primary source of an infants nutrition. The Mullen Scales of Early Learning and Infant Behavior Questionnaires-Revised were used to assess cognition and temperament, respectively, of fifty-four exclusively/predominantly breastfed infants in the first 6 months of life, whose follow-ups were conducted at 6-9, 9-12 and 12-18 months old. HM samples were obtained from the mothers of the participants at less than 6 months of life and analyzed for fatty acids (total monounsaturated fatty acids, polyunsaturated fatty acid, total saturated fatty acid (TSFA), arachidonic acid (ARA), docosahexaenoic acid (DHA), ARA/DHA, omega-6/omega-3 polyunsaturated fatty acids ratio (n-6/n-3)), phospholipids (phosphatidylcholine, phosphatidylethanolamine (PE), phosphatidylinositol (PI), sphingomyelin) and choline (free choline, phosphocholine (PCho), glycerophosphocholine). Feature selection was performed to select nutrients associated with cognition and temperament, respectively. The combined effects of selected nutrients were analyzed using multiple regression. A positive association between the arachidonic acid (ARA) and surgency was observed (p = 0.024). Significant effect of DHA, n-6/n-3, PE and TSFA concentrations on receptive language (R2 = 0.39, p = 0.025), and the elevated ARA, PCho, and PI with increased surgency (R2 = 0.43, p = 0.003) was identified, suggesting that DHA and ARA may have distinct roles for temperament and language functions. Furthermore, the exploratory association analyses suggest that the effects of HM nutrients on R.L. and surgency may persist beyond the first 6 months of life, particularly surgency at 12-18 months (p = 0.002). Our studies highlighted that various HM nutrients work together to support the development of cognition and temperament traits during early infancy.

developmental biology↗

bioRxiv2021

Real-time motion monitoring improves functional MRI data quality in infants

Imaging the infant brain with MRI has improved our understanding of early stages of neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are commonly scanned while asleep, they commonly exhibit motion during scanning, causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Hence, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD [≤] 0.2mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging.

neuroscience↗