Poster No:
274
Submission Type:
Abstract Submission
Authors:
ryoko yoshinare1, masatoshi yamashita1, yoshifumi mizuno1, akemi tomoda1
Institutions:
1university of Fukui, Eiheiji-cho, Yoshida-gun, Fukui prefecture
First Author:
ryoko yoshinare
university of Fukui
Eiheiji-cho, Yoshida-gun, Fukui prefecture
Co-Author(s):
akemi tomoda
university of Fukui
Eiheiji-cho, Yoshida-gun, Fukui prefecture
Introduction:
The neurodevelopmental outcomes of Late Preterm (LP) children born between 34 to 36 weeks of gestation remain uncertain. This study aimed to elucidate neurodevelopmental outcomes in LP children using data from the Adolescent Brain Cognitive Development (ABCD) Study, a large U.S. cohort study.
Methods:
Participants aged 9-11 years were categorized into the LP group (34-36 weeks) and the full-term (FT) group (≥40 weeks) based on gestational age. Baseline and 2-year follow-up data for analysis. We excluded participants with no data on gestational age, maternal age at birth, and with abnormal values on height, weight, body mass index (BMI), and no quality check for structure MRI (sMRI), diffusion tensor imaging (DTI), resting-state functional MRI (rs-fMRI). In rs-fMRI, frames with a framewise displacement (FD) of 0.2mm or more were excluded. Statistical analyses were conducted using a linear mixed model. We compared the LP group to the FT group with the following variables as dependent variables: gender, age, race, handedness, maternal age at birth, BMI, puberty score, parental education, household income, and intracranial volume as fixed effects, and family ID, site, and twin as random effects. Clinical symptoms were measured using the Child Behavior Checklist (CBCL), defined as a T-score higher than 65 on the DSM-5. Cognitive function was calculated using the NIH toolbox. We extracted 68 cortical regions labeled with the Desikan-Killiany Atlas-based classification for cortical regional volume, cortical thickness, and surface area, and 14 subcortical regions labeled with atlas-based segmentation for subcortical regions (Desikan, 2006). We adopted a linear mixed model with each brain volume, cortical thickness, and surface area as the dependent variable. We extracted 35 major white matter tracts by analyzing the Atlas Tract (Hagler, 2009). The bilateral subcortical regions analyzed through Automatic Subcortical Segmentation (ASEG) included 14 regions (Fischl, 2002). The key indices of white matter fibers focused on were fractional anisotropy (FA) and mean diffusivity (MD). We adopted a linear mixed model with each white matter tract, cortical, and subcortical region modeled as the dependent variable. We examined the connectivity within 12 networks based on the Gordon Atlas (Gordon, 2016). We applied a linear mixed model with each network as the dependent variable.
Results:
Results of sMRI showed that the LP group (n=1,093) had smaller volumes in some brain regions (left postcentral gyrus, right middle temporal gyrus, right pars orbitalis, right supramarginal gyrus, left insula, right insula) relative to the FT group (n=8,984), although these differences resolved after 2 years. No significant differences appeared in cortical thickness, surface area, cognitive function, and clinical symptoms. DTI analysis revealed that the LP group (n=997) had a significantly lower mean diffusivity (MD) value only in the left inferior longitudinal fasciculus than the FT group (n=8,193) in white matter tracts, which remained lower at 2 years. Some fractional anisotropy (FA) values on cortical regions were significantly higher (left lingual gyrus, left calcarine sulcus, left supramarginal gyrus, left transverse temporal gyrus, right lingual gyrus, right calcarine sulcus), remained high in the left lingual gyrus at 2 years. In the subcortical regions examined, the MD value was significantly lower only in the right pallidum, but no difference was observed at 2 years. Rs-fMRI analysis detected no significant difference between the two groups.
Conclusions:
The results of this study confirm that LP children exhibit unique neurodevelopmental trajectories. However, they suggest that many differences resolve with maturation, indicating the potential for positive outcomes.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Education, History and Social Aspects of Brain Imaging:
Education, History and Social Aspects of Brain Imaging 2
Keywords:
Cognition
Development
FUNCTIONAL MRI
PEDIATRIC
STRUCTURAL MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
Structural MRI
Diffusion MRI
Neuropsychological testing
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
Free Surfer
Provide references using APA citation style.
Desikan, R.S. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31(3),968-80.
Fischl, B. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3),341-55.
Gordon, E.M. (2016) Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb Cortex, 26,288-303.
Hagler, D.J Jr. (2009). Automated white-matter tractography using a probabilistic diffusion tensor atlas: application to temporal lobe epilepsy. Human Brain Mapp, 30(5),1535-47.
Hagler, D.J Jr. (2019). Image processing and analysis methods for the adolescent brain cognitive development study. Neuroimage, 202,116091.
No