Abstract #0053

Brain Ventricular and Microstructural Correlates of Executive Dysfunction in Congenital Heart Disease Using Explainable Machine Learning

Vincent Kyu Lee^1,2, William Thomas Reynolds^2,3, Benjamin Meyers⁴, Julia Wallace⁴, Daryaneh Badaly⁵, Cecilia Lo⁶, Ashok Panigrahy^1,3,4, and Rafael Ceschin^3,4

¹Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States, ²Radiology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, United States, ³Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States, ⁴Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States, ⁵Learning and Development Center, Child Mind Institute, New York, NY, United States, ⁶Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States

Synopsis

Keywords: Adolescents, Neuro, Congenital Heart Disease Neurodevelopment Machine LearningThis study examined cerebrospinal fluid volumes as neuroimaging features and their role in predicting specific executive function impairments among adolescents with congenital heart disease using explainable machine learning models. The findings showed CSF volumes were among the most important predictors of executive function inhibition domain with 3 CSF volumes ranked amongst the top 20, and 4 more CSF volumes among the top 20% of all features. Selective increased lateral ventricular volume in the frontal regions in CHD patients may be secondary to loss of white matter integrity in the uncinate fasciculus (emotional regulation) and subsequently lead to inhibitory dysfunction.

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