Structural Magnetic Resonance Imaging-Based Surface Morphometry Analysis of Pediatric Down Syndrome.

Down syndrome (DS) is a genetic disorder characterized by intellectual disability whose etiology includes an additional partial or full copy of chromosome 21. Brain surface morphometry analyses can potentially assist in providing a better understanding of structural brain differences, and may help characterize DS-specific neurodevelopment. We performed a retrospective surface morphometry study of 73 magnetic resonance imaging (MRI) examinations of DS patients (aged 1 day to 22 years) and compared them to a large cohort of 993 brain MRI examinations of neurotypical participants, aged 1 day to 32 years.

A Brain Morphometry Study with Across-Site Harmonization Using a ComBat-Generalized Additive Model in Children and Adolescents.

Regional anatomical structures of the brain are intimately connected to functions corresponding to specific regions and the temporospatial pattern of genetic expression and their functions from the fetal period to old age. Therefore, quantitative brain morphometry has often been employed in neuroscience investigations, while controlling for the scanner effect of the scanner is a critical issue for ensuring accuracy in brain morphometric studies of rare orphan diseases due to the lack of normal reference values available for multicenter studies. This study aimed to provide across-site normal reference values of global and regional brain volumes for each sex and age group in children and adolescents.

Random matrix theory tools for the predictive analysis of functional magnetic resonance imaging examinations.

Purpose: Random matrix theory (RMT) is an increasingly useful tool for understanding large, complex systems. Prior studies have examined functional magnetic resonance imaging (fMRI) scans using tools from RMT, with some success. However, RMT computations are highly sensitive to a number of analytic choices, and the robustness of findings involving RMT remains in question.

Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics.

Supervised machine learning classification is the most common example of artificial intelligence (AI) in industry and in academic research. These technologies predict whether a series of measurements belong to one of multiple groups of examples on which the machine was previously trained. Prior to real-world deployment, all implementations need to be carefully evaluated with hold-out validation, where the algorithm is tested on different samples than it was provided for training, in order to ensure the generalizability and reliability of AI models.

Morphological Abnormalities in Early-Onset Schizophrenia Revealed by Structural Magnetic Resonance Imaging.

Schizophrenia is a pathological condition characterized by delusions, hallucinations, and a lack of motivation. In this study, we performed a morphological analysis of regional biomarkers in early-onset schizophrenia, including cortical thicknesses, surface areas, surface curvature, and volumes extracted from T1-weighted structural magnetic resonance imaging (MRI) and compared these findings with a large cohort of neurotypical controls. Results demonstrate statistically significant abnormal presentation of the curvature of select brain regions in early-onset schizophrenia with large effect sizes, inclusive of the pars orbitalis, pars triangularis, posterior cingulate cortex, frontal pole, orbital gyrus, lateral orbitofrontal gyrus, inferior occipital gyrus, as well as in medial occipito-temporal, lingual, and insular sulci.