Kim, W. H. | Search bioRxiv

bioRxiv2022

Fractions strategy differences in those born extremely preterm

IntroductionTo investigate the effects of different strategies and cognitive load we explored brain hemodynamic responses associated with the use of different strategies to solve subtraction of fractions. We focused on those born extremely preterm (EPT; <28 weeks gestation) as they are known to have cognitive challenges and struggle with mathematics. We also included a group of full-term (FT) peers for comparison. MethodsFunctional MRI was acquired while the participants mentally solved fraction equations using either a strategy based on improper or mixed fractions. Different fraction item types were given, which affected respective required cognitive loads per strategy. Diffusion and T1-weighted structural images were also acquired. ResultsThe EPT and FT groups differed in terms of task-related hemodynamic responses. Functional group differences were greatest when strategies were applied to item types that result in high cognitive load. Other findings showed reduced white and grey matter volume and reduced white matter connectivity in widespread areas in the EPT group compared to the FT group. ConclusionThe understanding of function and structure presented here may help inform pedagogical practices by allowing for tailoring of mathematical education through identifying suitable strategy adoption that depends on item type, to circumvent weaknesses in cognitive skills.

neuroscience↗

bioRxiv2019

BrainNET: Inference of brain network topology using Machine Learning

ObjectiveTo develop a new fMRI network inference method, BrainNET, that utilizes an efficient machine learning algorithm to quantify contributions of various regions of interests (ROIs) in the brain to a specific ROI. MethodsBrainNET is based on Extremely Randomized Trees (ERT) to estimate network topology from fMRI data and modified to generate an adjacency matrix representing brain network topology, without reliance on arbitrary thresholds. Open source simulated fMRI data of fifty subjects in twenty-eight different simulations under various confounding conditions with known ground truth was used to validate the method. Performance was compared with correlation and partial correlation (PC). The real-world performance was then evaluated in a publicly available Attention-deficit/hyperactivity disorder (ADHD) dataset including 134 Typically Developing Children (mean age: 12.03, males: 83), 75 ADHD Inattentive (mean age: 11.46, males: 56) and 93 ADHD Combined (mean age: 11.86, males: 77) subjects. Network topologies in ADHD were inferred using BrainNET, correlation, and PC. Graph metrics were extracted to determine differences between the ADHD groups. ResultsBrainNET demonstrated excellent performance across all simulations and varying confounders in identifying true presence of connections. In the ADHD dataset, BrainNET was able to identify significant changes (p< 0.05) in graph metrics between groups. No significant changes in graph metrics between ADHD groups was identified using correlation and PC.

bioengineering↗