optimal multivariate transfer entropy to determine differences in short and long-range eeg connectivity in children with adhd and healthy children

Purpose: investigating brain connectivity using electroencephalogram (eeg) is a valuable method for studying mental disorders, such as attention-deficit/hyperactivity disorder (adhd), and optimizing and developing measures of effective connectivity can provide new insights into differences in brain communication in such disorders. multivariate transfer entropy (mute) is a measure of causal connectivity that quantifies the influence of multiple variables on each other in a system. in this study, the mute measure was modified by incorporating an interaction delay parameter in connectivity calculations to create a measure with self-prediction optimality, which we named mutespo. materials and methods: we applied  to investigate eeg effective connectivity in healthy and adhd children performing an attention task across five frequency bands and to compare brain connectivity differences between the two groups using statistical analysis.results: our analysis revealed that children with adhd exhibited excessive short-distance connections in all frequency bands while healthy children demonstrated stronger long-range connections in the alpha and gamma frequency bands. moreover, excessive short-distance connectivity was observed in the delta and theta frequency bands in all brain regions, as well as in the alpha, beta, and gamma frequency bands between the central and parietal regions in children with adhd. these connectivity patterns may contribute to impaired attention functions by impeding effective information transmission and reducing information processing speed in the brains of children with adhd.conclusion: our analysis presents a novel methodology for measuring effective connectivity and elucidates the differences in eeg brain connectivity between children with adhd and healthy children.