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Abstract
Objectives: The electrophysiological characteristics of attention-deficit/hyperactivity disorder (ADHD) and recent machine-learning methods promise easy-to-use approaches that can complement existing diagnostic tools when sufficiently large samples are used. Neuroalgorithms are models of multidimensional brain networks by means of which ADHD patient data can be separated from healthy control data.
Methods: Spontaneous electroencephalographic and event-related potential (ERP) data were collected three times over the course of 2 years from a multicentre sample of adults comprising 181 patients with ADHD and 147 healthy controls. Spectral power and ERP amplitude and latency measures were used as input data for a semi-automatic machine-learning framework.
Results: ADHD patients and healthy controls could be classified with a sensitivity ranging from 75% to 83% and specificity values of 71% to 77%. In the analysis of the repeated measurements, sensitivity values of the selected logistic regression model remained high (72% and 76%), while specificity values slightly decreased over time (64% and 67%).
Conclusions: Implementation of the system in clinical practice requires facilities to track affected networks, as well as expertise in neuropathophysiology. Therefore, the use of neuroalgorithms can enhance the diagnostic process by making it less subjective and more reliable and linking it to the underlying pathology.
Acknowledgments
We would like to thank the supporting foundations: Hirschmann Foundation, Uniscientia Foundation, Hand in Hand Anstalt, Fondation Claude & Giuliana, Propter Homines Foundation, Karl Mayer Foundation, Senta Herrmann Foundation, Maiores Foundation, Unus pro multis Foundation. Without their support, research activity of the kind described above wouldn’t have been possible.
Statement of interest
The authors of the publication did not receive direct research support from the above mentioned foundations and no other financial support besides the ordinary salary from Brain and Trauma Foundation. Andreas Müller and Juri Kropotov hold stocks of and serve on the board of directors of HBImed AG.
