Diagnosis of autism spectrum disorder based on functional brain networks and machine learning.

Autor: Alves CL; Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil. caroline.lourenco.alves@gmail.com.; BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany. caroline.lourenco.alves@gmail.com., Toutain TGLO; Health Sciences Institute(HSI), Federal University of Bahia (UFBA), Salvador, Bahia, Brazil., de Carvalho Aguiar P; Hospital Israelita Albert Einstein, São Paulo, Brazil.; Department of Neurology and Neurosurgery, Federal University of São Paulo, São Paulo, Brazil., Pineda AM; Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil., Roster K; Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil., Thielemann C; BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany., Porto JAM; Institute of Physics of São Carlos (IFSC), University of São Paulo (USP), São Paulo, Brazil., Rodrigues FA; Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 May 18; Vol. 13 (1), pp. 8072. Date of Electronic Publication: 2023 May 18.
DOI: 10.1038/s41598-023-34650-6
Abstrakt: Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.
(© 2023. The Author(s).)
Databáze: MEDLINE
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