Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82313
Title: Machine learning classification of ADHD and HC by multimodal serotonergic data
Authors: Kautzky, A
Vanicek, T
Philippe, C
Kranz, GS 
Wadsak, W
Mitterhauser, M
Hartmann, A
Hahn, A
Hacker, M
Rujescu, D
Kasper, S
Lanzenberger, R
Issue Date: 2020
Source: Translational psychiatry, 7 Apr. 2020, v. 10, 104, p. 1-9
Abstract: Serotonin neurotransmission may impact the etiology and pathology of attention-deficit and hyperactivity disorder (ADHD), partly mediated through single nucleotide polymorphisms (SNPs). We propose a multivariate, genetic and positron emission tomography (PET) imaging classification model for ADHD and healthy controls (HC). Sixteen patients with ADHD and 22 HC were scanned by PET to measure serotonin transporter (SERT') binding potential with [C-11]DASB. All subjects were genotyped for thirty SNPs within the HTR1A, HTR1B, HTR2A and TPH2 genes. Cortical and subcortical regions of interest (ROI) were defined and random forest (RF) machine learning was used for feature selection and classification in a five-fold cross-validation model with ten repeats. Variable selection highlighted the ROI posterior cingulate gyrus, cuneus, precuneus, pre-, para- and postcentral gyri as well as the SNPs HTR2A rs1328684 and rs6311 and HTR1B rs130058 as most discriminative between ADHD and HC status. The mean accuracy for the validation sets across repeats was 0.82 (+/- 0.09) with balanced sensitivity and specificity of 0.75 and 0.86, respectively. With a prediction accuracy above 0.8, the findings underlying the proposed model advocate the relevance of the SERT as well as the HTR1B and HTR2A genes in ADHD and hint towards disease-specific effects. Regarding the high rates of comorbidities and difficult differential diagnosis especially for ADHD, a reliable computer-aided diagnostic tool for disorders anchored in the serotonergic system will support clinical decisions.
Publisher: Nature Publishing Group
Journal: Translational psychiatry 
EISSN: 2158-3188
DOI: 10.1038/s41398-020-0781-2
Rights: © The Author(s) 2020
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
The following publication Kautzky, A., Vanicek, T., Philippe, C. et al. Machine learning classification of ADHD and HC by multimodal serotonergic data. Transl Psychiatry 10, 104 (2020) is available at https://dx.doi.org/10.1038/s41398-020-0781-2
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

37
Citations as of Jul 14, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.