Machine learning classification of ADHD and HC by multimodal serotonergic data

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 [ 11 C] 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.

Use of machine learning to classify adult ADHD and other conditions based on the Conners’ Adult ADHD Rating Scales

“…Studies explicitly based on self-rating scales could not be found which stresses the importance for further research in this area. Connecting the literature to our analyses, we conclude that research groups in the area of ADHD relied predominantly on SVM, however the Random forest approach was also used 29 . It is known that in some challengeable cases GBMs may outperform Random Forest models.…”

“…In a visual GO/NOGO task, special ERP components were able to differentiate between adult ADHD subjects and controls with an accuracy of 92% using SVM with tenfold cross-validation 28 . Evaluating PET imaging and genetic predictors within the serotonergic system, an accuracy of .82 could be achieved for classification of ADHD and controls using Random Forest with fivefold cross-validation 29 . The Random Forest approach was used to investigate how multiple genetic and environmental factors jointly contribute to ADHD, or to examine whether hyperactivity persists in male and female adults with ADHD 30 , 31 .…”

An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection

“…This disadvantage is not elaborate along with external validation. Although there are some reports without the external dataset [13,19,23,24], the performance of our DLA should be assessed using an independent external validation dataset to validate our results in the future. Second, this cohort included only patients that were referred to the study center with suspected AIS [4,10].…”

“…All data in the validation dataset were independent of the files in the training dataset. The shuffling into five datasets and five-fold cross validation was conducted 10 times in this process [19].…”

ADGRL3, FGF1 and DRD4: Linkage and Association with Working Memory and Perceptual Organization Candidate Endophenotypes in ADHD

“…Future studies could greatly benefit from the use of high-throughput genotyping/sequencing for the identification of putative causal variants underpinning perceptual organization in ADHD, leading to the identification of genetic profiles better responding to specific ADHD treatments and the development of translational medicine approaches [180,181]. Furthermore, these variants could also be used for developing predictive models, based on machine learning and artificial intelligence [182][183][184], for ADHD diagnosis and the identification of severe ADHD cases [97,115,120,185] in the clinical setting.…”