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Related Experiment Video

Updated: Dec 24, 2025

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Machine learning classification of ADHD and HC by multimodal serotonergic data.

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This study developed a machine learning model combining genetic data and brain imaging to classify attention-deficit/hyperactivity disorder (ADHD). The model achieved high accuracy, highlighting the role of serotonin transporter genes in ADHD.

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Area of Science:

  • Neuroscience
  • Genetics
  • Psychiatry

Background:

  • Serotonin neurotransmission is implicated in attention-deficit/hyperactivity disorder (ADHD) etiology and pathology.
  • Single nucleotide polymorphisms (SNPs) in serotonin-related genes may mediate these effects.

Purpose of the Study:

  • To develop and validate a multivariate classification model for ADHD using genetic data and positron emission tomography (PET) imaging.
  • To identify key genetic markers and brain regions discriminative of ADHD.

Main Methods:

  • PET imaging with [11C]DASB to measure serotonin transporter (SERT) binding potential in 16 ADHD patients and 22 healthy controls (HC).
  • Genotyping for 30 SNPs in HTR1A, HTR1B, HTR2A, and TPH2 genes.
  • Random forest machine learning with five-fold cross-validation for feature selection and classification.

Main Results:

  • The model identified specific SNPs (HTR2A rs1328684, rs6311; HTR1B rs130058) and brain regions (posterior cingulate gyrus, cuneus, precuneus, pre/para/postcentral gyri) as most discriminative.
  • Achieved a mean prediction accuracy of 0.82 (±0.09) with balanced sensitivity (0.75) and specificity (0.86).

Conclusions:

  • The findings support the relevance of SERT, HTR1B, and HTR2A genes in ADHD.
  • The developed model shows potential as a computer-aided diagnostic tool for ADHD, aiding clinical decisions, especially given high comorbidity rates.