ArticleFrontiers in computational neuroscience2017
A Computational Model for the Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Based on Functional Brain Volume.
Article in Frontiers in computational neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 35 citations in OpenAlex.
- Genetic and environmental contributions to ReHo and fALFF in early adolescence vary across brain regions.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Article
- Digital assessments for children and adolescents with ADHD: a scoping review.Frontiers in digital health · 2024Article
- A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis.Healthcare (Basel, Switzerland) · 2023Review
- Structural and diffusion-weighted brain imaging predictors of attention-deficit/hyperactivity disorder and its symptomology in very young (4- to 7-year-old) children.The European journal of neuroscience · 2022Article
- Interdisciplinary Tensions When Developing Digital Interventions Supporting Individuals With ADHD.Frontiers in digital health · 2022Article
- Localization and Diagnosis of Attention-Deficit/Hyperactivity Disorder.Healthcare (Basel, Switzerland) · 2021Article
- Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning.Frontiers in neuroscience · 2021Article
- Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2019Article
- Machine learning studies on major brain diseases: 5-year trends of 2014-2018.Japanese journal of radiology · 2019Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
In this paper, we investigated the problem of computer-aided diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) using machine learning techniques. With the ADHD-200 dataset, we developed a Support Vector Machine (SVM) model to classify ADHD patients from typically developing controls (TDCs), using the regional brain volumes as predictors. Conventionally, the volume of a brain region was considered to be an anatomical feature and quantified using structural magnetic resonance images. One major contribution of the present study was that we had initially proposed to measure the regional brain volumes using fMRI images. Brain volumes measured from fMRI images were denoted as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.