Identifying ADHD boys by very-low frequency prefrontal fNIRS fluctuations during a rhythmic mental arithmetic task
[Objective] Computer-aided diagnosis of attention-deficit/hyperactivity disorder (ADHD) aims to provide useful adjunctive indicators to support more accurate and cost-effective clinical decisions. Deep- and machine-learning (ML) techniques are increasingly used to identify neuroimaging-based feature...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2023 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositório: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/353065 |
| Acesso em linha: | http://hdl.handle.net/10261/353065 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Logistic regression Linear discriminant analysis Attention-deficit/hyperactivity disorder Machine learning Functional near-infrared spectroscopy Mental arithmetic |
| Resumo: | [Objective] Computer-aided diagnosis of attention-deficit/hyperactivity disorder (ADHD) aims to provide useful adjunctive indicators to support more accurate and cost-effective clinical decisions. Deep- and machine-learning (ML) techniques are increasingly used to identify neuroimaging-based features for objective assessment of ADHD. Despite promising results in diagnostic prediction, substantial barriers still hamper the translation of the research into daily clinic. Few studies have focused on functional near-infrared spectroscopy (fNIRS) data to discriminate ADHD condition at the individual level. This work aims to develop an fNIRS-based methodological approach for effective identification of ADHD boys via technically feasible and explainable methods. |
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