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Identifying ADHD boys by very-low frequency prefrontal fNIRS fluctuations during a rhythmic mental arithmetic task
Título : Identifying ADHD boys by very-low frequency prefrontal fNIRS fluctuations during a rhythmic mental arithmetic task |
Autor : Ortuño Miró, Sergio Molina Rodríguez, Sergio Belmonte Martínez, Carlos Ibáñez Ballesteros, Joaquín |
Editor : IOP Publishing |
Departamento: Instituto de Neurociencias Departamentos de la UMH::Fisiología |
Fecha de publicación: 2023-05-23 |
URI : https://hdl.handle.net/11000/30633 |
Resumen :
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. Approach. fNIRS signals recorded from superficial and deep tissue layers of
the forehead were collected from 15 clinically referred ADHD boys (average age 11.9 years) and 15
non-ADHD controls during the execution of a rhythmic mental arithmetic task. Synchronization
measures in the time-frequency plane were computed to find frequency-specific oscillatory
patterns maximally representative of the ADHD or control group. Time series distance-based
features were fed into four popular ML linear models (support vector machine, logistic regression
(LR), discriminant analysis and naïve Bayes) for binary classification. A ‘sequential forward
floating selection’ wrapper algorithm was adapted to pick out the most discriminative features.
Classifiers performance was evaluated through five-fold and leave-one-out cross-validation (CV)
and statistical significance by non-parametric resampling procedures. Main results. LR and linear
discriminant analysis achieved accuracy, sensitivity and specificity scores of near 100% (p < .001)
for both CV schemes when trained with only three key wrapper-selected features, arising from
surface and deep oscillatory components of very low frequency. Significance. We provide
preliminary evidence that very-low frequency fNIRS fluctuations induced/modulated by a
rhythmic mental task accurately differentiate ADHD boys from non-ADHD controls,
outperforming other similar studies. The proposed approach holds promise for finding functional
biomarkers reliable and interpretable enough to inform clinical practice.
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Palabras clave/Materias: attention-deficit/hyperactivity disorder functional near-infrared spectroscopy machine learning classification mental arithmetic logistic regression linear discriminant analysis |
Tipo de documento : info:eu-repo/semantics/article |
Derechos de acceso: info:eu-repo/semantics/openAccess |
DOI : 10.1088/1741-2552/acad2b |
Aparece en las colecciones: Artículos Fisiología
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La licencia se describe como: Atribución-NonComercial-NoDerivada 4.0 Internacional.