Key result
SVM-based fNIRS outperforms neural networks with ~92% accuracy in identifying impulsive adolescents.
Why the study?
The gold standard for diagnosing impulsivity relies on subjective clinical interviews, behavioral questionnaires, and rating scales, motivating development of an objective fNIRS-based classification approach.
Can an fNIRS-based machine learning classification approach accurately identify impulsive adolescents?
Cross-Sectional (n=71)
Can an fNIRS-based machine learning classification approach accurately identify impulsive adolescents?
Absolute Event Rate: 92.2% vs 90.16%
p-value: p=0.005
An fNIRS-based machine learning approach combining clinical, behavioral, and neurophysiological features can accurately identify impulsive adolescents, offering a potential computer-aided diagnostic tool.
May support fNIRS-ML screening for adolescent impulsivity; leaves open prospective validation before clinical adoption.
Background. The gold standard for diagnosing impulsivity relies on clinical interviews, behavioral questionnaires and rating scales which are highly subjective. Objective. The aim of this study was to develop a functional near infrared spectroscopy (fNIRS) based classification approach for correct identification of impulsive adolescents. Taking into account the multifaceted nature of impulsivity, we propose that combining informative features from clinical, behavioral and neurophysiological domains might better elucidate the neurobiological distinction underlying symptoms of impulsivity. Approach . Hemodynamic and behavioral information was collected from 38 impulsive adolescents and from 33 non-impulsive adolescents during a Stroop task with concurrent fNIRS recordings. Connectivity-based features were computed from the hemodynamic signals and a neural efficiency metric was computed by fusing the behavioral and connectivity-based features. We tested the efficacy of two commonly used supervised machine-learning methods, namely the support vector machines (SVM) and artificial neural networks (ANN) in discriminating impulsive adolescents from their non-impulsive peers when trained with multi-domain features. Wrapper method was adapted to identify the informative biomarkers in each domain. Classification accuracies of each algorithm were computed after 10 runs of a 10-fold cross-validation procedure, conducted for 7 different combinations of the 3-domain feature set. Main results. Both SVM and ANN achieved diagnostic accuracies above 90% when trained with Wrapper-selected clinical, behavioral and fNIRS derived features. SVM performed significantly higher than ANN in terms of the accuracy metric (92.2% and 90.16%, respectively, p = 0.005). Significance. Preliminary findings show the feasibility and applicability of both machine-learning based methods for correct identification of impulsive adolescents when trained with multi-domain data involving clinical interviews, fNIRS based biomarkers and neuropsychiatric test measures. The proposed automated classification approach holds promise for assisting the clinical practice of diagnosing impulsivity and other psychiatric disorders. Our results also pave the path for a computer-aided diagnosis perspective for rating the severity of impulsivity.
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Erdoğan et al. (2021) conducted a cross-sectional in Impulsivity (n=71). Support vector machines (SVM) trained with multi-domain fNIRS features vs. Artificial neural networks (ANN) was evaluated on Diagnostic accuracy in discriminating impulsive adolescents from non-impulsive peers (p=0.005). An fNIRS-based classification using support vector machines achieved 92.2% diagnostic accuracy in identifying impulsive adolescents, outperforming artificial neural networks (90.16%, p=0.005).
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