Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers.

Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naïve bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81% and specificities above 94%. SVM had the highest performance in terms of accuracy (85.1 ± 1.77%), sensitivity (84 ± 1.7%), specificity (95 ± 0.5%), precision (86 ± 1.6%), and F1-score (85 ± 1.7%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction.

Identification of impulsive adolescents with a functional near infrared spectroscopy (fNIRS) based decision support system.

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.