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    Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers
    (MDPI, 2022-01-01) Erdogan, Sinem Burcu; Yukselen, Gulnaz
    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 (naive 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.
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    Diagnosis and Treatment in Vestibular Migraine
    (TURKISH NEUROLOGICAL SOC, 2020-01-01) Dikmen, Pinar Yalinay
    Vestibular migraine (VM) is the most common cause of recurrent spontaneous vertigo. Inclusion of the diagnostic criteria for VM in the last edition of the International Classification of Headache Disorders published in 2013 promoted the establishment of unity in terminology as well as increasing awareness about VM. The diagnosis of VM is based on clinical history. In studies related to VM, it has been shown that headache and vertigo may not always occur simultaneously, and vertigo may present in different patterns in individual attacks of patients. This variation creates difficulty in the diagnosis of VM. Therefore, the presence of headache should always be questioned in young and middle-aged patients who present with vertigo. Headache that was present previously may have disappeared years ago, hence a detailed patient history is the most essential tool in the diagnosis of VM. There may be some findings showing central and peripheral vestibular involvement in the neurotologic examination during ictal and interictal periods. However, there is no pathognomonic laboratory test