The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.

Machine learning approaches for medical decision-making processes are valuable when both high classification accuracy and less feature requirements are satisfied. Artificial neural networks (ANNs) successfully meet the first goal with its adaptive engine, while natureinspired algorithms are focusing on the feature selection (FS) process in order to eliminate less informative and less discriminant features. Besides engineering applications of ANN and FS algorithms, medical informatics is another emerging field using similar methods for medical data processing. Classification of psychiatric disorders is one of the major focus of medical informatics using artificial intelligence approaches. Being one of the most debilitating psychiatric diseases, bipolar disorder (BD) is frequently misdiagnosed as unipolar disorder (UD), leading to suboptimal treatment and poor outcomes. Thus, discriminating UD and BD at earlier stages of illness could therefore help to facilitate efficient and specific treatment. The use of quantitative electroencephalography (EEG) cordance as a biomarker has greatly enhanced the clinical utility of EEG in psychiatric and neurological subjects. In this context, the paper puts forward a study using two-step hybridized methodology: particle swarm optimization (PSO) algorithm for FS process and ANN for training process. The noteworthy performance of ANN-PSO approach stated that it is possible to discriminate 31 bipolar and 58 unipolar subjects using selected features from alpha and theta frequency bands with 89.89 % overall classification accuracy.

ObjectiveThe combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN).MethodsThe artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation.ResultsThe ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively.ConclusionPotential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values.

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AimThe relationship between psychological stress and platelet activation has been widely studied. It is well known that platelets may reflect certain biochemical changes that occur in the brain when different mental conditions occur. Platelet 5-hydroxytryptamine (5-HT) is also extensively studied in psychiatry. The mean platelet volume (MPV), the accurate measure of platelet size, has been considered a marker and determinant of platelet function. The aim of the present study was to search for any probable difference in the MPV of subjects with panic disorder (PD).MethodsA total of 37 drug-free subjects, aged 18 to 65 years, diagnosed with PD, with or without agoraphobia, according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth edition (DSM-IV) criteria and 45 healthy control subjects were included in the study. Platelet count and MPV were measured and recorded for each subject.ResultsThere were no statistically significant differences between groups in terms of female/male ratio, age, or body mass index between the PD group and control group (P=0.91, P=0.82, and P=0.93, respectively). The MPV was found to be significantly lower in the PD group compared with the control group (8.8±0.9 fL vs 9.2±0.8 fL; P=0.02). All the participants had MPV values in the standard range of 6.9–10.8 fL.ConclusionWe concluded that abnormalities of the 5-HT1A receptor function in the central nervous system of subjects with a diagnosis of PD are also mirrored in as an alteration in platelet activity. Measurements of platelet activity may be used as a tool for neuropsychiatric and psychopharmacological research and for studying how certain mental diseases and medications affect the central nervous system.

ObjectiveRepetitive transcranial magnetic stimulation (rTMS) can enhance the excitement of the brain through adjusting the biological activities of the cerebral cortex and has wide biological effects, making it one basic mechanism of therapy for depression. In the treatment of unipolar depressive disorder, almost in every treatment method, hypomanic and manic shifts can be observed. There is still a lack of data regarding manic and hypomanic symptoms triggered by rTMS applications.MethodWe describe four cases with unipolar depression in which high-frequency rTMS over the left dorsolateral prefrontal cortex applied as an add-on antidepressive strategy may have induced a hypomanic episode.ResultsIn these cases, 25 Hz rTMS combined with antidepressants may have contributed to the occurrence of hypomanic symptoms.ConclusionUsing an intensive methodology of rTMS may induce hypomanic or manic symptoms.

ObjectiveInteractions between psychological, biological and environmental factors are important in development of trichotillomania and skin picking. The aim of this study is to determine the relationship of traumatic life events, symptoms of post-traumatic stress disorder and dissociation in patients with diagnoses of trichotillomania and skin picking disorder.MethodsThe study included patients who was diagnosed with trichotillomania (n=23) or skin picking disorder (n=44), and healthy controls (n=37). Beck Depression Inventory, Traumatic Stress Symptoms Scale and Dissociative Experiences Scale were administered. All groups checked a list of traumatic life events to determine the exposed traumatic events.ResultsThere was no statistical significance between three groups in terms of Dissociative Experiences Scale scores (P=0.07). But Beck Depression Inventory and Traumatic Stress Symptoms Scale scores of trichotillomania and skin picking groups were significantly higher than the control group. Subjects with a diagnosis of trichotillomania and skin picking reported statistically significantly higher numbers of traumatic and negative events in childhood compared to healthy subjects.ConclusionWe can conclude that trauma may play a role in development of both trichotillomania and skin picking. Increased duration of trichotillomania or skin picking was correlated with decreased presence of post-traumatic stress symptoms. The reason for the negatively correlation of severity of post-traumatic stress symptoms and self-harming behavior may be speculated as developing trichotillomania or skin picking symptoms helps the patient to cope with intrusive thoughts related to trauma. Future longitudinal research must focus on whether trauma and post-traumatic stress or trichotillomania and skin picking precede the development of mental disorder.

The aim of the present study was to assess the safety and effectiveness of high-frequency repetitive transcranial magnetic stimulation (rTMS) in pregnant patients with depression. Thirty depressed pregnant patients received rTMS over the left prefrontal cortex for 6 days in a week, from Monday to Saturday for 3 weeks. The rTMS intensity was set at 100% of the motor threshold. A 25-Hz stimulation with a duration of 2 s was delivered 20 times with 30-s intervals. A session comprised 1,000 magnetic pulses. Depression was rated using the 17-item Hamilton depression rating scale (HAMD) before and after treatment. Response was defined as a 50% reduction of the HAMD score. Patients with HAMD scores less than 8 were considered to be in remission. The mean HAMD score for the study group decreased from 26.77 ± 5.58 to 13.03 ± 6.93 (p < 0.001) after 18 sessions of rTMS. After the treatment period, 41.4% of the study group demonstrated significant mood improvements as indexed by a reduction of more than 50% on the HAMD score. In addition, 20.7% attained remission (HAMD score < 8), 34.5% achieved a partial response, and 3.4% had worsening in HAMD scores at the end of treatment. Treatment was well tolerated, and no significant adverse effects were reported. rTMS was well tolerated and found to be statistically and clinically effective in pregnant patients with treatment-resistant depression. This study contributed to the existing evidence of the antidepressant effect of rTMS in the treatment of depression in pregnancy.