Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data.

Objective: Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other psychiatric illnesses, making diagnosis difficult. The availability of biomarkers could be useful for aiding in diagnosis, although prior neuroimaging studies were unable to provide such biomarkers. Method: In this study, patients with OCD were classified from healthy controls using 2 different hybrid deep learning models: one-dimensional convolutional neural networks (1DCNN) together with long-short term memory (LSTM) and gradient recurrent units (GRU), respectively. Results: Both models exhibited exceptional classification accuracies in cross-validation and external validation phases. The mean classification accuracies in the cross-validation stage were 90.88% and 85.91% for the 1DCNN-LSTM and 1DCNN-GRU models, respectively. The inferior frontal, temporal, and occipital electrodes were predominant in providing discriminative features. Conclusion: Our findings underscore the potential of hybrid deep learning architectures utilizing EEG data to effectively differentiate patients with OCD from healthy controls. This promising approach holds implications for advancing clinical decision-making by offering valuable insights into diagnostic markers for OCD.

Dissociative Aggression Triggered by Headache.

Headache is generally perceived as a negative symptom focused on oneself. However, there are reports suggesting that patients suffering from pain, especially headache, can be aggressive. The precise nature of the link between headache and aggression is not known. Here, we describe a homicidal attack, triggered by headache, in a middle-aged man. The patient's background and the characteristics of the attack suggested a dissociative behavior. The case shows that headache may be a trigger for homicidal behavior. Case-control studies are needed to determine the prevalence of aggressive tendencies in patients with headache.

Use of EEG for Predicting Treatment Response to Transcranial Magnetic Stimulation in Obsessive Compulsive Disorder.

Aim. In this study we assessed the predictive power of quantitative EEG (qEEG) for the treatment response to right frontal transcranial magnetic stimulation (TMS) in obsessive compulsive disorder (OCD) using a machine learning approach. Method. The study included 50 OCD patients (35 responsive to TMS, 15 nonresponsive) who were treated with right frontal low frequency stimulation and identified retrospectively from Uskudar Unversity, NPIstanbul Brain Hospital outpatient clinic. All patients were diagnosed with OCD according to the DSM-IV-TR and DSM-5 criteria. We first extracted pretreatment band powers for patients. To explore the prediction accuracy of pretreatment EEG, we employed machine learning methods using an artificial neural network model. Results. Among 4 EEG bands, theta power successfully discriminated responsive from nonresponsive patients. Responsive patients had more theta powers for all electrodes as compared to nonresponsive patients. Discussion. qEEG could be helpful before deciding about treatment strategy in OCD. The limitations of our study are moderate sample size and limited number of nonresponsive patients and that treatment response was defined by clinicians and not by using a formal symptom measurement scale. Future studies with larger samples and prospective design would show the role of qEEG in predicting TMS response better.

High Frequencies in QEEG Are Related to the Level of Insight in Patients With Schizophrenia.

Lack of insight is a neurocognitive problem commonly encountered in patients with psychotic disorders that negatively affects treatment compliance and prognosis. Measurement of insight is based on self-report scales, which are limited due to subjectivity. This study aimed to determine the correlation between resting state beta and gamma power in 23 patients with schizophrenia and insight. It was observed that as beta and gamma power measured via qualitative electroencephalography (qEEG) increased the level of insight decreased. Negative correlation was found in F3, C3, Cz for gamma activity and in F3 and C3 for beta activity. This finding indicates that resting state qEEG could be used to evaluate the level of insight in patients with schizophrenia.

The Use of Quantitative EEG for Differentiating Frontotemporal Dementia From Late-Onset Bipolar Disorder.

The behavioral variant frontotemporal dementia (bvFTD) usually emerges with behavioral changes similar to changes in late-life bipolar disorder (BD) especially in the early stages. According to the literature, a substantial number of bvFTD cases have been misdiagnosed as BD. Since the literature lacks studies comparing differential diagnosis ability of electrophysiological and neuroimaging findings in BD and bvFTD, we aimed to show their classification power using an artificial neural network and genetic algorithm based approach. Eighteen patients with the diagnosis of bvFTD and 20 patients with the diagnosis of late-life BD are included in the study. All patients' clinical magnetic resonance imaging (MRI) scan and electroencephalography recordings were assessed by a double-blind method to make diagnosis from MRI data. Classification of bvFTD and BD from total 38 participants was performed using feature selection and a neural network based on general algorithm. The artificial neural network method classified BD from bvFTD with 76% overall accuracy only by using on EEG power values. The radiological diagnosis classified BD from bvFTD with 79% overall accuracy. When the radiological diagnosis was added to the EEG analysis, the total classification performance raised to 87% overall accuracy. These results suggest that EEG and MRI combination has more powerful classification ability as compared with EEG and MRI alone. The findings may support the utility of neurophysiological and structural neuroimaging assessments for discriminating the 2 pathologies.

Brainstem reflexes are hyperactive in patients with drug-induced akathisia.

Akathisia is a sensori-motor phenomenon which is generally encountered as an adverse effect of antidopaminergic medications suggesting involvement of dopaminergic pathways. We recently showed nociceptive flexor reflex was altered in akathisia as compared to restless legs syndrome and therefore, these findings may indicate co-involvement of pathways other than dopaminergic ones. To examine functional status of different pathways, we investigated auditory startle reflex (ASR), startle response to somatosensory input (SSS), and trigemino-cervical reflex (TCR) in a group of patients with akathisia. Consecutive seven patients with drug-induced akathisia and age- and gender-matched healthy subjects were prospectively included in the study. The diagnosis was made by appropriate clinical criteria. Brainstem reflexes, ASR, SSS, and TCR were examined in all participants. The probability, onset latency, amplitude, and duration were measured and compared between groups. The probability and amplitudes of ASRs were significantly increased and durations of ASRs and TCRs were prolonged in the patient group. Latencies of all responses as well as patterns of startle responses were similar between groups. The results reveal hyperactivity of the ASR and TCR in drug-induced akathisia. Hyperactive ASRs and TCRs also confirm suprasegmental hypodopaminergic state in akathisia. Although we keep in mind the confounding effects due to concurrent antidopaminergic treatments and the small sample group, we speculate that hyperactive ASRs and TCRs might be related to deficient control by forebrain and limbic-mainly amygdala-network in patients with drug-induced akathisia.

Lower limb flexor reflex: Comparisons between drug-induced akathisia and restless legs syndrome.

BACKGROUND AND OBJECTIVE: Akathisia is characterized by restlessness and crawling sensations similar to restless legs syndrome (RLS). Long latency flexor reflex (LLFR) which has helped to advance RLS pathophysiology has never been investigated in akathisia. Due to the clinical commonalities of akathisia and RLS, we investigated the behavior of LLFR in patients with akathisia aiming to understand pathophysiology of akathisia. PATIENTS AND METHODS: Seven patients with neuroleptic-induced akathisia, 12 drug-naïve patients with primary RLS and 17 healthy subjects were prospectively enrolled in the study. LLFR was recorded from unilateral tibialis anterior (TA) and long head of biceps femoris (BF) muscles after stimulating the sole by trains of electrical stimuli. We measured amplitude, latency, duration, presence of response and compared between three groups. RESULTS: One-way ANOVA showed mean durations of early and late responses recorded over TA were the longest in akathisia group compared to both RLS group and healthy subjects (p=0.012). The spatial spread of LLFR in akathisia patients was comparable to those of healthy subjects whereas presence of response on BF was significantly less in akathisia than RLS group. CONCLUSIONS: Our findings indicate increased excitability of LLFR pathway in akathisia group. These findings are probably due to lack of inhibition originated in regions other than those known to downregulate in RLS.

Treatment with group psychotherapy for chronic psychogenic nonepileptic seizures.

Although there have been a number of psychotherapy trials for chronic psychogenic nonepileptic seizures, evidence-based treatment options are limited. We developed an eclectic group psychotherapy which combines psychoeducation and behavioral and psychoanalytic techniques. Nine patients completed 12 weeks of psychotherapy. Patients were interviewed with SCID-I. They also filled in the following measures at the beginning and end of the therapy: Beck Depression Inventory, Dissociative Experiences Scale, Spielberger State-Trait Anxiety Scale, SF-36 Life Quality Scale, and Toronto Alexithymia Scale. Seizure frequency was assessed before and after the therapy and on follow-up visits at the fourth, sixth, ninth, and twelfth months. After one year of follow-up, the decrease in seizure frequency was highly significant (p<0.001). In addition, we observed significant improvements in the mental health subscale of the SF-36 (p=0.03) and the state (p=0.006) and trait (p=0.02) subscales of the Spielberger State-Trait Anxiety Scale at the end of the therapy. These results suggest that group psychotherapy might be a treatment option for chronic psychogenic nonepileptic seizures.