The relationship between subclinical hypothyroidism and depression is still controversial. Our objective was to compare the prevalence of depressive symptoms and major depressive disorder in a population of patients affected by subclinical hypothyroidism and a control group without thyroid disease. The authors enrolled 123 consecutive outpatients affected by subclinical hypothyroidism undergoing follow-up at the endocrinology department of San Paolo Hospital in Milan and 123 controls without thyroid disease under the charge of general physicians.All patients and controls underwent an evaluation by means of a psychiatric interview; Hamilton Rating Scale for Depression (HAM-D); Montgomery-Asberg Depression Rating Scale (MADRS); and serum thyroid stimulating hormone, free T4, and free T3 levels. Patients were also screened for thyroid peroxidase antibodies and thyroglobulin antibodies. Patients affected by subclinical hypothyroidism had a prevalence of depressive symptoms of 63.4% at HAM-D and 64.2% at MADRS; 22 patients (17.9%) had a diagnosis of depressive episode (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision criteria). The control group had a prevalence of depressive symptoms of 27.6% at HAM-D and 29.3% at MADRS, and only seven controls had a diagnosis of depressive episode. The prevalence of depressive symptoms between these two groups was statistically different. This study underlines a strong association between subclinical hypothyroidism and depressive symptoms, which could have some important diagnostic and therapeutic implications in the clinical practice.

Deep brain stimulation (DBS) has been proposed for severe, chronic, treatment-refractory obsessive-compulsive disorder (OCD) patients. Although serious adverse events can occur, only a few studies report on the safety profile of DBS for psychiatric disorders. In a prospective, open-label, interventional multi-center study, we examined the safety and efficacy of electrical stimulation in 30 patients with DBS electrodes bilaterally implanted in the anterior limb of the internal capsule. Safety, efficacy, and functionality assessments were performed at 3, 6, and 12 months post implant. An independent Clinical Events Committee classified and coded all adverse events (AEs) according to EN ISO14155:2011. All patients experienced AEs (195 in total), with the majority of these being mild (52% of all AEs) or moderate (37%). Median time to resolution was 22 days for all AEs and the etiology with the highest AE incidence was 'programming/stimulation' (in 26 patients), followed by 'New illness, injury, condition' (13 patients) and 'pre-existing condition, worsening or exacerbation' (11 patients). Sixteen patients reported a total of 36 serious AEs (eight of them in one single patient), mainly transient anxiety and affective symptoms worsening (20 SAEs). Regarding efficacy measures, Y-BOCS reduction was 42% at 12 months and the responder rate was 60%. Improvements in GAF, CGI, and EuroQol-5D index scores were also observed. In sum, although some severe AEs occurred, most AEs were mild or moderate, transient and related to programming/stimulation and tended to resolve by adjustment of stimulation. In a severely treatment-resistant population, this open-label study supports that the potential benefits outweigh the potential risks of DBS.

Differentiating epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) is commonly based on electroencephalogram and concurrent video recordings (vEEG). Here, we demonstrate that these two types of seizures can be discriminated based on signals related to autonomic nervous system activity recorded via wearable sensors. We used Empatica E4 Wristband sensors worn on both arms in vEEG confirmed seizures, and machine learning methods to train classifiers, specifically, extreme gradient boosting (XGBoost). Classification performance achieved a predictive accuracy of 78 ± 1.5% on previously unseen data for whether a seizure was epileptic or psychogenic, which is 6 standard deviations above the baseline of 68% accuracy. Our dataset contained altogether 35 seizures from 18 patients out of which 8 patients had 13 convulsive seizures. Prediction of seizure type was based on simple features derived from the segments of autonomic activity measurements (electrodermal activity, body temperature, blood volume pulse, and heart rate) and forearm acceleration. Features related to heart rate and electrodermal activity were ranked as the top predictors in XGBoost classifiers. We found that patients with PNES had a higher ictal heart rate and electrodermal activity than patients with ES. In contrast to existing published studies of mainly convulsive seizures, our classifier focuses on autonomic signals to differentiate convulsive or nonconvulsive semiology ES from PNES. Our results show that autonomic activity recorded via wearable sensors provides promising signals for detection and discrimination of psychogenic and epileptic seizures, but more work is necessary to improve the predictive power of the model.