Comparing the Anti-Depressive Effect of Electroconvulsive Therapy (ECT) Versus Transcranial Magnetic Stimulation (TMS) in the Treatment of Patients with Depression.

BACKGROUND: Electroconvulsive therapy (ECT) is one of the most effective treatments for depressive disorders. However, ECT has a number of limitations, such as significant side effects in the neurocognitive domain and the requirement for general anesthesia. Transcranial magnetic stimulation (TMS) is an intervention that applies electric stimulation to the brain without causing convulsions, thus representing an attractive alternative to ECT. The aim of our study is to review systematic reports of the effectiveness of ECT and TMS in the treatment of depressive spectrum disorders. SUBJECTS AND METHODS: We performed search queries in PubMed and eLibrary databases, which retrieved 391 articles, of which 14 met our inclusion criteria for the analysis. The articles comprised three comparisons: TMS vs SHAM, ECT vs sham ECT (SECT), and ECT vs PHARM. The protocol parameters analyzed for TMS were coil type, targeted brain area, amplitude of resting motor threshold, duration of session, number of sessions in total and per week, number and pulses per session and inter-train pause. For ECT, we evaluated the type of ECT device, targeted brain area, type of stimuli, and for ECT vs PHARM we recorded types of anesthesia and antidepressant medication. RESULTS: Three of 6 studies showed a therapeutic effect of TMS compared to placebo; efficacy was greater for TMS frequency exceeding 10 Hz, and with stimulation of two areas of cerebral cortex rather than a single area. There was insufficient data to identify a relationship between the success of TMS and intertrain pause (IP). Three of four studies showed a therapeutic effect of ECT compared to placebo. Three studies of bilateral ECT showed a significant reduction in depression scores compared to the SECT groups. ECT protocols with brief pulses were generally of lesser efficacy. Four of 5 ECT vs PHARM studies showed superior efficacy of ECT compared to PHARM. Among several antidepressants, only the ketamine study showed greater efficacy compared to ECT. CONCLUSIONS: There of six TMS studies and 7 of 9 ECT studies showed efficacy in reducing depressive symptoms. A prospective study of crossover design might reveal the relative efficacies of ECT and TMS.

Neuronetwork Approach in the Early Diagnosis of Depression.

BACKGROUND: Depression is a common mental illness, with around 280 million people suffering from depression worldwide. At present, the main way to quantify the severity of depression is through psychometric scales, which entail subjectivity on the part of both patient and clinician. In the last few years, deep (machine) learning is emerging as a more objective approach for measuring depression severity. We now investigate how neural networks might serve for the early diagnosis of depression. SUBJECTS AND METHODS: We searched Medline (Pubmed) for articles published up to June 1, 2023. The search term included Depression AND Diagnostics AND Artificial Intelligence. We did not search for depression studies of machine learning other than neural networks, and selected only those papers attesting to diagnosis or screening for depression. RESULTS: Fifty-four papers met our criteria, among which 14 using facial expression recordings, 14 using EEG, 5 using fMRI, and 5 using audio speech recording analysis, whereas 6 used multimodality approach, two were the text analysis studies, and 8 used other methods. CONCLUSIONS: Research methodologies include both audio and video recordings of clinical interviews, task performance, including their subsequent conversion into text, and resting state studies (EEG, MRI, fMRI). Convolutional neural networks (CNN), including 3D-CNN and 2D-CNN, can obtain diagnostic data from the videos of the facial area. Deep learning in relation to EEG signals is the most commonly used CNN. fMRI approaches use graph convolutional networks and 3D-CNN with voxel connectivity, whereas the text analyses use CNNs, including LSTM (long/short-term memory). Audio recordings are analyzed by a hybrid CNN and support vector machine model. Neural networks are used to analyze biomaterials, gait, polysomnography, ECG, data from wrist wearable devices, and present illness history records. Multimodality studies analyze the fusion of audio features with visual and textual features using LSTM and CNN architectures, a temporal convolutional network, or a recurrent neural network. The accuracy of different hybrid and multimodality models is 78-99%, relative to the standard clinical diagnoses.

Neurological and Psychiatric Aspects of Biological Markers for the Provision of Medical Care to Patients with Spinal Muscular Atrophy 5q.

BACKGROUND: Spinal muscular atrophy (SMA) is a rare genetic disorder, in which, for the common childhood onset forms, loss of function of the SMA 5q gene leads to disability and death before adulthood. Symptomatic treatment focusses on respiratory and nutritional support, and physical therapy, but there is little consideration of psychiatric manifestations of SMA. The aim of this study was to explore blood biomarker levels, electromyography (EMG) data, and clinical manifestations, including psychiatric impairments, in patients with SMA 5q. Our objectives were twofold: First, to assess the clinical relevance of standard biomarkers, i.e., creatinine, creatine kinase (CK), and lactate dehydrogenase (LDH) levels, and second, to obtain data supporting the development of an effective prognostic algorithm for the course of this disease. RESULTS: We analyzed retrospective data from 112 medical records of 58 registered patients (2008-2022) with SMA. At the time of last registration, the 58 patients had a mean age 38.4 years [13.68; 55.0], of whom 32 (52%) were female. The subgroup of 21 pediatric patients had a mean age 12.32 years [6.57; 13.93], of whom 14 (24%) were girls. The ICD-10 diagnoses were as follows: G12.0 (n=7, 12%, children), G12.1 (n=14, 24% children; n=29, 50% adults), G12.8 (n=6, 10% adults), G12.9 (n=2, 1% adults). The archival data on psychiatric status indicated emotional lability (n=6, 10.3%), fatigue (n=10, 17.2%), and tearfulness (n=3, 5.2%) in some patients. There were no significant subgroup differences in serum creatinine and CK levels, but there were significant differences in LDH levels between the G12.0, G12.1, G12.8, and G12.9 subgroups. Among the serum biomarkers, only LDH levels showed significant differences among the subgroups of SMA 5q patients; higher levels in the G12.1, G12.8, and G12.9 groups compared to the G12.0 (infantile) group related to age, weight, gender, and level of physical activity. Data on psychiatric status were insufficient to identify group differences and associations with biomarker levels. Likewise, longitudinal data on repeat hospitalizations did not indicate associations with biomarker levels. CONCLUSIONS: Creatinine, CK, and LDH levels were insufficient for monitoring and predicting the course of SMA. Further prospective research is needed to elaborate the weak relationships between CK levels, the dynamics of the clinical presentation, and therapeutic interventions, and to investigate psychiatric co-morbidities in SMA 5q patients.

Dietary Patterns as Modifiable Risk Factors for Depression: a Narrative Review.

BACKGROUND: The role of nutrition in treating clinical depression has been widely discussed. Unhealthy lifestyle patterns, like lack of physical activity, junk food consumption, and irregular sleep patterns are common in depressed patients. Considering the mental and physical side-effects, the daily nutrition of these patients seems to be a plausible option for reducing depressive symptoms and enhancing treatment results. METHODS: A PubMed search was done for meta-analyses published from January 2018 to June 2023 with the query: (diet) AND (psychiatric disorder) AND (depression). We selected meta-analyses that met specific criteria like including the entire diet or specific diet patterns and having depression or depressive symptoms as a primary or secondary outcome. RESULTS: Out of 28 papers found, the 9 meta-analyses, selected for review, revealed different types of correlation between dietary patterns and the symptoms of depression and anxiety. Healthy diets were associated with higher intake of fruits, vegetables, nuts, and lower intake of pro-inflammatory food items like processed meats and trans fats. Adherence to such diets showed a negative association with incident depression in cross-sectional and longitudinal studies. A diet mostly including ultra-processed foods was associated with higher odds of depressive and anxiety symptoms. Women were found to be more susceptible than men both in developing the depressive symptoms with unhealthy diet and in reducing the symptoms of depression and anxiety with improvement of diet quality. Statistically significant improvement in symptoms of depression and anxiety in both sexes was observed in study groups assigned for individual consultations of a dietician and a psychotherapist when compared with group sessions or general recommendations. CONCLUSIONS: Research on the correlation of healthy dietary patterns and symptoms of depression and anxiety has mainly focused on non-clinical populations. The evidence supports an inverse association between healthy eating habits and symptoms of depression. Further research should be encouraged on the eating habits of clinically depressed individuals and the underlying physiological mechanisms of uncontrolled food intake.