uency is actually beneficial, when the goal is to produce persistent synaptic changes.Introduction Neuromodulation is an important group of therapeutic modalities for neuropsychiatric disorders. Prior studies have focused on efficacy and adverse events associated with neuromodulation. Less is known regarding the influence of neuromodulation treatments on suicidality. This systematic review sought to examine the effects of various neuromodulation techniques on suicidality. Methods A systematic review of the literature from 1940 to 2020 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline was conducted. Any reported suicide-related outcome, including suicidal ideation, suicide intent, suicide attempt, completed suicide in reports were considered as a putative measure of treatment effect on suicidality. Results The review identified 129 relevant studies. An exploratory analysis of a randomized controlled trial comparing the effects of sertraline and transcranial direct-current stimulation (tDCS) for treating depression reported a decrease in suicidal ideationf suicidality are urgently needed. Rituximab order Systematic Review Registration https//www.crd.york.ac.uk/prospero/display_record.php?RecordID=125599, identifier CRD42019125599.Background Transient ischemic attack (TIA) has a high incidence of recurrent vascular events. Hypoperfusion is one of the factors that are closely correlated with 7-day recurrence of TIA. This study aimed to evaluate the power of hypoperfusion shown on magnetic resonance (MR) perfusion imaging in predicting the incidence of 7-day recurrence of ischemic events after TIA. Methods/Design REATTACK is a prospective multi-centered cohort study on the correlation between MR perfusion and TIA recurrence. Ninety patients aged ≥18 years with recent ( less then 7 days after onset) clinical TIA will be continuously included. All the patients will undergo diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) assessments within 24 h after the onset of TIA. The subjects will then be divided into a PWI positive group and a PWI negative group according to the time-to-maximum of the residue function (T max ). PWI will be repeated after 7 days and in 3 months. The primary clinical outcome will be the recurrence of TIA within 7 days after the onset of TIA. Secondary outcomes will be the recurrence of TIA in 3 months and modified Rankin scale (mRS) score. A chi-square test will be performed to compare the difference in the incidence of recurrent TIA between the two groups, and rank sum test in the mRS score. Multivariate logistic regression will be simultaneously performed to analyze the risk factors for the recurrence of TIA. Discussion The results of this study will confirm whether abnormal T max helps to identify the patients with TIA who have high risks of recurrent ischemic events. This would largely improve the prognosis of patients with TIA. Trial Registration www.chictr.org.cn, registration number ChiCTR2000031863, registered on 12 April 2020.Deep neural networks (DNNs) used for brain-computer interface (BCI) classification are commonly expected to learn general features when trained across a variety of contexts, such that these features could be fine-tuned to specific contexts. While some success is found in such an approach, we suggest that this interpretation is limited and an alternative would better leverage the newly (publicly) available massive electroencephalography (EEG) datasets. We consider how to adapt techniques and architectures used for language modeling (LM) that appear capable of ingesting awesome amounts of data toward the development of encephalography modeling with DNNs in the same vein. We specifically adapt an approach effectively used for automatic speech recognition, which similarly (to LMs) uses a self-supervised training objective to learn compressed representations of raw data signals. After adaptation to EEG, we find that a single pre-trained model is capable of modeling completely novel raw EEG sequences recorded with differing hardware, and different subjects performing different tasks. Furthermore, both the internal representations of this model and the entire architecture can be fine-tuned to a variety of downstream BCI and EEG classification tasks, outperforming prior work in more task-specific (sleep stage classification) self-supervision.Degenerative cervical myelopathy (DCM) damages the spinal cord, resulting in long-term neurological impairment including motor and visual deficits. Given that visual feedback is crucial in guiding movements, the visual disorder may be a cause of motor deficits in patients with DCM. It has been shown that increased functional connectivity between secondary visual cortices and cerebellum, which are functionally related to the visually guided movements, was correlated with motor function in patients with DCM. One possible explanation is that the information integration between these regions was increased to compensate for impaired visual acuity in patients with DCM and resulted in better visual feedback during motor function. However, direct evidence supporting this hypothesis is lacking. To test this hypothesis and explore in more detail the information flow within the "visual-cerebellum" system, we measured the effective connectivity (EC) among the "visual-cerebellum" system via dynamic causal modeling and thesustaining better motor function in patients with DCM.Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset, neurodevelopmental disorder, whereas major depressive disorder (MDD) is a mood disorder that typically emerges in adulthood. Accumulating evidence suggests that these seemingly unrelated psychiatric disorders, whose symptoms even appear antithetical [e.g., psychomotor retardation in depression vs. hyperactivity (psychomotor acceleration) in ADHD], are in fact associated with each other. Thus, individuals with ADHD exhibit high comorbidity with MDD later in life. Moreover, genetic studies have shown substantial overlaps of susceptibility genes between ADHD and MDD. Here, we propose a novel and testable hypothesis that the habenula, the epithalamic brain region important for the regulation of monoamine transmission, may be involved in both ADHD and MDD. The hypothesis suggests that an initially hypoactive habenula during childhood in individuals with ADHD may undergo compensatory changes during development, priming the habenula to be hyperactive in response to stress exposure and thereby increasing vulnerability to MDD in adulthood.