Active versus sham DLPFC-NAc rTMS for depressed adolescents with anhedonia using resting-state functional magnetic resonance imaging (fMRI): a study protocol for a randomized placebo-controlled trial.

BACKGROUND: Anhedonia, which is defined as the inability to feel pleasure, is considered a core symptom of major depressive disorder (MDD). It can lead to several adverse outcomes in adolescents, including heightened disease severity, resistance to antidepressants, recurrence of MDD, and even suicide. Specifically, patients who suffer from anhedonia may exhibit a limited response to selective serotonin reuptake inhibitors (SSRIs) and cognitive behavioral therapy (CBT). Previous researches have revealed a link between anhedonia and abnormalities within the reward circuitry, making the nucleus accumbens (NAc) a potential target for treatment. However, since the NAc is deep within the brain, repetitive transcranial magnetic stimulation (rTMS) has the potential to modulate this specific region. Recent advances have enabled treatment technology to precisely target the left dorsolateral prefrontal cortex (DLPFC) and modify the functional connectivity (FC) between DLPFC and NAc in adolescent patients with anhedonia. Therefore, we plan to conduct a study to explore the safety and effectiveness of using resting-state functional connectivity magnetic resonance imaging (fcMRI)-guided rTMS to alleviate anhedonia in adolescents diagnosed with MDD. METHODS: The aim of this article is to provide a study protocol for a parallel-group randomized, double-blind, placebo-controlled experiment. The study will involve 88 participants who will be randomly assigned to receive either active rTMS or sham rTMS. The primary object is to measure the percentage change in the severity of anhedonia, using the Snaith-Hamilton Pleasure Scale (SHAPS). The assessment will be conducted from the baseline to 8-week post-treatment period. The secondary outcome includes encompassing fMRI measurements, scores on the 17-item Hamilton Rating Scale for Depression (HAMD-17), the Montgomery Asberg Depression Rating Scale (MADRS), the Chinese Version of Temporal Experience of Pleasure Scale (CV-TEPS), and the Chinese Version of Beck Scale for Suicide Ideation (BSI-CV). The Clinical Global Impression (CGI) scores will also be taken into account, and adverse events will be monitored. These evaluations will be conducted at baseline, as well as at 1, 2, 4, and 8 weeks. DISCUSSION: If the hypothesis of the current study is confirmed, (fcMRI)-guided rTMS could be a powerful tool to alleviate the core symptoms of MDD and provide essential data to explore the mechanism of anhedonia. TRIAL REGISTRATION: ClinicalTrials.gov NCT05544071. Registered on 16 September 2022.

Lactate: a prospective target for therapeutic intervention in psychiatric disease.

ABSTRACT: Although antipsychotics that act via monoaminergic neurotransmitter modulation have considerable therapeutic effect, they cannot completely relieve clinical symptoms in patients suffering from psychiatric disorders. This may be attributed to the limited range of neurotransmitters that are regulated by psychotropic drugs. Recent findings indicate the need for investigation of psychotropic medications that target less-studied neurotransmitters. Among these candidate neurotransmitters, lactate is developing from being a waste metabolite to a glial-neuronal signaling molecule in recent years. Previous studies have suggested that cerebral lactate levels change considerably in numerous psychiatric illnesses; animal experiments have also shown that the supply of exogenous lactate exerts an antidepressant effect. In this review, we have described how medications targeting newer neurotransmitters offer promise in psychiatric diseases; we have also summarized the advances in the use of lactate (and its corresponding signaling pathways) as a signaling molecule. In addition, we have described the alterations in brain lactate levels in depression, anxiety, bipolar disorder, and schizophrenia and have indicated the challenges that need to be overcome before brain lactate can be used as a therapeutic target in psychopharmacology.

Cerebral cortical regions always connect with each other via the shortest paths.

In human society, the choice of transportation mode between two cities is largely influenced by the distance between the regions. Similarly, when neurons communicate with each other within the cerebral cortex, do they establish their connections based on their physical distance? In this study, we employed a data-driven approach to explore the relationships between fiber length and corresponding geodesic distance between the fiber's two endpoints on brain surface. Diffusion-MRI-derived fiber streamlines were used to represent extra-cortical axonal connections between neurons or cortical regions, while geodesic paths between cortical points were employed to simulate intra-cortical connections. The results demonstrated that the geodesic distance between two cortical regions connected by a fiber streamline was greater than the fiber length most of the time, indicating that cortical regions tend to choose the shortest path for connection; whether it be an intra-cortical or extra-cortical route, especially when intra-cortical routes within cortical regions are longer than potential extrinsic fiber routes, there is an increased probability to establish fiber routes to connect the both regions. These findings were validated in a group of human brains and may provide insights into the underlying mechanisms of neuronal growth, connection, and wiring.

Survey on natural language processing in medical image analysis. / 自然语言处理在医学影像分析中的应用.

Recent advancement in natural language processing (NLP) and medical imaging empowers the wide applicability of deep learning models. These developments have increased not only data understanding, but also knowledge of state-of-the-art architectures and their real-world potentials. Medical imaging researchers have recognized the limitations of only targeting images, as well as the importance of integrating multimodal inputs into medical image analysis. The lack of comprehensive surveys of the current literature, however, impedes the progress of this domain. Existing research perspectives, as well as the architectures, tasks, datasets, and performance measures examined in the present literature, are reviewed in this work, and we also provide a brief description of possible future directions in the field, aiming to provide researchers and healthcare professionals with a detailed summary of existing academic research and to provide rational insights to facilitate future research.