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Clinical efficacy and safety of vortioxetine as an adjuvant drug for patients with bipolar depression

Journal of Zhejiang University-science B(Biomedicine & Biotechnology) 2025年第1期26卷 26-38页

作者： Chunxiao DAI Yaoyang FU Xuanwei LI Meihua LIN Yinbo LI Xiao LI Keke HUANG Chengcheng ZHOU Jian XIE Qingwei ZHAO Shaohua HU Department of Psychiatry the First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China Department of Clinical Psychology Hangzhou First People’s HospitalHangzhou 310006China Department of Clinical Pharmacy the First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China Fuyang Third People’s Hospital Hangzhou 311402China Nanhu Brain-Computer Interface Institute Hangzhou 311100China The Zhejiang Key Laboratory of Precision Psychiatry Hangzhou 310003China School of Brain Science and Brain Medicine and MOE Frontier Science Center for Brain Science and Brain-Machine IntegrationZhejiang University School of MedicineHangzhou 310012China Brain Research Institute of Zhejiang University Hangzhou 310003China The State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhou 310003China Zhejiang Engineering Center for Mathematical Mental Health Hangzhou 310003China

Objective:Whether vortioxetine has a utility as an adjuvant drug in the treatment of bipolar depression remains *** study aimed to validate the efficacy and safety of vortioxetine in bipolar ***:Patients with bipolarⅡdepression were enrolled in this prospective,two-center,randomized,12-week pilot *** main indicator for assessing treatment effectiveness was a Montgomery-Asberg Depression Rating Scale(MADRS)of≥50%.All eligible patients initially received four weeks of lurasidone *** who responded well continued to receive this kind of ***,no-response patients were randomly assigned to either valproate or vortioxetine treatment for eight *** comprehensively comparing the results of MADRS over a period of 4-12 weeks,a systematic analysis was conducted to determine whether vortioxetine could be used as an adjuvant drug for treating bipolar ***:Thirty-seven patients responded to lurasidone monotherapy,and 60 patients were randomly assigned to the valproate or vortioxetine group for eight *** two weeks of combined valproate or vortioxetine treatment,the MADRS score in the vortioxetine group was significantly lower than that in the valproate *** was no difference in the MADRS scores between the two groups at 8 and 12 *** incidence of side effects did not significantly differ between the valproate and vortioxetine ***,three patients in the vortioxetine group appeared to switch to mania or ***:This study suggested that lurasidone combination with vortioxetine might have potential benefits to bipolar II depression in the early stage,while disease progression should be monitored closely for the risk of switching to mania.

关键词： BipolarⅡdepression Lurasidone Vortioxetine Combination

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Domain-Agnostic Co-Evolution of Generalizable Parallel Algorithm Portfolios

arXiv

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arXiv 2025年

作者： Wang, Zhiyuan Liu, Shengcai Yang, Peng Tang, Ke The Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Generalization is the core objective when training optimizers from data. However, limited training instances often constrain the generalization capability of the trained optimizers. Co-evolutionary approaches address this challenge by simultaneously evolving a parallel algorithm portfolio (PAP) and an instance population to eventually obtain PAPs with good generalization. Yet, when applied to a specific problem class, these approaches have a major limitation. They require practitioners to provide instance generators specially tailored to the problem class, which is often non-trivial to design. This work proposes a general-purpose, off-the-shelf PAP construction approach, named domain-agnostic co-evolution of parameterized search (DACE), for binary optimization problems where decision variables take values of 0 or 1. The key innovation of DACE lies in its neural network-based domain-agnostic instance representation and generation mechanism that delimitates the need for domain-specific instance generators. The strong generality of DACE is validated across three real-world binary optimization problems: the complementary influence maximization problem (CIMP), the compiler arguments optimization problem (CAOP), and the contamination control problem (CCP). Given only a small set of training instances from these classes, DACE, without requiring any domain knowledge, constructs PAPs with better generalization performance than existing approaches on all three classes, despite their use of domain-specific instance generators. Copyright © 2025, The Authors. All rights reserved.

关键词： Consensus algorithm

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Large Language Model-Based Benchmarking Experiment Settings for Evolutionary Multi-Objective Optimization

arXiv

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arXiv 2025年

作者： Pang, Lie Meng Ishibuchi, Hisao Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

When we manually design an evolutionary optimization algorithm, we implicitly or explicitly assume a set of target optimization problems. In the case of automated algorithm design, target optimization problems are usually explicitly shown. Recently, the use of large language models (LLMs) for the design of evolutionary multi-objective optimization (EMO) algorithms have been examined in some studies. In those studies, target multi-objective problems are not always explicitly shown. It is well known in the EMO community that the performance evaluation results of EMO algorithms depend on not only test problems but also many other factors such as performance indicators, reference point, termination condition, and population size. Thus, it is likely that the designed EMO algorithms by LLMs depends on those factors. In this paper, we try to examine the implicit assumption about the performance comparison of EMO algorithms in LLMs. For this purpose, we ask LLMs to design a benchmarking experiment of EMO algorithms. Our experiments show that LLMs often suggest classical benchmark settings: Performance examination of NSGA-II, MOEA/D and NSGA-III on ZDT, DTLZ and WFG by HV and IGD under the standard parameter specifications. Copyright © 2025, The Authors. All rights reserved.

关键词： Population statistics

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Plasma proteomics identifies proteins and pathways associated with incident depression in 46,165 adults

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science Bulletin 2025年第4期70卷 573-586页

作者： Jujiao Kang Liu Yang Tianye Jia Wei Zhang Lin-Bo Wang Yu-Jie Zhao Jia You Yue-Ting Deng Yi-Jun Ge Wei-Shi Liu Yi Zhang Yi-Lin Chen Xiao-Yu He Barbara J.Sahakian Yucheng TYang Xing-Ming Zhao Jin-Tai Yu Jianfeng Feng Wei Cheng Institute of Science and Technology for Brain-Inspired Intelligence(ISTBI) Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan UniversityMinistry of EducationShanghai 200433China Department of Neurology and National Center for Neurological Disorders Huashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceShanghai Medical CollegeFudan UniversityShanghai 200433China Department of Psychiatry University of CambridgeCambridge CB20SZUK Department of Computer Science University of WarwickCoventry CV47ALUK School of Data Science Fudan UniversityShanghai 200438China

Proteomic alterations preceding the onset of depression offer valuable insights into its development and potential *** data from 46,165 UK Biobank participants and 2920 plasma proteins profiled at baseline,we conducted a longitudinal analysis with a median follow-up of 14.5 years to explore the relationship between plasma proteins and incident *** regression was then used to assess associations between depression-related proteins and brain structures,genetic factors,and stress-related *** analysis identified 157 proteins associated with incident depression(P<1.71×10^(-5)),including novel associations with proteins such as GAST,PLAUR,LRRN1,BCAN,and ***,higher expression levels of GDF15(P=6.18×10-26)and PLAUR(P=2.88×10^(-14))were linked to an increased risk of depression,whereas higher levels of LRRN1(P=4.28×10^(-11))and ITGA11(P=3.68×10^(-9))were associated with a decreased *** of the 157 proteins is correlated with brain regions implicated in depression,including the hippocampus and middle temporal ***,these protein alterations were strongly correlated with stress-related events,including self-harm events,adult,and childhood *** pathway enrichment analysis highlighted the critical roles of the immune *** and TNF emerged as key proteins in the protein-protein interaction ***3A2,newly linked to incident depression(P=4.35×10^(-10)),was confirmed as a causal factor through Mendelian randomization *** summary,our research identified the proteomic signatures associated with the onset of depression,highlighting its potential for early intervention and tailored therapeutic avenues.

关键词： Plasma proteins Depression brain structure

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Pose as a Modality: A Psychology-Inspired Network for Personality Recognition with a New Multimodal Dataset

arXiv

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arXiv 2025年

作者： Tang, Bin Pan, Ke-Qi Zheng, Miao Zhou, Ning Sui, Jia-Lu Zhu, Dandan Deng, Cheng-Long Kuai, Shu-Guang School of Computer Science and Technology Shanghai Institute of Artificial Intelligence for Education Lab of Artificial Intelligence for Education East China Normal University Shanghai China School of Psychology and Cognitive Science Institute of Brain and Education Innovation Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention Shanghai Key Laboratory of Brain Functional Genomics Key Laboratory of Brain Functional Genomics East China Normal University Shanghai China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China NYU-ECNU Institute of Brain and Cognitive Science New York University Shanghai Shanghai China

In recent years, predicting Big Five personality traits from multimodal data has received significant attention in artificial intelligence (AI). However, existing computational models often fail to achieve satisfactory performance. Psychological research has shown a strong correlation between pose and personality traits, yet previous research has largely ignored pose data in computational models. To address this gap, we develop a novel multimodal dataset that incorporates full-body pose data. The dataset includes video recordings of 287 participants completing a virtual interview with 36 questions, along with self-reported Big Five personality scores as labels. To effectively utilize this multimodal data, we introduce the Psychology-Inspired Network (PINet), which consists of three key modules: Multimodal Feature Awareness (MFA), Multimodal Feature Interaction (MFI), and Psychology-Informed Modality Correlation Loss (PIMC Loss). The MFA module leverages the Vision Mamba Block to capture comprehensive visual features related to personality, while the MFI module efficiently fuses the multimodal features. The PIMC Loss, grounded in psychological theory, guides the model to emphasize different modalities for different personality dimensions. Experimental results show that the PINet outperforms several state-of-the-art baseline models. Furthermore, the three modules of PINet contribute almost equally to the model’s overall performance. Incorporating pose data significantly enhances the model’s performance, with the pose modality ranking mid-level in importance among the five modalities. These findings address the existing gap in personality-related datasets that lack full-body pose data and provide a new approach for improving the accuracy of personality prediction models, highlighting the importance of integrating psychological insights into AI frameworks. Copyright © 2025, The Authors. All rights reserved.

关键词： Psychoacoustic

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Temporal-Aware Spiking Transformer Hashing Based on 3D-DWT

arXiv

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arXiv 2025年

作者： Mei, Zihao Li, Jianhao Zhang, Bolin Wang, Chong Guo, Lijun Li, Guoqi Qian, Jiangbo Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Institute of Automation Chinese Academy of Sciences Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Beijing100045 China

With the rapid growth of dynamic vision sensor (DVS) data, constructing a low-energy, efficient data retrieval system has become an urgent task. Hash learning is one of the most important retrieval technologies which can keep the distance between hash codes consistent with the distance between DVS data. As spiking neural networks (SNNs) can encode information through spikes, they demonstrate great potential in promoting energy efficiency. Based on the binary characteristics of SNNs, we first propose a novel supervised hashing method named Spikinghash with a hierarchical lightweight structure. Spiking WaveMixer (SWM) is deployed in shallow layers, utilizing a multilevel 3D discrete wavelet transform (3D-DWT) to decouple spatiotemporal features into various low-frequency and high-frequency components, and then employing efficient spectral feature fusion. SWM can effectively capture the temporal dependencies and local spatial features. Spiking Self-Attention (SSA) is deployed in deeper layers to further extract global spatiotemporal information. We also design a hash layer utilizing binary characteristic of SNNs, which integrates information over multiple time steps to generate final hash codes. Furthermore, we propose a new dynamic soft similarity loss for SNNs, which utilizes membrane potentials to construct a learnable similarity matrix as soft labels to fully capture the similarity differences between classes and compensate information loss in SNNs, thereby improving retrieval performance. Experiments on multiple datasets demonstrate that Spikinghash can achieve state-of-the-art results with low energy consumption and fewer parameters. Copyright © 2025, The Authors. All rights reserved.

关键词： Discrete wavelet transforms

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An improved BCI-VR hybrid interactive system based on a mixed template CCA method

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Biomedical Signal Processing and Control 2025年 108卷

作者： Niu, Lan An, Li Bin, Jianxiong Zhao, Yinbin Kang, Xiaoyang Zhang, Lihua Ge, Hongjuan Ji Hua Laboratory Guangdong Province Foshan528000 China Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science Institute of AI and Robotics Academy for Engineering and Technology FUDAN University Shanghai200433 China Nanjing University of Aeronautics and Astronautics Jiangsu Province Nanjing210000 China

The brain-computer interface (BCI) controlled systems are proven to have a wide range of applications both in reality and virtual reality (VR). In this study, we implemented a BCI-VR hybrid system for unmanned aerial vehicle (UAV) control based on the steady-state visual evoked potential (SSVEP) method. It can blend virtual content with physical space smoothly, and users see the UAV and visual stimulation in time. The SSVEP method requires users to complete commands within a timeline set in synchronization mode. The improved CCA model is an innovative proposal. It can directly identify the user's non-control state or a control state under the current SSVEP paradigm without requiring the cooperation of other biological signals or alterations to the paradigm. Additionally, it does not necessitate the training of a large amount of offline data. The control state and NC state can be well identified by the proposed method. The average accuracy of the system reaches 93.15 % ± 4.57 %, and the information transfer rate is 31.612 ± 4.439 bits/min in the first online task. All ten participants completed the free-trajectory mission according to their wishes. The average accuracy of the second task of the online experiment is 91.31 % ± 5.37 %. The proposed system exhibited an excellent performance based on a Mixed Template CCA Method. The experimental results demonstrated the flexibility and practicability of the interactive BCI-VR hybrid system. © 2025 Elsevier Ltd

关键词： Drones

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EASING SEASICKNESS THROUGH ATTENTION REDIRECTION WITH A MINDFULNESS-BASED brain-computer interface

arXiv

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arXiv 2025年

作者： Bao, Xiaoyu Xu, Kailin Zhu, Jiawei Huang, Haiyun Li, Kangning Huang, Qiyun Li, Yuanqing School of Automation Science and Engineering South China University of Technology Guangzhou China Research Center for Brain-Computer Interface Pazhou Laboratory Guangzhou China School of Artificial Intelligence South China Normal University Foshan China School of Psychology South China Normal University Guangzhou China South China Brain-Computer Interface Technology Co. Ltd. Guangzhou China

Seasickness is a prevalent issue that adversely impacts both passenger experiences and the operational efficiency of maritime crews. While techniques that redirect attention have proven effective in alleviating motion sickness symptoms in terrestrial environments, applying similar strategies to manage seasickness poses unique challenges due to the prolonged and intense motion environment associated with maritime travel. In this study, we propose a mindfulness brain-computer interface (BCI), specifically designed to redirect attention with the aim of mitigating seasickness symptoms in real-world settings. Our system utilizes a single-channel headband to capture prefrontal electroencephalography (EEG) signals, which are then wirelessly transmitted to computing devices for the assessment of mindfulness states. The results are transferred into real-time feedback as mindfulness scores and audiovisual stimuli, facilitating a shift in attentional focus from physiological discomfort to mindfulness practices. A total of 43 individuals participated in a real-world maritime experiment consisted of three sessions: a real-feedback mindfulness session, a resting session, and a pseudofeedback mindfulness session. Notably, 81.39% of participants reported that the mindfulness BCI intervention was effective, and there was a significant reduction in the severity of seasickness, as measured by the Misery Scale (MISC). Furthermore, EEG analysis revealed a decrease in the theta/beta ratio, corresponding with the alleviation of seasickness symptoms. A decrease in overall EEG band power during the real-feedback mindfulness session suggests that the mindfulness BCI fosters a more tranquil and downregulated state of brain activity. Together, this study presents a novel nonpharmacological, portable, and effective approach for seasickness intervention, with the potential to enhance the cruising experience for both passengers and crews. © 2025, CC BY-NC-ND.

关键词： Electroencephalography

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A Habenula Neural Biomarker Simultaneously Tracks Weekly and Daily Symptom Variations during Deep brain Stimulation Therapy for Depression

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Shi Qi, Yu Hu, Shaohua Wei, Ning Zhang, Jianmin Zhu, Junming Wu, Hemmings Hu, Hailan Yang, Yuxiao Wang, Yueming Zhejiang University College of Computer Science and Technology Hangzhou 310058 China Zhejiang University School of Medicine First Affiliated Hospital Hangzhou 310003 China Zhejiang University School of Medicine Second Affiliated Hospital Hangzhou 310058 China Zhejiang University School of Brain Science and Brain Medicine Hangzhou 310058 China Nanhu Brain-computer Interface Institute MOE Frontier Science Center for Brain Science and Brain-machine Integration Hangzhou China Zhejiang University State Key Laboratory of Brain-machine Intelligence Department of Neurosurgery Second Affiliated Hospital School of Medicine College of Computer Science and Technology Hangzhou 310058 China

Objective: Deep brain stimulation (DBS) targeting the lateral habenula (LHb) is a promising therapy for treatment-resistant depression (TRD) but its clinical effect has been variable, which can be improved by adaptive DBS (aDBS) guided by a neural biomarker of depression symptoms. Existing neural biomarkers, however, cannot simultaneously track slow and fast symptom dynamics, do not sufficiently respond to stimulation parameters, and lack neurobiological interpretability, which hinder their use in developing aDBS. Methods: We conducted a study on one TRD patient who achieved remission following a 41-week LHb DBS treatment, during which we assessed slow symptom variations using weekly clinical ratings and fast variations using daily self-reports. We recorded daily LHb local field potentials (LFP) concurrently with the reports during the entire treatment process. We then used machine learning methods to identify a personalized depression neural biomarker from spectral and temporal LFP features. Results: The neural biomarker was identified from classification of high and low depression symptom states with a cross-validated accuracy of 0.97. It further simultaneously tracked both weekly (slow) and daily (fast) depression symptom variation dynamics, achieving test data explained variance of 0.74 and 0.63 respectively and responded to DBS frequency alterations. Finally, it can be neurobiologically interpreted as indicating LHb excitatory and inhibitory balance changes during DBS treatment. Conclusion: By collecting and analyzing a unique personalized dataset of weekly and daily LFP recordings and symptom evaluations, we identified a high-performance neural biomarker for depression during LHb DBS. Significance: Our results hold promise to facilitate future aDBS for treating TRD. © 2013 IEEE.

关键词： biomarker Deep brain stimulation depression habenula local field potential machine learning

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Development of yellow-light TiO2 integrated photonics

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Optics Letters 2025年第11期50卷 3489-3492页

作者： Zequn Chen Yiheng Tang Maoliang Wei Xiaojing Li Boshu Sun Yingchun Wu Sishuo Zou Ji Huang Ke Si Wei Gong Hongtao Lin Lan Li Institute of Advanced Technology Westlake Institute for Advanced Study Hangzhou 310024 China Zhejiang Key Laboratory of 3D Micro/Nano Fabrication and Characterization School of Engineering Westlake University Hangzhou 310030 China The State Key Lab of Brain-Machine Intelligence College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China MOE Frontier Science Center for Brain Science & Brain-Machine Integration Zhejiang University Hangzhou 310027 China Nanhu Brain-Computer Interface Institute Hangzhou 311100 China College of Optical Science and Engineering Zhejiang University Hangzhou 310027 China Westlake Institute for Optoelectronics Fuyang Hangzhou Zhejiang 311421 China

Visible-light photonic integrated circuits (PICs) are rapidly emerging as a crucial technology to overcome the scaling challenges in quantum information processing and biosensing. Titanium dioxide (TiO 2 ), with its high transparency in the visible range, is regarded as a promising material for facilitating the development of high-performance visible-light integrated systems. In this paper, we introduce a series of passive TiO 2 integrated devices, which include waveguides, multimode interferometers (MMIs), and thermo-optic switches (TOSs), monolithically integrated for the first time to our knowledge at a working wavelength of 589 nm. A propagation loss of 5.46 ± 0.17 dB/cm demonstrates a significant improvement over previous reports on yellow-light TiO 2 waveguides. This study marks the first fabrication of a TOS operating at 589 nm, featuring a TiO 2 core and polymer cladding. Notably, it achieves a power consumption (P π ) of 8.24 mW, which represents a threefold reduction compared to silicon nitride-based TOSs operating in the visible spectrum. These results offer new insight into the design and fabrication of visible waveguide-integrated devices, paving the way for future emerging applications, such as quantum information, sensors, and biomedical science.

关键词： Biosensors Effective refractive index Information processing Photonics Quantum information Titanium dioxide

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