目标和宗旨
彭玉佳研究员领导的抑郁与焦虑障碍计算神经实验室(Depression & Anxiety Computational Neuroscience Lab,DACN)主要聚焦于临床心理学的基础研究,并结合认知神经和人工智能的交叉研究,致力于探究抑郁和焦虑障碍的心理与神经机制以及治疗方法。
研究方法
- 行为学手段,包括心理物理学,眼动技术
- 脑成像,包括脑电,核磁共振成像,脑磁图和近红外成像
- 问卷量表,线上调查和实验
- 计算建模,机器学习
研究内容
- 抑郁与焦虑障碍的认知神经机制。当前科学界对于抑郁症和焦虑症的发展机制还存在很多未知,难以实现精神疾病的早期诊断和预测。社交恐惧症是焦虑症中一个重要的分支,体现为对于社交行为和场合的极度焦虑和回避,严重影响了病人的正常工作和生活,且为病人就医和寻求治疗带来了极大的阻碍,从而形成一个恶性循环。社交恐惧症处于多种疾病交叉的中心,具有复杂的认知、情绪和行为的个体差异。然而,对于社交恐惧的理解还存在很多未知,并忽视了同样重要的且包含大量社会信息的身体运动和社交运动。实验室结合心理物理学、眼动捕捉、脑成像以及生理信号记录,从纵向的时间维度和横向的多数据维度,研究社交恐惧病人对于运动中社会信息的加工特异性。
- 抑郁与焦虑障碍的纵向发展机制。从青春期至成年的过渡时期,该人生阶段伴随着前所未有的挑战、生活压力及人际关系,同时处于情绪和焦虑障碍发病的高峰时期。实验室主要关注大脑活动和情绪障碍症状维度随时间变化的关系。注重时间维度上的纵向追踪,探究从青春期至成年期的发病诱因和神经发展机制,以及环境和家庭因素对于情绪、认知和神经网络的调节。
- 跨精神疾病的认知及情绪加工研究。目前对于精神疾病的研究大多局限于离散的DSM-V诊断类别,而该分类诊断系统导致了精神疾病间的共病性和异质性,为研究的进一步推进带来困难。以孤独症举例,病人常常并发抑郁症和焦虑症的症状,这对临床诊断和开发有效的治疗方案带来了很多挑战。聚焦于孤独症和社交恐惧症群体,实验室目标深入推进跨诊断类别的认知和情绪加工层面的比较,推进个体差异的理论研究和个性化治疗的应用研究,系统探究孤独症和社交恐惧症在运动加工和社会认知方面的交叉和差异。
- 计算建模在临床心理学研究中的应用。通过计算模型和数据挖掘,来建模临床病人的认知特点,比较病人与常人的认知行为差异,理解精神疾病背后的机制,预期实现基于多维度数据的诊断和分类,以及发病的早期预测。
公众号
科研队伍
负责人
彭玉佳 ([email protected], www.yujiapeng.com)
博士生
王愉茜 ([email protected])
科研助理
路迪 ([email protected])
硕士生
李自立 ([email protected])
江欣 ([email protected])
彭旱雨 ([email protected])
代表性论文
Peng, Y. *, Knotts, J. D. *, Young, K. S., Bookheimer, S. Y., Nusslock, R., Zinbarg, R. E., ... & Craske, M. G. # (2022). Threat neurocircuitry predicts the development of anxiety and depression symptoms in a longitudinal study. Biological psychiatry: cognitive neuroscience and neuroimaging. https://doi.org/10.1016/j.bpsc.2021.12.013
Peng, Y.*, Knotts, J.D.*#, Taylor, C.T., Craske, M.G., Stein, M.B., Bookheimer, S., Young, K.S., Simmons, A.N., Yeh, H., Ruiz, J., Paulus, P.M. (2021). Failure to identify robust latent variables of positive or negative valence processing across units of analysis. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 6(5), 518-526.
Shu, T.#, Peng, Y., Zhu, S., & Lu, H. (2021). A unified psychological space for human perception of physical and social events. Cognitive Psychology. 128. 101398.
Peng, Y. #, Lu, H., & Johnson, S. P. (2021). Infant perception of causal motion produced by humans and inanimate objects. Infant Behavior and Development, 64, 101615.
Peng, Y. #, Lee, H., Shu, T., & Lu, H. (2020). Exploring biological motion perception in two-stream convolutional neural networks. Vision Research, 178, 28-40.
Chiang J.N. #, Peng, Y., Lu, H., Holyoak, K.J., & Monti, M.M. (2020). Distributed code for semantic relations predicts neural activity during analogical reasoning. Journal of Cognitive Neuroscience, 1-13.
Peng, Y. #, Ichien, N., & Lu, H. (2020). Causal actions enhance perception of continuous body movements. Cognition, 194, 104060,
Ogren, M.#, Kaplan, B., Peng, Y., Johnson, K. L., & Johnson, S. P. (2019). Motion or emotion: Infants discriminate emotional biological motion based on low-level visual information. Infant Behavior and Development, 57, 101324.
Tsang, T., Ogren, M., Peng, Y., Nguyen, B., Johnson, K.L. & Johnson S.P. # (2018). Infant perception of sex differences in biological motion displays. Journal of Experimental Child Psychology, 173, 338–350.
Keane, B. P.*, Peng, Y.*, Demmin, D., Silverstein, S. M., & Lu, L. (2018). Intact perception of coherent motion, dynamic rigid form, and biological motion in chronic schizophrenia. Psychiatry Research, 268, 53-59.
Shu, T.*#, Peng, Y.*, Fan, L., Zhu, S., & Lu, H. (2017). Perception of human interaction based on motion trajectories: from aerial videos to decontextualized animations. Topics in Cognitive Science, 10(1), 225-241.
Peng, Y. #, Thurman, S., & Lu, H. (2017). Causal action: A fundamental constraint on perception and inference about body movements. Psychological Science, 28(6), 798-807.
van Boxtel, J. #, Peng, Y., Su, J., & Lu, H. (2016). Individual differences in high-level biological motion tasks correlate with autistic traits. Vision Research, 141, 136-144.
Chen, J., Yu, Q., Zhu, Z., Peng, Y., & Fang, F.# (2016). Spatial summation revealed in the earliest visual evoked component C1 and the effect of attention on its linearity. Journal of Neurophysiology, 115(1), 500-509.
Chen, J., He, Y., Zhu, Z., Zhou, T., Peng, Y., Zhang, X., & Fang, F.# (2014). Attention-dependent early cortical suppression contributes to crowding. The Journal of Neuroscience, 34(32), 10465-10474.
Lu, J. *, & Peng, Y.*# (2014). Brain-computer interface for cyberpsychology: components, methods, and applications. International Journal of Cyber Behavior, Psychology and Learning (IJCBPL), 4(1), 1-14.