WeChat Mini Program
Old Version Features

    • 磁共振导航定位高频重复经颅磁刺激治疗抑郁发作的初步研究

    胡希文,李轶,马洁华,董介正,菊轩,刘健,谭忠林

    Chinese Journal of Nervous and Mental Diseases（2018）

    Cited 4|Views11
    Abstract
    目的 探讨重复经颅磁刺激(repetitive transcranial magnetic stimulation,rTMS)治疗中应用磁共振导航定位与"标准5 cm法"定位的位置差异.方法 对12例抑郁发作患者分别采用磁共振导航定位和5 cm定位法定位背外侧前额叶(dorsolateral prefrontal cortex,DLPFC),比较两者位置的差异,并使用汉密尔顿抑郁量表(Hamilton depression rating scale,HAMD)观察抗抑郁治疗效果.结果 磁共振引导下定位与传统定位的DLPFC相距平均(17.42±9.62)mm,该距离具有统计学意义(t=6.00,P<0.01).共有9例患者接受超过5次rTMS治疗(最少6次,最多25次),rTMS治疗前后的HAMD分别为(24.56±4.50)分与(7.78±4.71)分,差异具有统计学意义(t=-11.20,P<0.01),其中有4例患者达到临床痊愈.结论 磁共振导航定位与标准5 cm方法定位的位置存在明显差异,在rTMS抗抑郁治疗研究中采用磁共振引导定位,能够更精准定位DLPFC区域.
    More
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined