Cerebral Cortex:约会对象合不合适,一起扫个功能磁共振

2021-06-22 MedSci原创 MedSci原创

这是第一个阐明兼容性感觉的神经基础,并强调静息状态功能磁共振成像(fMRI)在预测人际交往结果方面的潜力的研究。

找到一个合适的浪漫伴侣是具有挑战性的,这就是为什么婚恋服务有一个强大的经济市场。为了帮助人们找到他们的灵魂伴侣,一些婚介服务利用算法,基于自我报告的数据,在用户开始交流之前预测潜在适合的伴侣。然而,这些算法的性能目前是非常有限的。在这样的算法中,通过对社会关系的研究,人格的相似性和互补性都被认为是兼容性的重要因素。人们更喜欢和性格相同且具有互补依恋不安全感(焦虑型或回避型)的人约会。他们也喜欢与具有互补的人际风格(支配或顺从)的伙伴进行二人互动。因此,这种心理构念有助于预测个体的相容性。即使在信息技术非常发达的情况下,基于自我报告的特征和偏好来预测异性恋者的最初兼容性也并不成功。

为了克服自我报告测量的局限性并预测兼容性,Shogo Kajimura等使用了来自静息态功能磁共振成像(fMRI)数据的功能连通性剖面,该数据提供丰富的个人特定信息,足以预测社会认知任务中的心理结构和激活模式。研究结果发表在Cerebral Cortex杂志。

研究流程

在收集了静息状态fmri的数据几天后,参与者进行了一项闪电约会实验,他们与每一位异性参与者进行3分钟的闪电约会。我们的机器学习算法利用实验前获得的功能连接配置文件的相似性(不相似性),成功地预测了实验中的成对是否兼容。个体之间功能连接的相似性和差异性以及这些多元关系有助于预测,因此表明了互补性(观察到的差异性)的重要性,以及个体和潜在伴侣在最初吸引阶段的相似性。

身份分类结果。(A)在所有频带上,自我-自我对(深色分布)的整体功能连通性相似性显著高于自我-他人对(浅色分布)。* FDR校正后P<0.05。(B)对的真标签与每个频带随机标签的分类精度差异的分布。竖线表示概率水平。F1: 0.109-0.199 Hz, F2: 0.055-0.109 Hz, F3: 0.027-0.055 Hz, F4: 0.014-0.027 Hz, FDR校正后的P<0.05。

前100个特征值,即每个频带用于身份分类的功能连通性差异的绝对值。红线和蓝线分别表示基于相似性和差异性的贡献。圆上的点表示roi,其大小由涉及roi的显著特征值的总数定义。F1: 0.109-0.199 Hz F2: 0.055-0.109 Hz F3: 0.027-0.055 Hz

特性的属性,显著地促进了兼容性分类。(A,B)100个特征值,即功能连接之间的绝对值,这导致了每个频段的成功兼容性分类。红色和蓝色的线条分别代表相似度和不相似的贡献。圆圈上的点代表ROIs,它的大小是由ROIs参与的重要特征值的总数定义的。(C,E)roi水平对分类的贡献。热和冷色ROIs分别显示了ROI的相似度和不相似的贡献。(D,F)在大脑网络方面对分类的连接的比率。红色和蓝色的矩阵分别显示了相似度和不相似的贡献。F1:0.109-0.199 Hz,F2:0.055-0.109 Hz。

该研究结果表明,显著性网络、边缘区和小脑对相容性感觉尤为重要。

该研究试图通过静息状态fMRI数据来预测闪电约会后男女关系的兼容性。HCP测试-重测数据集(身份分类)的指标评估结果表明,通过对比高频波段(即F1和F2)的功能连通性可以对关系的兼容性进行分类。结果表明,兼容对的整体功能连通性相似性并不高于不兼容对的整体功能连通性相似性,兼容分类得到了功能连通性与身份分类显著差异的支持。这可能反映了一个事实,即相容性取决于个体和潜在伴侣之间的相似性和互补性。这是第一个阐明兼容性感觉的神经基础,并强调静息状态功能磁共振成像(fMRI)在预测人际交往结果方面的潜力的研究。

综上所述,目前的研究表明,异性恋个体的初始兼容性不能通过自我报告的心理结构来预测,但可以通过静息态功能磁共振成像数据的功能连通性来预测。个体之间的功能联系和这些多元关系的相似性和差异性的贡献表明了互补性的重要性以及个体和潜在伴侣之间的相似性。该研究强调了神经成像在检查社会环境中的复杂现象中的效用。

原文出处

Shogo Kajimura, Ayahito Ito, Keise Izuma, Brain Knows Who Is on the Same Wavelength: Resting-State Connectivity Can Predict Compatibility of a Female–Male Relationship, Cerebral Cortex, 2021;, bhab143, https://doi.org/10.1093/cercor/bhab143

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    2021-06-23 莉莉安 Lillian C.

    人是会变的…

    0

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    2021-06-23 Au revoir

    哈哈哈很有趣#学习#

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