Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, 14642, USA.
Del Monte Institute for Neuroscience, University of Rochester Medical Center, Rochester, NY, 14642, USA.
Nat Commun. 2020 Nov 20;11(1):5916. doi: 10.1038/s41467-020-19630-y.
Everyone experiences common events differently. This leads to personal memories that presumably provide neural signatures of individual identity when events are reimagined. We present initial evidence that these signatures can be read from brain activity. To do this, we progress beyond previous work that has deployed generic group-level computational semantic models to distinguish between neural representations of different events, but not revealed interpersonal differences in event representations. We scanned 26 participants' brain activity using functional Magnetic Resonance Imaging as they vividly imagined themselves personally experiencing 20 common scenarios (e.g., dancing, shopping, wedding). Rather than adopting a one-size-fits-all approach to generically model scenarios, we constructed personal models from participants' verbal descriptions and self-ratings of sensory/motor/cognitive/spatiotemporal and emotional characteristics of the imagined experiences. We demonstrate that participants' neural representations are better predicted by their own models than other peoples'. This showcases how neuroimaging and personalized models can quantify individual-differences in imagined experiences.
每个人对常见事件的体验都不同。这导致了个人记忆，当事件被重新想象时，这些记忆可能为个体身份提供神经特征。我们提出了初步的证据，表明这些特征可以从大脑活动中读取。为此，我们超越了以前的工作，以前的工作已经部署了通用的群体水平计算语义模型来区分不同事件的神经表示，但没有揭示出事件表示中的人际差异。我们使用功能磁共振成像对 26 名参与者的大脑活动进行了扫描，当他们生动地想象自己亲身经历 20 种常见场景（例如跳舞、购物、婚礼）时。我们没有采用一刀切的方法来对场景进行泛化建模，而是根据参与者对想象体验的感觉/运动/认知/时空和情绪特征的口头描述和自我评分，构建了个人模型。我们证明，参与者的神经表示可以通过他们自己的模型更好地预测，而不是其他人的模型。这展示了神经影像学和个性化模型如何量化想象体验中的个体差异。
