Decoding individual identity from brain activity elicited in imagining common experiences

被引:0
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作者
Andrew James Anderson
Kelsey McDermott
Brian Rooks
Kathi L. Heffner
David Dodell-Feder
Feng V. Lin
机构
[1] University of Rochester Medical Center,Department of Neuroscience
[2] University of Rochester Medical Center,Del Monte Institute for Neuroscience
[3] University of Rochester Medical Center,Elaine C. Hubbard Center for Nursing Research on Aging, School of Nursing
[4] University of Arizona,Neuroscience
[5] University of Rochester Medical Center,Department of Psychiatry
[6] University of Rochester Medical Center,Division of Geriatrics and Aging, Department of Medicine
[7] University of Rochester,Department of Psychology
[8] University of Rochester Medical Center,Department of Neurology
[9] University of Rochester,Department of Brain and Cognitive Sciences
来源
Nature Communications | / 11卷
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摘要
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.
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