Byrge Lisa, Kennedy Daniel P
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
Netw Neurosci. 2019 Feb 1;3(2):363-383. doi: 10.1162/netn_a_00068. eCollection 2019.
Connectome fingerprinting-a method that uses many thousands of functional connections in aggregate to identify individuals-holds promise for individualized neuroimaging. A better characterization of the features underlying successful fingerprinting performance-how many and which functional connections are necessary and/or sufficient for high accuracy-will further inform our understanding of uniqueness in brain functioning. Thus, here we examine the limits of high-accuracy individual identification from functional connectomes. Using ∼3,300 scans from the Human Connectome Project in a split-half design and an independent replication sample, we find that a remarkably small "thin slice" of the connectome-as few as 40 out of 64,620 functional connections-was sufficient to uniquely identify individuals. Yet, we find that no specific connections or even specific networks were necessary for identification, as even small random samples of the connectome were sufficient. These results have important conceptual and practical implications for the manifestation and detection of uniqueness in the brain.
连接组指纹识别——一种综合利用成千上万条功能连接来识别个体的方法——有望实现个性化神经成像。更好地刻画成功的指纹识别性能背后的特征——为实现高精度需要多少以及哪些功能连接是必要的和/或充分的——将进一步增进我们对大脑功能独特性的理解。因此，我们在此研究从功能连接组进行高精度个体识别的局限性。在一项对半分割设计中，我们使用了来自人类连接组计划的约3300次扫描以及一个独立的复制样本，我们发现连接组中极小的“薄片”——在64620条功能连接中仅40条——就足以唯一地识别个体。然而，我们发现识别并不需要特定的连接甚至特定的网络，因为即使是连接组的小随机样本也足够了。这些结果对大脑独特性的表现和检测具有重要的概念和实际意义。
