Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications, and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including multilayer perceptrons of demographic, psychological, serological, chronic morbidity, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted individual psychology better than the coincidence of common chronic morbidity (p<0.05). Serology predicted common morbidity (p<0.05) and was best predicted by it (p<0.001), followed by structural neuroimaging (p<0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the brain.

翻译：我们对人群水平人类脑组织结构的认知尚未转化为在个体水平预测功能差异的能力，这限制了临床应用，并对推断机制的泛化性提出质疑。目前尚不清楚这种困难源于大脑缺乏个体化生物学模式，还是受限于现有模型和计算资源的解析能力。本研究以前所未有的规模系统探究数据与计算对此类模式的可解析性。基于英国生物银行（UK Biobank）23810名独立参与者，我们通过结构性与功能性神经影像数据的所有可用组合，系统评估了25项个体生物学特征的可预测性。历时4526 GPU*小时计算，我们训练、优化并评估了700个脱离样本的个体预测模型，包括针对人口统计学、心理学、血清学、慢性共病及功能连接性特征的多层感知器，以及基于宏观和微观脑结构影像的单模态与多模态三维卷积神经网络模型。研究显示：性别（平衡准确率99.7%）、年龄（平均绝对误差2.048岁，R²=0.859）和体重（平均绝对误差2.609公斤，R²=0.625）具有极高可预测性（三项指标均达当前最优性能），而其他特征的可预测性却出奇低下。结构或功能影像对个体心理特征的预测能力均未优于普通慢性共病的偶然一致性（p<0.05）。血清学可预测常见共病（p<0.05），且其本身亦被共病最佳预测（p<0.001），其次为结构性神经影像（p<0.05）。本研究结果表明，从大脑解码个体层面特征或需更高信息量的影像或更强大的模型。