Large Connectome Model：多任务学习场景下由脑-环境交互赋能的脑连接组fMRI基础模型
Large Connectome Model: An fMRI Foundation Model of Brain Connectomes Empowered by Brain-Environment Interaction in Multitask Learning Landscape
AI总结：
针对fMRI基础模型自监督预训练与下游脑结局任务不对齐的问题，提出结合脑-环境交互的多任务预训练+半监督微调的Large Connectome Model，在多类神经影像临床任务上取得优异表现。
摘要：
可靠的功能神经影像基础模型对推动临床应用至关重要，当前AI模型的性能受样本量有限的严重制约。为此，学界已付出大量努力，利用可扩展的自监督学习在海量未标记fMRI数据上预训练大模型。由于自监督未必与脑-结局关系对齐，多数基础模型在下游任务（如疾病结局预测）上表现欠佳。我们借助丰富的环境变量、人口统计学数据以及前所未有的大量功能神经影像，将脑建模构建为多任务学习，并提出一种可扩展的模型架构：（i）通过对多种脑-环境交互（BEI）进行分词实现多任务预训练；（ii）通过分配预训练BEI的伪标签实现半监督微调。我们在多种应用上评估了该基础模型，包括性别预测、人类行为识别，以及自闭症、帕金森病、阿尔茨海默病和精神分裂症的疾病早期诊断，实验结果表现优异，表明其在推动当前神经影像应用融入临床常规方面具有巨大潜力。
英文摘要：
A reliable foundation model of functional neuroimages is critical to promote clinical applications where the performance of current AI models is significantly impeded by a limited sample size. To that end, tremendous efforts have been made to pretraining large models on extensive unlabeled fMRI data using scalable self-supervised learning. Since self-supervision is not necessarily aligned with the brain-to-outcome relationship, most foundation models are suboptimal to the downstream task, such as predicting disease outcomes. By capitalizing on rich environmental variables and demographic data along with an unprecedented amount of functional neuroimages, we form the brain modeling as a multitask learning and present a scalable model architecture for (i) multitask pretraining by tokenizing multiple brain-environment interactions (BEI) and (ii) semi-supervised finetuning by assigning pseudo-labels of pretrained BEI. We have evaluated our foundation model on a variety of applications, including sex prediction, human behavior recognition, and disease early diagnosis of Autism, Parkinson's disease, Alzheimer's disease, and {Schizophrenia}, where promising results indicate the great potential to facilitate current neuroimaging applications in clinical routines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
