Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, QC H3W 1W6, Canada; Department of Psychology, Université de Montréal, Montreal QC H3C 3J7, Canada.
Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, QC H3W 1W6, Canada.
Neuroimage. 2021 May 1;231:117847. doi: 10.1016/j.neuroimage.2021.117847. Epub 2021 Feb 12.
A key goal in neuroscience is to understand brain mechanisms of cognitive functions. An emerging approach is "brain decoding", which consists of inferring a set of experimental conditions performed by a participant, using pattern classification of brain activity. Few works so far have attempted to train a brain decoding model that would generalize across many different cognitive tasks drawn from multiple cognitive domains. To tackle this problem, we proposed a multidomain brain decoder that automatically learns the spatiotemporal dynamics of brain response within a short time window using a deep learning approach. We evaluated the decoding model on a large population of 1200 participants, under 21 different experimental conditions spanning six different cognitive domains, acquired from the Human Connectome Project task-fMRI database. Using a 10s window of fMRI response, the 21 cognitive states were identified with a test accuracy of 90% (chance level 4.8%). Performance remained good when using a 6s window (82%). It was even feasible to decode cognitive states from a single fMRI volume (720ms), with the performance following the shape of the hemodynamic response. Moreover, a saliency map analysis demonstrated that the high decoding performance was driven by the response of biologically meaningful brain regions. Together, we provide an automated tool to annotate human brain activity with fine temporal resolution and fine cognitive granularity. Our model shows potential applications as a reference model for domain adaptation, possibly making contributions in a variety of domains, including neurological and psychiatric disorders.
神经科学的一个主要目标是理解认知功能的大脑机制。一种新兴的方法是“大脑解码”，它包括使用大脑活动模式分类来推断参与者执行的一组实验条件。到目前为止，很少有工作试图训练一个能够跨多个认知领域的多个不同认知任务进行泛化的大脑解码模型。为了解决这个问题，我们提出了一种多领域大脑解码器，它使用深度学习方法在短时间窗口内自动学习大脑反应的时空动态。我们在来自人类连接组计划任务 fMRI 数据库的 1200 名参与者的大样本中评估了解码模型，该模型涵盖了六个不同认知领域的 21 种不同实验条件。使用 fMRI 响应的 10 秒窗口，可以以 90%的测试准确率（4.8%的机会水平）识别 21 种认知状态。使用 6 秒窗口时性能仍然良好（82%）。甚至可以从单个 fMRI 体积（720ms）解码认知状态，性能随血流动力学响应的形状而变化。此外，显着性图分析表明，高解码性能是由具有生物学意义的大脑区域的反应驱动的。总之，我们提供了一种自动工具，可实现具有精细时间分辨率和精细认知粒度的人类大脑活动注释。我们的模型具有作为域自适应参考模型的潜在应用，可能在包括神经和精神障碍在内的各种领域做出贡献。
