Learning to decode cognitive states from brain images
Learning to decode cognitive states from brain images
复制标题
DOI：
10.1023/b:mach.0000035475.85309.1b
复制
发表时间：
2004-10-01
期刊：
影响因子：
7.5
通讯作者：
Newman, S
中科院分区：
文献类型：
--
作者：
Mitchell, TM;Hutchinson, R;Newman, S
在过去的十年中，功能磁共振成像(FMRI)已经成为一种强大的新工具，可以收集关于人脑活动的大量数据。一个典型的功能磁共振实验可以每隔半秒产生一张与人类大脑活动相关的三维图像，空间分辨率为几毫米。就像在其他现代经验科学中一样，这种新的工具已经导致了大量新的数据，并相应地需要新的数据分析方法。我们描述了最近的研究，将机器学习方法应用于基于在单个时间间隔上观察到的fRMI数据对人类受试者的认知状态进行分类的问题。特别是，我们提供了案例研究，在这些案例中，我们已经成功地训练分类器来区分认知状态，例如(1)人类主体是在看图片还是句子，(2)主体是在阅读模棱两可的句子还是非歧义的句子，以及(3)主体正在阅读的词是否是描述食物、人、建筑等的词。这个学习问题为从极高维(10(5)特征)、极稀疏(数十个训练样本)、噪声数据中学习分类器提供了一个有趣的案例研究。本文总结了在这三个案例研究中获得的结果，以及在这种情况下如何成功地应用机器学习方法来训练分类器的经验教训。
Over the past decade, functional Magnetic Resonance Imaging ( fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. A typical fMRI experiment can produce a three-dimensional image related to the human subject's brain activity every half second, at a spatial resolution of a few millimeters. As in other modern empirical sciences, this new instrumentation has led to a flood of new data, and a corresponding need for new data analysis methods. We describe recent research applying machine learning methods to the problem of classifying the cognitive state of a human subject based on fRMI data observed over a single time interval. In particular, we present case studies in which we have successfully trained classifiers to distinguish cognitive states such as ( 1) whether the human subject is looking at a picture or a sentence, ( 2) whether the subject is reading an ambiguous or non-ambiguous sentence, and ( 3) whether the word the subject is viewing is a word describing food, people, buildings, etc. This learning problem provides an interesting case study of classifier learning from extremely high dimensional (10(5) features), extremely sparse ( tens of training examples), noisy data. This paper summarizes the results obtained in these three case studies, as well as lessons learned about how to successfully apply machine learning methods to train classifiers in such settings.