The Mind Research Network, 1101 Yale Blvd, NE, Albuquerque, NM 87106, USA.
Neuroimage. 2010 May 15;51(1):123-34. doi: 10.1016/j.neuroimage.2010.01.069. Epub 2010 Jan 28.
Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.
从同一个体收集多任务脑成像数据现在已经成为医学成像研究中的常见做法。在本文中，我们提出了一种简单而有效的模型，即“CCA+ICA”，作为多任务数据融合的强大工具。这种联合盲源分离（BSS）模型利用了两种多元方法：典型相关分析和独立成分分析，以实现高估计精度，并提供两个数据集之间的正确连接，其中源可以具有公共或不同数据集之间的相关性。在模拟和真实 fMRI 应用中，我们将提出的方案与其他联合 BSS 模型进行了比较，并检验了不同的建模假设。选择两个任务的对比图像：感觉运动（SM）和 Sternberg 工作记忆（SB），来自于一般线性模型（GLM），用于贡献真实的多任务 fMRI 数据，这些数据均来自 50 名精神分裂症患者和 50 名健康对照者。当检查与疾病持续时间的关系时，CCA+ICA 显示与颞叶激活呈显著负相关。此外，CCA+ICA 将感觉运动皮层定位为两个任务的组判别区域，并确定了 SM 中的颞上回和 SB 中的前额叶皮层为任务特异性组判别脑网络。总之，我们将新方法与具有不同假设的一些竞争方法进行了比较，并对每种方法在连接两个任务方面的假设得出了一致的结果。这种方法填补了从脑成像数据中识别生物标志物的现有多元方法中的空白。
