Ramezani Mahdi, Abolmaesumi Purang, Marble Kris, Trang Heather, Johnsrude Ingrid
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada,
Brain Imaging Behav. 2015 Jun;9(2):149-61. doi: 10.1007/s11682-014-9292-1.
Classification of individuals based on patterns of brain activity observed in functional MRI contrasts may be helpful for diagnosis of neurological disorders. Prior work for classification based on these patterns have primarily focused on using a single contrast, which does not take advantage of complementary information that may be available in multiple contrasts. Where multiple contrasts are used, the objective has been only to identify the joint, distinct brain activity patterns that differ between groups of subjects; not to use the information to classify individuals. Here, we use joint Independent Component Analysis (jICA) within a Support Vector Machine (SVM) classification method, and take advantage of the relative contribution of activation patterns generated from multiple fMRI contrasts to improve classification accuracy. Young (age: 19-26) and older (age: 57-73) adults (16 each) were scanned while listening to noise alone and to speech degraded with noise, half of which contained meaningful context that could be used to enhance intelligibility. Functional contrasts based on these conditions (and a silent baseline condition) were used within jICA to generate spatially independent joint activation sources and their corresponding modulation profiles. Modulation profiles were used within a non-linear SVM framework to classify individuals as young or older. Results demonstrate that a combination of activation maps across the multiple contrasts yielded an area under ROC curve of 0.86, superior to classification resulting from individual contrasts. Moreover, class separability, measured by a divergence criterion, was substantially higher when using the combination of activation maps.
基于功能磁共振成像对比中观察到的大脑活动模式对个体进行分类，可能有助于神经系统疾病的诊断。先前基于这些模式进行分类的工作主要集中在使用单一对比，而没有利用多个对比中可能存在的互补信息。在使用多个对比的情况下，目标仅仅是识别不同受试者组之间不同的联合、独特的大脑活动模式；而不是利用这些信息对个体进行分类。在这里，我们在支持向量机（SVM）分类方法中使用联合独立成分分析（jICA），并利用多个功能磁共振成像对比产生的激活模式的相对贡献来提高分类准确率。对年轻（年龄：19 - 26岁）和年长（年龄：57 - 73岁）的成年人（各16名）在单独听噪音以及听被噪音干扰的语音时进行扫描，其中一半的语音包含可用于提高可懂度的有意义语境。基于这些条件（以及一个安静的基线条件）的功能对比在jICA中用于生成空间上独立的联合激活源及其相应的调制剖面。调制剖面在非线性SVM框架内用于将个体分类为年轻或年长。结果表明，多个对比的激活图组合产生的ROC曲线下面积为0.86，优于单个对比的分类结果。此外，使用激活图组合时，通过散度准则测量的类可分离性显著更高。
