Habeck Christian, Krakauer John W, Ghez Claude, Sackeim Harold A, Eidelberg David, Stern Yaakov, Moeller James R
Cognitive Neuroscience Division, Taub Institute, and Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
Neural Comput. 2005 Jul;17(7):1602-45. doi: 10.1162/0899766053723023.
In neuroimaging studies of human cognitive abilities, brain activation patterns that include regions that are strongly interactive in response to experimental task demands are of particular interest. Among the existing network analyses, partial least squares (PLS; McIntosh, 1999; McIntosh, Bookstein, Haxby, & Grady, 1996) has been highly successful, particularly in identifying group differences in regional functional connectivity, including differences as diverse as those associated with states of awareness and normal aging. However, we address the need for a within-group model that identifies patterns of regional functional connectivity that exhibit sustained activity across graduated changes in task parameters. For example, predictions of sustained connectivity are commonplace in studies of cognition that involve a series of tasks over which task difficulty increases (Baddeley, 2003). We designed ordinal trend analysis (OrT) to identify activation patterns that increase monotonically in their expression as the experimental task parameter increases, while the correlative relationships between brain regions remain constant. Of specific interest are patterns that express positive ordinal trends on a subject-by-subject basis. A unique feature of OrT is that it recovers information about functional connectivity based solely on experimental design variables. In particular, there is no requirement by OrT to provide either a quantitative model of the uncertain relationship between functional brain circuitry and subject variables (e.g., task performance and IQ) or partial information about the regions that are functionally connected. In this letter, we provide a step-by-step recipe of the computations performed in the new OrT analysis, including a description of the inferential statistical methods applied. Second, we describe applications of OrT to an event-related fMRI study of verbal working memory and H(2)15O-PET study of visuo-motor learning. In sum, OrT has potential applications to not only studies of young adults and their cognitive abilities, but also studies of normal aging and neurological and psychiatric disease.
在人类认知能力的神经影像学研究中，大脑激活模式（包括那些在响应实验任务需求时具有强烈交互作用的区域）特别令人感兴趣。在现有的网络分析方法中，偏最小二乘法（PLS；McIntosh，1999；McIntosh，Bookstein，Haxby，& Grady，1996）已经取得了巨大成功，尤其是在识别区域功能连接性的组间差异方面，包括与意识状态和正常衰老相关的各种差异。然而，我们关注的是需要一种组内模型，该模型能够识别在任务参数逐渐变化过程中表现出持续活动的区域功能连接模式。例如，在涉及一系列任务难度逐渐增加的认知研究中，对持续连接性的预测很常见（Baddeley，2003）。我们设计了序数趋势分析（OrT）来识别随着实验任务参数增加其表达单调增加的激活模式，同时大脑区域之间的相关关系保持不变。特别感兴趣的是在个体基础上表现出正序数趋势的模式。OrT的一个独特之处在于它仅基于实验设计变量来恢复有关功能连接性的信息。具体而言，OrT不需要提供功能性脑回路与个体变量（如任务表现和智商）之间不确定关系的定量模型，也不需要提供有关功能连接区域的部分信息。在这封信中，我们提供了新的OrT分析中执行的计算的逐步方法，包括所应用的推断统计方法的描述。其次，我们描述了OrT在言语工作记忆的事件相关功能磁共振成像研究和视觉运动学习的H(2)15O-PET研究中的应用。总之，OrT不仅在年轻成年人及其认知能力的研究中有潜在应用，而且在正常衰老以及神经和精神疾病的研究中也有潜在应用。
