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 applicatio to not only studies of young adults and their cognitive abilities, but also studies of normal aging and neurological and psychiatric disease.
ASJC Scopus subject areas
- Control and Systems Engineering
- Artificial Intelligence