Reproducibility of Results and Dynamic Causal Modeling in fMRI: The New Perspectives in fMRI Research Award

The central aim of cognitive neuroscience over the past two decades has been to formulate, support, and test predictions about the interaction of the brain and the mind. Methods of mapping the brain using functional neuroimaging have proven invaluable for observing cognitively induced neurophysiological processes at work in the living brain and have provided the basis for many current theories of cognitive function (Posner & DiGirolamo, 2000). However, the ability to craft testable predictions about the robust nature of brain activation results should not depend only upon the identification of brain areas having sufficiently small statistical p values, per se. Consideration also needs to be given to the potential for activation patterns to be repeated in independent replication. If fMRI results are unreliable, how can they be used to construct theories of cognitive function? On the other hand, it has become increasingly clear that it is the networks of connectivity between brain areas that underlie cognitive processing, not simply the number and magnitude of activation local maxima. Many cognitive processes, such as visual attention for faces and objects (Kanwisher & Wojciulik, 2000; Ishai, Ungerleider, Martin, Schouten, & Haxby, 1999), require not only the active participation of a distributed collection of brain regions for visual processing but also continuous communication between these regions. This implies information encoding, integration, and exchange within large-scale brain networks that may mediate top–down and bottom–up processes. Indeed, how large-scale patterns of connectivity are dynamically modulated over time is likely to be fundamental in understanding many complex cognitive operations (Horwitz, Tagamets, & McIntosh, 1999). In this issue of JOCN we are delighted to present two articles as winners of the ‘‘2003 New Perspectives in fMRI Research Award.’’ This is the second year of the award, sponsored by The fMRI Data Center (fMRIDC) and the Journal of Cognitive Neuroscience, which recognizes brain researchers who, in drawing from the fMRIDC archive of published fMRI study data, perform unique analyses that broaden the scope of the original imaging study research findings. The development and application of novel fMRI methods and interpretation against the benchmark of published empirical data serve to enhance the previously reported results and, in so doing, clarify thinking in cognitive neuroscience. Articles submitted for the award were specially reviewed by experts in the fields of functional neuroimaging and cognitive neuroscience. Two articles, in particular, stood out because both were focused toward improving and enhancing the ability to make testable predictions about cognitive processes from fMRI data. The article by Liou et al. explores a practical means for assessing the likely reproducibility of brain activation results. Replications of neuroimaging studies have occurred only rarely in the literature (e.g., Chawla et al., 1999; Zarahn, Aguirre, & D’Esposito, 2000), presumably due to the economics of performing fMRI studies and the strong desire of researchers to make unique contributions to the field. Still, as the sample sizes in many neuroimaging studies tend to be rather small, the potential for reproducibility or ‘‘statistical power’’ in both PET and fMRI has likewise tended to be unimpressive (Desmond & Glover, 2002; Van Horn, Ellmore, Esposito, & Berman, 1998). This suggests that even if a study is replicated, the odds may not be in favor of finding a pattern of results consistent with the original investigation. Liou et al. contrast the general linear model approach with that of an empirical Bayesian approach, finding the latter to be more sensitive to detecting activation within subjects but may not overcome the fact that individual subjects are highly variable. This agrees with related work on individual differences observed in episodic memory (Miller et al., 2002). Nevertheless, estimating and mapping the potential reproducibility of neuroimaging results within and between subjects is likely to be helpful in identifying brain areas that activate consistently. Identifying the brain areas involved in a task, however, is only a prelude to identifying how these areas interact and are mediated by one another over time. Indeed, the essence of fMRI is not a static map of isolated regional activity but the observation of the temporal fluctuations in BOLD signal induced by the task and by inputs from other brain areas. How brain areas are networked is not often the subject of cognitive neuroimaging investigations but deserves increased examination to determine the role of brain connectivity mediating cognitive processes. An excellent example of representing the dynamic interactions among brain areas is given by Mechelli Dartmouth College