Cognitive neuroscience 2.0: building a cumulative science of human brain function

Abstract

Cognitive neuroscientists increasingly recognize that continued progress in understanding human brain function will require not only the acquisition of new data, but also the synthesis and integration of data across studies and laboratories. Here we review ongoing efforts to develop a more cumulative science of human brain function. We discuss the rationale for an increased focus on formal synthesis of the cognitive neuroscience literature, provide an overview of recently developed tools and platforms designed to facilitate the sharing and integration of neuroimaging data, and conclude with a discussion of several emerging developments that hold even greater promise in advancing the study of human brain function.

Figure 1. Meta-analysis of neuroimaging data: methods and application

A) Coordinate-based meta-analysis. Whereas early kernel-based approaches were based on simply aggregating coordinates across a set of studies [37, 50], recent approaches explicitly test replicability across studies and allow for weighting by sample size and study quality [6, 25]. The diagram shows the procedure for one of the newer techniques, Multilevel Kernel Density Analysis [51, 52]. Reported peaks are separated by contrast map (often synonymous with study) and convolved with a spatial smoothing kernel. A weighted average map is constructed, considering sample size and other measures of study quality. The map is thresholded by randomizing the locations of the within-study activation regions many times (e.g., 10,000) and calculating the null-hypothesis distribution of the maximum across the image. This threshold provides family-wise error rate control, so that any region in the resulting thresholded map can be interpreted as more consistently activated across studies than would be expected by chance. Similar methods are available for comparing two or more task conditions (see [40]). B) Results in the sublenticular extended amygdala (Amy) from a meta-analysis comparing emotional tasks across emotion types (adapted from Table 1 in [53]). Amygdala responses are not specific for fear. C) Results in the periaqueductal gray (PAG), hypothalamus (Hy), and amygdala across studies (adapted from [51]). Replicable activation in the PAG points towards new hypothesis about PAG's previously under-appreciated role in human emotion.

Figure 2. A schematic depicting the structure of the Cognitive Atlas

The Cognitive Atlas (http://www.cognitiveatlas.org) aims to formally represent mental concepts and their relationships to the tasks that are meant to measure them. In this example, a subset of concepts in the domain of executive function is depicted, along with a task (the stop-signal task) that is thought to measure one of these components. Mental concepts (i.e., any process, representation, or concept related to mental function) can be related to one another in a number of ways, including basic ontological relations (such as “is-a” and “part-of”) as well as temporal precedence relations (“precedes”) and semantic relations such synonymy. Tasks are defined in terms of their particular experimental conditions, the contrasts between conditions that generally define experimental effects, and measured variables (called “indicators”). Specific contrasts (e.g., subtractions between conditions) are related to specific mental concepts by the measurement relation (“is-measured-by”), which formalizes the relations between mental constructs and task manipulations that are often left implicit in cognitive neuroscience research. Tasks can also be related to one another in a family-tree relation (derives-from), which represents the “task phylogeny” [54] describing the historical evolution of psychological tasks.