Abstract
A key challenge in the field of cognitive neuroscience is to identify discriminable cognitive functions, and then map these functions to brain activity. In the current study, we set out to explore the relationships between performance arising from different cognitive tasks thought to tap different domains of cognition, and then to test whether these distinct latent cognitive abilities also are subserved by corresponding “latent” brain substrates. To this end, we tested a large sample of adults under the age of 40 on twelve cognitive tasks as they underwent fMRI scanning. Exploratory factor analysis revealed 4-factor model, dissociating tasks into processes corresponding to episodic memory retrieval, reasoning, speed of processing and vocabulary. An analysis of the topographic covariance patterns of the BOLD-response acquired during each task similarity also converged on four neural networks that corresponded to the 4 latent factors. These results suggest that distinct ontologies of cognition are subserved by corresponding distinct neural networks.
Highlights
Two longstanding challenges in the field of cognitive neuroscience are to (1) isolate cognitive functions, and (2) map brain activity to these cognitive functions [1]
While the 5-factor model had marginally better Root Mean Square Error of Approximation (RMSEA) and SRMSR values compared to the 4-factor model [30,31], the four-factor model both converges with previous findings [12,16,20,32,33,34] and is a more parsimonious model
An exploratory factor analysis with goemin rotation including a 3, 4- and 5-factor model of the correlations between z-statistics of task-pairs calculated from the group-level maps was conducted
Summary
Two longstanding challenges in the field of cognitive neuroscience are to (1) isolate cognitive functions, and (2) map brain activity to these cognitive functions [1]. Since the advent of functional brain imaging, tens of thousands of fMRI studies have been conducted, attempting to investigate the neural pathways that lead to specific behavioral outcomes. To synthesis and aggregate the results from individual studies, different forms of “informatics-driven” approaches have been developed [1,2,3,4]. One of the most commonly used approach is the meta-analysis, in which data from many studies that each use different tasks to probe a particular cognitive function—working memory training [5] or social rejection [6], for example—are pooled to look for common behavioral patterns of results or neural substrates.
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