Architecture of fluid intelligence and working memory revealed by lesion mapping

Abstract

Although cognitive neuroscience has made valuable progress in understanding the role of the prefrontal cortex in human intelligence, the functional networks that support adaptive behavior and novel problem solving remain to be well characterized. Here, we studied 158 human brain lesion patients to investigate the cognitive and neural foundations of key competencies for fluid intelligence and working memory. We administered a battery of neuropsychological tests, including the Wechsler Adult Intelligence Scale (WAIS) and the N-Back task. Latent variable modeling was applied to obtain error-free scores of fluid intelligence and working memory, followed by voxel-based lesion-symptom mapping to elucidate their neural substrates. The observed latent variable modeling and lesion results support an integrative framework for understanding the architecture of fluid intelligence and working memory and make specific recommendations for the interpretation and application of the WAIS and N-Back task to the study of fluid intelligence in health and disease.

Underlying sources of cognitive-anatomical variation in multi-modal neuroimaging and cognitive testing

“…We were able to identify joint sources of anatomical and cognitive variation at multiple levels broadly consistent with previous functional and lesion literature on the distributed nature of high-level cognitive abilities including with areas of the core “rich club,” fronto-parietal network, and the white matter tracts that form the anatomical basis for functional communications within this network (Engle et al, 1999a; Barbey et al, 2014). …”

“…This residual factor, produced by a data-driven approach, provides converging evidence for the importance of these regions to cognitive function (Barbey et al, 2014; Colom et al, 2009; Luders et al, 2009; McDaniel, 2005), and unlike the first-level components it cannot be interpreted as an artifact of demographic variations or gross morphology because these sources of variance were removed in the first-level analysis. Thus, we suggest that the novel, data-driven first-level sources of variation may serve as useful controls in exploring high-level hypotheses for links between anatomical variations to cognitive function.…”

“…Yet lesion studies robustly link cognitive factors to anatomy (Allen et al, 2006; Barbey et al, 2012; Barbey et al, 2014), as do functional and resting state imaging (Buckner et al, 2008; Von den Heuvel and Sporns, 2011; Wang et al, 2013). Specific anatomical hypotheses developed from such data, the parieto-frontal integration theory (PFIT), maps high-level cognitive factors such as fluid intelligence to a network of superior parietal and frontal regions that integrate multiple sources of information in service of a goal (Jung and Haier, 2007).…”

Brain structural changes following adaptive cognitive training assessed by Tensor-Based Morphometry (TBM)

“…The failure to detect statistically significant changes at the hypothesized brain regions (mainly in frontal and parietal cortices) may account for the lack of results in this regard. Indeed, the large-scale lesion study reported by Barbey et al (2014) might be consistent with this view. Analyzing the overlap between fluid intelligence (Gf) and four working memory distinguishable cognitive factors (verbal/numeric working memory, spatial working memory, working memory manipulation, and working memory monitoring) meager overlap was found for Gf and monitoring processes (as assessed by 1-back, 2-back, and 3-back tasks): the overlap was circumscribed to a small region within the right inferior parietal cortex.…”

Making Cognitive Latent Variables Manifest: Distinct Neural Networks for Fluid Reasoning and Processing Speed

“…With the advent of fMRI imaging, neural substrates of general and fluid intelligence have been mapped in the brain, primarily to frontoparietal and temporal locations (Barbey, Colom, Paul, & Grafman, 2014; Hampshire, Thompson, Duncan, & Owen, 2011; Preusse, van der Meer, Deshpande, Krueger, & Wartenburger, 2011; Colom, Karama, Jung, & Haier, 2010; Woolgar et al, 2010). Furthermore, rigorous attempts at identifying general and specific components of intelligence by combining neuroimaging and use of latent variable modeling have been undertaken recently (Hampshire, Highfield, Parkin, & Owen, 2012).…”