A watershed model of individual differences in fluid intelligence

Abstract

Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates these three explanatory levels in a principled manner in a single statistical model, with processing speed and white matter figuring as intermediate endophenotypes. We fit this model in a large (N=555) adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. The model fit the data well, outperforming competing models and providing evidence for a many-to-one mapping between white matter integrity, processing speed and fluid intelligence. The model can be naturally extended to integrate other cognitive domains, endophenotypes and genotypes.

Obesity associated with increased brain age from midlife

“…Although crystallized intelligence increases with age, fluid intelligence decreases with declining brain function (Horn and Cattell, 1967, Salthouse, 2009). Although previous studies have linked white-matter integrity, processing speed and fluid intelligence (Kievit et al., 2016), our results suggest that BMI does not additionally influence the age and brain structure relationship with cognition. More generally, differences in demographic, clinical (e.g., cognitive status), and socioeconomic variables controlled for may also contribute to the heterogeneity in the literature regarding the relationship between adiposity and neurodegeneration in population-based studies.…”

Crystallized and fluid intelligence are predicted by microstructure of specific white‐matter tracts

“…The most upstream variables were the 10 major white-matter tract mTBFA, which were modeled as influencing only the two latent variables. Thus, the single-factor summarization of intelligence scales in Kievit et al (2016, Kievit et al, 2018 lead to a misallocation of variance of the intelligence measures that will affect the full model. were identified: reasoning, spatial ability, memory, PS, and vocabulary (Salthouse, 2004), which are very closely related but not identical to the four independent indexes of the WAIS-III: PO or Reasoning (PO), PS, VC, and WM.…”

“…This graph fulfills a Markov property that can be explained as follows: If variable A is connected to B and B to C, and there is no other direct or indirect path from A to C, B "totally mediates" the influence of A on C. In other words, B "screens off" A from C. Under appropriate conditions (Pearl, 2000), the resulting graphs allow some inference about mechanistic causal relations. This framework was leveraged by Kievit et al (2012Kievit et al ( , 2016 and Kievit, Fuhrmann, Borgeest, Simpson-Kent, and Henson (2018) to provide an SEM specification for the study of the relation of FA with intelligence, work that is worth summarizing in the next paragraph. A particularly useful type of SEM is the Multiple Indicator, Multiple Causes (MIMIC) introduced by Jöreskog and Goldberger (2006) in which latent variables are introduced as mediators between two sets of observed variables.…”

“…Subsequent to the Kievit et al (2016) proposal of the watershed model for intelligence regularized regression methods were integrated into SEM/MIMIC modeling (Jacobucci, Brandmaier, & Kievit, 2019).…”

Theory of Cognitive Aging in Parkinson Disease

“…Cognitive decline across the life span has been most successfully characterized using a multivariate model [10,11]. Age-related changes in cognitive performance have been linked to a reduction in cognitive resources.…”