Latent functional connectivity underlying multiple brain states

Functional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to primarily reflect the brain's intrinsic network architecture, which is thought to be broadly relevant to brain function because it persists across brain states. However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting patterns of connectivity shared across many brain states, may better capture intrinsic FC relative to measures derived from resting state alone. We estimated latent FC in relation to 7 highly distinct task states (24 task conditions) and resting state using fMRI data from 352 participants from the Human Connectome Project. Latent FC was estimated independently for each connection by applying leave-one-task-out factor analysis on the state FC estimates. Compared to resting-state connectivity, we found that latent connectivity improves generalization to held-out brain states, better explaining patterns of both connectivity and task-evoked brain activity. We also found that latent connectivity improved prediction of behavior, measured by the general intelligence factor psychometric g. Our results suggest that patterns of FC shared across many brain states, rather than just resting state, better reflects general, state-independent connectivity. This affirms the notion of "intrinsic" brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor.

The Breadth of Impacts from the Abecedarian Project Early Intervention on IQ Subtests

Early life interventions impacting cognitive abilities are most often followed by post-treatment fadeout. Some have hypothesized that persistence is unlikely when gains are specific to trained skills, or more specifically, distinguishable from impacts on general cognitive ability (classically modeled as a hierarchical factor, so-called psychometric g). Using measurement invariance testing and multiple indicators multiple causes models, we investigated impacts on IQ subtests from the Abecedarian early childhood intervention (n = 107). We found that 1) observed impacts on IQ scores from age 5 to age 21 were consistent with persistent positive effects on g; 2) subtest-specific variance that was differentiable from changes on g did fade. Together, these findings indicated that Abecedarian early impact persisted across a range of cognitive skills, providing some evidence for the hypothesis that breadth and persistence of impacts from educational interventions are related.

Exploratory and Confirmatory Factor Analysis of the Universal Nonverbal Intelligence Test–Second Edition: Testing Dimensionality and Invariance Across Age, Gender, Race, and Ethnicity

This study examined key assumptions underlying the interpretation of one of the most widely used multidimensional nonverbal tests of intelligence, the Universal Nonverbal Intelligence Test–Second Edition (UNIT2). Specifically, we examined the dimensionality of the UNIT2 and the interpretive relevance of its factors. We also examined the invariance of constructs measured by the UNIT2 across age groups, gender, race, and ethnicity. Structural analyses were conducted using data from 1,802 individuals aged 5 to 21 years who participated in the norming of the UNIT2. Results indicate that the UNIT2 is primarily a measure of psychometric g. Tests of invariance indicate that the factors measured by the UNIT2 are calibrated differently across age, gender, and racial groups. The Memory, Quantitative, and Reasoning factors represent psychometric g quite well. However, there is insufficient unique, reliable variance for the interpretation of the index scores reflecting the Memory, Quantitative, and Reasoning factors. Based on the results of this study, we question whether the administration of multidimensional nonverbal tests of intelligence is worth the time and effort when unidimensional tests may provide the same information.