[Article Review] Unraveling Brain and Cognitive Changes: A Deep Dive into GALAMMs

Reference

Sørensen, Ø., Fjell, A. M., & Walhovd, K. B. (2023). Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models. Psychometrika, 88(2), 456-486. https://doi.org/10.1007/s11336-023-09910-z

Review

In their 2023 study, Sørensen, Fjell, and Walhovd introduced generalized additive latent and mixed models (GALAMMs) to analyze clustered data. They developed these models primarily to address applications in cognitive neuroscience. Their method leverages a scalable maximum likelihood estimation algorithm, utilizing advanced computational techniques like the Laplace approximation, sparse matrix computation, and automatic differentiation. Crucially, this approach allows for a variety of mixed response types, heteroscedasticity, and crossed random effects.

The authors further illustrated the applicability of GALAMMs by presenting two case studies. The first highlighted how these models could comprehensively capture lifespan trajectories of various cognitive abilities, including episodic memory, working memory, and executive function. Such findings were drawn from widely used cognitive tests like the California Verbal Learning Test, digit span tests, and Stroop tests. In their second case, the researchers explored the impact of socioeconomic status on brain structure, specifically delving into the relationship between educational and income levels with hippocampal volumes, gauged via magnetic resonance imaging (MRI). Their results posited that by integrating semiparametric estimation with latent variable modeling, GALAMMs can offer a more nuanced depiction of how both the brain and cognition evolve throughout an individual's life.

Overall, this study presents a promising tool for the analysis of complex data structures, especially in the realm of cognitive neuroscience. While the authors provided solid evidence from their case studies, it would be beneficial to see how GALAMMs fare in a broader range of applications. Moreover, the efficacy of these models in different sample sizes, beyond moderate ones, remains a question worth exploring in future research.

[Article Review] Uncovering the Brain's Response to Socioeconomic Status: A Longitudinal Study

Reference

McDermott, C. L., Seidlitz, J., Nadig, A., Liu, S., Clasen, L. S., Blumenthal, J. D., ... & Raznahan, A. (2019). Longitudinally Mapping Childhood Socioeconomic Status Associations with Cortical and Subcortical Morphology. Journal of Neuroscience, 39(8), 1365-1373. https://doi.org/10.1523/JNEUROSCI.1808-18.2018

Review

In the study conducted by McDermott et al. (2019), the researchers sought to examine the associations between childhood socioeconomic status (SES) and structural brain development in a longitudinal manner. By analyzing 1,243 MRI scans from 623 youth aged 5 to 25 years, the authors provided a comprehensive understanding of the relationship between SES and cortical and subcortical morphology.

The results indicated positive associations between SES and the total volumes of the brain, cortical sheet, and four separate subcortical structures. These associations were stable across the entire age range studied. Moreover, the authors found areal expansion in specific cortical and subcortical regions, such as lateral prefrontal, anterior cingulate, lateral temporal, and superior parietal cortices, as well as ventrolateral thalamic and medial amygdala-hippocampal subregions, to be associated with higher SES. The findings from meta-analyses of functional imaging data suggest that the cortical correlates of SES are primarily focused on brain systems that support sensorimotor functions, language, memory, and emotional processing.

In conclusion, McDermott et al. (2019) demonstrated that anatomical variation within a subset of the identified regions partially mediates the positive association between SES and IQ, while also identifying neuroanatomical correlates of SES that exist independently of IQ variation. The study offers valuable insights into the potential neuroanatomical mediators linking SES and cognitive outcomes, paving the way for future research on this topic.