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

Analyzing Latent Traits with Generalized Additive Latent and Mixed Models (GALAMMs)

Sørensen, Fjell, and Walhovd’s 2023 research introduces Generalized Additive Latent and Mixed Models (GALAMMs), a methodological advancement designed for analyzing complex clustered data. This approach holds particular relevance for cognitive neuroscience, offering robust tools for examining how cognitive and neural traits develop over time.

Background

Traditional models used in cognitive neuroscience often face challenges when handling non-linear relationships, mixed response types, or crossed random effects. GALAMMs were developed to address these limitations, leveraging maximum likelihood estimation techniques, including the Laplace approximation and sparse matrix computation. This method builds on advancements in computational science, allowing researchers to model intricate data structures with greater flexibility.

Key Insights

• Capturing Lifespan Cognitive Changes: The authors demonstrated how GALAMMs can model trajectories for episodic memory, working memory, and executive function. Using data from standard cognitive assessments such as the California Verbal Learning Test and digit span tests, the study provided detailed insights into age-related changes in cognitive abilities.
• Investigating Socioeconomic Impacts on Brain Structure: A second case study highlighted how socioeconomic factors, such as education and income, influence hippocampal volumes. These findings were derived from magnetic resonance imaging (MRI) data and revealed the nuanced interplay between environmental factors and neural structures.
• Integration of Semiparametric and Latent Variable Modeling: GALAMMs combine semiparametric estimation techniques with latent variable approaches, enabling a more nuanced understanding of brain-cognition relationships across the lifespan.

Significance

By introducing GALAMMs, the authors have provided a versatile tool that extends the capacity to analyze complex data structures in neuroscience and related fields. This approach allows researchers to better understand how cognitive and neural characteristics evolve, offering applications in areas such as developmental studies, aging research, and the analysis of social determinants of health.

Future Directions

While GALAMMs have shown promise in modeling moderate-sized datasets, further research is needed to test their scalability with larger or smaller samples. Expanding their use to other fields could also validate their versatility and effectiveness. Additional studies could refine the models further by exploring their application to non-linear relationships in varied contexts.

Conclusion

Sørensen, Fjell, and Walhovd’s study highlights the potential of GALAMMs in addressing challenges associated with analyzing complex, clustered data in cognitive neuroscience. By improving the ability to capture intricate patterns in lifespan development, their work contributes significantly to the study of brain and cognitive aging, as well as the broader understanding of human development.

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

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

The Influence of Childhood Socioeconomic Status on Brain Development

McDermott et al. (2019) conducted a longitudinal study examining how childhood socioeconomic status (SES) is associated with structural brain development. By analyzing over 1,200 MRI scans from youth aged 5 to 25 years, the researchers explored connections between SES and the anatomy of the brain, offering important insights into cognitive and emotional development.

Background

Socioeconomic status has long been studied for its impact on educational outcomes and cognitive performance. However, its influence on brain development has only recently become a focus of neuroimaging research. McDermott et al.’s study builds on this work by identifying specific cortical and subcortical regions affected by SES, highlighting how these variations relate to cognitive and emotional processing.

Key Insights

• Positive Associations Between SES and Brain Volume: Higher SES was linked to larger total brain, cortical, and subcortical volumes across the studied age range.
• Regional Variations in Brain Anatomy: SES correlated with areal expansion in the lateral prefrontal, anterior cingulate, lateral temporal, and superior parietal cortices, as well as in subregions such as the ventrolateral thalamus and medial amygdala-hippocampus.
• Functional Implications: These cortical regions are involved in sensorimotor functions, language, memory, and emotional regulation, indicating SES’s impact on systems critical for daily functioning and long-term cognitive outcomes.

Significance

The findings from this study contribute to understanding the biological pathways through which SES influences cognitive abilities. The identification of neuroanatomical mediators, some independent of IQ, suggests that SES shapes both structural and functional brain development in ways that extend beyond traditional measures of intelligence. This research highlights the role of social and environmental factors in shaping developmental trajectories.

Future Directions

Future research could focus on the specific environmental mechanisms underlying these associations. For example, examining the roles of access to education, nutrition, or emotional support may provide further clarity. Additionally, expanding studies to include more diverse populations could help generalize findings and inform interventions aimed at mitigating disparities linked to SES.

Conclusion

This longitudinal study by McDermott et al. (2019) underscores the relationship between childhood socioeconomic status and brain development. By linking SES to specific neuroanatomical variations, the research provides a foundation for understanding how social conditions influence cognitive and emotional growth. These findings reinforce the need to address socioeconomic disparities as part of efforts to support healthy development.

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