Longitudinally Mapping Childhood Socioeconomic Status Associations with Cortical and Subcortical Morphology
Bibliographic record
Abstract
Childhood socioeconomic status (SES) impacts cognitive development and mental health, but its association with human structural brain development is not yet well characterized. Here, we analyzed 1243 longitudinally acquired structural MRI scans from 623 youth (299 female/324 male) to investigate the relation between SES and cortical and subcortical morphology between ages 5 and 25 years. We found positive associations between SES and total volumes of the brain, cortical sheet, and four separate subcortical structures. These associations were stable between ages 5 and 25. Surface-based shape analysis revealed that higher SES is associated with areal expansion of lateral prefrontal, anterior cingulate, lateral temporal, and superior parietal cortices and ventrolateral thalamic, and medial amygdalo-hippocampal subregions. Meta-analyses of functional imaging data indicate that cortical correlates of SES are centered on brain systems subserving sensorimotor functions, language, memory, and emotional processing. We further show that anatomical variation within a subset of these cortical regions partially mediates the positive association between SES and IQ. Finally, we identify neuroanatomical correlates of SES that exist above and beyond accompanying variation in IQ. Although SES is clearly a complex construct that likely relates to development through diverse, nondeterministic processes, our findings elucidate potential neuroanatomical mediators of the association between SES and cognitive outcomes. SIGNIFICANCE STATEMENT Childhood socioeconomic status (SES) has been associated with developmental disparities in mental health, cognitive ability, and academic achievement, but efforts to understand underlying SES–brain relationships are ongoing. Here, we leverage a unique developmental neuroimaging dataset to longitudinally map the associations between SES and regional brain anatomy at high spatiotemporal resolution. We find widespread associations between SES and global cortical and subcortical volumes and surface area and localize these correlations to a distributed set of cortical, thalamic, and amygdalo-hippocampal subregions. Anatomical variation within a subset of these regions partially mediates the positive relationship between SES and IQ. Our findings help to localize cortical and subcortical systems that represent candidate biological substrates for the known relationships between SES and cognition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".