Diffusion tensor imaging of neurocognitive profiles in a community cohort living in marginal housing
Bibliographic record
Abstract
OBJECTIVE: We investigated white matter differences associated with distinct neurocognitive profiles derived from a large cohort of marginally housed persons with comorbid physical and mental illnesses. Our prior work identified three profile cluster groups: a high functioning group (Cluster 1), a low functioning group with relative strength in decision-making (Cluster 3), and an intermediary group with a relative decision-making weakness (Cluster 2). This study extends previous findings of cortical gray matter differences between these groups with evidence for putative neurodevelopmental abnormalities in the low cognitive functioning group (i.e., Cluster 3). We hypothesized that altered white matter diffusion would be associated with the lowest functioning neurocognitive profile and would be associated with previously observed gray matter differences. METHOD: Participants from a socially impoverished neighborhood in Vancouver, Canada underwent neurocognitive evaluation and neuroimaging. We performed Tract-Based Spatial Statistics using diffusion tensor imaging data from 184 participants to examine whole-brain differences in white matter microstructure between cluster analytically derived neurocognitive profiles, as well as unitary neurocognitive measures. Correlations between frontal gray and white matter were also examined. RESULTS: Cluster 3 showed increased diffusion in predominately bilateral frontal and interhemisphere tracts (vs. Clusters 1 and 2), with relatively greater diffusion in the left hemisphere (vs. Cluster 1). Differences in radial diffusivity were more prominent compared with axial diffusivity. A weak association between regional frontal fractional anisotropy and previously defined abnormalities in gyrification was observed. CONCLUSIONS: In a socially marginalized sample, we established several patterns in the covariation of white matter diffusion and neurocognitive functioning. These patterns elucidate the neurobiological substrates and vulnerabilities that are apt to underlie functional impairments inherent to this complex and heterogeneous population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".