Characterising the association between posterior parietal metabolite levels and cortical macrostructure in a cohort spanning childhood to adulthood
Bibliographic record
Abstract
Abstract Postnatal brain development is characterised by dynamic macrostructural changes, including cortical thinning and cortical flattening during childhood and adolescence. These macro-structural changes are parallel with developmental changes in brain neurochemistry, probed in the human brain using Magnetic Resonance Spectroscopy (MRS). This includes neurotransmitters such glutamate and gamma-aminobutyric acid (GABA), as well as building blocks of neuronal and associated tissue such as N-acetyl aspartate (NAA), and those involved in metabolism such as creatine (Cr). While previous research has linked MRS-measured neuro-metabolite levels to bulk tissue composition (e.g., gray matter, white matter, and cerebrospinal fluid), the relationship between neurochemistry and more granular macrostructural metrics, such as cortical thickness, area, volume, and local gyrification, remains unexplored. This study investigates the association between MRS-measured neuro-metabolite levels in the posterior parietal cortex (PPC) and PPC-voxel cortical macrostructural metrics in a developmental cohort of 86 individuals aged 5–35 years. We also examine whether PPC metabolite concentrations associate with whole-brain structural metrics to determine whether associations are region-specific or more broadly generalisable. Our findings reveal significant positive associations between PPC cortical thickness, volume, local gyrification index (LGI) and Glx (glutamate + glutamine) levels, likely because differences in cortical microstructure , including dendritic arbour complexity, contributes to variation in both cortical macrostructure and Glx activity across development. Additionally, PPC Glx:GABA+ ratio negatively associated with subcortical gray matter volume, while PPC total NAA positively associated with cerebral white matter volume, suggesting a link between regional neurochemistry and broader brain structure. These results highlight the importance of accounting for macrostructural and broader brain structural characteristics when interpreting the neuroanatomical correlates of MRS-measured metabolites, beyond controlling for bulk tissue composition. This approach is particularly crucial when comparing neuro-metabolite levels across groups with known structural differences, such as developmental cohorts or individuals with neurodevelopmental conditions. Key points Posterior parietal cortex (PPC) Glx levels are positively associated with PPC cortical thickness and local gyrification index, likely because differences in cortical microstructure, including dendritic arbour complexity, contributes to both cortical macrostructure and neuro-metabolic traits across development. The PPC Glx:GABA+ ratio is negatively associated with subcortical gray matter volume, while PPC total NAA is positively associated with cerebral white matter volume, suggesting a link between regional neurochemistry and broader brain structure. These findings emphasise the importance of considering more detailed macrostructural characteristics, as well as bulk tissue composition (white matter, gray matter, cerebral spinal fluid), when interpreting MRS-measured metabolite differences.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".