Characterising the association between posterior parietal metabolite levels and cortical macrostructure in a cohort spanning childhood to adulthood.

Postnatal brain development is characterised by dynamic macrostructural changes, including cortical thinning and cortical flattening during childhood and adolescence. These macrostructural 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., grey 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. Our findings reveal significant and positive age-dependent 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 local cortical macrostructure and Glx activity across development. We further show age-independent associations between PPC Cr and local cortical surface area and volume, suggesting that inter-individual variability in these cortical macrostructural metrics is linked to underlying tissue energetic properties. These results highlight the importance of accounting for macrostructural characteristics when interpreting the neuroanatomical correlates of MRS-measured neuro-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.