Fig. 5: Microstructure reflects neurotransmitter receptor & transporter densities and cognitive functioning.

A PLS correlation between receptor/transporter densities and microstructural metrics revealed a statistically significant principal gradient (p = 0.002) that explained 65.4% of the covariance. Equivalent PLS correlation analysis between cognitive task activation maps and microstructural metrics revealed a principal gradient (p = 0.001) that explained 78.9% of the covariance. B Structural and C receptor/transporter loadings showed universally positive weightings, except for KFA, myelin, ODI and GABAa and NET. D Structural and E receptor/transporter scores had strong correspondence with each other (r = 0.678, p = 0.0002) and showed the most positive weighting in paralimbic cortex and the most negative weighting in idiotypic and unimodal cortex. F Structural loadings from PLS analysis with cognitive maps mirrored those from PLS analysis with receptor/transporter densities and the two corresponding structural scores (D, H) were almost identical (r = 0.995, p < 0.0001). G The twenty most positive cognitive loadings were associated with emotional and reward processing, while the twenty most negative loadings were associated with sensory processing/integration, attention, judgement, and planning. I The cognitive scores were well correlated with the structural scores (r = 0.83, p = 0.0004) and even the receptor/transporter scores (r = 0.62, 0.0004). Both the receptor \((r=-0.62,{p}=0.0036)\) and structural \((r=-0.76,{p}=0.0012)\) scores (D, E) showed statistically significant correspondence with the first principal component of gene expression. Similarly, the cognitive \((r=-0.69,{p}=0.006)\) and the structural \((r=-0.72,{p}=0.01)\) scores (H, I) also showed statistically significant association with the first principal component of gene expression.