Prenatal exposure to maternal cigarette smoking and structural properties of the human corpus callosum.

Alterations induced by prenatal exposure to nicotine have been observed in experimental (rodent) studies. While numerous developmental outcomes have been associated with prenatal exposure to maternal cigarette smoking (PEMCS) in humans, the possible relation with brain structure is less clear. Here we sought to elucidate the relation between PEMCS and structural properties of human corpus callosum in adolescence and early adulthood in a total of 1,747 youth. We deployed three community-based cohorts of 446 (age 25-27 years, 46% exposed), 934 (age 12-18 years, 47% exposed) and 367 individuals (age 18-21 years, 9% exposed). A mega-analysis revealed lower mean diffusivity in the callosal segments of exposed males. We speculate that prenatal exposure to maternal cigarette smoking disrupts the early programming of callosal structure and increases the relative portion of small-diameter fibres.

Structural properties of the human corpus callosum: Multimodal assessment and sex differences.

A number of structural properties of white matter can be assessed in vivo using multimodal magnetic resonance imaging (MRI). We measured profiles of R1 and R2 relaxation rates, myelin water fraction (MWF) and diffusion tensor measures (fractional anisotropy [FA], mean diffusivity [MD]) across the mid-sagittal section of the corpus callosum in two samples of young individuals. In Part 1, we compared histology-derived axon diameter (Aboitiz et al., 1992) to MRI measures obtained in 402 young men (19.55 ± 0.84 years) recruited from the Avon Longitudinal Study on Parents and Children. In Part 2, we examined sex differences in FA, MD and magnetization transfer ratio (MTR) across the corpus callosum in 433 young (26.50 ± 0.51 years) men and women recruited from the Northern Finland Birth Cohort 1986. We found that R1, R2, and MWF follow the anterior-to-posterior profile of small-axon density. Sex differences in mean MTR were similar across the corpus callosum (males > females) while these in FA differed by the callosal segment (Body: M>F; Splenium: F>M). We suggest that the values of R1, R2 and MWF are driven by high surface area of myelin in regions with high density of "small axons".

Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data.

Graph theory (GT) is a powerful framework for quantifying topological features of neuroimaging-derived functional and structural networks. However, false positive (FP) connections arise frequently and influence the inferred topology of networks. Thresholding is often used to overcome this problem, but an appropriate threshold often relies on a priori assumptions, which will alter inferred network topologies. Four common network metrics (global efficiency, mean clustering coefficient, mean betweenness and smallworldness) were tested using a model tractography dataset. It was found that all four network metrics were significantly affected even by just one FP. Results also show that thresholding effectively dampens the impact of FPs, but at the expense of adding significant bias to network metrics. In a larger number (n=248) of tractography datasets, statistics were computed across random group permutations for a range of thresholds, revealing that statistics for network metrics varied significantly more than for non-network metrics (i.e., number of streamlines and number of edges). Varying degrees of network atrophy were introduced artificially to half the datasets, to test sensitivity to genuine group differences. For some network metrics, this atrophy was detected as significant (p<0.05, determined using permutation testing) only across a limited range of thresholds. We propose a multi-threshold permutation correction (MTPC) method, based on the cluster-enhanced permutation correction approach, to identify sustained significant effects across clusters of thresholds. This approach minimises requirements to determine a single threshold a priori. We demonstrate improved sensitivity of MTPC-corrected metrics to genuine group effects compared to an existing approach and demonstrate the use of MTPC on a previously published network analysis of tractography data derived from a clinical population. In conclusion, we show that there are large biases and instability induced by thresholding, making statistical comparisons of network metrics difficult. However, by testing for effects across multiple thresholds using MTPC, true group differences can be robustly identified.