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.</p

Novel genetic loci associated with hippocampal volume

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function

Transcranial magnetic stimulation of frontal oculomotor regions during smooth pursuit

Both the frontal eye fields (FEFs) and supplementary eye fields (SEFs) are known to be involved in smooth pursuit eye movements. It has been shown recently that stimulation of the smooth-pursuit area of the FEF [frontal pursuit area (FPA)] in monkey increases the pursuit response to unexpected changes in target motion during pursuit. In the current study, we applied transcranial magnetic stimulation (TMS) to the FPA and SEF in humans during sinusoidal pursuit to assess its effects on the pursuit response to predictable, rather than unexpected, changes in target motion. For the FPA, we found that TMS applied immediately before the target reversed direction increased eye velocity in the new direction, whereas TMS applied in mid-cycle, immediately before the target began to slow, decreased eye velocity. For the SEF, TMS applied at target reversal increased eye velocity in the new direction but had no effect on eye velocity when applied at mid-cycle. TMS of the control region (leg region of the somatosensory cortex) did not affect eye velocity at either point. Previous stimulation studies of FPA during pursuit have suggested that this region is involved in controlling the gain of the transformation of visual signals into pursuit motor commands. The current results suggest that the gain of the transformation of predictive signals into motor commands is also controlled by the FPA. The effect of stimulation of the SEF is distinct from that of the FPA and suggests that its role in sinusoidal pursuit is primarily at the target direction reversal

Saguenay Youth Study: A multi-generational approach to studying virtual trajectories of the brain and cardio-metabolic health

This paper provides an overview of the Saguenay Youth Study (SYS) and its parental arm. The overarching goal of this effort is to develop trans-generational models of developmental cascades contributing to the emergence of common chronic disorders, such as depression, addictions, dementia and cardio-metabolic diseases. Over the past 10 years, we have acquired detailed brain and cardio-metabolic phenotypes, and genome-wide genotypes, in 1029 adolescents recruited in a population with a known genetic founder effect. At present, we are extending this dataset to acquire comparable phenotypes and genotypes in the biological parents of these individuals. After providing conceptual background for this work (transactions across time, systems and organs), we describe briefly the tools employed in the adolescent arm of this cohort and highlight some of the initial accomplishments. We then outline in detail the phenotyping protocol used to acquire comparable data in the parents

Texture analysis in brain T2 and diffusion MRI differentiates histology-verified grey and white matter pathology types in multiple sclerosis

© 2022 Elsevier B.V.Background: Multiple sclerosis (MS) is a complex disease of the central nervous system involving several types of brain pathology that are difficult to characterize using conventional imaging methods. New method: We originated novel texture analysis and machine learning approaches for classifying MS pathology subtypes as compared with 2 common advanced MRI measures: magnetization transfer ratio (MTR) and fractional anisotropy (FA). Texture analysis used an optimized grey level co-occurrence matrix method with histology-informed 7T T2-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) from 15 MS and 12 control brain specimens. DTI analysis took an innovative approach that assessed the texture across diffusion directions upsampled from 30 to 90. Tissue types included de- and re-myelinated lesions and normal-appearing areas in both grey and white matter, and diffusely abnormal white matter. Data analyses were stepwise, including: (1) group-wise classification using random forest algorithms based on all or individual imaging parameters; (2) parameter importance ranking; and (3) pairwise analysis using top-ranked features. Results: Texture analysis performed better than MTR and FA, with T2 texture performed the best. T2 texture measures ranked the highest in classifying most grey and white matter tissue types, including de- versus re-myelinated lesions and among grey matter lesion subtypes (accuracy=0.86–0.59; kappa=0.60–0.41). Diffusion texture best differentiated normal appearing and control white matter. Comparison with existing methods: There is no established method in imaging for differentiating MS pathology subtypes. In combined texture analysis and machine learning studies, there is also no direct evidence comparing conventional with advanced MRI measures for assessing MS pathology. Further, this study is unique in conducting innovative texture analysis with DTI following data-augmentation using robust methods. Conclusions: T2 and diffusion MRI texture analysis integrated with machine learning may be valuable approaches for characterizing MS pathology