These questions assess your understanding of basic concepts in statistics that we will be covering in detail in this course. a. Go to PubMed (https://pubmed.ncbi.nlm.nih.gov/) and download the article "Cigarette smoking and gray matter brain volumes in middle age adults: the CARDIA Brain MRI sub-study" by Elbejjani et al. (2019) published in Transl. Psychiatry. Read the abstract. Explain what the following sentence means (focusing on the term 95% CI) in lay terms: "Compared to never-smokers, current smokers had smaller total GM volume (-8.86 cm (95%CI = -13.44, -4.29)). b. Go to Pubmed and read the abstract for "Association of relative brain age with tobacco smoking, alcohol consumption, and genetic variants" by Ning et al. in Sci Rep. 2020. Explain to a freshman who has not taken a statistics course what the p value means in this sentence: "We found that daily or almost daily consumption of tobacco and alcohol were both significantly associated with increased RBA (P < 0.001). Cigarette smoking and gray matter brain volumes in middle age adults: the CARDIA Brain MRI sub-study Martine Elbejjani 1, Reto Auer 2, David R Jacobs Jr 3, Thaddeus Haight 1, Christos Davatzikos 4, David C Goff Jr R Nick Bryan Lenore J Launer 6 Affiliations ' expand PMID: 30741945 PMCID: PMC6370765 DOI: 10.1038/s41398-019-0401-1 Free PMC article Abstract Cigarette smoking has been associated with dementia and dementia-related brain changes, notably gray matter (GM) volume atrophy. These associations are thought to reflect the co- morbidity of smoking and vascular, respiratory, and substance use/psychological conditions. However, the extent and localization of the smoking-GM relationship and the degree to which vascular, respiratory, and substance use/psychological factors influence this relationship remain unclear. In the Coronary Artery Risk Development in Young Adults CARDIA cohort (n = 698; 52% women; 40% black participants; age = 50.3 (SD = 3.5)), we examined the associations of smoking status with total GM volume and GM volume of brain regions linked to neurocognitive and addiction disorders. Linear regression models were used to adjust for vascular, respiratory, and substance use/psychological factors and to examine whether they modify the smoking-GM relationship. Compared to never-smokers, current smokers had smaller total GM volume (-8.86 cm (95%CI = -13.44, -4.29). Adjustment for substance use/psychological - but not vascular or respiratory - factors substantially attenuated this association (coefficients = -5.54 (95% CI = -10.32, -0.76); -8.33 (95% CI = -12.94, -3.72); -7.69 (95% CI = -6.95, -4.21), respectively). There was an interaction between smoking and alcohol use such that among alcohol non-users, smoking was not related to GM volumes and among alcohol users, those who currently smoked had -12 cm smaller total GM, specifically in the frontal and temporal lobes, amygdala, cingulate, and insula. Results suggest a large-magnitude association between smoking and smaller GM volume at middle age, accounting for vascular, respiratory, and substance use/psychological factors, and that the association was strongest in alcohol users. Regions suggested to be most vulnerable are those where cognition and addiction processes overlap. Association of relative brain age with tobacco smoking, alcohol consumption, and genetic variants Kaida Ning 12, Lu Zhao 1, Will Matloff 13, Fengzhu Sun 2, Arthur W Toga Affiliations expand 4 PMID: 32001736 PMCID: PMC6992742 DOI: 10.1038/s41598-019-56089-4 Free PMC article Abstract Brain age is a metric that quantifies the degree of aging of a brain based on whole-brain anatomical characteristics. While associations between individual human brain regions and environmental or genetic factors have been investigated, how brain age is associated with those factors remains unclear. We investigated these associations using UK Biobank data. We first trained a statistical model for obtaining relative brain age (RBA), a metric describing a subject's brain age relative to peers, based on whole-brain anatomical measurements, from training set subjects (n = 5,193). We then applied this model to evaluation set subjects (n = 12,115) and tested the association of RBA with tobacco smoking, alcohol consumption, and genetic variants. We found that daily or almost daily consumption of tobacco and alcohol were both significantly associated with increased RBA (P < 0.001). We also found SNPs significantly associated with RBA (p-value < 5E-8). The SNP most significantly associated with RBA is located in MAPT gene. Our results suggest that both environmental and genetic factors are associated with structural brain aging.