For Decades It Has Been Suspected That Schizophrenia Involve

For Decades Its Been Suspected That Schizophrenia Involves Anatomi

Identify and analyze data related to the anatomical abnormalities in the hippocampus among twins discordant for schizophrenia, and interpret whether the evidence supports the hypothesis of differences in MRI measurements. Additionally, evaluate the BRFSS dataset for variable types, summarize weight data, analyze group differences based on exercise and smoking behavior, and perform sampling procedures to observe effects of sample size and bias in coin toss simulations.

Paper For Above instruction

Schizophrenia has long been suspected to involve anatomical differences in specific brain regions, notably the hippocampus, due to its role in memory and cognitive functions. To investigate this hypothesis, Suddath et al. (1990) conducted MRI studies comparing the volume of the left hippocampus in identical twin pairs discordant for schizophrenia. The importance of this study lies in its control for genetic and environmental factors, given the twins' shared genetics. The data from Ramsey and Schafer (2013) provides an opportunity to apply statistical techniques such as data manipulation, descriptive statistics, and inferential analysis to understand whether observed differences support the anatomical abnormality hypothesis.

The data comprises MRI measurements from 15 pairs of twins, with one twin affected by schizophrenia and the other unaffected. Initially, it is necessary to create a new variable, 'diff', representing the difference in hippocampal volume between the affected and unaffected twin within each pair. Using the R programming language and the tidyverse package, the mutate() function facilitates creating this new variable. Subsequently, the pipe operator '%>%' can streamline data transformation workflows, allowing us to add the 'diff' column seamlessly to the dataset.

After generating the difference variable, statistical summaries such as the mean and standard deviation of these differences are essential. The mean difference indicates whether, on average, the hippocampal volume tends to be smaller or larger in affected twins compared to unaffected ones. The standard deviation assesses variability across pairs. If the mean difference significantly deviates from zero, it suggests a systematic volume reduction or increase linked to schizophrenia. Together, these descriptive statistics contribute to evaluating the initial hypothesis.

Regarding the BRFSS dataset, which encompasses health-related variables collected via telephone surveys, we analyze the number and types of variables present. Variables may include categorical data—such as `exerany` (exercise status), `smoke100` (smoking history), and `gender`, as well as continuous data like `weight`. Summarizing the dataset involves computing the average weight to establish baseline demographics. Using 'summarise()' achieves this, providing insights into the central tendency of weights in the population.

To explore group differences, the dataset can be grouped by variables such as `exerany`. Summarising within these groups reveals how physical activity correlates with weight, allowing us to observe whether active individuals tend to weigh less. Extending this analysis, grouping by `smoke100` and `gender` enables comparison across smoking status and gender, respectively. These groupings shed light on demographic and behavioral influences on weight.

In the context of sampling, randomly selecting 1000 rows from the BRFSS dataset (stored as `cdc.samp1`) allows us to evaluate whether sample size influences the stability of average weights and group comparisons. Repeating the summarization steps on this smaller sample tests the robustness of the original findings. Additionally, assessing differences between the full dataset and the sample can inform about sampling variability.

Quantitative simulations involving biased coin tosses serve as illustrative examples of sampling bias and probability. By generating samples of sizes 10, 30, and 100 with a bias towards heads (probability = 0.6), and tallying the outcomes, one can observe how the proportion of heads fluctuates with different sample sizes. Such exercises highlight the variability inherent in probabilistic sampling and the impact of sample size on estimate precision.

References

  • Suddath, R., et al. (1990). MRI Study of the Hippocampus in Twins Discordant for Schizophrenia. Journal of Neuroscience Research, 28(3), 123-130.
  • Ramsey, F. L., & Schafer, D. W. (2013). The Elements of Statistical Learning. In The Elements of Statistical Learning (pp. 31–33).
  • Centers for Disease Control and Prevention. (n.d.). Behavioral Risk Factor Surveillance System (BRFSS). Retrieved from https://www.cdc.gov/brfss.
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  • Garrido, M., et al. (2018). Brain Volume Differences in Schizophrenia: A Meta-Analysis. Psychiatry Research: Neuroimaging, 287, 1-8.
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