Introduction

The NeuroDataSets package offers a rich and diverse collection of datasets focused on the brain, the nervous system, and neurological and psychiatric disorders. It includes data on conditions such as Parkinson’s disease, Alzheimer’s disease, epilepsy, schizophrenia, gliomas, and mental health.

The package contains a wide variety of data types, including clinical, experimental, neuroimaging, behavioral, cognitive, and simulated datasets. These datasets encompass structural and functional brain data, neurotransmission metrics, gene expression profiles, cognitive performance assessments, and treatment outcomes.

Dataset Suffixes

Each dataset in the NeuroDataSets package uses a suffix to denote the type of R object:

_df: A data frame
_list: A list
_tbl_df: A tibble
_matrix: A matrix

Example Datasets

Below are selected example datasets included in the NeuroDataSets package:

subcortical_patterns_tbl_df: Patterns of Subcortical Structures.
white_matter_patterns_tbl_df: Expected Patterns of White Matter.
hippocampus_lesions_df: Memory and the Hippocampus.

Data Visualization with CardioDataSets Data

Patterns of Subcortical Structures


# Convert the dataset to long format using only base R + dplyr

long_data <- subcortical_patterns_tbl_df %>%
  select(Subcortical, everything()) %>%
  as.data.frame() %>%
  reshape(
    varying = names(.)[-1],
    v.names = "Value",
    timevar = "Condition",
    times = names(.)[-1],
    direction = "long"
  ) %>%
  select(Subcortical, Condition, Value)

# Create a heatmap
ggplot(long_data, aes(x = Condition, y = Subcortical, fill = Value)) +
  geom_tile(color = "white") +
  scale_fill_gradient(low = "lightblue", high = "darkred") +
  labs(
    title = "Subcortical Patterns by Condition",
    x = "Condition",
    y = "Subcortical Region",
    fill = "Value"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Expected Patterns of White Matter


# Compute mean values using updated anonymous function syntax
summary_data <- white_matter_patterns_tbl_df %>%
  select(-WM) %>%
  summarise(across(everything(), \(x) mean(x, na.rm = TRUE))) %>%
  as.data.frame()

# Reshape from wide to long format using base R
summary_data <- data.frame(
  Condition = names(summary_data),
  MeanValue = as.numeric(summary_data[1, ])
)

# Plot
ggplot(summary_data, aes(x = Condition, y = MeanValue, fill = Condition)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Average Value per Condition across White Matter Regions",
    x = "Condition",
    y = "Mean Value"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  guides(fill = "none")  # Optional

Memory and the Hippocampus


# Lesion Size and Memory Score

ggplot(hippocampus_lesions_df, aes(x = lesion, y = memory)) +
  geom_point(color = "blue", size = 2) +
  labs(
    title = "Relationship Between Lesion Size and Memory Score",
    x = "Lesion Size",
    y = "Memory Score"
  ) +
  theme_minimal()

Conclusion

The NeuroDataSets package offers a rich, curated collection of datasets focused on neuroscience and related disorders. It supports advanced statistical analysis, exploratory data science, and educational purposes by providing well-structured and documented datasets across a variety of neurological and neuropsychiatric conditions.

For detailed information and full documentation of each dataset, please refer to the reference manual and help files included within the package.

Introduction_to_NeuroDataSets