NeuroDataSets: A Comprehensive Collection of Neuroscience and Brain-Related Datasets

Description

This package provides a wide variety of datasets focused on the brain, nervous system, and related disorders including Parkinson's disease, Alzheimer's, epilepsy, schizophrenia, gliomas, and mental health.

Details

NeuroDataSets: A Comprehensive Collection of Neuroscience and Brain-Related Datasets

A Comprehensive Collection of Neuroscience and Brain-Related Datasets.

Author(s)

Maintainer: Renzo Caceres Rossi arenzocaceresrossi@gmail.com

See Also

Useful links:

• https://github.com/lightbluetitan/neurodatasets

Allen Brain Atlas Phenotype Data

Description

This dataset, aba_phenotype_data_df, is a data frame containing brain tissue phenotype measurements from the Allen Brain Atlas Aging, Dementia, and TBI study. The data includes immunohistochemistry markers for microglia and astrocytes across 377 brain samples, intended for correlation analyses with expression data.

Usage

data(aba_phenotype_data_df)

Format

A data frame with 377 observations and 4 variables:

structure_acronym.x

Character: Brain structure acronym

ihc_iba1_ffpe

Numeric: IBA1 immunohistochemistry measurement (microglia marker)

ihc_gfap_ffpe

Numeric: GFAP immunohistochemistry measurement (astrocyte marker)

id

Character: Sample identification code

Details

The dataset name has been kept as 'aba_phenotype_data_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the BRETIGEA package version 1.0.3. Original data from: Allen Brain Atlas Aging, Dementia, and TBI study.

Ability and Intelligence Tests

Description

This dataset, ability_intelligence_list, is a list containing psychometric data from six cognitive tests administered to 112 individuals. The list includes a covariance matrix, variable means, and observation count for tests measuring various intellectual abilities.

Usage

data(ability_intelligence_list)

Format

A list with 3 components:

cov

Numeric matrix [6×6]: Test score covariance matrix

center

Numeric vector [6]: Variable means

n.obs

Numeric: Number of observations (112)

Details

The dataset name has been kept as 'ability_intelligence_list' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'list' indicates that the dataset is a list object. The original content has not been modified.

Source

Data taken from the educationR package version 0.10

Adolescent Mental Health Study

Description

This dataset, adolescent_mental_health_df, is a data frame containing mental health assessments from the National Longitudinal Study of Adolescent Health. The data includes depression and anxiety measures for 4,344 students in grades 7-12 from a cross-sectional sample analyzed by Warne (2014).

Usage

data(adolescent_mental_health_df)

Format

A data frame with 4,344 observations and 3 variables:

grade

Ordered factor with 6 levels: School grade (7-12)

depression

Integer: Depression symptom score

anxiety

Integer: Anxiety symptom score

Details

The dataset name has been kept as 'adolescent_mental_health_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the heplots package version 1.7.4. Original analysis: Warne, R.T. (2014) A primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists. Practical Assessment, Research & Evaluation, 19(1).

Smoking and Alzheimer's Disease

Description

This dataset, alzheimer_smoking_df, is a data frame containing case-control data from a study examining the association between smoking and Alzheimer's disease. The study included 538 participants with information on smoking status, disease classification, and gender.

Usage

data(alzheimer_smoking_df)

Format

A data frame with 538 observations and 3 variables:

smoking

Factor: Smoking status of participants (4 levels)

disease

Factor: Disease classification including Alzheimer's diagnosis (3 levels)

gender

Factor: Participant's gender (2 levels)

Details

The dataset name has been kept as 'alzheimer_smoking_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the coin package version 1.4-3. Original study: Salib, E. and Hillier, V. (1997). A case-control study of smoking and Alzheimer's disease. International Journal of Geriatric Psychiatry 12: 295-300.

Alzheimer's Disease Biomarkers Study

Description

This dataset, alzheimers_biomarkers_tbl_df, is a tibble containing clinical data from 333 patients in a study of Alzheimer's disease biomarkers. The study included patients with mild cognitive impairment and healthy controls, with measurements of demographic characteristics, apolipoprotein E genotype, protein biomarkers (including Abeta, Tau, and pTau), and clinical dementia scores.

Usage

data(alzheimers_biomarkers_tbl_df)

Format

A tibble with 333 observations and 131 variables:

age

Numeric: Patient age

male

Numeric: Indicator for male gender (1 = male, 0 = female)

Genotype

Factor: Apolipoprotein E genotype

Class

Factor: Clinical classification (e.g., healthy, impaired)

Ab_42

Numeric: Amyloid-beta 42 protein measurement

tau

Numeric: Tau protein measurement

p_tau

Numeric: Phosphorylated Tau protein measurement

[131 additional biomarker variables]

Numeric measurements of various proteins and biomarkers

Details

The dataset name has been kept as 'alzheimers_biomarkers_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'tbl_df' indicates that the dataset is a tibble. The original content has not been modified.

Source

Data taken from the modeldata package version 1.4.0. Original study: Craig-Schapiro R, Kuhn M, Xiong C, et al. (2011) Multiplexed Immunoassay Panel Identifies Novel CSF Biomarkers for Alzheimer's Disease Diagnosis and Prognosis. PLoS ONE 6(4): e18850.

Brain Structure in Bilingual Humans

Description

This dataset, bilingual_brains_df, is a data frame containing measurements of second language proficiency scores and gray matter density in the left inferior parietal region from 22 observations.

Usage

data(bilingual_brains_df)

Format

A data frame with 22 observations and 2 variables:

proficiency

Numeric vector representing second language proficiency scores (summary of reading, writing, and speech)

greymatter

Numeric vector representing density of gray matter in the left inferior parietal region

Details

The dataset name has been kept as 'bilingual_brains_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the abd package version 0.2-8

Blood-Brain Barrier

Description

This dataset, blood_brain_barrier_df, is a data frame containing experimental measurements from a rat study investigating sugar-infusion methods for temporary blood-brain barrier disruption. The barrier's protective function was assessed through multiple biological markers.

Usage

data(blood_brain_barrier_df)

Format

A data frame with 34 observations and 9 variables:

Brain

Integer: Brain tissue measurement (units?)

Liver

Integer: Liver tissue measurement (units?)

Time

Numeric: Experimental time measurement (hours)

Treatment

Factor with 2 levels: Experimental treatment groups

Days

Integer: Observation period (days)

Sex

Factor with 2 levels: Animal sex (Male/Female)

Weight

Integer: Subject weight (grams)

Loss

Numeric: Physiological loss measurement

Tumor

Integer: Tumor presence indicator (0/1)

Details

The dataset name has been kept as 'blood_brain_barrier_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the Sleuth3 package version 1.0-6. Original reference: Ramsey, F.L. and Schafer, D.W. (2013) The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed), Cengage Learning.

Mammal Brain Size and Litter Size Relationship

Description

This dataset, brain_litter_mammals_df, is a data frame comparing relative brain weights between 96 mammalian species divided by reproductive strategy: 51 species with small litters (< 2 offspring) and 45 species with large litters (\geq 2 offspring).

Usage

data(brain_litter_mammals_df)

Format

A data frame with 96 observations and 2 variables:

BrainSize

Numeric: Relative brain weight measurement (encephalization quotient or similar metric)

LitterSize

Factor with 2 levels: Reproductive strategy ("Small" (< 2) and "Large" (\geq 2) litter sizes)

Details

The dataset name has been kept as brain_litter_mammals_df to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix df indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the Sleuth3 package version 1.0-6. Original reference: Ramsey, F.L. and Schafer, D.W. (2002) The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed), Duxbury.

Brain Size and IQ Study Data

Description

This dataset, brain_size_iq_df, is a data frame containing neurocognitive measurements from a study examining relationships between brain size, gender, and intelligence. The data include 40 right-handed psychology students with no neurological history, selected based on extreme Scholastic Aptitude Test scores.

Usage

data(brain_size_iq_df)

Format

A data frame with 40 observations and 7 variables:

ID

Numeric: Participant identification number

GENDER

Factor with 2 levels: Participant's gender (Male/Female)

FSIQ

Numeric: Full Scale IQ score

VIQ

Numeric: Verbal IQ score

PIQ

Numeric: Performance IQ score

MRI

Numeric: Brain size measurement from MRI (in cubic cm)

IQDI

Factor with 2 levels: IQ group classification (High/Low)

Details

The dataset name has been kept as 'brain_size_iq_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the sur package version 1.0.4. Original study: Willerman, L., Schultz, R., Rutledge, J.N. and Bigler, E.D. (1991) In Vivo Brain Size and Intelligence. Intelligence, 15, 223-228.

Brain Activity in String Players

Description

This dataset, brain_string_players_df, is a data frame containing neurophysiological measurements from a study of 15 violin and other string instrument players. The data examines the relationship between years of musical practice and measured brain activity levels in relevant cortical regions.

Usage

data(brain_string_players_df)

Format

A data frame with 15 observations and 2 variables:

Years

Integer: Years of musical practice

Activity

Numeric: Brain activity measurement (likely fMRI or similar neuroimaging units)

Details

The dataset name has been kept as 'brain_string_players_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the Sleuth3 package version 1.0-6. Original reference: Ramsey, F.L. and Schafer, D.W. (2013) The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed), Cengage Learning.

BRAiNS Cohort Cognitive States Matrix

Description

This dataset, brains_cognitive_matrix, is a matrix containing the states and covariates of 649 participants enrolled in the BRAiNS cohort at the University of Kentucky's Alzheimer's Disease Research Center. The data includes longitudinal cognitive assessments and various health covariates across multiple visits.

Usage

data(brains_cognitive_matrix)

Format

A matrix with 6240 observations and 13 variables:

ID

Integer: Participant identification number

visitno

Integer: Visit number

prstate

Integer: Previous cognitive state

custate

Integer: Current cognitive state

bagec

Integer: Baseline age centered

famhx

Integer: Family history of dementia (0 = No, 1 = Yes)

HBP

Integer: History of high blood pressure (0 = No, 1 = Yes)

apoe4

Integer: APOE \varepsilon_4 allele carrier status (0 = Non-carrier, 1 = Carrier)

smk1

Integer: Smoking status indicator 1

smk2

Integer: Smoking status indicator 2

smk3

Integer: Smoking status indicator 3

lowed

Integer: Low education indicator (0 = No, 1 = Yes)

headinj

Integer: History of head injury (0 = No, 1 = Yes)

Details

The dataset name has been kept as brains_cognitive_matrix to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix matrix indicates that the dataset is a matrix. The original content has not been modified.

Source

Data taken from the RRMLRfMC package version 0.4.0. Original study: University of Kentucky's Alzheimer's Disease Research Center BRAiNS cohort.

Effects of Cocaine on Dopamine Receptors

Description

This dataset, cocaine_dopamine_df, is a data frame containing measurements of dopamine receptor blockade and perceived high levels from 34 human subjects as determined by PET scans.

Usage

data(cocaine_dopamine_df)

Format

A data frame with 34 observations and 2 variables:

percent.blocked

Integer vector representing percent of dopamine receptors blocked

high

Integer vector representing perceived level of high from PET scans

Details

The dataset name has been kept as 'cocaine_dopamine_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the abd package version 0.2-8

Dopamine \beta-Hydroxylase Activity in Schizophrenia

Description

This dataset, 'dopamine_schizophrenia_tbl_df', is a tibble containing measurements of dopamine \beta-hydroxylase (DBH) activity in 25 schizophrenic patients treated with antipsychotic medication. The data compares DBH levels between patient groups.

Usage

data(dopamine_schizophrenia_tbl_df)

Format

A tibble with 25 observations and 2 variables:

dbh

Integer: Dopamine \beta-hydroxylase activity level (nmol/(mL\cdothr))

group

Character: Treatment/patient group classification

Details

The dataset name has been kept as dopamine_schizophrenia_tbl_df to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix tbl_df indicates that the dataset is a tibble. The original content has not been modified.

Source

Data taken from the BSDA package version 1.2.2

Epilepsy Treatment Randomized Controlled Trial

Description

This dataset, epilepsy_RCT_tbl_df, is a tibble containing data from a randomized controlled trial of progabide for epilepsy treatment. The trial recorded seizure counts for 59 patients at baseline and four follow-up visits.

Usage

data(epilepsy_RCT_tbl_df)

Format

A tibble with 59 observations and 8 variables:

id

Integer: Patient identification number

treat

Factor with 2 levels: Treatment group (progabide/control)

base

Integer: Baseline seizure count

age

Integer: Patient age in years

y1

Integer: Seizure count at first follow-up

y2

Integer: Seizure count at second follow-up

y3

Integer: Seizure count at third follow-up

y4

Integer: Seizure count at fourth follow-up

Details

The dataset name has been kept as 'epilepsy_RCT_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'tbl_df' indicates that the dataset is a tibble. The original content has not been modified.

Source

Data taken from the pubh package version 2.0.0

SANAD Epilepsy Drug Treatment Quality of Life Study

Description

This dataset, epilepsy_drug_qol_df, is a data frame containing quality of life measurements from the SANAD randomized controlled trial comparing carbamazepine and lamotrigine in 544 epilepsy patients. QoL assessments were collected at baseline, 3 months, 1 year and 2 years using validated instruments.

Usage

data(epilepsy_drug_qol_df)

Format

A data frame with 1,852 observations and 9 variables:

id

Integer: Patient identification number

with.time

Numeric: Time to withdrawal/discontinuation (days)

trt

Factor with 2 levels: Treatment group (carbamazepine/lamotrigine)

with.status

Integer: Withdrawal status indicator

time

Numeric: Assessment time point (days since baseline)

anxiety

Numeric: Anxiety score (from QoL measure)

depress

Numeric: Depression score (from QoL measure)

aep

Numeric: Adverse effects profile score

with.status2

Numeric: Alternative withdrawal coding

Details

The dataset name has been kept as 'epilepsy_drug_qol_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the joineRML package version 0.4.7. Original study: Marson, A.G., et al. (2007) The SANAD study of effectiveness of carbamazepine, gabapentin, lamotrigine, oxcarbazepine, or topiramate for treatment of partial epilepsy: an unblinded randomised controlled trial. The Lancet, 369(9566), 1000-1015.

Epileptic Seizures Clinical Drug Trial

Description

This dataset, epilepsy_drug_trial_df, is a data frame containing seizure counts from a clinical trial of anti-epileptic medication. The data includes seizure frequency measurements along with treatment indicators and patient covariates for 295 observations.

Usage

data(epilepsy_drug_trial_df)

Format

A data frame with 295 observations and 6 variables:

seizures

Numeric: Count of epileptic seizures

id

Integer: Patient identification number

treat

Numeric: Treatment indicator

expind

Numeric: Exposure period indicator

timeadj

Numeric: Adjusted time period

age

Numeric: Patient age in years

Details

The dataset name has been kept as 'epilepsy_drug_trial_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the faraway package version 1.0.9

Patterns of Gray Matter in Schizophrenia

Description

This dataset, gm_expected_patterns_tbl_df, is a tibble containing expected patterns of gray matter in schizophrenia derived from large-scale meta-analyses by the ENIGMA consortium. It includes data from multiple neurological and psychiatric conditions for comparison.

Usage

data(gm_expected_patterns_tbl_df)

Format

A tibble with 33 observations and 16 variables:

GM

Character vector indicating gray matter regions

SSD

Numeric vector of expected patterns for schizophrenia spectrum disorder

MDD

Numeric vector of expected patterns for major depressive disorder

AD_ADNI

Numeric vector of expected patterns for Alzheimer's disease (ADNI cohort)

AD_ADNIOSYRIX

Numeric vector of expected patterns for Alzheimer's disease (ADNI+OSYRIX cohort)

BD

Numeric vector of expected patterns for bipolar disorder

PD

Numeric vector of expected patterns for Parkinson's disease

Diabetes

Numeric vector of expected patterns for diabetes

HighBP

Numeric vector of expected patterns for high blood pressure

HighLipids

Numeric vector of expected patterns for high lipids

MET

Numeric vector of expected patterns for metabolic syndrome

DS_22q

Numeric vector of expected patterns for 22q11.2 deletion syndrome

Suicide

Numeric vector of expected patterns for suicide

OCD_pediatric

Numeric vector of expected patterns for pediatric OCD

OCD_adult

Numeric vector of expected patterns for adult OCD

AN

Numeric vector of expected patterns for anorexia nervosa

Details

The dataset name has been kept as 'gm_expected_patterns_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble. The original content has not been modified in any way.

Source

Data taken from the RVIpkg package version 0.3.2.

Neurotransmission in Guinea Pig Brains

Description

This dataset, guineapig_neurotransmission_df, is a data frame containing measurements of spontaneous current amplitudes recorded from individual brain cells in adult guinea pigs. The study investigated whether synaptic transmission occurs in quantal units, which would manifest as multimodal amplitude distributions with regularly spaced peaks.

Usage

data(guineapig_neurotransmission_df)

Format

A data frame with 346 observations and 1 variable:

y

Numeric: Peak amplitude of spontaneous synaptic currents (pA or similar units)

Details

The dataset name has been kept as 'guineapig_neurotransmission_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the boot package version 1.3-31. Original study: Paulsen, O. and Heggelund, P. (1994) The quantal size at retinogeniculate synapses determined from spontaneous and evoked EPSCs in guinea-pig thalamic slices. Journal of Physiology, 480, 505–511.

Memory and the Hippocampus

Description

This dataset, hippocampus_lesions_df, is a data frame containing measurements of spatial memory scores and percent lesion of the hippocampus from 57 observations.

Usage

data(hippocampus_lesions_df)

Format

A data frame with 57 observations and 2 variables:

lesion

Numeric vector representing percent lesion of the hippocampus

memory

Numeric vector representing spatial memory scores

Details

The dataset name has been kept as 'hippocampus_lesions_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the abd package version 0.2-8

Mammal Brain and Body Size

Description

This dataset, mammals_brain_body_df, is a data frame containing comparative neuroanatomical and life history data for 96 mammalian species. The data examine the relationship between brain size, body size, and reproductive characteristics across different mammal species.

Usage

data(mammals_brain_body_df)

Format

A data frame with 96 observations and 5 variables:

Species

Factor with 96 levels: Mammalian species names

Brain

Numeric: Brain weight (grams)

Body

Numeric: Body weight (kilograms)

Gestation

Integer: Gestation period (days)

Litter

Numeric: Average litter size

Details

The dataset name has been kept as 'mammals_brain_body_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the Sleuth3 package version 1.0-6. Original study: Allison, T. and Cicchetti, D.V. (1976) Sleep in Mammals: Ecological and Constitutional Correlates. Science, 194, 732-734.

Cross-Species Brain Cell Marker Genes

Description

This dataset, markers_brain_df, is a data frame containing the top 1,000 marker genes for each of six major brain cell types (astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and OPCs) identified through meta-analysis of both human and mouse brain gene expression data.

Usage

data(markers_brain_df)

Format

A data frame with 6,000 observations and 2 variables:

markers

Character: Gene symbol for cell-type specific marker (human/mouse orthologs)

cell

Character: Cell type classification (astrocytes/endothelial/microglia/neurons/oligodendrocytes/OPCs)

Details

The dataset name has been kept as 'markers_brain_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the BRETIGEA package version 1.0.3. Derived from: Meta-analysis of human and mouse brain cell-type specific gene expression datasets.

Human Brain Cell Marker Genes

Description

This dataset, markers_human_brain_df, is a data frame containing the top 1,000 marker genes for each of six major brain cell types (astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and OPCs) identified through meta-analysis of human brain gene expression data.

Usage

data(markers_human_brain_df)

Format

A data frame with 5,500 observations and 2 variables:

markers

Character: Gene symbol for cell-type specific marker

cell

Character: Cell type classification (astrocytes/endothelial/microglia/neurons/oligodendrocytes/OPCs)

Details

The dataset name has been kept as 'markers_human_brain_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the BRETIGEA package version 1.0.3.

Mouse Brain Cell Marker Genes

Description

This dataset, markers_mouse_brain_df, is a data frame containing the top 1,000 marker genes for each of six major brain cell types (astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and OPCs) identified through meta-analysis of mouse brain gene expression data.

Usage

data(markers_mouse_brain_df)

Format

A data frame with 5,430 observations and 2 variables:

markers

Character: Gene symbol for cell-type specific marker

cell

Character: Cell type classification (astrocytes/endothelial/microglia/neurons/oligodendrocytes/OPCs)

Details

The dataset name has been kept as 'markers_mouse_brain_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the BRETIGEA package version 1.0.3. Original study: Mckenzie AT, Wang M, Hauberg ME, et al. (2018) Brain Cell Type Specific Gene Expression and Co-expression Network Architectures. Scientific Reports, 8(1), 8868.

Migraine Headache Treatment

Description

This dataset, migraine_treatment_df, is a data frame containing clinical data on 4,152 migraine treatment cases collected by Tammy Kostecki-Dillon. The data includes treatment details, headache characteristics, and patient demographics.

Usage

data(migraine_treatment_df)

Format

A data frame with 4,152 observations and 9 variables:

id

Integer: Patient identification number

time

Integer: Time measurement (likely days or hours)

dos

Integer: Treatment dosage

hatype

Factor with 3 levels: Headache type classification

age

Integer: Patient age in years

airq

Numeric: Air quality index measurement

medication

Factor with 3 levels: Medication type

headache

Factor with 2 levels: Headache presence/severity

sex

Factor with 2 levels: Patient sex

Details

The dataset name has been kept as 'migraine_treatment_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the carData package version 3.0-5. Original collection: Kostecki-Dillon, T. (Year not specified) Migraine Treatment Study.

Cranial Capacity in Neanderthals and Modern Humans

Description

This dataset, neanderthal_brains_df, is a data frame containing measurements of brain size (lnbrain) and body mass (lnmass) from 39 specimens of Neanderthals and early modern humans, identified by species.

Usage

data(neanderthal_brains_df)

Format

A data frame with 39 observations and 3 variables:

ln.mass

Numeric vector representing natural logarithm of body mass

ln.brain

Numeric vector representing natural logarithm of brain size

species

Factor indicating species with 2 levels (Neanderthals and early modern humans)

Details

The dataset name has been kept as 'neanderthal_brains_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the abd package version 0.2-8

Neurophysiological Point Process Data

Description

This dataset, neuro_pointprocess_matrix, is a matrix containing times of observed neuronal firing in windows of 250ms surrounding stimulus application in human subjects. Each row represents an experimental replication (469 total replicates), with values indicating spike times relative to stimulus onset.

Usage

data(neuro_pointprocess_matrix)

Format

A numeric matrix with 469 observations (rows) and 6 variables (columns):

[,1:6]

Numeric: Spike times (milliseconds) relative to stimulus onset, with NA representing no spike in that trial window

Details

The dataset name has been kept as 'neuro_pointprocess_matrix' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'matrix' indicates that the dataset is a matrix. The original content has not been modified.

Source

Data taken from the boot package version 1.3-31. Original collection: Dr. S.J. Boniface, Neurophysiology Unit, Radcliffe Infirmary, Oxford.

Neurocognitive Performance in Psychosis

Description

This dataset, neurocognitive_psychiatric_df, is a data frame containing comprehensive neurocognitive assessments from a study comparing performance patterns in schizophrenia, schizoaffective disorder, and controls. The data includes 242 observations across multiple cognitive domains using a psychosis-specific neurocognitive battery.

Usage

data(neurocognitive_psychiatric_df)

Format

A data frame with 242 observations and 10 variables:

Dx

Factor with 3 levels: Diagnostic group (Schizophrenia/Schizoaffective/Control)

Speed

Integer: Processing speed score

Attention

Integer: Attention/vigilance score

Memory

Integer: Working memory score

Verbal

Integer: Verbal learning score

Visual

Integer: Visual learning score

ProbSolv

Integer: Problem solving score

SocialCog

Integer: Social cognition score

Age

Integer: Participant age in years

Sex

Factor with 2 levels: Participant sex

Details

The dataset name has been kept as 'neurocognitive_psychiatric_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the heplots package version 1.7.4. Original research: Hartman, L.I. (2016) Schizophrenia and Schizoaffective Disorder: One Condition or Two? Unpublished PhD dissertation, York University.

OASIS Aging-Dementia Longitudinal MRI

Description

This dataset, oasis_dementia_mri_df, is a data frame containing longitudinal neuroimaging and clinical data from 150 older adults (60-96 years) with repeated MRI scans over multiple visits. The study includes both nondemented and demented individuals, with 373 total imaging sessions featuring 3-4 T1-weighted scans per session.

Usage

data(oasis_dementia_mri_df)

Format

A data frame with 373 observations and 15 variables:

Subject.ID

Character: Unique subject identifier

MRI.ID

Character: Unique MRI session identifier

Group

Factor with 3 levels: Diagnostic group classification

Visit

Integer: Visit number

MR.Delay

Integer: Days since first visit

Gender

Character: Subject gender

Hand

Character: Handedness

Age

Integer: Subject age in years

EDUC

Integer: Years of education

SES

Integer: Socioeconomic status

MMSE

Integer: Mini-Mental State Examination score (0-30)

CDR

Numeric: Clinical Dementia Rating (0-3)

eTIV

Integer: Estimated total intracranial volume (mm³)

nWBV

Numeric: Normalized whole brain volume

ASF

Numeric: Atlas scaling factor

Details

The dataset name has been kept as 'oasis_dementia_mri_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified.

Source

Data taken from the jointest package version 1.0. Original study: Marcus, D.S. et al. (2007) Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented and Demented Older Adults. Journal of Cognitive Neuroscience, 19(9), 1498-1507.

Dopamine Agonists as Adjunct Therapy in Parkinson’s

Description

This dataset, parkinsons_dopamine_list, is a list containing information from 7 studies investigating the mean lost work-time reduction in patients given 4 dopamine agonists and placebo as adjunct therapy for Parkinson's disease. There is placebo and four active drugs coded 2 to 5.

Usage

data(parkinsons_dopamine_list)

Format

A list with 5 components:

Outcomes

Numeric vector containing the outcomes (mean lost work-time reduction)

SE

Numeric vector containing standard errors for the outcomes

Treat

Character vector indicating the treatment (placebo or drug codes 2-5)

Study

Numeric vector indicating the study number (1-7)

Treat.order

Character vector showing the treatment order (placebo and drugs 2-5)

Details

The dataset name has been kept as 'parkinsons_dopamine_list' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package and assists users in identifying its specific characteristics. The suffix 'list' indicates that the dataset is a list. The original content has not been modified in any way.

Source

Data taken from the bnma package version 1.6.0.

Pediatric High-Grade Glioma Clinical Dataset

Description

This dataset, pediatric_glioma_tbl_df, is a tibble containing comprehensive clinical and tumor characteristics for 57 pediatric patients with high-grade glioma. The data includes 22 variables covering demographic, symptomatic, pathological, treatment, and outcome measures.

Usage

data(pediatric_glioma_tbl_df)

Format

A tibble with 57 observations and 22 variables:

Age

Numeric: Patient age in years

Gender

Character: Patient gender

Headache

Character: Headache presence/characteristics

Epilepsy

Character: Epilepsy status

Hemparesis

Character: Hemiparesis presence

increaseICT

Character: Increased intracranial pressure indicators

Pathology

Character: Tumor pathology classification

Pathology_Grade

Numeric: WHO tumor grade (III-IV)

Thalamic_extension

Character: Thalamic involvement

Bil_extension

Character: Bilateral extension

Post_extension

Character: Posterior fossa extension

BrainStem_extension

Character: Brainstem involvement

MultiFocality

Character: Multifocal tumor presence

Midlineshift

Character: Midline shift presence

Edema

Character: Peritumoral edema characteristics

Approx_Tumor_Vol

Numeric: Estimated tumor volume (cm³)

ExtentofSurgicalresection

Character: Surgical resection extent

Shunt

Character: Ventricular shunt presence

ResidualsizeonMRI

Character: Post-surgical residual tumor

Neurostate

Character: Neurological status

PSBeforeRT

Numeric: Performance status pre-radiotherapy

Died

Character: Mortality outcome

Details

The dataset name has been kept as 'pediatric_glioma_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package. The suffix 'tbl_df' indicates that the dataset is a tibble. The original content has not been modified.

Source

Kaggle dataset: Pediatric High-Grade Glioma Dataset. URL: https://www.kaggle.com/datasets/amraam/pediatric-high-grade-glioma-dataset

Sleep and Learning Performance

Description

This dataset, sleep_performance_df, is a data frame containing measurements of the increase in slow-wave sleep and corresponding improvements in spatial learning tasks from 10 human subjects.

Usage

data(sleep_performance_df)

Format

A data frame with 10 observations and 2 variables:

sleep

Integer vector representing increase in slow-wave sleep (units)

improvement

Integer vector representing improvement in spatial learning tasks (units)

Details

The dataset name has been kept as 'sleep_performance_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the abd package version 0.2-8

Patterns of Subcortical Structures

Description

This dataset, subcortical_patterns_tbl_df, is a tibble containing expected patterns of subcortical structures in schizophrenia derived from large-scale meta-analyses by the ENIGMA consortium. It includes data from multiple neurological and psychiatric conditions for comparison.

Usage

data(subcortical_patterns_tbl_df)

Format

A tibble with 8 observations and 16 variables:

Subcortical

Character vector indicating subcortical regions

SSD

Numeric vector of expected patterns for schizophrenia spectrum disorder

MDD

Numeric vector of expected patterns for major depressive disorder

AD_ADNI

Numeric vector of expected patterns for Alzheimer's disease (ADNI cohort)

AD_ADNIOSYRIX

Numeric vector of expected patterns for Alzheimer's disease (ADNI+OSYRIX cohort)

BD

Numeric vector of expected patterns for bipolar disorder

PD

Numeric vector of expected patterns for Parkinson's disease

Diabetes

Numeric vector of expected patterns for diabetes

HighBP

Numeric vector of expected patterns for high blood pressure

HighLipids

Numeric vector of expected patterns for high lipids

MET

Numeric vector of expected patterns for metabolic syndrome

DS_22q

Numeric vector of expected patterns for 22q11.2 deletion syndrome

Suicide

Numeric vector of expected patterns for suicide

OCD_pediatric

Numeric vector of expected patterns for pediatric OCD

OCD_adult

Numeric vector of expected patterns for adult OCD

AN

Numeric vector of expected patterns for anorexia nervosa

Details

The dataset name has been kept as 'subcortical_patterns_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble. The original content has not been modified in any way.

Source

Data taken from the RVIpkg package version 0.3.2

View Available Datasets in NeuroDataSets

Description

This function lists all datasets available in the 'NeuroDataSets' package. If the 'NeuroDataSets' package is not loaded, it stops and shows an error message. If no datasets are available, it returns a message and an empty vector.

Usage

view_datasets_neuro()

Value

A character vector with the names of the available datasets. If no datasets are found, it returns an empty character vector.

Examples

if (requireNamespace("NeuroDataSets", quietly = TRUE)) {
  library(NeuroDataSets)
  view_datasets_neuro()
}

Expected Patterns of White Matter

Description

This dataset, white_matter_patterns_tbl_df, is a tibble containing expected patterns of white matter in schizophrenia derived from large-scale meta-analyses by the ENIGMA consortium. It includes data from multiple neurological and psychiatric conditions for comparison.

Usage

data(white_matter_patterns_tbl_df)

Format

A tibble with 24 observations and 15 variables:

WM

Character vector indicating white matter regions

SSD

Numeric vector of expected patterns for schizophrenia spectrum disorder

MDD

Numeric vector of expected patterns for major depressive disorder

AD_ADNI

Numeric vector of expected patterns for Alzheimer's disease (ADNI cohort)

AD_ADNIOSYRIX

Numeric vector of expected patterns for Alzheimer's disease (ADNI+OSYRIX cohort)

BD

Numeric vector of expected patterns for bipolar disorder

Diabetes

Numeric vector of expected patterns for diabetes

HighBP

Numeric vector of expected patterns for high blood pressure

HighLipids

Numeric vector of expected patterns for high lipids

MET

Numeric vector of expected patterns for metabolic syndrome

DS_22q

Numeric vector of expected patterns for 22q11.2 deletion syndrome

PTSD

Numeric vector of expected patterns for post-traumatic stress disorder

TBI

Numeric vector of expected patterns for traumatic brain injury

OCD_pediatric

Numeric vector of expected patterns for pediatric OCD

OCD_adult

Numeric vector of expected patterns for adult OCD

Details

The dataset name has been kept as 'white_matter_patterns_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the NeuroDataSets package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble. The original content has not been modified in any way.

Source

Data taken from the RVIpkg package version 0.3.2