Summary statistics

This page contains descriptions and examples to load case-control datasets from several ENIGMA Working Groups. These ENIGMA summary statistics contain the following data: effect sizes for case-control differences (d_icv), standard error (se_icv), lower bound of the confidence interval (low_ci_icv), upper bound of the confidence interval (up_ci_icv), number of controls (n_controls), number of patiens (n_patients), observed p-values (pobs), false discovery rate (FDR)-corrected p-value (fdr_p).

ENIGMA’s standardized protocols for data processing, quality assurance, and meta-analysis of individual subject data were conducted at each site. For site-level meta-analysis, all research centres within a given specialized Working Group tested for case vs. control differences using multiple linear regressions, where diagnosis (e.g., healthy controls vs. individuals with epilepsy) was the predictor of interest, and subcortical volume, cortical thickness, or surface area of a given brain region was the outcome measure. Case-control differences were computed across all regions using either Cohen’s d effect sizes or t-values, after adjusting for different combinations of age, sex, site/scan/dataset, intracranial volume, IQ (see below for disease-specific models).

Can’t find the data you’re searching for? 🙈

Let us know what’s missing and we’ll try and fetch that data for you and implement it in our toolbox. Get in touch with us here. If you have locally stored summary statistics on your computer, check out our tutorials on how to import data and accordingly take advantage of all the ENIGMA TOOLBOX functions.

* 📸 indicates case-control tables used in the code snippets.

22q11.2 deletion syndrome

Available summary statistics tables

From Sun et al., 2020, Mol Psychiatry | age, sex, data set/site, and ICV* correction; FDR correction available

*only for surface area measures

↪ CortThick_case_vs_controls 📸

↪ CortSurf_case_vs_controls 📸

↪ CortThick_psychP_vs_psychN (+/- psychosis)

↪ CortSurf_psychP_vs_psychN (+/- psychosis)

From Ching et al., 2020, Am J Psychiatry | age, age^2, sex, scan site, and ICV correction; FDR correction available

↪ SubVol_case_vs_controls

↪ SubVol_case_vs_controls_AD (A-D deletion)

↪ SubVol_case_vs_controls_AB (A-B deletion)

↪ SubVol_AB_vs_AD

↪ SubVol_psychP_vs_psychN (+/- psychosis)

Python | meta

Matlab | meta

>>> from enigmatoolbox.datasets import load_summary_stats

>>> # Load summary statistics for ENIGMA-22q
>>> sum_stats = load_summary_stats('22q')

>>> # Get case-control cortical thickness and surface area tables
>>> CT = sum_stats['CortThick_case_vs_controls']
>>> SA = sum_stats['CortSurf_case_vs_controls']

>>> # Extract Cohen's d values
>>> CT_d = CT['d_icv']
>>> SA_d = SA['d_icv']

% Load summary statistics for ENIGMA-22q
sum_stats = load_summary_stats('22q');

% Get case-control cortical thickness and surface area tables
CT = sum_stats.CortThick_case_vs_controls;
SA = sum_stats.CortSurf_case_vs_controls;

% Extract Cohen's d values
CT_d = CT.d_icv;
SA_d = SA.d_icv;

Attention deficit hyperactivity disorder

Available summary statistics tables

From Hoogman et al., 2019, Am J Psychiatry | mega-analysis; age, sex, and ICV* correction; FDR correction available

*only for surface area measures

ALL AGES

↪ CortThick_case_vs_controls_allages

↪ CortSurf_case_vs_controls_allages

ADULTS (age 22-63 years)

↪ CortThick_case_vs_controls_adult 📸

↪ CortSurf_case_vs_controls_adult 📸

ADOLESCENTS (age 15-21 years)

↪ CortThick_case_vs_controls_adolescent

↪ CortSurf_case_vs_controls_adolescent

CHILDREN (age 4-14 years)

↪ CortThick_case_vs_controls_pediatric

↪ CortSurf_case_vs_controls_pediatric

From Hoogman et al., 2017, Lancet Psychiatry | mega-analysis; age, sex, ICV, and site correction; p<0.0156 for FDR correction at q=0.05; mean [(left+right)/2] region of interest volume

ALL AGES

↪ SubVol_case_vs_controls_allages

ADULTS (age≥22 years)

↪ SubVol_case_vs_controls_adult

ADOLESCENTS (age 15-21 years)

↪ SubVol_case_vs_controls_adolescent

CHILDREN (age⩽14 years)

↪ SubVol_case_vs_controls_pediatric

Python | meta

Matlab | meta

>>> from enigmatoolbox.datasets import load_summary_stats

>>> # Load summary statistics for ENIGMA-ADHD
>>> sum_stats = load_summary_stats('adhd')

>>> # Get case-control cortical thickness and surface area tables
>>> CT = sum_stats['CortThick_case_vs_controls_adult']
>>> SA = sum_stats['CortSurf_case_vs_controls_adult']

>>> # Extract Cohen's d values
>>> CT_d = CT['d_icv']
>>> SA_d = SA['d_icv']

% Load summary statistics for ENIGMA-ADHD
sum_stats = load_summary_stats('adhd');

% Get case-control cortical thickness and surface area tables
CT = sum_stats.CortThick_case_vs_controls_adult;
SA = sum_stats.CortSurf_case_vs_controls_adult;

% Extract Cohen's d values
CT_d = CT.d_icv;
SA_d = SA.d_icv;

Autism spectrum disorder

Available summary statistics tables

From van Rooij et al., 2018, Am J Psychiatry | age, sex, IQ, and ICV* correction; FDR correction available (uncorrected p-values not provided); mean* [(left+right)/ 2)] region of interest volume

*only for subcortical volume measures

↪ CortThick_case_vs_controls_meta_analysis 📸

↪ CortThick_case_vs_controls_mega_analysis

↪ SubVol_case_vs_controls_meta_analysis

Python | meta

Matlab | meta

>>> from enigmatoolbox.datasets import load_summary_stats

>>> # Load summary statistics for ENIGMA-Autism
>>> sum_stats = load_summary_stats('asd')

>>> # Get case-control cortical thickness table
>>> CT = sum_stats['CortThick_case_vs_controls_meta_analysis']

>>> # Extract Cohen's d values
>>> CT_d = CT['d_icv']

% Load summary statistics for ENIGMA-Autism
sum_stats = load_summary_stats('asd');

% Get case-control cortical thickness table
CT = sum_stats.CortThick_case_vs_controls_meta_analysis;

% Extract Cohen's d values
CT_d = CT.d_icv;

Bipolar disorder

Available summary statistics tables

From Hibar al., 2018, Mol Psychiatry | age, sex, and ICV* correction; FDR correction available

*only for surface area measures

ADULTS (age⩾25 years)

↪ CortThick_case_vs_controls_adult 📸

↪ CortSurf_case_vs_controls_adult 📸

↪ CortThick_typeI_vs_typeII_adult

↪ CortSurf_typeI_vs_typeII_adult

ADOLESCENTS/YOUNG ADULTS (age<25 years)

↪ CortThick_case_vs_controls_adolescent

↪ CortSurf_case_vs_controls_adolescent

↪ CortThick_typeI_vs_typeII_adolescent

↪ CortSurf_typeI_vs_typeII_adolescent

From Hibar al., 2016, Mol Psychiatry | age, sex, and ICV correction; p<4.91E-3 for FDR correction at q=0.05; mean [(left+right)/2] region of interest volume

↪ SubVol_case_vs_controls_typeI

↪ SubVol_case_vs_controls_typeII

↪ SubVol_typeII_vs_typeI

Python | meta

Matlab | meta

>>> from enigmatoolbox.datasets import load_summary_stats

>>> # Load summary statistics for ENIGMA-BD
>>> sum_stats = load_summary_stats('bipolar')

>>> # Get case-control surface area table
>>> CT = sum_stats['CortThick_case_vs_controls_adult']
>>> SA = sum_stats['CortSurf_case_vs_controls_adult']

>>> # Extract Cohen's d values
>>> CT_d = CT['d_icv']
>>> SA_d = SA['d_icv']

% Load summary statistics for ENIGMA-BD
sum_stats = load_summary_stats('bipolar');

% Get case-control surface area table
CT = sum_stats.CortThick_case_vs_controls_adult;
SA = sum_stats.CortSurf_case_vs_controls_adult;

% Extract Cohen's d values
CT_d = CT.d_icv;
SA_d = SA.d_icv;

Epilepsy

Available summary statistics tables

From Whelan al., 2018, Brain | age, sex, and ICV correction; Bonferroni correction p<1.49E-4; FDR correction also available

↪ CortThick_case_vs_controls_allepilepsy

↪ SubVol_case_vs_controls_allepilepsy

↪ CortThick_case_vs_controls_gge

↪ SubVol_case_vs_controls_gge

↪ CortThick_case_vs_controls_ltle 📸

↪ SubVol_case_vs_controls_ltle 📸

↪ CortThick_case_vs_controls_rtle

↪ SubVol_case_vs_controls_rtle

↪ CortThick_case_vs_controls_allotherepilepsy

↪ SubVol_case_vs_controls_allotherepilepsy

Python | meta

Matlab | meta

>>> from enigmatoolbox.datasets import load_summary_stats

>>> # Load summary statistics for ENIGMA-Epilepsy
>>> sum_stats = load_summary_stats('epilepsy')

>>> # Get case-control subcortical volume and cortical thickness tables
>>> SV = sum_stats['SubVol_case_vs_controls_ltle']
>>> CT = sum_stats['CortThick_case_vs_controls_ltle']

>>> # Extract Cohen's d values
>>> SV_d = SV['d_icv']
>>> CT_d = CT['d_icv']

% Load summary statistics for ENIGMA-Epilepsy
sum_stats = load_summary_stats('epilepsy');

% Get case-control subcortical volume and cortical thickness tables
SV = sum_stats.SubVol_case_vs_controls_ltle;
CT = sum_stats.CortThick_case_vs_controls_ltle;

% Extract Cohen's d values
SV_d = SV.d_icv;
CT_d = CT.d_icv;

Major depressive disorder

Available summary statistics tables

From Schmaal et al., 2017, Mol Psychiatry | age, sex, and scan site correction; FDR correction available

ADULTS (age>21 years)

↪ CortThick_case_vs_controls_adult 📸

↪ CortSurf_case_vs_controls_adult 📸

↪ CortThick_case_vs_controls_adult_firstepisode

↪ CortSurf_case_vs_controls_adult_firstepisode

↪ CortThick_case_vs_controls_adult_recurrent

↪ CortSurf_case_vs_controls_adult_recurrent

↪ CortThick_firstepisode_vs_recurrent_adult

↪ CortSurf_firstepisode_vs_recurrent_adult

↪ CortThick_case_vs_controls_adult_early (age of onset⩽21 years)

↪ CortSurf_case_vs_controls_adult_early (age of onset⩽21 years)

↪ CortThick_case_vs_controls_adult_late (age of onset>21 years)

↪ CortSurf_case_vs_controls_adult_late (age of onset>21 years)

↪ CortThick_early_vs_late_adult

↪ CortSurf_early_vs_late_adult

ADOLESCENTS (age⩽21 years)

↪ CortThick_case_vs_controls_adolescent

↪ CortSurf_case_vs_controls_adolescent

↪ CortThick_case_vs_controls_adolescent_firstepisode

↪ CortSurf_case_vs_controls_adolescent_firstepisode

↪ CortThick_case_vs_controls_adolescent_recurrent

↪ CortSurf_case_vs_controls_adolescent_recurrent

↪ CortThick_firstepisode_vs_recurrent_adolescent

↪ CortSurf_firstepisode_vs_recurrent_adolescent

From Schmaal et al., 2016, Mol Psychiatry | age, sex, ICV, and scanner differences correction; Bonferroni correction p<5.6E-3; mean [(left+right)/2] region of interest volume

↪ SubVol_case_vs_controls

↪ SubVol_case_vs_controls_late (age of onset>21 years)

↪ SubVol_case_vs_controls_early (age of onset⩽21 years)

↪ SubVol_late_vs_early

↪ SubVol_case_vs_controls_firstepisode

↪ SubVol_case_vs_controls_recurrent

↪ SubVol_recurrrent_vs_firstepisode

Python | meta

Matlab | meta

>>> from enigmatoolbox.datasets import load_summary_stats

>>> # Load summary statistics for ENIGMA-MDD
>>> sum_stats = load_summary_stats('depression')

>>> # Get case-control cortical thickness and surface area tables
>>> CT = sum_stats['CortThick_case_vs_controls_adult']
>>> SA = sum_stats['CortSurf_case_vs_controls_adult']

>>> # Extract Cohen's d values
>>> CT_d = CT['d_icv']
>>> SA_d = SA['d_icv']

% Load summary statistics for ENIGMA-MDD
sum_stats = load_summary_stats('depression');

% Get case-control cortical thickness and surface area tables
SV = sum_stats.SubVol_case_vs_controls_adult;
CT = sum_stats.CortThick_case_vs_controls_adult;
SA = sum_stats.CortSurf_case_vs_controls_adult;

% Extract Cohen's d values
SV_d = SV.d_icv;
CT_d = CT.d_icv;
SA_d = SA.d_icv;

Obsessive-compulsive disorder

Available summary statistics tables

From Boedhoe et al., 2018, Am J Psychiatry | age, sex, scan site, and ICV* correction; FDR correction available

*only for surface area measures

ADULTS (age≥18 years)

↪ CortThick_case_vs_controls_adult 📸

↪ CortSurf_case_vs_controls_adult 📸

↪ CortThick_medicatedcase_vs_controls_adult

↪ CortSurf_medicatedcase_vs_controls_adult

PEDIATRIC (age<18 years)

↪ CortThick_case_vs_controls_pediatric

↪ CortSurf_case_vs_controls_pediatric

↪ CortThick_medicatedcase_vs_controls_pediatric

↪ CortSurf_medicatedcase_vs_controls_pediatric

From Boedhoe et al., 2017, Am J Psychiatry | age, sex, scan site, and ICV correction; Bonferroni correction p<5.6E-3; mean [(left+right)/2] region of interest volume

ADULTS (age≥18 years)

↪ SubVol_case_vs_controls_adult

↪ SubVol_medicatedcase_vs_controls_adult

↪ SubVol_unmedicatedcase_vs_controls_adult

↪ SubVol_medicatedcase_vs_unmedicated_adult

↪ SubVol_case_vs_controls_adult_late (age of onset≥18 years)

↪ SubVol_case_vs_controls_adult_early (age of onset<18 years)

↪ SubVol_late_vs_early_adult

↪ SubVol_case_vs_controls_adult_depression (as comorbidity)

↪ SubVol_case_vs_controls_adult_nodepression

↪ SubVol_depression_vs_nodepression_adult

↪ SubVol_case_vs_controls_adult_anxiety (as comorbidity)

↪ SubVol_case_vs_controls_adult_noanxiety

↪ SubVol_anxiety_vs_noanxiety_adult

PEDIATRIC (age<18 years)

↪ SubVol_case_vs_controls_pediatric

↪ SubVol_medicatedcase_vs_controls_pediatric

↪ SubVol_unmedicatedcase_vs_controls_pediatric

↪ SubVol_medicatedcase_vs_unmedicated_pediatric

Python | meta

Matlab | meta

>>> from enigmatoolbox.datasets import load_summary_stats

>>> # Load summary statistics for ENIGMA-OCD
>>> sum_stats = load_summary_stats('ocd')

>>> # Get case-control cortical thickness and surface area tables
>>> CT = sum_stats['CortThick_case_vs_controls_adult']
>>> SA = sum_stats['CortSurf_case_vs_controls_adult']

>>> # Extract Cohen's d values
>>> CT_d = CT['d_icv']
>>> SA_d = SA['d_icv']

% Load summary statistics for ENIGMA-OCD
sum_stats = load_summary_stats('ocd');

% Get case-control cortical thickness and surface area tables
CT = sum_stats.CortThick_case_vs_controls_adult;
SA = sum_stats.CortSurf_case_vs_controls_adult;

% Extract Cohen's d values
CT_d = CT.d_icv;
SA_d = SA.d_icv;

Schizophrenia

Available summary statistics tables

From van Erp et al., 2018, Biol Psychiatry | age and sex correction; FDR correction available

↪ CortThick_case_vs_controls 📸

↪ CortSurf_case_vs_controls 📸

From van Erp et al., 2016, Mol Psychiatry | age, sex, scan site, and ICV correction; Bonferroni correction p<5.6E-3

↪ SubVol_case_vs_controls

↪ SubVol_case_vs_controls_mean (mean [(left+right)/ 2)] region of interest volume)

Python | meta

Matlab | meta

>>> from enigmatoolbox.datasets import load_summary_stats

>>> # Load summary statistics for ENIGMA-Schizophrenia
>>> sum_stats = load_summary_stats('schizophrenia')

>>> # Get case-control cortical thickness and surface area tables
>>> CT = sum_stats['CortThick_case_vs_controls']
>>> SA = sum_stats['CortSurf_case_vs_controls']

>>> # Extract Cohen's d values
>>> CT_d = CT['d_icv']
>>> SA_d = SA['d_icv']

% Load summary statistics for ENIGMA-schizophrenia
sum_stats = load_summary_stats('schizophrenia');

% Get case-control cortical thickness and surface area tables
CT = sum_stats.CortThick_case_vs_controls;
SA = sum_stats.CortSurf_case_vs_controls;

% Extract Cohen's d values
CT_d = CT.d_icv;
SA_d = SA.d_icv;