Load example data
This page contains descriptions and examples to load our example data, re-order subcortical labels, and z-score values!
This is an example dataset that includes 10 healthy controls (7 females, age±SD=33.3±8.8 years) and 10 individuals with epilepsy (7 females, age±SD=39.8±14.8 years).
Covariates | As per ENIGMA-Epilepsy protocol, covariate information includes SubjID (subjectID), Dx (diagnosis, 0=controls, 1=patients), SDx (sub-diagnosis, 0=controls, 1=non-lesional patients, 2=genetic generalized epilepsy (IGE/GGE) patients, 3=left TLE, 4=right TLE), Age (in years), Sex (1=males, 2=females), Handedness (1=right, 2=left), AO (age at onset in years, patients only), DURILL (duration of illness in years, patients only), and ICV (intracranial volume).
Subcortical volume | Subcortical grey matter volumes regroup data from 12 subcortical regions, bilateral hippocampus, and bilateral ventricles.
Cortical thickness | Cortical thickness was measured at each vertex as the Euclidean distance between white and pial surfaces, and subsequently averaged within each of the Desikan-Killiany parcels.
Cortical surface area | The cortical surface area of every Desikan-Killiany parcel is also provided as part of ENIGMA imaging protocols; this morphological measure is defined by the sum of the area of each of the triangles within the parcel.
py Python | mega
>>> from enigmatoolbox.datasets import load_example_data
>>> # Load all example data from an individual site >>> cov, metr1_SubVol, metr2_CortThick, metr3_CortSurf = load_example_data()
matlab Matlab | mega
% Load all example data from an individual site [cov, metr1_SubVol, metr2_CortThick, metr3_CortSurf] = load_example_data();
The column order from ENIGMA-derived subcortical volume matrices does not match the order in the connectivity matrices nor the pre-requisite for our subcortical visualization tools. But, hey, don't you worry! To re-order ENIGMA-derived subcortical volume data, you may use our reorder_sctx() function, which will re-order the columns of the subcortical volume dataset accordingly (i.e., alphabetically, with all left hemisphere structures first followed by all right hemisphere structures).
py Python | mega
>>> from enigmatoolbox.utils.useful import reorder_sctx
>>> # Re-order the subcortical data alphabetically and by hemisphere >>> metr1_SubVol_r = reorder_sctx(metr1_SubVol)
matlab Matlab | mega
% Re-order the subcortical data alphabetically and by hemisphere metr1_SubVol_r = reorder_sctx(metr1_SubVol);
With our example data loaded and re-ordered, we can then z-score data in patients, relative to controls, so that lower values correspond to greater atrophy. For simplicity, we also compute the mean z-score across all left TLE patients. These mean, z-scored vectors will be used in subsequent tutorials as measures of subcortical and cortical atrophy!
Prerequisites ↪ Re-order subcortical data <reorder_sctx>
py Python | mega
>>> from enigmatoolbox.utils.useful import zscore_matrix
>>> # Z-score patients' data relative to controls (lower z-score = more atrophy) >>> group = cov['Dx'].to_list() >>> controlCode = 0 >>> SV_z = zscore_matrix(metr1_SubVol_r.iloc[:, 1:-1], group, controlCode) >>> CT_z = zscore_matrix(metr2_CortThick.iloc[:, 1:-5], group, controlCode) >>> SA_z = zscore_matrix(metr3_CortSurf.iloc[:, 1:-5], group, controlCode)
>>> # Mean z-score values across individuals with from a specific group (e.g., left TLE, that is SDx == 3) >>> SV_z_mean = SV_z.iloc[cov[cov['SDx'] == 3].index, :].mean(axis=0) >>> CT_z_mean = CT_z.iloc[cov[cov['SDx'] == 3].index, :].mean(axis=0) >>> SA_z_mean = SA_z.iloc[cov[cov['SDx'] == 3].index, :].mean(axis=0)
matlab Matlab | mega
% Z-score patients' data relative to controls (lower z-score = more atrophy) group = cov.Dx; controlCode = 0; SV_z = zscore_matrix(metr1_SubVol_r(:, 2:end-1), group, controlCode); CT_z = zscore_matrix(metr2_CortThick(:, 2:end-5), group, controlCode); SA_z = zscore_matrix(metr3_CortSurf(:, 2:end-5), group, controlCode);
% Mean z-score values across individuals with from a specific group (e.g., left TLE, that is SDx == 3) SV_z_mean = array2table(mean(SV_z{find(cov.SDx == 3), :}, 1), ... 'VariableNames', SV_z.Properties.VariableNames); CT_z_mean = array2table(mean(CT_z{find(cov.SDx == 3), :}, 1), ... 'VariableNames', CT_z.Properties.VariableNames); SA_z_mean = array2table(mean(SA_z{find(cov.SDx == 3), :}, 1), ... 'VariableNames', SA_z.Properties.VariableNames);