Alzheimer's Disease versus Bipolar Disorder versus Health Control MRI data and processed results

README

The data is structured as follows:

Clinical_data folder contains the .csv that can be read by spreadsheet software, as well as from Python, Matlab or R. There are separate files for each biomarker. The file "clinical_data_id_age_gender.csv" contains the numerical random key of the patient for anonymity, diagnostic key, age and gender for each entry in the other files. The file "clinical_data_corrected.csv" can be ignored.

Diagnostic keywords: "crl" == healthy control, "tb" == bipolar disorder, "ea" == Alheimer's disease

Imaging data is nifti encoded. The name of the file starts with the diagnostic key followed by the numerical random key and some nemotechnic for the contents. For instance: "crl_132_diff_dti_FA_FA_to_target.nii.gz" is the spatially normalized FA data of healthy control 132. Imaging data can be read with FSL, SPM, and any other nifti reading soft.

Imaging folders contain the following data
DWI_origin - > the original diffusion weighted MRI data and their corresponding b-vector values

FA - > the FA coefficients computed using FSL

FA_to_target - > the FA volumes registered to MNI template using FSL tools

T1_preprocessed - > the T1-weighted volumes at 1mm resolution registered to the MNI template using FSL no-linear registration tools

T1_VBM_SPM_1mm - > the results of applying SPM implementation of voxel based morphometry (VBM) on the T1-weighted data, including results of the correlation between biomarkers and the detected clusters . Results can be checked using SPM (https://www.fil.ion.ucl.ac.uk/spm/)

TBSS_results -> contains track based spatial statistics (TBSS) results obtained with FSL software (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS)

Publications using this dataset

M. Graña, M. Termenon, A. Savio, A. Gonzalez-Pinto, J. Echeveste, J. M. Pérez, A. Besga, Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson’s correlation,  Neuroscience letters,Volume 502, Issue 3, 20 September 2011, Pages 225-229

A. Besga, M. Termenon, M. Graña, J. Echeveste, J. M. Perez, A. Gonzalez-Pinto "Discovering Alzheimer's disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features, Neuroscience Letters, Volume 520, Issue 1, 27 June 2012, Pages 71–76.

M. Termenon, M. Graña, A. Besga, J. Echeveste, A. Gonzalez-Pinto, Lattice Independent Component Analysis feature selection on Diffusion Weighted Imaging for Alzheimer’s Disease Classification, Neurocomputing (2013) Volume 114, 19 August 2013, Pages 132–141

Ariadna Besga, Itxaso González-Ortega, Enrique Echeburúa, Alexandre Savio, Borja Ayerdi, Darya Chyzhyk, Jose LM Madrigal, Juan C. Leza, Manuel Graña, Ana González-Pinto,  "Discrimination between Alzheimer’s Disease and Late Onset Bipolar Disorder using multivariate analysis" Frontiers in Aging Neuroscience, 7:231

Ariadna Besga-Basterra, Darya Chyzhyk, Itxaso González-Ortega, Alexandre Savio, Borja Ayerdi, Jon Echeveste, Manuel Graña, Ana González-Pinto,  Eigenanatomy on fractional anisotropy imaging provides white matter anatomical features discriminating between Alzheimer’s Disease and Late Onset Bipolar Disorder, Current Alzheimer Research, 13(5): 557 - 565 (2016)

Ariadna Besga, Darya Chyzhyk, Itxaso Gonzalez Ortega, Jon Echeveste, Marina Grana-Lecuona, Manuel Grana, Ana González-Pinto, White Matter Tract Integrity in Alzheimer’s Disease versus Late Onset Bipolar Disorder and its Correlation with Systemic Inflammation and Oxidative Stress Biomarkers, Frontiers in Aging Neuroscience, 9:179 (2017)