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

<strong>README</strong> 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<br> DWI_origin - &gt; the original diffusion weighted MRI data and their corresponding b-vector values FA - &gt; the FA coefficients computed using FSL FA_to_target - &gt; the FA volumes registered to MNI template using FSL tools T1_preprocessed - &gt; the T1-weighted volumes at 1mm resolution registered to the MNI template using FSL no-linear registration tools T1_VBM_SPM_1mm - &gt; 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 -&gt; contains track based spatial statistics (TBSS) results obtained with FSL software (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS) <strong>Publications using this dataset</strong> 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, <strong>Neuroscience Letters</strong>, 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)