Edinburgh Research Explorer

A test-retest fMRI dataset for motor, language and spatial attention functions

Dataset

Abstract

The following data comes from the study “A test-retest fMRI dataset for motor, language and spatial attention functions”, which is a test-retest dataset acquired to validate functional magnetic resonance imaging (fMRI) tasks used in pre-surgical planning. Five task-related fMRI time series (finger, foot and lip movement, overt verb generation, covert verb generation, overt word repetition, and landmark tasks) were used to investigate which protocols gave reliable single-subject results. Ten healthy participants in their fifties were scanned twice using an identical protocol 2-3 days apart. In addition to the fMRI sessions, high-angular resolution diffusion tensor MRI (DTI), and high-resolution 3D T1-weighted volume scans were acquired. Each subject was assigned a random, unique identifier using the DICOM confidential de-identification toolkit to replace their name and any other medical identification information. DICOM files for each scanning sequence were anonymized according to the Health Insurance Portability and Accountability Act guidelines, and DICOM to NIfTI conversion was performed using the dcm2nii tool. To prevent visual identification, the 3D T1-weighted volumes have been defaced using mri_deface. Seven NIfTI files are provided for each subject/session: five 4D fMRI, one 4D DTI, and one 3D T1-weighted volume scan. Due to the fact that the overt language tasks were scanned using sparse sampling, we were able to record and analyze each subject’s responses (not included due to privacy concerns). This analysis lead to exclusion of one session of one subject of the overt word repetition task, due to the fact that the subject failed to perform the task correctly. Data and its description are arranged according to the OpenfMRI layout. Together, this dataset provides a unique opportunity to investigate the reliability of different fMRI tasks, as well as methods and algorithms used to analyze, de-noise and combine fMRI, DTI and structural T1-weighted volume data. Related code in GitHub: https://github.com/chrisfilo/2010-Reliability-Study

ID: 18378410