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

GigaScience

Cyril R Pernet¹  Joanna M Wardlaw¹  Ian R Whittle³  Mark E Bastin¹  Amos Storkey²  Krzysztof J Gorgolewski² 

[1] Brain Research Imaging Centre, a SINAPSE Collaboration centre, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK;Institute for Adaptive and Neural Computation, University of Edinburgh, 10 Crichton Street, Edinburgh, EH8 9AB, UK;Division of Clinical Neurosciences, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK

关键词: DTI;    fMRI;    Motor;    Landmark;    Overt word repetition;    Covert verb generation;    Overt verb generation;    Test-retest;   

Others  :  861640
DOI  :  10.1186/2047-217X-2-6

 received in 2013-01-16, accepted in 2013-04-09,  发布年份 2013

【 摘 要 】

Background

Since its inception over twenty years ago, functional magnetic resonance imaging (fMRI) has been used in numerous studies probing neural underpinnings of human cognition. However, the between session variance of many tasks used in fMRI remains understudied. Such information is especially important in context of clinical applications. A test-retest dataset was acquired to validate fMRI tasks used in pre-surgical planning. In particular, 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.

Findings

Reliability analyses of fMRI data showed that the motor and language tasks were reliable at the subject level while the landmark task was not, despite all paradigms showing expected activations at the group level. In addition, differences in reliability were found to be mostly related to the tasks themselves while task-by-motion interaction was the major confounding factor.

Conclusions

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.

【 授权许可】

   
2013 Gorgolewski et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Gorgolewski KJ, Storkey AJ, Bastin ME, Whittle I, Pernet C: Single subject fMRI test-retest reliability metrics and confounding factors. Neuroimagein press
• [2]Gorgolewski KJ, Storkey AJ, Bastin ME, Pernet CR: Adaptive thresholding for reliable topological inference in single subject fMRI analysis. Front Hum Neurosci 2012, 6:1-14.
• [3]Ohgaki H, Kleihues P: Epidemiology and etiology of gliomas. Acta Neuropathol 2005, 109:93-108.
• [4]Hall DA, Haggard MP, Akeroyd MA, Palmer AR, Summerfield AQ, Elliott MR, Gurney EM, Bowtell RW: “Sparse” temporal sampling in auditory fMRI. Hum Brain Mapp 1999, 7:213-223.
• [5]Fink GR, Marshall JC, Shah NJ, Weiss PH, Halligan PW, Grosse-Ruyken M, Ziemons K, Zilles K, Freund HJ: Line bisection judgments implicate right parietal cortex and cerebellum as assessed by fMRI. Neurology 2000, 54:1324-1331.
• [6]Jones DK, Williams SC, Gasston D, Horsfield MA, Simmons A, Howard R: Isotropic resolution diffusion tensor imaging with whole brain acquisition in a clinically acceptable time. Hum Brain Mapp 2002, 15:216-230.
• [7]Aron AR, Gluck MA, Poldrack RA: Long-term test-retest reliability of functional MRI in a classification learning task. Neuroimage 2006, 29:1000-1006.
• [8]Duncan KJ, Pattamadilok C, Knierim I, Devlin JT: Consistency and variability in functional localisers. Neuroimage 2009, 46:1018-1026.
• [9]Mennes M, Biswal B, Castellanos FX, Milham MP: Making data sharing work: The FCP/INDI experience. Neuroimagein press
• [10]Bischoff-Grethe A, Ozyurt IB, Busa E, Quinn BT, Fennema-Notestine C, Clark CP, Morris S, Bondi MW, Jernigan TL, Dale AM, Brown GG, Fischl B: A technique for the deidentification of structural brain MR images. Hum Brain Mapp 2007, 28:892-903.
• [11]Parameter file 1 used. https://surfer.nmr.mgh.harvard.edu/pub/dist/mri_deface/talairach_mixed_with_skull.gca.gz webcite
• [12]Parameter file 2 used. https://surfer.nmr.mgh.harvard.edu/pub/dist/mri_deface/face.gca.gz webcite
• [13]Gorgolewski KJ, Storkey A, Bastin ME, Whittle IR, Wardlaw JM, Pernet CR: A test-retest functional MRI dataset for motor, language and spatial attention functions. GigaScience Database 2013.