This Indico event describes the content and planning for the course "Neuroimaging data analysis" (GBIO0034), formerly known as "Introduction to medical statistics" (STAT0722). The program is very similar but might be slightly updated from one year to the next, especially the later classes...
The course content is focused on the SPM software and its application to analyse neuroimaging data. The course will be illustrated with some example of data processing using the demo data from SPM. The material from last year's is still available; moreover the course in 2020 was taught online and the video recording are an excellent source of information. Not to be missed either, the slides and videos from past official SPM courses in London are all accessible online!
Class room & timetable
Six classes are planned, on Wednesdays from 14h till 17h with a mid-break. Those will take place in the "Big meeting" room, at the Cyclotron Research Centre, B30, on November 8, 15 & 29 and December 6, 13 & 20, as described here under. The exact program might be slightly adjusted.
For those who can only attend remotely, the course will tentatively be live streamed via Teams. Alternatively, you could check out the recording of the 2020 course or those of the official SPM courses in London, which cover about the same content.
Evaluation
This will consist in the oral presentation of a peer-reviewed article in the field of neuroimaging. The focus should be on the methodological aspects and data processing overall. Students are free to choose an article they find interesting but it should still be approved by C. Phillips.
Potential journals:
Potential papers,
Eklund et al. Cluster Failure: Why fMRI Inferences for Spatial Extent Have Inflated False-Positive Rates. PNAS 2016. http://www.ncbi.nlm.nih.gov/pubmed/27357684
Eklund et al. Cluster Failure Revisited: Impact of First Level Design and Physiological Noise on Cluster False Positive Rates. HBM 2019, https://doi.org/10.1073/pnas.1602413113
Esteban et al. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLOS ONE 2017, https://doi.org/10.1371/journal.pone.0184661
Esteban et al. FMRIPrep: a robust preprocessing pipeline for functional MRI. Nature Methods 2019, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6319393/
Gyger et al. Temporal trajectory of brain tissue property changes induced by electroconvulsive therapy. NeuroImage 2021, https://doi.org/10.1016/j.neuroimage.2021.117895
Huber et al. Evaluating the capabilities and challenges of layer-fMRI VASO at 3T. Aperture Neuro 2023, https://doi.org/10.52294/001c.85117
Mortamet et al. Automatic quality assessment in structural brain magnetic resonance imaging. Magnetic Resonance in Medicine 2009, https://doi.org/10.1002/mrm.21992
Pernet. Misconceptions in the use of the General Linear Model applied to functional MRI: a tutorial for junior neuro-imagers, Frontiers in Neuroscience 2014, https://www.frontiersin.org/articles/10.3389/fnins.2014.00001
Reuter et al. Head motion during MRI acquisition reduces gray matter volume and thickness estimates. NeuroImage 2015, https://doi.org/10.1016/j.neuroimage.2014.12.006
Smith & Nichols. Statistical Challenges in “Big Data” Human Neuroimaging. Neuron 2018, https://doi.org/10.1016/j.neuron.2017.12.018
Date, time & place: