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Neuroimaging data analysis (GBIO0034)

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B-30/0-000 - Big meeting room (CRC)

B-30/0-000 - Big meeting room

CRC

20
Christophe Phillips (GIGA in silico medicine & CRC in vivo imaging)
Description

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,

Finally the SPM documentation has been refreshed and is now also available in a friendly online format, this includes tutorials for all the demo datasets.

Class room & timetable

Six classes are planned, on Wednesdays from 14h till 17h with a mid-break. Those will take place in the "large meeting" room, aka. "Fluor" at the Cyclotron Research Centre, B30, on November 13, 20 & 27 and December 4, 11 & 18, as described here under. The course should also be broadcasted via Teams too but no guarantee about technical hickups! The exact program might be slightly adjusted.

Evaluation

This will consist in

  • the oral presentation (~20min) 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, for 2025-2026 academic year (updated from the 2024-2025 list):

  • Váša et al. Ultra-low-field brain MRI morphometry: Test–retest reliability and correspondence to high-field MRI. Imaging Neuroscience 2025, https://doi.org/10.1162/IMAG.a.930

  • Fortin et al. GOUHFI: A novel contrast- and resolution-agnostic segmentation tool for ultra-high-field MRI. Imaging Neuroscience 2025, https://doi.org/10.1162/IMAG.a.960

  • Wunderlich et al. Denoising strategies of functional connectivity MRI data in lesional and non-lesional brain diseases. Imaging Neuroscience 2025, https://doi.org/10.1162/IMAG.a.968

  • He et al. Common pitfalls during model specification in psychophysiological interaction analysis. Imaging Neuroscience 2025, https://doi.org/10.1162/IMAG.a.989 

  • Peterson et al. Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding. Imaging Neuroscience 2025, https://doi.org/10.1162/IMAG.a.162

  • Giubergia et al. Multi-echo versus single-echo EPI sequences for task-fMRI: A comparative study. Imaging Neuroscience 2025, https://doi.org/10.1162/IMAG.a.94

  • Shin et al. Estimation and Removal of Residual Motion Artifact in Retrospectively Motion-Corrected fMRI Data: A Comparison of Intervolume and Intravolume Motion Using Gold Standard Simulated Motion Data. Aperture Neuro, 2024. https://doi.org/10.52294/001c.123369

  • Adame-Gonzalez et al. FONDUE: Robust resolution-invariant denoising of MR images using Nested UNets. Imaging Neuroscience, 2024. https://doi.org/10.1162/imag_a_00374

  • 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

  • Huber et al. Evaluating the capabilities and challenges of layer-fMRI VASO at 3T. Aperture Neuro 2023, https://doi.org/10.52294/001c.85117

  •  

Date, time & place:

  • TBD.
  • at the  CRC (B30), large meeting room.
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