🔍 Can one hundred scans be linked to hearing loss? The case of the Courtois NeuroMod project

For over five years, the Courtois NeuroMod project scanned six participants weekly using fMRI — creating the largest individual-subject fMRI dataset ever collected.

MRI machines are loud, and participants wore MRI-compatible Sensimetrics earphones with foam inserts and additional custom over-the-ear protection. We still remained vigilant about potential impacts on auditory health.

📣 A new study, led by Eddie Fortier under the supervision of Adrian Fuente, and now published in PLOS ONE, presents the results of an auditory monitoring protocol conducted in parallel with CNeuroMod:
🔗 https://lnkd.in/eRinvsDH
Key Findings:

Across participants, we found no clinical signs of ear trauma immediately following scanning. Changes in detection thresholds were typically <10 dB, even in high-frequency ranges (>10 kHz) where variability was greatest.

One participant with pre-existing unilateral hearing loss was tested across five sessions. Their results were inconsistent — and in some cases, paradoxically showed improved sensitivity post-scan — likely due to test-retest variability and fatigue effects in the upper frequency range.

In long-term follow-up (up to 16 months delay), we observed no sustained hearing loss. While high-frequency measures remained variable, no clinically significant, consistent declines were found across the group.

🎧 While pure tone audiometry is a cognitively demanding test — especially following extended scanning sessions — our findings are reassuring: with proper hearing protection, even repeated, long-duration fMRI protocols like CNeuroMod can be conducted safely. See the paper for full results and a complete discussion.

The five-year CNeuroMod data collection phase is now complete, and we are deeply grateful to the participants who committed their time to this study, and to the Courtois Foundation for their visionary support.

We are now preparing a series of public data releases and publications that will continue to explore the many facets of this unique longitudinal dataset.

Stay tuned.

They* say that computational scientific paper is only advertisement for science. They say the actual science is the environment, data and code necessary to reproduce the figures in the paper. But are they actually right?

Could the paper, environment, data and code be merged together into a single reproducible object? Could papers be fully reproduced BEFORE they're even sent to scientific peer review?

Join us next month virtually or in Montreal for our first Neurolibre workshop, where we'll announce the beta launch of https://neurolibre.org and discuss the future of reproducible publishing. Registration is free: https://events.neurolibre.org/day/

(* is paraphrased from a famous quote by Buckheit and Donoho)

New preprint alert! https://arxiv.org/abs/2403.19421 Sana looked at generating brain encoding maps at voxel level resolution using @sklearn 's ridge regression.

She found the default methods available do not scale to the size and resolution of @cneuromod 's friends dataset: about 30 hours of fMRI per subject (~70k samples) at high spatial resolution (~2 mm isotropic voxels, ~260k brain targets) to be predicted frm ~16k latent features of VGG16, for a total of ~4B regression parameters.

She tried a slight modification of sklearn's parallelization strategy, simply distributing batches of brain targets across multiple CPUs. This scales very effectively with the number of threads & cpus.

Full brain and voxel level encoding maps are included for the first time in a cneuromod publication 🎉

**A todo list for procrastinators**. I've struggled with procrastination forever, sometimes severely. Over the years I've developed a system which works for me in order to get things done. I wrote this system down first for myself, but I figured some fellow procrastinators may find it useful as well.

Part 1 - Gentle introduction https://pbellec.github.io/todo-procrastinator/intro.html
Part 2 - Some background https://pbellec.github.io/todo-procrastinator/origins_todo.html
Part 3 - The actual "Todo list for procrastinators" https://pbellec.github.io/todo-procrastinator/how_todo_procrastinators.html
Part 4 - Some thoughts on the origins of procrastination https://pbellec.github.io/todo-procrastinator/origins_procrastinators.html

Note that the system can be implemented with any software to manage todo lists. I am personally using @obsidian which I absolutely love.

As a bonus I've added an illustration representing the character Madeline from the game Celeste, together with her inner self. I refer to that duo a lot in Part 4. This illustration is a Fan-art by [GomiGomiPomi](https://gomigomipomi.tumblr.com/) reproduced with permission from the author (Usage of my drawings is allowed as long as proper credits are given and it’s not for commercial purposes).