Soichi Hayashi · projects

Brain MRI Viewer

944 words5 min read#Neuroscience#Visualization#Web App

A little side project: my own T1-weighted anatomical brain scan, viewable right in the browser using FreeBrowse, the FreeSurfer project’s web viewer. No installs, no server-side processing — the NIfTI volume is fetched and rendered entirely client-side, with synchronized axial, sagittal, and coronal views.

It’s a fun way to poke around my own anatomy, and a nod to years spent building neuroimaging tools at brainlife.io — this time pointed at my own head instead of someone else’s dataset.

To make it more than gray levels to eyeball, I also ran FreeSurfer’s recon-all 8.2.0 on the scan (in Docker, on my home server) and turned the results into a full report page: the aparc+aseg parcellation — roughly a hundred labeled cortical and subcortical structures — as a toggleable overlay in the viewer, plus stat tiles, volume/thickness charts, and tables computed from the analysis.

The scan has been defaced (facial features removed) before publishing, so no facial features are visible in the volume. It’s for personal curiosity, not clinical use.

What the numbers made me worry about

Attaching percentiles to your own brain turns out to be an effective way to lose an evening’s sleep. Once the report showed my cortical thickness in the low single-digit percentiles, I worked through a chain of worries — and then, since I had the data and the tools, tested each one. Here they are, in the order they occurred to me.

“Thin temporal lobe — does this mean dementia?” My temporal-region thicknesses sit at low percentiles (temporal pole ~2nd, entorhinal 7–15th centile, at age 41). But zooming out: the thinning is diffuse, not focal — mean cortical thickness overall is at the 4th centile, and occipital, frontal, and parietal regions are all low together. Neurodegenerative thinning (like early Alzheimer’s) is regionally selective — medial temporal lobe first, sparing the rest — and it does not uniformly thin the whole cortex. Meanwhile the structural markers that do track neurodegeneration are unremarkable for me: ventricles at the 46th centile, hippocampus 14th/58th, amygdala 69th/41st. And diagnosis never rests on one cross-sectional number anyway — it’s cognitive symptoms plus change over time, plus biomarkers, read by a clinician.

“Right frontal pole is at the 0.001st centile — is my executive function impaired?” Looking across the whole cortex table: essentially the entire right frontal/temporal surface sits at or below the 3rd centile (lateral orbitofrontal 0.002nd, frontal pole 0.001st, superior frontal 1.5th, …) while the same regions on the left sit at 3rd–54th. No disease selectively thins ten contiguous right-hemisphere regions while sparing their left twins — a systematic measurement offset does exactly that. On top of that, the frontal pole is FreeSurfer’s least trustworthy region: tiny, at the edge of the volume, right above the sinuses where susceptibility artifacts distort the signal, and famously mis-traced. And even a genuine thickness value couldn’t tell me this — regional cortical thickness explains only a tiny fraction of the variance in cognitive performance, and executive function is measurable directly (neuropsych testing), not from a segmentation.

“Did the defacing step corrupt the analysis?” This one I could test directly. I registered the un-defaced original scan (from my BIDS export) into the defaced volume’s space and compared them inside the brain mask: the intensities are identical — a scale factor of 1.0002, and exactly 2 voxels out of 1.49 million changed by more than a rounding error. Defacing only removed tissue outside the brain.

“Was the brain extraction wrong?” I re-ran FreeSurfer’s entire preprocessing stage (recon-all -autorecon1, FS 6.0) on the un-defaced original and compared the brain masks. They agree at Dice 0.935: the defaced-input run cut a uniformly tighter boundary — a 1–2 voxel shell around the whole brain — and the total tissue it removed that the original keeps is 0.9 cm³, with no focal deficit anywhere near the regions I was worried about.

So why is my cortical thickness low?

Probably some combination of:

  • Anatomy trade-off. Cortical thickness trades off against surface area. I’m at the 76th centile for total surface area and 70th for intracranial volume — bigger, more folded cortices tend to run thinner. High-surface-area/thin-cortex is a known normal constitutional combination.
  • Scanner and processing offset. The reference curves (Bethlehem et al. 2022, ~124k scans) are harmonized to UK Biobank scanners. A single Siemens Prisma scan processed by a different FreeSurfer version carries residual offset that nobody corrects for — and a whole-cortex downward shift is what that looks like.
  • FreeSurfer’s least reliable regions. Temporal pole, entorhinal, and orbitofrontal measures are commonly discarded in research QA for exactly the artifacts described above.
  • Wide tails. At the 1st or 99th centile, the population model’s uncertainty is largest; a single noisy estimate lands in the tail more often than intuition suggests.

Do these numbers mean anything?

Honestly: I don’t really know how useful these segmentation results are if we can’t draw scientific conclusions out of these measures. What I can say after all this: they’re wonderful for looking at my own anatomy, and they’d be genuinely informative as a baseline — the scientifically meaningful quantity is change over time on the same scanner and pipeline, not any single percentile. But as evidence about my present or future cognition, one scan’s regional percentiles against a harmonized population band are close to uninterpretable. The right instrument for “is anything wrong with me” is a clinician, not recon-all. I’ve kept the report up anyway — it’s an honest record of what the pipeline measured, worries included.