Category Archives: Work

CompAge 2020

Here’s my poster:

AAIC 2020 presentations

Here are my presentations from AAIC 2020:

MRC-ULHA Zoom Seminar

Thanks to folks in the UCL MRC Unit for Lifelong Health and Ageing (and colleagues beyond) for hosting me for a Zoom seminar today (13 May 2020).

I very much appreciate the questions, discussion, and enthusiasm for my research.

Special thanks to Sarah-Naomi James for inviting me!

Here are the slides:

CMIC seminar

Thanks to colleagues in the UCL Centre for Medical Image Computing (CMIC) and Dementia Research Centre for turning out in force yesterday for my CMIC seminar (15 January 2020).

Looking forward to following up with new results as they come in now that my UKRI Future Leaders Fellowship has started!

Here are the slides:

Talk at the UK DRI (UCL)

Thanks to Marc Busche for the invitation, I gave a talk at the UK Dementia Research Institute at UCL on 31 October. Looking forward to following up with all the new leads for possible collaborations.

Here are the slides:

UCL postdoc job

Want to work with me on computational modelling of neurological disease progression like Alzheimer’s, multiple sclerosis, and prion diseases?

We also model normal ageing, and collaborate with teams working on neurodevelopment in infants and children.

Got a PhD in computer science, physics, maths, medical image computing, or a related field?

Then apply for this postdoc job at UCL.

Closing date is 15 Feb 2018.

Data-driven computational models of familial Alzheimer’s disease

The published version is available here:
Brain 141(5), awy050 (2018).

“The paper is a pleasure to read, as well as scientifically insightful.”
— Journal Editor

My latest paper on Alzheimer’s disease progression has been accepted in Brain. The preprint is on bioRxiv, available for free:

Data-driven models of dominantly-inherited Alzheimer’s disease progression
Neil Oxtoby, Alex(andra) Young, Dave Cash, Tammie Benzinger, Anne Fagan, John Morris, Randy Bateman, Nick Fox, Jon Schott, Danny Alexander
bioRxiv, 250654 (2018)

Familial AD (known more technically as “dominantly-inherited” AD or “autosomal dominant” AD) is very rare cause of dementia – about 1% of all AD. It’s caused by one of a family of genetic mutations inherited (50/50 chance) from a parent, and results in developing AD symptoms (memory loss, etc.), earlier than usual – in your 40s or 50s, rather than 60s or 70s.

Because this rare disease is dominantly inherited, it’s possible to identify people who carry one of the genetic mutations before symptoms appear. These people are usually recruited via their parents, after their parents have been diagnosed. This presymptomatic phase enables us to study familial AD progression before it’s too late, which is impractical for typical, non-familial AD (you’d need to observe many thousands of people, annually, over 10-20 years or more, and many of these wouldn’t develop AD). Further, during this pre symptomatic phase of familial AD it’s possible to estimate the number of years until the onset of symptoms in mutation carriers, called “EYO” (Estimated Years to Onset). This is because children often develop symptoms around the same age as their parents do: usually within about 5 years of the same age.

So, EYO represents a good, but not great, method/model for “staging” patients along the timeline of familial AD progression.

We wanted to see if data-driven disease progression modelling could do better.

In this paper, we analysed biomarker data including brain imaging data (MRI and PET), specific protein levels in spinal fluid, and scores on a cognitive test to build computational models of the sequence and timing of familial AD progression (specifically, event-based models and differential equation models). The data came from a global collection of volunteer participants including families affected by familial AD (parents and their adult children) in the DIAN dataset.

Our models do not use EYO (the current state of the art), and we predicted symptom onset more accurately than using EYO (within 1.3 years, compared to 5.5 years for EYO in our experiments).

Another win for computational, data-driven modelling of neurological diseases!

Next step: apply similar approaches to other diseases, and combine what we learn with the aim to produce a useful tool for identifying people at risk well before the disease has taken hold.

The paper is in production over at Brain and should be available soon.

Sequential disconnection of the brain in Alzheimer’s disease

My latest paper on Alzheimer’s disease progression is available in Frontiers in Neurology:

Data Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease
Neil Oxtoby, Sara Garbarino, Nick Firth, Jason Warren, Jon Schott, Danny Alexander
Front. Neurol., 8, 580 (2017)

Alzheimer’s disease is thought to be a “disconnection syndrome”, where brain regions becomes increasingly disconnected due to neurodegeneration. No-one has examined the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease.

Until now.

In this paper, I analysed brain imaging data (MRI) to build connectomes for healthy and diseased individuals from the public ADNI dataset, and summarised brain connectivity in health and disease using graph theory metrics.

These metrics were then plugged into our ever-reliable event-based model of disease progression (with an important tweak courtesy of Nick) in order to find the sequence of brain disconnections due to Alzheimer’s disease. The paper was published on 7 Nov 2017.

Imaging plus X

My work in the EuroPOND consortium is neatly summarised in our latest paper, where we review the emerging field of data-driven disease progression modelling. It’s open access, so anyone can download and read it for free from here:

Imaging plus X: multimodal models of neurodegenerative disease progression
Neil Oxtoby, Danny Alexander, for the EuroPOND Consortium
Current Opinion in Neurology 30, 371–379 (2017)


Technical: MRtrix ACT using GIF parcellation

Software: MRtrix 3 (0.3.15)
Pipeline: Anatomically Constrained Tractography (ACT)

Here in CMIC we analyse a lot of structural MR images using Jorge Cardoso’s Geodesic Information Flows (GIF) algorithm, which utilises the Neuromorphometrics parcellation. Jorge and colleagues currently offer a web service that will segment and parcellate your structural MRI using GIF: NiftyWeb.

I wanted to do some AC tractography and connectomics with MRtrix based on this tutorial, but using a GIF-based parcellation rather than FreeSurfer. Following the hints at the bottom of this ACT tutorial, I succeeded. Keep reading to find out how.

Making MRtrix ACT work using GIF segmentation/parcellation, rather than FreeSurfer

$MRTRIX – path to your mrtrix3 installation
$GIFDB – path to relevant GIF-specific files for ACT (if you’re lucky enough to have the GIF source code, you can generate these yourself, but I provide them below)

1. Calling 5ttgen to generate 5TT.mif

This creates a Five Tissue Type file (Cortical/Subcortical GM; WM; CSF; Pathological tissue). When using the freesurfer argument, this refers to the following script: $MRTRIX/scripts/src/_5ttgen/, which relies upon configuration files in the $MRTRIX/scripts/data folder:

How to modify this process for GIF
  • I wrote a MATLAB/Octave script GIFColourLUT_generator.m (not supplied here) to create the necessary config files:
  • I manually edited $MRTRIX/scripts/src/_5ttgen/ and saved it as
  • I added export GIFDB_HOME=$GIFDB to my bash profile (reminiscent of FREESURFER_HOME)
2. Connectome Lookup Table (LUT)

This step simply renumbers the ROIs, such that the numbers in the image no longer correspond to entries in the colour lookup table (the Neuromorphometrics ROI numbers), but to rows and columns of the connectome.

  • Create GIF version of the connectome LUT as:
    $MRTRIX/src/connectome/tables/gif_default.txt (mrtix v0.3.15)
    $MRTRIX/src/connectome/config/gif_default.txt (mrtix v0.3.14)
3. Rerun the HCP connectome tutorial using GIF

I leave this as an exercise for you.

You’ll need a GIF-processed T1 image, so you should submit the structural MR image (T1w_acpc_dc_restore_brain_GIF_Parcellation.nii.gz) to the NiftyWeb GIF parcellation service and save the resulting parcellated file in an appropriate location.

Differences if you want to use non-HCP data, such as ADNI

ADNI is single-shell diffusion data, so you need to modify the pipeline at the appropriate points. I have tested this out on processed data from ADNI (contact me if you don’t know how to download processed images from LONI). I found that the diffusion and structural images were misaligned by a linear translation, so I shifted them to the same origin using MRtrix:

mrinfo -transform ${T1_}.mif | cat >> ${T1_}_transform.txt
mrtransform -replace ${T1_}_transform.txt ${DTI_}.mif ${DTI_}_trans.mif
mrview ${T1_}.mif -overlay.load ${DTI_}_trans.mif -overlay.opacity 0.3