Dr. Neil Oxtoby

Many thanks to the organisers of the Rosetrees interdisciplinary workshop on neurodegenerative diseases of the brain yesterday (10 Feb, via Zoom).

I had a great time presenting my talk and discussing the physics of life, plus our work on disease progression modelling in the UCL POND group.

My slides are here:
Top-down and Bottom-up models of neurodegenerative disease progression

Look out for the recording of all the talks, which will appear on YouTube soon.

Here’s my poster:

http://neiloxtoby.com/work/wp-content/uploads/2020/09/35_Oxtoby.pdf

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: neiloxtoby.com/work/20200513-mrc_unit-seminar-lores

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.

[UPDATE]
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.

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.

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)