Vacancy: Research Assistant in Memory Clinic Image Computing

Apply Here (closing date: 23 May 2021)

I have a vacancy for a post-Masters / pre-PhD level person interested in medical image (MRI) data wrangling and analysis, including computational modelling of neurological diseases like Alzheimer’s and Lewy Body Disease.

One important goal for the project is to use existing computational methods (from the POND group) to build a differential diagnosis tool for dementias, Specifically Alzheimer’s Disease vs Lewy body Dementia.

This is exciting for two reasons in particular: we are looking at real world data from individuals in the prodromal stage of dementia.

If you’re not quite ready for a PhD and are interested in medical image computing, this could be the role for you. The ideal candidate would have a background in computer science, physics, maths, medical image computing, or a related field.

NISOx talks

I was invited to present today to the NeuroImaging Statistics Oxford (NISOx) reading group of Prof Thomas Nichols at the Oxford Big Data Institute.

I ended up giving two talks after my first talk (on TADPOLE Challenge) generated some interest in the event-based model!

Here are the slides:

Rosetrees Interdisciplinary workshop on neurodegenerative diseases of the brain

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

The recording of my talk is up on YouTube:
https://youtu.be/JdplymQmoHo?t=8177.

CompAge 2020

Here’s my poster:

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

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

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: neiloxtoby.com/work/20200115-cmic_seminar

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: neiloxtoby.com/work/20191031-ukdri-d3pm_for_trials

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

[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.