Dr. Neil Oxtoby

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)

Following a press-release today (23 March 2016), Dr. Laura Phipps from Alzheimer’s Research UK wrote a blog post about my research into Alzheimer’s disease and Parkinson’s disease. Check it out:
www.dementiablog.org/using-computers-to-decode-alzheimers

The official project description can be found here.

For more about my research, drop me a line.

Our POND team at UCL have modelled the changes in Alzheimer’s disease, confirming our current understanding of this disease, and providing a tool for diagnosis and prognosis. You can read about it for free in the journal Brain here (open access). Title and abstract below.

A data-driven model of biomarker changes in sporadic Alzheimer’s disease

Alexandra Young, Neil Oxtoby, Pankaj Daga, David Cash, ADNI, Nick Fox, Sebastien Ourselin, Jonathan Schott, and Daniel Alexander

We demonstrate the use of a probabilistic generative model to explore the biomarker changes occurring as Alzheimer’s disease develops and progresses. We enhanced the recently introduced event-based model for use with a multi-modal sporadic disease data set. This allows us to determine the sequence in which Alzheimer’s disease biomarkers become abnormal without reliance on a priori clinical diagnostic information or explicit biomarker cut points. The model also characterises uncertainty in the ordering and provides a natural patient staging system. Two hundred and eighty-five subjects (92 cognitively normal, 129 mild cognitive impairment, 64 Alzheimer’s disease) were selected from the Alzheimer’s Disease Neuroimaging Initiative with measurements of 14 Alzheimer’s disease-related biomarkers including cerebrospinal fluid proteins, regional magnetic resonance imaging brain volume and rates of atrophy measures, and cognitive test scores. We used the event-based model to determine the sequence of biomarker abnormality and its uncertainty in various population subgroups. We used patient stages assigned by the event-based model to discriminate cognitively normal subjects from those with Alzheimer’s disease, and predict conversion from mild cognitive impairment to Alzheimer’s disease and cognitively normal to mild cognitive impairment. The model predicts that cerebrospinal fluid levels become abnormal first, followed by rates of atrophy, then cognitive test scores, and finally regional brain volumes. In amyloid-positive (cerebrospinal fluid amyloid-β_1–42 < 192 pg/ml) or APOE-positive (one or more APOE4 alleles) subjects, the model predicts with high confidence that the cerebrospinal fluid biomarkers become abnormal in a distinct sequence: amyloid-β_1–42, phosphorylated tau, total tau. However, in the broader population total tau and phosphorylated tau are found to be earlier cerebrospinal fluid markers than amyloid-β_1–42, albeit with more uncertainty. The model’s staging system strongly separates cognitively normal and Alzheimer’s disease subjects (maximum classification accuracy of 99%), and predicts conversion from mild cognitive impairment to Alzheimer’s disease (maximum balanced accuracy of 77% over 3 years), and from cognitively normal to mild cognitive impairment (maximum balanced accuracy of 76% over 5 years). By fitting Cox proportional hazards models, we find that baseline model stage is a significant risk factor for conversion from both mild cognitive impairment to Alzheimer’s disease (P = 2.06 × 10⁻⁷) and cognitively normal to mild cognitive impairment (P = 0.033). The data-driven model we describe supports hypothetical models of biomarker ordering in amyloid-positive and APOE-positive subjects, but suggests that biomarker ordering in the wider population may diverge from this sequence. The model provides useful disease staging information across the full spectrum of disease progression, from cognitively normal to mild cognitive impairment to Alzheimer’s disease. This approach has broad application across neurodegenerative disease, providing insights into disease biology, as well as staging and prognostication.

Working with folk from my old stomping ground at the University of Liverpool, we discovered in the laboratory that a two-dimensional dusty plasma can be adequately described using the ideal gas law – even when shock waves are excited to melt the dust crystal.

The title and abstract are below, but you can read all about it in Physical Review Letters here (or for free on the arXiv here).

Ideal Gas Behavior of a Strongly Coupled Complex (Dusty) Plasma

N.P. Oxtoby, E.J. Griffith, C. Durniak, J.F. Ralph, and D. Samsonov

In a laboratory, a two-dimensional complex (dusty) plasma consists of a low-density ionized gas containing a confined suspension of Yukawa-coupled plastic microspheres. For an initial crystal-like form, we report ideal gas behaviour in this strongly coupled system during shock-wave experiments. This evidence supports the use of the ideal gas law as the equation of state for soft crystals such as those formed by dusty plasmas.

I have started my new role at University College London (UCL). I am working on computational models of neurodegenerative disease progression. Read more here.