White Matter Bundles and their Relationship to Language Decline Following Anterior Temporal Lobe Resection

1) Brief description of the context of the research including potential impact

Epilepsy in the UK alone affects 450,00 people with around 30,000 annually developing epilepsy. Pharmaceutical intervention with antiepileptic drugs results in a remission rate of 60%, leaving 40% with drug-resistant epilepsy. Anterior temporal lobe resection (ATLR) is a surgical intervention for temporal lobe epilepsy that results in a remission rate of up to 77%. ATLR is underutilised, principally because of concerns regarding adverse effects, with up to 30% acquiring a language deficit negatively impacting on their quality of life. This means that fewer patients opt for this treatment which has a good chance at rendering them seizure-free.

Magnetic resonance imaging (MRI) is routinely used preoperative to assess lesion location and language. These scans do not reveal a structural or functional cause for the language deficit. One potential explanation for the language deficit seen is damage to white matter bundles. Diffusion MRI is able to map white matter tracts, using this it has been shown that the greater lateralisation of white matter between functionally activated areas during language tasks to the dominant hemisphere is associated with greater post-surgical naming decline.

This project aims to investigate white matter bundles which are shown to be involved in language and run through the temporal lobe. If this project can predict any single or combination of white matter (WM) bundles accurately predicts language decline, we can outline this to surgeons to minimise language deficits. This alleviation of adverse side effects would make ATLR a more attractive choice for many, thus leading to a more cost-effective treatment and an improvement in their quality of life.

2) Aims and objectives

This project can be split into two general aims with sub-aims

1) Retrospectively investigate which WM bundle relates to the language deficit seen post-operatively from ATLR
a. Assess the quality of retrospective data
b. Investigate what processing methods make all retrospective data comparable
c. Develop an automated tractography algorithm reflecting the literature to reconstruct WM bundles in all patients
2) Using findings from 1. Plan ATLR based on avoiding areas critical to language function and assess the impact post-operatively
a. Assess which language tests best assess what we are investigating
b. Work with neurosurgical teams to identify how best to avoid critical regions

3) Novelty of the research methodology

There has been no research that investigates language and WM bundles to this extent in ATLR. The discovery of damage to which WM bundle or bundles causes a language deficit would not only help improve patients' lives but also help clarify the current understanding of white matter bundles and their function. In addition, there is currently no automated tractography algorithm that reflects the current literature for the bundles we're focusing on. The development of such a tool would allow more research groups to investigate this for projects relating to white matter bundles and their relationship to language. This will, in turn, improve the understanding of how these connections work

4) Alignment to EPSRC's strategies and research areas

This project aligns with the EPSRC research theme of healthcare technologies, particularly in optimising treatment by further tailoring surgery to the individual by minimising potential deficits. Furthermore, this project matches two additional EPSRC research areas: 1) Medical imaging as this project relies on multi-modal MRI data. 2) Software engineering as we will develop a new algorithm for automatic tractography of language-related bundles.

5) Any companies or collaborators involved
Sjoerd B. Vos, Peter N. Taylor, Pamela J. Thompson, Sallie Baxendale, Jane de Tisi, Gavin P. Winston, John S. Duncan
UCLH, Newcastle Uni, The Epilepsy Society, Epilepsy Research UK

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