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Published: Wednesday 14 April 2018

Published in the JNNP (Journal of Neurology, Neurosurgery and Psychiatry), new research suggests that a computer technique could help predict how well stroke survivors respond to language therapies for aphasia.

What is aphasia?

Aphasia is a very common condition after stroke and affects a person’s ability to use language. This not only includes problems with spoken conversations but can include problems with reading and writing too.  

Why is this research important?

Aphasia increases a person’s likelihood of becoming socially isolated, dependent and depressed.

A big question people can ask after stroke is what sort of recovery of their language abilities they are likely to make. This can be very hard for clinicians to predict, and frustrating for the stroke survivor and their loved ones.

Previous research by the PLORAS (Predicting Language Outcome and Recovery After Stroke) team at UCL has already made useful predictions about how long it will take aphasic patients to recover language abilities after stroke. To make these predictions, the PLORAS computer model uses information from scans of hundreds of aphasia patients’ brains, including the size, and location in the brain, of the damaged caused by stroke.

However the current study took this work a step further. It investigated whether a computer model could predict how aphasic patients responded to a language therapy, using similar brain scan information.

The ability to predict how well patients with aphasia may respond to therapies could help plan their care and help with patient selection for clinical trials in order to test these therapies.

What did the researchers do?

The study involved 23 participants with ‘central alexia’ (CA), a problem with reading after stroke that can happen to patients with aphasia. People with CA may be slow at reading, make frequent errors, and have problems talking and writing. 

All participants took part in training with an online therapy tool called iReadMore, which aims to improve aphasia patients’ single-word reading skills.

A computer model was created using information about the participants. The information included stroke details from their brain scans, demographic information (including age and gender), and findings from behavioural tests the participants took shortly after having their stroke. 

The model matched changes in participant reading ability after using iReadMore with the model information about them.

The study found that the model could predict 23% of the difference in how participants responded to iReadMore therapy, which was a statistically significant result.

Although this level of prediction may seem small, the researchers think the predictive ability of their model should increase with the number of participants included in the analysis. This means that a much larger sample of participants could provide a much more accurate prediction of response to treatment.

It’s early days in this field of research, but it’s hoped that the findings of the study will encourage further attempts to predict how aphasia patients respond to different language therapies, informed by the stroke damage seen on their brain scans.

Dr Tom Hope, Principal Investigator on the study, said:

"Treatments for aphasia often help some patients but not others. This study is exciting because it suggests that we can predict which patients might benefit from a given treatment in advance. This could be the basis of a more personalised medicine for people with aphasia: tuning treatment plans to the details of the strokes that individual patients have suffered."

About the researchers

Principal investigator on the study, Dr Tom Hope, is a Stroke Association Postdoctoral Fellow at the Wellcome Centre for Human Neuroimaging, University College London.

Dr Hope is responsible for building and refining the PLORAS prediction system.


This study was supported by the Medical Research Council, Wellcome Trust, the COLCIENCIAS Administrative Department of Science, Technology and Innovation and the Stroke Association in the UK.