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Professor Cathy Price is Professor of Cognitive Neuroscience at Queen Square Institute of Neurology. Director of the Wellcome Trust Centre for Neuroimaging (2016-2024).
To mark World Stroke Day, we caught up with Professor Cathy Price to talk about her research into the effect of stroke on brain function, particularly language. We delve into why some people’s language is more affected than others, what predicts recovery and where she sees the research field going in the future with help with AI and machine learning.
What inspired you to pursue a career in cognitive neuroscience, specialising in the neuroanatomy of language processing?
As a teenager, I became inspired to study the brain after my aunt mentioned that emotions are controlled by it. I thought that I could understand emotions by understanding the brain. During my university years (1981-84), I studied physiology and psychology, where I was introduced to neuropsychology. I was fascinated by how brain damage can lead to a variety of symptoms. The symptom that engaged me the most was from a “deep dyslexic” who looked at the word “YACHT” and said “Ship” even though there is no correspondence in the letters. I pursued this topic during my PhD (1985-1990) learning more about how brain damage results in specific processing impairments in object recognition and reading – but I was always frustrated by the lack of knowledge about the brain. After my PhD viva, my PhD examiners recommended me for a post-doctoral position working with the pioneers of the first brain scanner in the UK that was able to functionally map cognitive processes to different parts of the brain. I was then in the unique position to conduct the early functional brain imaging studies of language in the early 1990s. I’m still passionately pursuing this interest.
Throughout my career I’ve always been inspired by the people that I meet, especially by the patients. We often encounter patients who have been informed that they will recover from language deficits after a stroke within a year, yet they find themselves still struggling long after that timeline has passed. Then, on the other hand, we see patients who have been told they will never recover, only to surprise everyone by making significant progress. In both cases, the emotional toll is profound. Patients who were given false hope may feel misled and anxious about their future, while those who adapted to a bleak outlook may experience anger upon realising their potential for recovery. These scenarios highlight the importance of providing accurate information to patients and their families and have shaped my research, which focuses on predicting and explaining language difficulties after brain damage, such as stroke, to give patients realistic expectations for their recovery.
How can stroke affect language processing?
When a stroke occurs, it disrupts blood flow, leading to brain cell damage. This damage can impair various functions, including language processing, depending on which areas of the brain are affected. Damage to the brain regions important for language can lead to various communication challenges that manifest in different ways. For instance, some individuals may have difficulty forming complete sentences, resulting in speech that is short or fragmented. They might know what they want to say but struggle to find the right words or put their thoughts into coherent sentences.
Others may experience issues with speech fluency, producing sentences that are grammatically incorrect or lacking meaning. Additionally, some individuals may have difficulty with rhythm and intonation, making their speech sound monotone or awkward. These challenges highlight the complexity of language processing, as it involves multiple interconnected functions, including grammar, vocabulary, and the ability to convey meaning. The specific effects of the stroke can profoundly impact a person's ability to communicate effectively.
Why do some people recover their language quicker than others following a stroke?
Recovery from a stroke can vary widely among patients, largely depending on which parts of the brain are damaged. If critical areas responsible for language are affected, it can lead to more noticeable difficulties. The degree of recovery depends on whether (i) other parts of the brain, that can help compensate for the lost function, have been preserved and (ii) the patient is able to engage these compensatory neural systems. Factors, like age and overall health, may play a role in how quickly someone can engage available systems and recover. Overall, we need to focus on which areas have been preserved to support the recovery process.
Why do some people respond better to treatment for language problems following a stroke compared to others?
Research shows that speech and language therapy can help people who suffer with language deficits following a stroke (also known as aphasia), but therapy effectiveness can vary widely. Additionally, some patients may improve on their own, even without therapy, making it hard to know if the therapy really made a difference. The condition of the brain, the severity of the initial symptoms, and time in therapy undoubtedly influence therapy effectiveness. The type of therapy matters, too, as approaches that are personalised yield better results. Motivation and engagement also play crucial roles; those who are actively involved tend to see better outcomes. Fatigue can hinder progress, as tired individuals may struggle to participate fully. Social support and overall physical health also influence recovery. Understanding these variations helps clarify why treatment responses differ among individuals following a stroke.
How is technology, such as AI and machine learning, being used to improve prediction of recovery from aphasia after stroke?
Technology, including AI and machine learning, is increasingly enhancing predictions of recovery from aphasia after a stroke. Machine learning algorithms analyse vast amounts of data from clinical studies and patient records, identifying patterns that help predict outcomes based on individual characteristics, such as stroke severity and type, age and prior language skills, guiding clinicians in setting realistic goals. AI may also, in future, be able to create personalised rehabilitation plans by assessing specific patient needs and responses to therapy, optimising treatment effectiveness. Additionally, real-time monitoring through mobile apps and wearable devices may allow for tracking progress and adjusting strategies instantly. Most recently, natural language processing tools are being used to analyse speech patterns to provide insights into language abilities. By leveraging these technologies, healthcare professionals can make more informed decisions, ultimately improving outcomes for individuals with aphasia following a stroke.