Neural underpinnings of naturalistic speech rhythms underlying disorganised thought processing and the impact of vocal emotions on emotional states in clinical high-risk using OPM-MEG

   School of Psychology

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  Dr Hyojin Park, Dr Jack Rogers  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Person Specification

Applicants should have a strong background in neuroscience, mental health research, psychology, medicine, physics, computer science, experimental linguistics or a related discipline relevant to cognitive neuroscience/neuroimaging, and ideally a background in cognitive neuroimaging. They should have a commitment to research in neuroimaging and mental health and hold or realistically expect to obtain at least an Upper Second Class Honours Degree and ideally master’s degree in a relevant subject.

Informal enquiries should be directed to the project supervisor Dr Hyojin Park [Email Address Removed]

Research links and

Project details

Early detection of schizophrenia and other psychotic disorders is widely recognised as a critical need. Key symptoms like disorganised thoughts including atypical predictive processing and auditory hallucinations are central to schizophrenia and often appear before the first episode of psychosis, reflected in altered semantic and syntactic language processing1-3. Investigating these alterations in speech processing and prediction of speech as well as voice processing within real-life settings can provide powerful tools for identifying early signs of developing psychosis and enabling timely interventions. However, there is a scarcity of studies exploring the neural rhythmic underpinnings of spoken language and vocal emotions in intelligible speech processing and emotional states among CHR individuals.

Our previous research has shown that low-frequency rhythms tracked by inferior frontal and sensory-motor cortices serve as neurobiological markers in individuals without psychosis risk for audiovisual intelligible speech processing4,5. Additionally, advancements in natural language processing algorithms based on Large Language Models (LLMs) now allow us to investigate the neural mechanisms underpinning high-level semantic processing, such as extracting the gist of a narrative during naturalistic speech processing6.

Moreover, during speech perception, the manipulation of vocal emotions (e.g., happy, sad, afraid) influences our emotional states7, but it remains unknown how vocal emotions affect the emotional states of CHR young individuals. We will employ an innovative technique called rapid invisible frequency tagging (RIFT)8,9, which enhances cortical excitation by frequency-tagging responses to improve prosodic rhythm processing and subsequently affect intelligible speech processing including predictive utility and emotional states.

We will use OPM-MEG technology, which features a lightweight, wearable helmet that reduces concerns related to the brain-to-sensor gap, especially over the frontal regions, as compared to conventional MEG systems10. This advancement enables a more thorough investigation of the critical frontal-sensory-motor interactions involved in natural language processing in CHR individuals. By employing cutting-edge techniques and methodological approaches, our objective is to detect early indications of impaired speech and voice processing including predictive mechanisms in young individuals at CHR for psychosis.

How to apply

Click on the institution website which will direct you to the MRC AIM website which provide full information and the application forms to complete. Please ensure your application is submitted by the application deadline midday (GMT) Friday 12 January 2024 as late applications will not be considered.

Biological Sciences (4) Medicine (26)

Funding Notes

This is a fully funded studentship provided by the Medical Research Council.
If you are successful, you will receive a stipend (currently £18,622 per year for 2023/24) and a tuition fee waiver for 4 years.
Successful candidates will also receive an allowance for a laptop, a travel and conference allowance and an allowance for laboratory/PhD running costs.


1 Silva, A. M. et al. Syntactic complexity of spoken language in the diagnosis of schizophrenia: A probabilistic Bayes network model. Schizophr Res 259, 88-96, doi:10.1016/j.schres.2022.06.011 (2023).
2 Alonso-Sanchez, M. F., Limongi, R., Gati, J. & Palaniyappan, L. Language network self-inhibition and semantic similarity in first-episode schizophrenia: A computational-linguistic and effective connectivity approach. Schizophr Res 259, 97-103, doi:10.1016/j.schres.2022.04.007 (2023).
3 Hermans, K. et al. Temporal dynamics of suspiciousness and hallucinations in clinical high risk and first episode psychosis. Psychiatry Res 290, 113039, doi:10.1016/j.psychres.2020.113039 (2020).
4 Park, H., Ince, R. A., Schyns, P. G., Thut, G. & Gross, J. Frontal top-down signals increase coupling of auditory low-frequency oscillations to continuous speech in human listeners. Curr Biol 25, 1649-1653, doi:10.1016/j.cub.2015.04.049 (2015).
5 Park, H., Kayser, C., Thut, G. & Gross, J. Lip movements entrain the observers' low-frequency brain oscillations to facilitate speech intelligibility. Elife 5:e14521, doi:10.7554/eLife.14521 (2016).
6 Park, H. & Gross, J. Get the gist of the story: Neural map of topic keywords in multi-speaker environment. eLife 12, doi:10.7554/eLife.89703.1 (2023).
7 Aucouturier, J. J. et al. Covert digital manipulation of vocal emotion alter speakers' emotional states in a congruent direction. Proc Natl Acad Sci U S A 113, 948-953, doi:10.1073/pnas.1506552113 (2016).
8 Zhigalov, A., Herring, J. D., Herpers, J., Bergmann, T. O. & Jensen, O. Probing cortical excitability using rapid frequency tagging. NeuroImage 195, 59-66, doi:10.1016/j.neuroimage.2019.03.056 (2019).
9 Drijvers, L., Jensen, O. & Spaak, E. Rapid invisible frequency tagging reveals nonlinear integration of auditory and visual information. Hum Brain Mapp 42, 1138-1152, doi:10.1002/hbm.25282 (2021).
10 Boto, E. et al. Moving magnetoencephalography towards real-world applications with a wearable system. Nature 555, 657-661, doi:10.1038/nature26147 (2018).

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