Where is Stress Processed at Night? A Neuroimaging Study into Stress-Related Abnormality in Brain

Study Level

Visiting researcher, postdoc, PhD

Overview

Stress is something we can feel subjectively, but how exactly our brain processes stress? Why does stress often cause sleep problems? This study aims to explore the stress-related abnormality in brain activity especially during sleep. This study involves the use of wearable neuron imaging devices and salivary cortisol test.

Research activities

This research project may involve the following activities:

• systematic literature review on fNIR brain imaging, sleep and stress

• design and perform data collection experiment using wearable fNIR (Artinis Brite 24), smartwatches (Fitbit), wearable rings (OURA), continuous glucose monitor (FreeStyle Libre) and salivary biochemical test (SOMA cube; cortisol & sIgA)

• multimodal data retrieval, integration, and preprocessing

• multimodal physiological data analysis using statistical techniques, signal processing, spatio-temporal time series analysis, machine learning and data mining

• co-author research papers and give presentations in academic conferences

Outcomes

Upon conclusion of this research, we expect to develop:

• experience in designing and conducting data collection experiments with human subjects

• skills in data engineering and data science

• experience with the latest brain imaging technology and wearable health technology

• expertise in a variety of data analytics techniques

• domain knowledge in brain science, sleep and stress

• skills in project management and technical communication

Skills and experience

As the ideal candidate, you'll have a passion for brain science or sleep science and a strong background in signal processing/time series analysis/machine learning/data mining. Prior programming experience in Python or R will add extra advantage.

Publications

• Liang Z. (2021) Self-rated sleep quality is associated with hemodynamics in prefrontal cortex in the first sleep cycle. In Proceedings of the 3rd Congress of Asian Society of Sleep Medicine (ASSM), Beijing, China.

Recommended reading

• Sungho Tak, Jong Chul Ye. (2014) Statistical analysis of fNIRS data: A comprehensive review. Neuroimage 85(1): 72-91. doi: 10.1016/j.neuroimage.2013.06.016.

• Cooper, R.J., Selb, J., Gagnon, L., Phillip, D., Schytz, H.W., Iversen, H.K., Ashina, M., Boas,D.A. (2012). A systematic comparison of motion artifact correction techniques forfunctional near-infrared spectroscopy. Front. Neurosci. 6

• Robertson, F.C., Douglas, T.S., Meintjes, E.M. (2010). Motion artifact removal for func-tional near infrared spectroscopy: a comparison of methods. IEEE Trans. Biomed.Eng. 57 (6), 1377–1387.

• Paola Pinti, Felix Scholkmann, Antonia Hamilton, Paul Burgess and Ilias Tachtsidis. (2019) Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework. Front. Hum. Neurosci. 12:505.

• P. Pinti, M. F. Siddiqui, A. D. Levy, E. J. H. Jones & Ilias Tachtsidis. (2021) An analysis framework for the integration of broadband NIRS and EEG to assess neurovascular and neurometabolic coupling. Scientific Report 11:3977.

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