Evaluating Models of the Ageing BOLD Response

Henson, R. N.; Olszowy, W.; Tsvetanov, K. A.; Yadav, P. S.; Tyler, Lorraine K.; Brayne, Carol; Bullmore, Edward T.; Calder, Andrew C.; Cusack, Rhodri; Dalgleish, Tim; Duncan, John; Matthews, Fiona E.; Marslen-Wilson, William D.; Rowe, James B.; Shafto, Meredith A.; Campbell, Karen; Cheung, Teresa; Davis, Simon; Geerligs, Linda; Kievit, Rogier; McCarrey, Anna; Mustafa, Abdur; Price, Darren; Samu, David; Taylor, Jason R.; Treder, Matthias; Tsvetanov, Kamen A.; van Belle, Janna; Williams, Nitin; Mitchell, Daniel; Fisher, Simon; Eising, Else; Knights, Ethan; Bates, Lauren; Emery, Tina; Erzinçlioglu, Sharon; Gadie, Andrew; Gerbase, Sofia; Georgieva, Stanimira; Hanley, Claire; Parkin, Beth; Troy, David; Auer, Tibor; Correia, Marta; Gao, Lu; Green, Emma; Henriques, Rafael; Allen, Jodie; Amery, Gillian; Amunts, Liana; Barcroft, Anne; Castle, Amanda; Dias, Cheryl; Dowrick, Jonathan; Fair, Melissa; Fisher, Hayley; Goulding, Anna; Grewal, Adarsh; Hale, Geoff; Hilton, Andrew; Johnson, Frances; Joh...

Abstract

Neural activity cannot be directly observed using fMRI; rather it must be inferred from the hemodynamic responses that neural activity causes. Solving this inverse problem is made possible through the use of forward models, which generate predicted hemodynamic responses given hypothesised underlying neural activity. Commonly-used hemodynamic models were developed to explain data from healthy young participants; however, studies of ageing and dementia are increasingly shifting the focus toward elderly populations. We evaluated the validity of a range of hemodynamic models across the healthy adult lifespan: from basis sets for the linear convolution models commonly used to analyse fMRI studies, to more advanced models including nonlinear fitting of a parameterised hemodynamic response function (HRF) and nonlinear fitting of a biophysical generative model (hemodynamic modelling, HDM). Using an exceptionally large sample of participants, and a sensorimotor task optimized for detecting the shape of the BOLD response to brief stimulation, we first characterised the effects of age on descriptive features of the response (e.g., peak amplitude and latency). We then compared these to features from more complex nonlinear models, fit to four regions of interest engaged by the task, namely left auditory cortex, bilateral visual cortex, left (contralateral) motor cortex and right (ipsilateral) motor cortex. Finally, we validated the extent to which parameter estimates from these models have predictive validity, in terms of how well they predict age in cross-validated multiple regression. We conclude that age-related differences in the BOLD response can be captured effectively by models with three free parameters. Furthermore, we show that biophysical models like the HDM have predictive validity comparable to more common models, while additionally providing insights into underlying mechanisms, which go beyond descriptive features like peak amplitude or latency, and include estimation of nonlinear effects. Here, the HDM revealed that most of the effects of age on the BOLD response could be explained by an increased rate of vasoactive signal decay and decreased transit rate of blood, rather than changes in neural activity per se. However, in the absence of other types of neural/hemodynamic data, unique interpretation of HDM parameters is difficult from fMRI data alone, and some brain regions in some tasks (e.g., ipsilateral motor cortex) can show responses that are more difficult to capture using current models.

Citation

Henson, R. N., Olszowy, W., Tsvetanov, K. A., Yadav, P. S., Tyler, L. K., Brayne, C., Bullmore, E. T., Calder, A. C., Cusack, R., Dalgleish, T., Duncan, J., Matthews, F. E., Marslen-Wilson, W. D., Rowe, J. B., Shafto, M. A., Campbell, K., Cheung, T., Davis, S., Geerligs, L., Kievit, R., …Zeidman, P. (2024). Evaluating Models of the Ageing BOLD Response. Human Brain Mapping, 45(15), Article e70043. https://doi.org/10.1002/hbm.70043

• Journal Article Type: Article
• Acceptance Date: Sep 27, 2024
• Online Publication Date: Oct 18, 2024
• Publication Date: Oct 15, 2024
• Deposit Date: Nov 3, 2024
• Publicly Available Date: Nov 4, 2024
• Journal: Human Brain Mapping
• Print ISSN: 1065-9471
• Electronic ISSN: 1097-0193
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 45
• Issue: 15
• Article Number: e70043
• DOI: https://doi.org/10.1002/hbm.70043
• Public URL: https://hull-repository.worktribe.com/output/4908246