TY - JOUR
T1 - Evaluating Models of the Ageing BOLD Response
AU - Cam-CAN
AU - Henson, R. N.
AU - Olszowy, W.
AU - Tsvetanov, K. A.
AU - Yadav, P. S.
AU - Tyler, Lorraine K.
AU - Brayne, Carol
AU - Bullmore, Edward T.
AU - Calder, Andrew C.
AU - Cusack, Rhodri
AU - Dalgleish, Tim
AU - Duncan, John
AU - Henson, Richard N.
AU - Matthews, Fiona E.
AU - Marslen-Wilson, William D.
AU - Rowe, James B.
AU - Shafto, Meredith A.
AU - Campbell, Karen
AU - Cheung, Teresa
AU - Davis, Simon
AU - Geerligs, Linda
AU - Kievit, Rogier
AU - McCarrey, Anna
AU - Mustafa, Abdur
AU - Price, Darren
AU - Samu, David
AU - Taylor, Jason R.
AU - Treder, Matthias
AU - Tsvetanov, Kamen A.
AU - van Belle, Janna
AU - Williams, Nitin
AU - Mitchell, Daniel
AU - Fisher, Simon
AU - Eising, Else
AU - Knights, Ethan
AU - Bates, Lauren
AU - Emery, Tina
AU - Erzinçlioglu, Sharon
AU - Gadie, Andrew
AU - Gerbase, Sofia
AU - Georgieva, Stanimira
AU - Hanley, Claire
AU - Parkin, Beth
AU - Troy, David
AU - Auer, Tibor
AU - Correia, Marta
AU - Gao, Lu
AU - Green, Emma
AU - Henriques, Rafael
AU - Allen, Jodie
AU - Amery, Gillian
N1 - Publisher Copyright:
© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.
PY - 2024/10/15
Y1 - 2024/10/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85206855193&partnerID=8YFLogxK
U2 - 10.1002/hbm.70043
DO - 10.1002/hbm.70043
M3 - Article
C2 - 39422406
AN - SCOPUS:85206855193
SN - 1065-9471
VL - 45
JO - Human Brain Mapping
JF - Human Brain Mapping
IS - 15
M1 - e70043
ER -