Evidence map›Paper›PMID 39422406›Full record

ArticleHuman brain mapping2024

Evaluating Models of the Ageing BOLD Response.

R N Henson, W Olszowy, K A Tsvetanov, P S Yadav, Cam‐CAN, P Zeidman

Abstract read
In one paragraph

Article in Human brain mapping, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. How alexithymia shapes functional networks: Insights from a general population study.European psychiatry : the journal of the Association of European Psychiatrists · 2026
    Article
  6. Article
  7. Prefrontal Default Mode Network Interactions with Posterior Hippocampus during Exploration.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  8. Article
  9. Review
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

R N HensonMedical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.ORCID 0000-0002-0712-2639
W OlszowyWolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.ORCID 0000-0002-6080-6597
K A TsvetanovDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
P S YadavMedical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
Cam‐CAN
P ZeidmanWellcome Centre for Human Neuroimaging, University College London, London, UK.ORCID 0000-0003-3610-6619

Funding

Alzheimer's Society 602Biotechnology and Biological Sciences Research Council BB/H008217/1Guarantors of Brain G101149Medical Research Council SUAG/046/G101400Wellcome TrustWellcome Trust 203147/Z/16/Z
6 · The paper itself

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.

Indexed as

AgingMagnetic Resonance ImagingAdultAgedAged, 80 and overBrainBrain MappingFemaleHemodynamicsHumansMaleMiddle AgedModels, NeurologicalOxygenYoung AdultOxygen

Identifiers

PMID39422406
PMCPMC11487563

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.