Characterization of the hemodynamic response function in default mode network subregions for task-evoked negative BOLD responses

28/10/2026

AI Summary

Task-evoked negative BOLD responses scale linearly with task duration, supporting linear systems assumption for NBR.
Each DMN subregion exhibits a distinct negative HRF, differing from positive BOLD HRF and from other DMN regions.
Inter-regional HRF variability exceeded inter-task variability; iterative robust deconvolution reliably extracted regional HRFs unrelated to task performance.

TL;DR (AI Generated)

Our results demonstrated that each region of the DMN represented a unique HRF, which was not only substantially different from the HRF of the positive BOLD signal, but also different from the HRF extracted from the other DMN regions.

Altogether, we demonstrated NBR scales linearly with task duration, introduced a robust HRF extraction approach to obtain a distinct HRF for each DMN sub-regions, which were different from the HRF of the positive BOLD signal.

Basic summary

Neuroimage. 2026 Oct 27:122318. doi: 10.1016/j.neuroimage.2026.122318. Online ahead of print.

ABSTRACT

While the hemodynamic response function (HRF) of the positive blood oxygenated level dependent (BOLD) signal is well characterized in the functional magnetic resonance imaging (fMRI) field, studies investigating the shape and properties of the negative BOLD HRF, often observed in the default mode network (DMN) regions, are rare. In this study, we first investigated whether the duration-dependent scaling assumption for linear systems theory holds for the task-evoked negative BOLD response (NBR). Then, we obtained the shape of the HRF from each region of the DMN, using an in-house developed robust iterative deconvolution technique. Our results demonstrated that each region of the DMN represented a unique HRF, which was not only substantially different from the HRF of the positive BOLD signal, but also different from the HRF extracted from the other DMN regions. We assessed the consistency and variability of regional HRF estimates across six task-fMRI datasets collected within the same study cohort. When comparing the HRF across DMN sub-regions and across tasks, our results demonstrated a generally higher inter-regional variability compared to inter-task variability. Furthermore, our performance correlation analysis illustrated that the HRFs in the DMN sub-regions extracted by our iterative FIR method were not significantly related to the task performance. Altogether, we demonstrated NBR scales linearly with task duration, introduced a robust HRF extraction approach to obtain a distinct HRF for each DMN sub-regions, which were different from the HRF of the positive BOLD signal. The results suggest there is possibility that each node of the DMN might have different underlying neural and/or vascular mechanisms that evokes different NBR-HRF.

PMID:42946274 | DOI:10.1016/j.neuroimage.2026.122318

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