scholarly journals Robust Bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information

2003 ◽  
Vol 19 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Guillaume Marrelec ◽  
Habib Benali ◽  
Philippe Ciuciu ◽  
Mélanie Pélégrini-Issac ◽  
Jean-Baptiste Poline
NeuroImage ◽  
2001 ◽  
Vol 14 (1) ◽  
pp. 140-148 ◽  
Author(s):  
C. Gössl ◽  
L. Fahrmeir ◽  
D.P. Auer

NeuroImage ◽  
2014 ◽  
Vol 93 ◽  
pp. 59-73 ◽  
Author(s):  
Sébastien Proulx ◽  
Mouna Safi-Harb ◽  
Pierre LeVan ◽  
Dongmei An ◽  
Satsuki Watanabe ◽  
...  

2017 ◽  
Author(s):  
Josef Wilzén ◽  
Anders Eklund ◽  
Mattias Villani

AbstractInference from fMRI data faces the challenge that the hemodynamic system, that relates the underlying neural activity to the observed BOLD fMRI signal, is not known. We propose a new Bayesian model for task fMRI data with the following features: (i) joint estimation of brain activity and the underlying hemodynamics, (ii) the hemodynamics is modeled nonparametrically with a Gaussian process (GP) prior guided by physiological information and (iii) the predicted BOLD is not necessarily generated by a linear time-invariant (LTI) system. We place a GP prior directly on the predicted BOLD time series, rather than on the hemodynamic response function as in previous literature. This allows us to incorporate physiological information via the GP prior mean in a flexible way. The prior mean function may be generated from a standard LTI system, based on a canonical hemodynamic response function, or a more elaborate physiological model such as the Balloon model. This gives us the nonparametric flexibility of the GP, but allows the posterior to fall back on the physiologically based prior when the data are weak. Results on simulated data show that even with an erroneous prior for the GP, the proposed model is still able to discriminate between active and non-active voxels in a satisfactory way. The proposed model is also applied to real fMRI data, where our Gaussian process model in several cases finds brain activity where previously proposed LTI models, parametric and nonparametric, does not.


2018 ◽  
Vol 115 (43) ◽  
pp. E10206-E10215 ◽  
Author(s):  
Immanuel G. Elbau ◽  
Benedikt Brücklmeier ◽  
Manfred Uhr ◽  
Janine Arloth ◽  
Darina Czamara ◽  
...  

Ample evidence links dysregulation of the stress response to the risk for psychiatric disorders. However, we lack an integrated understanding of mechanisms that are adaptive during the acute stress response but potentially pathogenic when dysregulated. One mechanistic link emerging from rodent studies is the interaction between stress effectors and neurovascular coupling, a process that adjusts cerebral blood flow according to local metabolic demands. Here, using task-related fMRI, we show that acute psychosocial stress rapidly impacts the peak latency of the hemodynamic response function (HRF-PL) in temporal, insular, and prefrontal regions in two independent cohorts of healthy humans. These latency effects occurred in the absence of amplitude effects and were moderated by regulatory genetic variants of KCNJ2, a known mediator of the effect of stress on vascular responsivity. Further, hippocampal HRF-PL correlated with both cortisol response and genetic variants that influence the transcriptional response to stress hormones and are associated with risk for major depression. We conclude that acute stress modulates hemodynamic response properties as part of the physiological stress response and suggest that HRF indices could serve as endophenotype of stress-related disorders.


2021 ◽  
Author(s):  
Michele Lacerenza ◽  
Mauro Buttafava ◽  
Lorenzo Spinelli ◽  
Alberto Tosi ◽  
Alberto Dalla Mora ◽  
...  

2021 ◽  
Vol 125 (4) ◽  
pp. 1045-1057 ◽  
Author(s):  
Natasha de la Rosa ◽  
David Ress ◽  
Amanda J. Taylor ◽  
Jung Hwan Kim

We investigate dynamics of the negative hemodynamic response function (nHRF), the negative blood-oxygen-level-dependent (BOLD) response (NBR) evoked by a brief stimulus, in human early visual cortex. Here, we show that the nHRFs are not inverted versions of the corresponding pHRFs. The nHRF has complex dynamics that varied significantly with eccentricity. The results also show shift-invariant temporal linearity does not hold for the NBR.


NeuroImage ◽  
2020 ◽  
Vol 208 ◽  
pp. 116446 ◽  
Author(s):  
Henriette Lambers ◽  
Martin Segeroth ◽  
Franziska Albers ◽  
Lydia Wachsmuth ◽  
Timo Mauritz van Alst ◽  
...  

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