Literature DB >> 16466935

BOLD responses to stimuli: dependence on frequency, stimulus form, amplitude, and repetition rate.

P A Robinson1, P M Drysdale, H Van der Merwe, E Kyriakou, M K Rigozzi, B Germanoska, C J Rennie.   

Abstract

A quantitative theory is developed for the relationship between stimulus and the resulting blood oxygen level-dependent (BOLD) functional MRI signal. The relationship of stimuli to neuronal activity during evoked responses is inferred from recent physiology-based quantitative modeling of evoked response potentials (ERPs). A hemodynamic model is then used to calculate the BOLD response to neuronal activity having the form of an impulse, a sinusoid, or an ERP-like damped sinusoid. Using the resulting equations, the BOLD response is analyzed for different forms, frequencies, and amplitudes of stimuli, in contrast with previous research, which has mostly concentrated on sustained stimuli. The BOLD frequency response is found to be closely linear in the parameter ranges of interest, with the form of a low-pass filter with a weak resonance at approximately 0.07 Hz. An improved BOLD impulse response is systematically obtained which includes initial dip and post-stimulus undershoot for some parameter ranges. It is found that the BOLD response depends strongly on the precise temporal course of the evoked neuronal activity, not just its peak value or typical amplitude. Indeed, for short stimuli, the linear BOLD response is closely proportional to the time-integrated activity change evoked by the stimulus, regardless of amplitude. It is concluded that there can be widely differing proportionalities between BOLD and peak activity, that this is the likely reason for the low level of correspondence seen experimentally between ERP sources and BOLD measurements and that non-BOLD measurements, such as ERPs, can be used to correct for this effect to obtain improved activity estimates. Finally, stimulus sequences that optimize the signal-to-noise ratio in event-related BOLD fMRI (efMRI) experiments are derived using the hemodynamic transfer function.

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Year:  2006        PMID: 16466935     DOI: 10.1016/j.neuroimage.2005.12.026

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  19 in total

1.  Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

Authors:  Yi-Feng Wang; Zhiliang Long; Qian Cui; Feng Liu; Xiu-Juan Jing; Heng Chen; Xiao-Nan Guo; Jin H Yan; Hua-Fu Chen
Journal:  Hum Brain Mapp       Date:  2015-10-29       Impact factor: 5.038

2.  A novel method for integrating MEG and BOLD fMRI signals with the linear convolution model in human primary somatosensory cortex.

Authors:  Cathy Nangini; Fred Tam; Simon J Graham
Journal:  Hum Brain Mapp       Date:  2008-01       Impact factor: 5.038

Review 3.  Model driven EEG/fMRI fusion of brain oscillations.

Authors:  Pedro A Valdes-Sosa; Jose Miguel Sanchez-Bornot; Roberto Carlos Sotero; Yasser Iturria-Medina; Yasser Aleman-Gomez; Jorge Bosch-Bayard; Felix Carbonell; Tohru Ozaki
Journal:  Hum Brain Mapp       Date:  2009-09       Impact factor: 5.038

4.  Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations.

Authors:  Gustavo Deco; Adrián Ponce-Alvarez; Dante Mantini; Gian Luca Romani; Patric Hagmann; Maurizio Corbetta
Journal:  J Neurosci       Date:  2013-07-03       Impact factor: 6.167

5.  Shock-like haemodynamic responses induced in the primary visual cortex by moving visual stimuli.

Authors:  T C Lacy; K M Aquino; P A Robinson; M M Schira
Journal:  J R Soc Interface       Date:  2016-12       Impact factor: 4.118

6.  How local excitation-inhibition ratio impacts the whole brain dynamics.

Authors:  Gustavo Deco; Adrián Ponce-Alvarez; Patric Hagmann; Gian Luca Romani; Dante Mantini; Maurizio Corbetta
Journal:  J Neurosci       Date:  2014-06-04       Impact factor: 6.167

7.  Response-mode decomposition of spatio-temporal haemodynamics.

Authors:  J C Pang; P A Robinson; K M Aquino
Journal:  J R Soc Interface       Date:  2016-05       Impact factor: 4.118

8.  Inferring multi-scale neural mechanisms with brain network modelling.

Authors:  Michael Schirner; Anthony Randal McIntosh; Viktor Jirsa; Gustavo Deco; Petra Ritter
Journal:  Elife       Date:  2018-01-08       Impact factor: 8.140

9.  Hemodynamic traveling waves in human visual cortex.

Authors:  Kevin M Aquino; Mark M Schira; P A Robinson; Peter M Drysdale; Michael Breakspear
Journal:  PLoS Comput Biol       Date:  2012-03-22       Impact factor: 4.475

10.  Determination of Dynamic Brain Connectivity via Spectral Analysis.

Authors:  Peter A Robinson; James A Henderson; Natasha C Gabay; Kevin M Aquino; Tara Babaie-Janvier; Xiao Gao
Journal:  Front Hum Neurosci       Date:  2021-07-16       Impact factor: 3.169

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