Literature DB >> 31487547

Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration.

Michalis Kassinopoulos1, Georgios D Mitsis2.   

Abstract

Functional magnetic resonance imaging (fMRI) is widely viewed as the gold standard for studying brain function due to its high spatial resolution and non-invasive nature. However, it is well established that changes in breathing patterns and heart rate strongly influence the blood oxygen-level dependent (BOLD) fMRI signal and this, in turn, can have considerable effects on fMRI studies, particularly resting-state studies. The dynamic effects of physiological processes are often quantified by using convolution models along with simultaneously recorded physiological data. In this context, physiological response function (PRF) curves (cardiac and respiratory response functions), which are convolved with the corresponding physiological fluctuations, are commonly employed. While it has often been suggested that the PRF curves may be region- or subject-specific, it is still an open question whether this is the case. In the present study, we propose a novel framework for the robust estimation of PRF curves and use this framework to rigorously examine the implications of using population-, subject-, session- and scan-specific PRF curves. The proposed framework was tested on resting-state fMRI and physiological data from the Human Connectome Project. Our results suggest that PRF curves vary significantly across subjects and, to a lesser extent, across sessions from the same subject. These differences can be partly attributed to physiological variables such as the mean and variance of the heart rate during the scan. The proposed methodological framework can be used to obtain robust scan-specific PRF curves from data records with duration longer than 5 min, exhibiting significantly improved performance compared to previously defined canonical cardiac and respiration response functions. Besides removing physiological confounds from the BOLD signal, accurate modeling of subject- (or session-/scan-) specific PRF curves is of importance in studies that involve populations with altered vascular responses, such as aging subjects.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Breathing; Cardiac activity; Physiological confounds; Physiological noise correction; fMRI artifacts

Mesh:

Year:  2019        PMID: 31487547     DOI: 10.1016/j.neuroimage.2019.116150

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


  9 in total

1.  Advancing motion denoising of multiband resting-state functional connectivity fMRI data.

Authors:  John C Williams; Philip N Tubiolo; Jacob R Luceno; Jared X Van Snellenberg
Journal:  Neuroimage       Date:  2022-01-13       Impact factor: 6.556

2.  Resting-state functional MRI signal fluctuation amplitudes are correlated with brain amyloid-β deposition in patients with mild cognitive impairment.

Authors:  Norman Scheel; Takashi Tarumi; Tsubasa Tomoto; C Munro Cullum; Rong Zhang; David C Zhu
Journal:  J Cereb Blood Flow Metab       Date:  2021-12-03       Impact factor: 6.960

3.  The neurocognitive correlates of brain entropy estimated by resting state fMRI.

Authors:  Ze Wang
Journal:  Neuroimage       Date:  2021-02-20       Impact factor: 6.556

4.  Hemodynamic and metabolic correspondence of resting-state voxel-based physiological metrics in healthy adults.

Authors:  Shengwen Deng; Crystal G Franklin; Michael O'Boyle; Wei Zhang; Betty L Heyl; Paul A Jerabek; Hanzhang Lu; Peter T Fox
Journal:  Neuroimage       Date:  2022-01-20       Impact factor: 7.400

5.  Vascular origins of low-frequency oscillations in the cerebrospinal fluid signal in resting-state fMRI: Interpretation using photoplethysmography.

Authors:  Ahmadreza Attarpour; James Ward; J Jean Chen
Journal:  Hum Brain Mapp       Date:  2021-02-27       Impact factor: 5.038

6.  Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity.

Authors:  Josh Neudorf; Shaylyn Kress; Ron Borowsky
Journal:  Brain Struct Funct       Date:  2021-10-11       Impact factor: 3.270

7.  Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain.

Authors:  Felix Scholkmann; Ilias Tachtsidis; Martin Wolf; Ursula Wolf
Journal:  Neurophotonics       Date:  2022-07-11       Impact factor: 4.212

8.  Extracting electrophysiological correlates of functional magnetic resonance imaging data using the canonical polyadic decomposition.

Authors:  Dylan Mann-Krzisnik; Georgios D Mitsis
Journal:  Hum Brain Mapp       Date:  2022-05-14       Impact factor: 5.399

9.  Altered Relationship Between Heart Rate Variability and fMRI-Based Functional Connectivity in People With Epilepsy.

Authors:  Michalis Kassinopoulos; Ronald M Harper; Maxime Guye; Louis Lemieux; Beate Diehl
Journal:  Front Neurol       Date:  2021-06-10       Impact factor: 4.003

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.