scholarly journals Computing hemodynamic response functions from concurrent spectral fiber-photometry and fMRI data

2022 ◽  
Vol 9 (03) ◽  
Author(s):  
Tzu-Hao H. Chao ◽  
Wei-Ting Zhang ◽  
Li-Ming Hsu ◽  
Domenic H. Cerri ◽  
Tzu-Wen Wang ◽  
...  
Keyword(s):  
Hemodynamic Response ◽  
Fmri Data ◽  
Response Functions

Detecting task-dependent functional connectivity in GIMME with person-specific hemodynamic response functions

2021 ◽  
Author(s):  
Kelly Anne Duffy ◽  
Zachary F. Fisher ◽  
Cara A. Arizmendi ◽  
Peter C.M. Molenaar ◽  
Joseph Hopfinger ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Hemodynamic Response ◽  
Response Functions

A semi-parametric model of the hemodynamic response for multi-subject fMRI data

NeuroImage ◽  
2013 ◽  
Vol 75 ◽  
pp. 136-145 ◽  
Author(s):  
Tingting Zhang ◽  
Fan Li ◽  
Lane Beckes ◽  
James A. Coan
Keyword(s):  
Hemodynamic Response ◽  
Parametric Model ◽  
Fmri Data

Problems in estimating hemodynamic response parameters from fMRI data

1996 ◽  
Vol 4 (4) ◽  
pp. 265-272 ◽  
Author(s):  
James A. Sorenson ◽  
Xiaoming Wang
Keyword(s):  
Hemodynamic Response ◽  
Fmri Data

Deconvolution of neural dynamics from fMRI data using a spatiotemporal hemodynamic response function

NeuroImage ◽  
2014 ◽  
Vol 94 ◽  
pp. 203-215 ◽  
Author(s):  
K.M. Aquino ◽  
P.A. Robinson ◽  
M.M. Schira ◽  
M. Breakspear
Keyword(s):  
Response Function ◽  
Hemodynamic Response ◽  
Neural Dynamics ◽  
Fmri Data ◽  
Hemodynamic Response Function

scholarly journals An investigation of positive and inverted hemodynamic response functions across multiple visual areas

2014 ◽  
Vol 35 (11) ◽  
pp. 5550-5564 ◽  
Author(s):  
Alexander M. Puckett ◽  
Jedidiah R. Mathis ◽  
Edgar A. DeYoe
Keyword(s):  
Hemodynamic Response ◽  
Response Functions ◽  
Visual Areas

scholarly journals Modeling of the Hemodynamic Responses in Block Design fMRI Studies

2013 ◽  
Vol 34 (2) ◽  
pp. 316-324 ◽  
Author(s):  
Zuyao Y Shan ◽  
Margaret J Wright ◽  
Paul M Thompson ◽  
Katie L McMahon ◽  
Gabriella G A M Blokland ◽  
...  
Keyword(s):  
Response Function ◽  
Goodness Of Fit ◽  
Block Design ◽  
Onset Time ◽  
Hemodynamic Response ◽  
Fmri Data ◽  
Parameter Estimates ◽  
Block Designs ◽  
Hemodynamic Response Function ◽  
Time To Peak

The hemodynamic response function (HRF) describes the local response of brain vasculature to functional activation. Accurate HRF modeling enables the investigation of cerebral blood flow regulation and improves our ability to interpret fMRI results. Block designs have been used extensively as fMRI paradigms because detection power is maximized; however, block designs are not optimal for HRF parameter estimation. Here we assessed the utility of block design fMRI data for HRF modeling. The trueness (relative deviation), precision (relative uncertainty), and identifiability (goodness-of-fit) of different HRF models were examined and test–retest reproducibility of HRF parameter estimates was assessed using computer simulations and fMRI data from 82 healthy young adult twins acquired on two occasions 3 to 4 months apart. The effects of systematically varying attributes of the block design paradigm were also examined. In our comparison of five HRF models, the model comprising the sum of two gamma functions with six free parameters had greatest parameter accuracy and identifiability. Hemodynamic response function height and time to peak were highly reproducible between studies and width was moderately reproducible but the reproducibility of onset time was low. This study established the feasibility and test–retest reliability of estimating HRF parameters using data from block design fMRI studies.

scholarly journals Robust and automated motion correction for real infant fNIRS data

2020 ◽  
Author(s):  
Lindsey J Powell
Keyword(s):  
Motion Correction ◽  
Hemodynamic Response ◽  
Motion Artifacts ◽  
Response Functions ◽  
Correction Procedure ◽  
Response Signal ◽  
Hemodynamic Responses ◽  
Differential Responses ◽  
Difficult Challenge

Although many approaches have been proposed, removing motion artifacts from developmental fNIRS data remains a difficult challenge. In particular, the lack of consistency in motion correction approaches across experimental reports suggests that the field has not yet identified an algorithm that consistently removes the majority of motion contamination while retaining hemodynamic responses, regardless of the idiosyncrasies of particular datasets. Some existing approaches remove the same fraction of variance from each participant’s data; others use participant data to set filtering parameters in ways that result in more stringent thresholds for low-motion participants than high-motion participants. Both types of approach risk leaving artifacts in data from participants with the most motion, while removing signal from participants with the least motion. In contrast, the procedure proposed here identifies and filters motion artifacts on the basis of a fixed, physiologically-justified threshold, so that amount of variance removed is closely associated with the prevalence of motion in each participant’s data. Across multiple contrasts from real experimental datasets, this procedure effectively removes motion artifacts while retaining the hemodynamic response signal, allowing the detection of differential responses to conditions, and recovering canonical hemodynamic response functions for both oxygenated and deoxygenated timecourses, indicated by robust negative correlations between the two hemoglobin types. This motion correction procedure would be appropriate to preregister as a planned component of the preprocessing stream in future fNIRS research.

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