Hemodynamic response function parameters obtained from resting-state functional MRI data in soldiers with trauma

Functional magnetic resonance imaging (fMRI) is an indirect measure of brain activity, i.e. it is a convolution of the latent (unmeasured) neural signal and the hemodynamic response function (HRF). As such, the HRF has been shown to vary across brain regions and individuals. The shape of the HRF is controlled by both neural and non-neural factors. The shape of the HRF can be characterized by three parameters (response height, time-to-peak and full-width at half-max). The data presented here provides the three HRF parameters at every voxel, obtained from U.S. Army soldiers (N=87) diagnosed with posttraumatic stress disorder (PTSD), with comorbid PTSD and mild-traumatic brain injury (mTBI), and matched healthy combat controls. Findings from this data and further interpretations are available in our recent research study (Rangaprakash et al., 2017) [1]. This data is a valuable asset in studying the impact of HRF variability on fMRI data analysis, specifically resting state functional connectivity.

[1]  G. Tononi,et al.  Stimulus Set Meaningfulness and Neurophysiological Differentiation: A Functional Magnetic Resonance Imaging Study , 2014, bioRxiv.

[2]  Ferenc Domoki,et al.  Mechanisms involved in the cerebrovascular dilator effects of N-methyl-d-aspartate in cerebral cortex , 2007, Brain Research Reviews.

[3]  Murray B Stein,et al.  Developing an optimal short‐form of the PTSD Checklist for DSM‐5 (PCL‐5) , 2019, Depression and anxiety.

[4]  Xi-Nian Zuo,et al.  REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing , 2011, PloS one.

[5]  Steven Laureys,et al.  Posterior Cingulate Cortex-Related Co-Activation Patterns: A Resting State fMRI Study in Propofol-Induced Loss of Consciousness , 2014, PloS one.

[6]  Thomas S. Denney,et al.  Hemodynamic variability in soldiers with trauma: Implications for functional MRI connectivity studies , 2017, NeuroImage: Clinical.

[7]  Jonathan D. Power,et al.  Recent progress and outstanding issues in motion correction in resting state fMRI , 2015, NeuroImage.

[8]  Thomas A. Daniel,et al.  Compromised hippocampus‐striatum pathway as a potential imaging biomarker of mild‐traumatic brain injury and posttraumatic stress disorder , 2017, Human brain mapping.

[9]  Karl J. Friston,et al.  Statistical parametric mapping , 2013 .

[10]  C. Nievergelt,et al.  Diagnostic Utility of the Posttraumatic Stress Disorder (PTSD) Checklist for Identifying Full and Partial PTSD in Active-Duty Military , 2015, Assessment.

[11]  Chaogan Yan,et al.  DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI , 2010, Front. Syst. Neurosci..

[12]  Mark D'Esposito,et al.  Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses , 2004, NeuroImage.

[13]  Keith D. Cicerone,et al.  Persistent postconcussion syndrome: The structure of subjective complaints after mild traumatic brain injury , 1995 .

[14]  Guorong Wu,et al.  A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data , 2012, Medical Image Anal..

[15]  S. Muthukumaraswamy,et al.  Individual variability in the shape and amplitude of the BOLD‐HRF correlates with endogenous GABAergic inhibition , 2012, Human brain mapping.

[16]  Andrea Bergmann,et al.  Statistical Parametric Mapping The Analysis Of Functional Brain Images , 2016 .

[17]  Bidhan Lamichhane,et al.  The Neural Basis of Perceived Unfairness in Economic Exchanges , 2014, Brain Connect..

[18]  Hang Joon Jo,et al.  Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression , 2012, Brain Connect..