Dataset Information

Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner.

ABSTRACT: Functional magnetic resonance imaging (fMRI), being an indirect measure of brain activity, is mathematically defined as a convolution of the unmeasured latent neural signal and the hemodynamic response function (HRF). The HRF is known to vary across the brain and across individuals, and it is modulated by neural as well as non-neural factors. Three parameters characterize the shape of the HRF, which is obtained by performing deconvolution on resting-state fMRI data: response height, time-to-peak and full-width at half-max. The data provided here, obtained from 47 healthy adults, contains these three HRF parameters at every voxel in the brain, as well as HRF parameters from the default-mode network (DMN). In addition, we have provided functional connectivity (FC) data from the same DMN regions, obtained for two cases: data with deconvolution (HRF variability minimized) and data with no deconvolution (HRF variability corrupted). This would enable researchers to compare regional changes in HRF with corresponding FC differences, to assess the impact of HRF variability on FC. Importantly, the data was obtained in a 7T MRI scanner. While most fMRI studies are conducted at lower field strengths, like 3T, ours is the first study to report HRF data obtained at 7T. FMRI data at ultra-high fields contains larger contributions from small vessels, consequently HRF variability is lower for small vessels at higher field strengths. This implies that findings made from this data would be more conservative than from data acquired at lower fields, such as 3T. Results obtained with this data and further interpretations are available in our recent research study (Rangaprakash et al., in press) [1]. This is a valuable dataset for studying HRF variability in conjunction with FC, and for developing the HRF profile in healthy individuals, which would have direct implications for fMRI data analysis, especially resting-state connectivity modeling. This is the first public HRF data at 7T.

SUBMITTER: Rangaprakash D

PROVIDER: S-EPMC5988211 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner.

Rangaprakash D D Wu Guo-Rong GR Marinazzo Daniele D Hu Xiaoping X Deshpande Gopikrishna G

Data in brief 20180106

Functional magnetic resonance imaging (fMRI), being an indirect measure of brain activity, is mathematically defined as a convolution of the unmeasured latent neural signal and the hemodynamic response function (HRF). The HRF is known to vary across the brain and across individuals, and it is modulated by neural as well as non-neural factors. Three parameters characterize the shape of the HRF, which is obtained by performing deconvolution on resting-state fMRI data: response height, time-to-peak ...[more]

PMID: 29876476

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Project description:Understanding sources of electromagnetic interference are important in designing any electronic system. This is especially true when combining positron emission tomography (PET) and magnetic resonance imaging (MRI) in a multimodality system as coupling between the subsystems can degrade the performance of either modality. For this reason, eliminating radio frequency (RF) interference and gradient-induced eddy currents have been major challenges in building simultaneous hybrid PET/MRI systems. MRI requires negligible RF interference at the Larmor resonance frequency, while RF interference at almost any frequency may corrupt PET data. Moreover, any scheme that minimizes these interactions would, ideally, not compromise the performance of either subsystem. This paper lays out a plan to resolve these problems. A carbon fiber composite material is found to be a good RF shield at the Larmor frequency (300MHz in this work) while introducing negligible gradient eddy currents. This carbon fiber composite also provides excellent structural support for the PET detector components. Low frequency electromagnetic radiation (81kHz here) from the switching power supplies of the gradient amplifiers was also found to interfere with the PET detector. Placing the PET detector module between two carbon fiber tubes and grounding the inner carbon fiber tube to the PET detector module ground reduced this interference. Further reductions were achieved by adding thin copper (Cu) foil on the outer carbon fiber case and electrically grounding the PET detector module so that all 3 components had a common ground, i.e. with the PET detector in an electrostatic cage. Finally, gradient switching typical in MRI sequences can result in count losses in the particular PET detector design studied. Moreover, the magnitude of this effect depends on the location of the detector within the magnet bore and which MRI gradient is being switched. These findings have a bearing on future designs of PET/MRI systems.

Project description:Aims Currently, there are only a few studies concerning brain functional alterations after acute alcohol exposure, and the majority of existing studies attach more importance to the spatial properties of brain function without considering the temporal properties. The current study adopted sliding window to investigate the resting-state brain networks in healthy volunteers after acute alcohol intake and to explore the dynamic changes in network connectivity. Materials and methods Twenty healthy volunteers were enrolled in this study. Blood-oxygen-level-dependent (BOLD) data prior to drinking were obtained as control, while that 0.5 and 1 h after drinking were obtained as the experimental group. Reoccurring functional connectivity patterns (states) were determined following group independent component analysis (ICA), sliding window analysis and k-means clustering. Between-group comparisons were performed with respect to the functional connectivity states fractional windows, mean dwell time, and the number of transitions. Results Three optimal functional connectivity states were identified. The fractional windows and mean dwell time of 0.5 h group and 1 h group increased in state 3, while the fraction window and mean dwell time of 1 h group decreased in state 1. State 1 is characterized by strong inter-network connections between basal ganglia network (BGN) and sensorimotor network (SMN), BGN and cognitive executive network (CEN), and default mode network (DMN) and visual network (VN). However, state 3 is distinguished by relatively weak intra-network connections in SMN, VN, CEN, and DMN. State 3 was thought to be a characteristic connectivity pattern of the drunk brain. State 1 was believed to represent the brain’s main connection pattern when awake. Such dynamic changes in brain network connectivity were consistent with participants’ subjective feelings after drinking. Conclusion The current study reveals the dynamic change in resting-state brain functional network connectivity before and after acute alcohol intake. It was discovered that there might be relatively independent characteristic functional network connection patterns under intoxication, and the corresponding patterns characterize the clinical manifestations of volunteers. As a valuable imaging biomarker, dynamic functional network connectivity (dFNC) offers a new approach and basis for further explorations on brain network alterations after alcohol consumption and the alcohol-related mechanisms for neurological damage.

Dataset Information

Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner.

Publications

Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner.

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