Rangaprakash D, Wu Guo-Rong, Marinazzo Daniele, Hu Xiaoping, Deshpande Gopikrishna
AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.
Data Brief. 2018 Jan 6;17:1175-1179. doi: 10.1016/j.dib.2018.01.003. eCollection 2018 Apr.
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.
功能磁共振成像（fMRI）作为一种对大脑活动的间接测量方法，在数学上被定义为未测量的潜在神经信号与血液动力学响应函数（HRF）的卷积。已知HRF在大脑中以及个体之间会有所不同，并且它受到神经和非神经因素的调节。通过对静息态fMRI数据进行反卷积获得的HRF形状由三个参数表征：响应高度、峰值时间和半高宽。此处提供的数据来自47名健康成年人，包含大脑中每个体素的这三个HRF参数，以及默认模式网络（DMN）的HRF参数。此外，我们还提供了来自同一DMN区域的功能连接性（FC）数据，针对两种情况获取：进行反卷积的数据（HRF变异性最小化）和未进行反卷积的数据（HRF变异性被破坏）。这将使研究人员能够将HRF的区域变化与相应的FC差异进行比较，以评估HRF变异性对FC的影响。重要的是，这些数据是在7T磁共振成像扫描仪中获得的。虽然大多数fMRI研究是在较低场强（如3T）下进行的，但我们的研究是第一项报告在7T下获得的HRF数据的研究。超高场强下的fMRI数据中小血管的贡献更大，因此在较高场强下小血管的HRF变异性较低。这意味着从这些数据得出的结果将比从较低场强（如3T）获取的数据得出的结果更为保守。利用这些数据获得的结果及进一步的解释可在我们最近的研究（Rangaprakash等人，即将发表）[1]中找到。这是一个用于结合FC研究HRF变异性以及建立健康个体HRF图谱的宝贵数据集，这将对fMRI数据分析，尤其是静息态连接性建模有直接影响。这是首个公开的7T HRF数据。
