five

Auditory HRF

收藏
OpenNeuro2026-05-09 更新2026-05-16 收录
下载链接:
https://openneuro.org/datasets/ds007780
下载链接
链接失效反馈
官方服务:
资源简介:
# Dataset overview This OpenNeuro release contains two fMRI datasets acquired on a 3 T Siemens Prisma scanner as part of a study characterising the auditory hemodynamic response function (HRF) using fast fMRI. The two datasets differ in acquisition parameters but employ the same experimental paradigm, enabling assessment of reproducibility across sessions and acquisition protocols. --- ## MRI acquisition Both datasets were acquired with a repetition time (TR) of 1 s and whole-brain coverage (48 slices), but differed in several parameters: - Bandwidth: 2620 Hz (Original dataset) vs. 1985 Hz (Replication dataset) - Voxel resolution: 2 mm vs. 2.5 mm isotropic - Echo time (TE): 35.2 ms vs. 28 ms --- ## Experimental task Participants passively listened to brief (500 ms) environmental sounds presented with long inter-stimulus intervals (>18.5 s), designed to allow characterisation of the full auditory HRF. Each fMRI session consisted of 6 runs of this passive listening paradigm. --- ## Datasets ### Original dataset - Participants completed 2 fMRI sessions. - Each session included 6 functional runs. - Participants additionally completed one multi-parameter mapping (MPM) session. ### Replication dataset - Participants completed 4 fMRI sessions. - Each session included 6 functional runs using the same auditory stimuli. Participants 1–4 in the Original dataset are the same individuals as Participants 1–4 in the Replication dataset. --- ## Data quality and notes - Participants were scanned using a head fixation device to minimise motion. Each participant has one MPM available. - `sub-rep05` is missing a fieldmap for `ses-01`. --- ## Intended use The dataset is intended for methodological work on HRF estimation, fast fMRI, and auditory cortex, as well as for reuse in studies of auditory perception and hemodynamic modelling.
创建时间:
2026-05-09
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作