The Bergen Breakfast Scanning Club dataset: a deep brain imaging dataset

Abstract

Populational brain imaging methods based on group averages provide valuable insights into the general functions of the brain. However, they often overlook the inherent inter- and intra-subject variability, limiting our understanding of individual differences. To address this limitation, researchers have turned to big datasets and deep brain imaging datasets. Big datasets enable the exploration of inter-subject variations, while deep brain imaging datasets, involving repeated scanning of multiple subjects over time, offer detailed insights into intra-subject variability. Despite the availability of numerous big datasets, the number of deep brain imaging datasets remains limited. In this article, we present a deep brain imaging dataset derived from the Bergen Breakfast Scanning Club (BBSC) project. The dataset comprises data collected from three subjects who underwent repeated scanning over the course of approximately one year. Specifically, three types of data chunks were collected: behavioral data, functional brain data, and structural brain data. Functional brain images, encompassing magnetic resonance spectroscopy (MRS) and resting-state functional magnetic resonance imaging (fMRI), along with their anatomical reference T1-weighted brain images, were collected twice a week during the data collection period. In total, 38, 40, and 25 sessions of functional data were acquired for subjects 1, 2, and 3, respectively. On the other hand, structural brain images, including T2-weighted brain images, diffusion-weighted images (DWI), and fluid-attenuated inversion recovery (FLAIR) images, were obtained once a month. A total of 10, 9, and 6 sessions were collected for subjects 1, 2, and 3, respectively. The primary objective of this article is to provide a comprehensive description of the data acquisition protocol employed in the BBSC project, as well as detailed insights into the preprocessing steps applied to the acquired data.