The Cuban Human Brain Mapping Project, a young and middle age population-based EEG, MRI, and cognition dataset.

The Cuban Human Brain Mapping Project (CHBMP) repository is an open multimodal neuroimaging and cognitive dataset from 282 young and middle age healthy participants (31.9 ± 9.3 years, age range 18-68 years). This dataset was acquired from 2004 to 2008 as a subset of a larger stratified random sample of 2,019 participants from La Lisa municipality in La Habana, Cuba. The exclusion criteria included the presence of disease or brain dysfunctions. Participant data that is being shared comprises i) high-density (64-120 channels) resting-state electroencephalograms (EEG), ii) magnetic resonance images (MRI), iii) psychological tests (MMSE, WAIS-III, computerized go-no go reaction time), as well as iv,) demographic information (age, gender, education, ethnicity, handedness, and weight). The EEG data contains recordings with at least 30 minutes in duration including the following conditions: eyes closed, eyes open, hyperventilation, and subsequent recovery. The MRI consists of anatomical T1 as well as diffusion-weighted (DWI) images acquired on a 1.5 Tesla system. The dataset presented here is hosted by Synapse.org and available at https://chbmp-open.loris.ca .

The MNI data-sharing and processing ecosystem.

Neuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of "Big Data", there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing.