brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research

Scatter plots of four diffusion metrics (FA, MD, OD, ICVF) derived from denoised data of subject 1 using the DWI data of subject 2 and 3 as training data, respectively.

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Different diffusion metrics (FA, MD, ICVF, and OD) computed from SoS, SENSE1, and dSoS images showing on a representative slice.

The corresponding histograms are shown in the bottom. For ICVF and OD, the dSoS data (green) is more similar to the gold standard SENSE1 data (blue) than the original SoS data (red). The benefit of our denoising procedure is most evident in ICVF and OD, the two measures that are derived from higher-order diffusion models that rely on high b-value images, although some improvement can also be seen in the MD value.</p

SENSE1-dSoS correlations of diffusion metrics (FA, MD, OD, and ICVF).

For the dSoS data used in this table, the model was trained on data that was merged from the other two subjects. The dSoS data for SUBJ 1, for example, was obtained by applying a model trained on the combined data of SUBJ 2 and SUBJ 3 to SUBJ 1’s SoS data.</p

Schematic drawing of the architecture of the 1D CNN model for denoising.

“16x1x16” means the dimension of convolutional operation: kernel size = 16, depth = 1, number of filters = 16.</p