From Molecules to Networks: Siibra Integrates Brain Data into a Unified Atlas

In the current issue of the renowned journal Nature Methods, siibra is introduced: a software suite that integrates data from different multimodal sources into a comprehensive atlas of the human brain and makes it easily accessible—for interactive exploration as well as for automated and reproducible data analyses, simulations, and AI applications. Siibra is developed by an international team of scientists under the leadership of the Institute of Neuroscience and Medicine (INM-1) at Forschungszentrum Jülich.

The siibra tool suite integrates data from diverse sources into a comprehensive atlas of the human brain, making information on brain architecture usable for neuroscience, medicine, and AI development—from the molecular and cellular to the macroscopic level. (Forschungszentrum Jülich)

To better understand the human brain, information from various levels must be integrated: from molecules and cells to their organization to entire networks. A central challenge is that these data are often scattered across sources and organized differently. They originate from different methods such as microscopy, MRI, or connectivity analyses, exist in formats ranging from images to tables, and rely on different spatial reference systems and conceptual taxonomies.

This is exactly where siibra comes in (“Software Interfaces for Interacting with Brain Atlases”). The software suite includes an interactive 3D web viewer, a Python library, and a web API. It facilitates access to existing resources, brings distributed brain data together in a common reference system—an atlas—and makes them available in interoperable formats. Siibra is already used within EBRAINS, the European digital research platform for neuroscience. There, it provides the technical backbone of the Human Brain Atlas, linking macroscopic reference spaces such as MNI with microscopic models like BigBrain, and using the cytoarchitectonic maps of Julich Brain—a three-dimensional atlas of the human brain covering cortical and subcortical regions—as an anatomical reference.

Methodological Strength

Siibra enables access to different data types, for example about cells, molecules, fibers, functions, and connections in the brain. This includes particularly large image data in the gigabyte or terabyte range, which cannot easily be downloaded and stored locally. Through siibra, data for specific regions can be queried and accessed efficiently. This creates seamless workflows from atlas orientation to fully reproducible, data-driven analyses.

The brain atlas developed with siibra connects brain regions and the variability of brain structure with multiple levels of brain organization, from cells to large-scale networks. (Forschungszentrum Jülich)

Another advantage of siibra is the probabilistic assignment of coordinates, regions, and image patches to brain areas. This is important because brains differ anatomically, and this inter-individual variability must be taken into account. It also allows measurements with spatial uncertainty to be reliably interpreted without relying on rigid boundaries. This is especially relevant for studies that align imaging, histological, or electrophysiological data with atlas information.

Case Study

How this approach translates in practice is shown by a case study re-evaluating deep brain stimulation target regions in the thalamus, such as the VIM, an important relay station in the brain. Reanalyzing previously published data with siibra suggests that this nucleus may sometimes be confused with neighboring regions when localizing stimulations. The example demonstrates siibra’s potential to contribute to more precise stimulations by linking detailed microstructural maps with patient imaging data.

Challenges and Outlook

Bringing previously separated datasets together into a common spatial reference system opens up new perspectives, for example for digital brain models, AI-assisted analyses, and clinical applications, as illustrated by deep brain stimulation.

To which degree these perspectives can be realized depends on the availability and quality of highly precise brain data on which siibra can operate. Not at least, their global availability with high access rates will play a crucial role. Siibra will continually adapt to the needs of the scientific community, with planned additions including new AI-driven analysis tools and connections to additional international data platforms.

Original publication

Dickscheid, T., Gui, X., Simsek, A. N., Schiffer, C., Mangin, J.-F., Leprince, Y., Jirsa, V., Bjaalie, J. G., Leergaard, T. B., Bludau, S., & Amunts, K. (2026). Siibra: A software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources. Nature Methods, doi: 10.1038/s41592-026-03159-x