We are involved with the creation of multiple electronic brain atlases, extensively used by the neuroimaging community. In addition to the educational propose, these atlases allow the quantification of multiple contrasts and MRI properties per anatomical structure, facilitating the translation and interpretation of the results. We published the first white matter atlas,  which was adopted by major software, such as FSL, 3D Slicer and MRICron. We then created the first digital atlas of deep gray matter structures based on susceptibility weighted images, later extending the effort to multi-atlases. We also created the first digital, 3D, deformable atlas of brain arterial territories based on lesion distributions in more than a thousand stroke lesions, freely downloadable at https://www.nitrc.org/projects/arterialatlas

In collaboration with clinical scientists, we study relationships between brain damage and functional deficits, particularly in stroke. These studies made clear the need of a large, well-curated database and of automated technologies for reproducible, large scale data processing. We create free and accessible tools to quantify the ischemic stroke core and perfusion deficits, using a large sample (3,000) of clinical images of patients with acute strokes. Our tools are suited to imaging experts and non-experts working on translational research, as they have minimal computational requirements, work in local CPUs with a single command line. They output lesion volume, 3D digital segmentation, and quantification of damage per brain structure and per vascular territory, in real time.

Abnormal patterns and brain pathologies often involve subtle and heterogeneous changes in multiple domains. To access this multimodal information, in collaboration with Dr. Susumu Mori and Dr. Michael Miller, we develop and test technologies for automated, public, and accessible atlas-based analysis of structural images, diffusion-weighted images, resting state fMRI, susceptibility weighted images, and arterial spin labeling.

In combination with these technologies, we have been applying artificial intelligence and high-throughput information to characterize diseases and individual brain patterns, for content-based image retrieval, to study the pathological basis of clinical conditions, and for patient stratification.

The quantification of morphometry and photometry in brain MRIs and of their deviations from the healthy pattern, enabled us to characterize diverse aspects of normal and abnormal brain development, disease models, to access relationships between anatomy and function, and to trace longitudinal progression of diseases.

In collaboration with clinical scientists, we helped to identify potential biomarkers for clinical modeling and therapeutics.