Neuroimaging and Applied Computational Anatomy Lab (NIACAL), Northwestern University, Chicago, IL
About our group:
Our group focuses on developing complex biomarkers for neuropsychiatric disorders, particularly schizophrenia and Alzheimer's disease, using the tools of computational anatomy developed in conjunction with our collaborators. To achieve this goal, we develop and implement precise measures of neuroanatomical structure and then relate these measures to cognition, brain activation and specific elements of clinical psychopathology. One of our primary focuses is to describe changes in any of these measures that may occur during disease progression or treatment. As an adjunct to such studies, we also conduct clinical trials with special emphasis on interventions that aim to improve cognition.
About our XNAT project(s):
The Northwestern University Schizophrenia Data and Software Tool (NUSDAST) is a database for sharing neuroimaging and related data with select collaborators or the general community. You can upload images, view them, add demographic and clinical data, and much more. The data are all stored in a searchable, secure web accessible database.
The Northwestern University Neuroimaging Data Archive (NUNDA) is a resource for managing study data collected by the Northwestern University neuroimaging community.
Summary: To make structural magnetic resonance (MR) imaging data, genotyping data, and neurocognitive data as well as analysis tools available to the schizophrenic research community.
Details:
In collaboration with John G. Csernansky, MD, Lizzie Gilman Professor and Chair of Psychiatry and Behavioral Sciences at Northwestern Feinberg School of Medicine, Michael I. Miller, PhD, Professor of Biomedical Engineering and Director of the Center for Imaging Science at the Johns Hopkins University, and Daniel Marcus, PhD, Research Assistant Professor of Radiology at Washington University School of Medicine, and Director of the Neuroinformatics Research Group at Washington University, we are making structural magnetic resonance (MR) imaging data, genotyping data, and neurocognitive data as well as analysis tools available to the schizophrenic research community.
Sharing data and tools across a research community adds tremendous value to the efforts of that community. The project, entitled “Schizophrenia Data and Software Tool Federation using Biomedical Informatics Research Network (BIRN) Infrastructure,” is funded by the National Institute of Mental Health, using the BIRN as a platform for data sharing and informatics tool sharing that extends beyond the neuroimaging researchers involved in the test beds, to include other areas of neuroscience beyond imaging, and to include biomedical research beyond neuroscience.
As part of the growing field of Computational Anatomy (CA), our group has developed tools to identify and characterize brain structural abnormalities in schizophrenia, some of which meet the criteria for disease endophenotypes. In this effort, we have collected high resolution magnetic resonance (MR) datasets from more than 270 subjects using the same MR scanner platform and sequences. Longitudinal MR data are also available on a majority of these subjects. Using CA tools, we have generated surface maps for all of the deep subcortical structures (i.e., hippocampus, amygdala, thalamus, caudate nucleus, nucleus accumbens, putamen and globus pallidus). In addition, smaller datasets of variables related to the volume, thickness and surface area of cortical structures (e.g., the cingulate gyrus) have been generated. Finally, we have constructed manual segmentation datasets for all these structures, which can be used for the validation of new computational methods.
When made publically accessible, these high resolution scans and the associated structural data will be invaluable to the neuroscience community in many ways. First, other groups of scientists will be able to use these data to generate or test new hypotheses related to the maldevelopment of brain structures and neural networks in individuals with schizophrenia. Second, scientists in other groups would be able to rapidly replicate findings produced using their own datasets. Third, the data could be used to test and validate new brain mapping tools. Further, the CA pipeline designed for the analysis of these datasets, which consists of landmarking and diffeomorphic mapping tools along with training and validation datasets, will enable others to study other MR datasets collected from other clinical samples. A key group of intended users of our distribution is researchers who may have their own imaging data but lack software tools for mapping subcortical brain structures. In that group, our software tools would be needed and used on our training data, therefore enabling these researchers to obtain structural measurements in their images.
To determine if our data can help your research, please visit our data portal tool and see what we have available. Guests can specify search parameters to isolate the data that meets their needs. Those with accounts on XNAT Central will be able to access the data, as well.
Image 1: Data Sharing Schematic
Our goals for the XNAT Workshop:
To share our experience and learn from others.