Workflow-based approaches to neuroimaging analysis

Surgical Workflow Management Schemata for Cataract Procedures

Objective: Workflow guidance of surgical activities is a challenging task. Because of variations in patient properties and applied surgical techniques, surgical processes have a high variability. The objective of this study was the design and implementation of a surgical workflow management system (SWFMS) that can provide a robust guidance for surgical activities. We investigated how many surgical process models are needed to develop a SWFMS that can guide cataract surgeries robustly.

Methods: We used 100 cases of cataract surgeries and acquired patient-individual surgical process models (iSPMs) from them. Of these, randomized subsets iSPMs were selected as learning sets to create a generic surgical process model (gSPM). These gSPMs were mapped onto workflow nets as work-flow schemata to define the behavior of the SWFMS. Finally, 10 iSPMs from the disjoint set were simulated to validate the workflow schema for the surgical processes. The measurement was the successful guidance of an iSPM.

Results: We demonstrated that a SWFMS with a workflow schema that was generated from a subset of 10 iSPMs is sufficient to guide approximately 65% of all surgical processes in the total set, and that a subset of 50 iSPMs is sufficient to guide approx. 80% of all processes.

Conclusion: We designed a SWFMS that is able to guide surgical activities on a detailed level. The study demonstrated that the high inter-patient variability of surgical processes can be considered by our approach.

Is it time to re-prioritize neuroimaging databases and digital repositories?

The development of in vivo brain imaging has lead to the collection of large quantities of digital information. In any individual research article, several tens of gigabytes-worth of data may be represented-collected across normal and patient samples. With the ease of collecting such data, there is increased desire for brain imaging datasets to be openly shared through sophisticated databases. However, very often the raw and pre-processed versions of these data are not available to researchers outside of the team that collected them. A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Though early sociological and technical concerns have been addressed, they have not been ameliorated altogether for many in the field. In this article, we review the progress made in neuroimaging databases, their role in data sharing, data management, potential for the construction of brain atlases, recording data provenance, and value for re-analysis, new publication, and training. We feature the LONI IDA as an example of an archive being used as a source for brain atlas workflow construction, list several instances of other successful uses of image databases, and comment on archive sustainability. Finally, we suggest that, given these developments, now is the time for the neuroimaging community to re-prioritize large-scale databases as a valuable component of brain imaging science.

Visualizing data mining results with the brede tools

A few neuroinformatics databases now exist that record results from neuroimaging studies in the form of brain coordinates in stereotaxic space. The Brede Toolbox was originally developed to extract, analyze and visualize data from one of them - the BrainMap database. Since then the Brede Toolbox has expanded and now includes its own database with coordinates along with ontologies for brain regions and functions: The Brede Database. With Brede Toolbox and Database combined, we setup automated workflows for extraction of data, mass meta-analytic data mining and visualizations. Most of the Web presence of the Brede Database is established by a single script executing a workflow involving these steps together with a final generation of Web pages with embedded visualizations and links to interactive three-dimensional models in the Virtual Reality Modeling Language. Apart from the Brede tools I briefly review alternate visualization tools and methods for Internet-based visualization and information visualization as well as portals for visualization tools.

Database-managed grid-enabled analysis of neuroimaging data: the CNARI framework

Functional magnetic resonance imaging (fMRI) has led to an enormous growth in the study of cognitive neuroanatomy, and combined with advances in high-field electrophysiology (and other methods), has led to a fast-growing field of human neuroscience. Technological advances in both hardware and software will lead to an ever more promising future for fMRI. We have developed a new computational framework that facilitates fMRI experimentation and analysis, and which has led to some rethinking of the nature of experimental design and analysis. The Computational Neuroscience Applications Research Infrastructure (CNARI) incorporates novel methods for maintaining, serving, and analyzing massive amounts of fMRI data. By using CNARI, it is possible to perform naturalistic, network-based, statistically valid experiments in systems neuroscience on a very large scale, with ease of data manipulation and analysis, within reasonable computational time scales. In this article, we describe this infrastructure and then illustrate its use on a number of actual examples in both cognitive neuroscience and neurological research. We believe that these advanced computational approaches will fundamentally change the future shape of cognitive brain imaging with fMRI.