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REX: response exploration for neuroimaging datasets.

Authors
Journal
Neuroinformatics
1539-2791
Publisher
Springer-Verlag
Publication Date
Volume
5
Issue
4
Identifiers
DOI: 10.1007/s12021-007-9001-y
Keywords
  • Animals
  • Brain
  • Brain Mapping
  • Database Management Systems
  • Diagnostic Imaging
  • Humans
  • Image Processing
  • Computer-Assisted
  • Programming Languages
  • Signal Processing
  • Computer-Assisted
Disciplines
  • Computer Science

Abstract

Neuroimaging technologies produce large and complex datasets. The challenge of comprehensively analysing the recorded dynamics remains an important field of research. The whole-brain linear modelling of hypothesised response dynamics and experimental effects must utilise simple basis sets, which may not detect unexpected or complex signal effects. These unmodelled effects can influence statistical mapping results, and provide important additional clues to the underlying neural dynamics. They can be detected via exploration of the raw signal, however this can be difficult. Specialised visualisation tools are required to manage the huge number of voxels, events and scans. Many effects can be occluded by noise in individual voxel time-series. This paper describes a visualisation framework developed for the assessment of entire neuroimaging datasets. While currently available tools tend to be tied to a specific model of experimental effects, this framework includes a novel metadata schema that enables the rapid selection and processing of responses based on easily-adjusted classifications of scans, brain regions, and events. Flexible event-related averaging and process pipelining capabilities enable users to investigate the effects of preprocessing algorithms and to visualise power spectra and other transformations of the data. The framework has been implemented as a MATLAB package, REX (Response Exploration), which has been utilised within our lab and is now publicly available for download. Its interface enables the real-time control of data selection and processing, for very rapid visualisation. The concepts outlined in this paper have general applicability, and could provide significant further functionality to neuroimaging databasing and process pipeline environments.

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