Identifying spatial relationships in neural processing using a multiple classification approach.
The application of statistical classification methods to in vivo functional neuroimaging data makes it possible to explore spatial patterns in task-related changes in neural processing. Cluster analysis is one group of descriptive statistical procedures that can assist in identifying classes of brain regions that exhibit similar task-related functionality. In practice, a limitation of cluster analysis is that the performances of clustering algorithms rely on unknown characteristics of the data, making it difficult to determine which procedure best suits a particular analysis. We present a multiple classification approach that incorporates numerous algorithms, evaluates the associated classifications, and either selects a plausible partition relative to the others considered or pools the results from the numerous methods. The multiple classification approach utilizes a new performance criterion, called the relative information (RI) measure, to evaluate the quality of the candidate partitions and as the basis for producing a composite classification image. Employing multiple classifications, rather than a single algorithm, our methodology increases the chance of detecting the functional relationships within the data and, therefore, produces more reliable results. We apply our methodology to a PET study to explore spatial relationships in measured brain function associated with increasing blood alcohol concentration levels, and we perform a simulation study to evaluate the performance of RI.
DuBois Bowman
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Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA. dbowma3@sph.emory.edu
Patel
Rajan
R
D000435:Alcoholic Intoxication / Q000000981:diagnostic imaging* / Q000503:physiopathology; D000465:Algorithms; D001288:Attention / Q000502:physiology*; D001921:Brain / Q000000981:diagnostic imaging* / Q000503:physiopathology; D001931:Brain Mapping; D016000:Cluster Analysis; D003625:Data Collection / Q000706:statistics & numerical data; D004305:Dose-Response Relationship, Drug; D000431:Ethanol / Q000097:blood; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008432:Mathematical Computing; D009415:Nerve Net / Q000000981:diagnostic imaging / Q000503:physiopathology; D049268:Positron-Emission Tomography / Q000706:statistics & numerical data*; D011597:Psychomotor Performance / Q000502:physiology*; D012039:Regional Blood Flow / Q000502:physiology; D015203:Reproducibility of Results
D000431:Ethanol