title: Bold-Independent Computational Entropy Assesses Functional Donut-Like Structures in Brain fMRI Images.
abstract: We introduce a novel method for the measurement of information level in fMRI (functional Magnetic Resonance Imaging) neural data sets, based on image subdivision in small polygons equipped with different entropic content. We show how this method, called maximal nucleus clustering (MNC), is a novel, fast and inexpensive image-analysis technique, independent from the standard blood-oxygen-level dependent signals. MNC facilitates the objective detection of hidden temporal patterns of entropy/information in zones of fMRI images generally not taken into account by the subjective standpoint of the observer. This approach befits the geometric character of fMRIs. The main purpose of this study is to provide a computable framework for fMRI that not only facilitates analyses, but also provides an easily decipherable visualization of structures. This framework commands attention because it is easily implemented using conventional software systems. In order to evaluate the potential applications of MNC, we looked for the presence of a fourth dimension's distinctive hallmarks in a temporal sequence of 2D images taken during spontaneous brain activity. Indeed, recent findings suggest that several brain activities, such as mind-wandering and memory retrieval, might take place in the functional space of a four dimensional hypersphere, which is a double donut-like structure undetectable in the usual three dimensions. We found that the Rényi entropy is higher in MNC areas than in the surrounding ones, and that these temporal patterns closely resemble the trajectories predicted by the possible presence of a hypersphere in the brain.
authors.affiliation: Department of Electrical and Computer Engineering, University of ManitobaWinnipeg, MB, Canada; Department of Mathematics, Adıyaman UniversityAdıyaman, Turkey; Department of Mathematics, Faculty of Arts and Sciences, Adıyaman UniversityAdıyaman, Turkey; Computational Intelligence Laboratory, University of ManitobaWinnipeg, MB, Canada.
authors.forename: James F
authors.initials: JF
authors.lastname: Peters
authors.affiliation: Department of Applied Computer Science, University of Winnipeg Winnipeg, MB, Canada.
authors.forename: Sheela
authors.initials: S
authors.lastname: Ramanna
authors.affiliation: Department of Physics, Center for Nonlinear Science, University of North Texas Denton, TX, USA.
authors.forename: Arturo
authors.initials: A
authors.lastname: Tozzi
authors.affiliation: Department of Mathematics, Faculty of Arts and Sciences, Adıyaman UniversityAdıyaman, Turkey; Computational Intelligence Laboratory, University of ManitobaWinnipeg, MB, Canada.
authors.forename: Ebubekir
authors.initials: E
authors.lastname: İnan
keywords: Voronoï tessellation; fMRI; hypersphere; nucleus clustering; strong proximity; temporal pattern