The Discovery of Rules from Brain Images

Tsukimoto, Hiroshi; Morita, Chie

doi:10.1007/3-540-49292-5_18

Cited by 16 publications

(6 citation statements)

References 2 publications

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“…The results are linear formulas of the form Since the naive approach always runs exponentially to complete the second step and becomes unrealistic in practice, a better algorithm is to generate terms from low order to high order while applying a pruning strategy. Experiments on artificial data showed: 94 (1) when there are no correlations between adjacent attributes, the accuracies are almost the same as the accuracies of C4.5, and (2) when there are strong correlations between adjacent attributes, the algorithm works better than C4.5 in terms of the accuracy of the result.…”

Section: Other Methodsmentioning

confidence: 88%

“…92 Spatial-lattice models are often applied to image analysis; these techniques are often based on Markov random fields, with inference techniques based on various modifications of likelihood-maximization procedures. 93 Another alternative is to divide brain images into meshes, treat functions as classes and meshes as attributes, and find rules like 'A and B ) positive', which means if mesh A and mesh B are active, then some function is positive/on: 94 thus the problem can be reduced to supervised inductive learning. The usual inductive learning algorithms such as C4.5 95 do not work for this problem, however, because (1) there are strong correlations between attributes and (2) there are usually too many attributes (say 100 Â 100 = 10 000) and too few samples (say 100).…”

Section: Other Methodsmentioning

confidence: 99%

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Data mining in brain imaging

Megalooikonomou¹,

Ford²,

Shen³

et al. 2000

stat methods med res

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Data mining in brain imaging is an emerging field of high importance for providing prognosis, treatment, and a deeper understanding of how the brain functions. The field of data mining addresses the question of how best to use this data to discover new knowledge and improve the process of decision making. The discovery of associations between human brain structures and functions (i.e. human brain mapping) has been recognized as the main goal of the Human Brain Project, 1 which is a high-priority project funded by several government initiatives. Mining problems can be grouped in three categories: 2 identifying classifications, finding sequential patterns, and discovering associations. Although data mining is a powerful knowledge discovery technique, there are constraints in the way it can be applied: it is applicationdependent, different applications usually require different mining techniques, and data must be of a certain size and format.3 In this paper we survey current mining methods, give a critical review of the main computational obstacles that lie behind our ability to perform automatic data mining on brain imaging and propose some solutions.There are various problems in mining of brain images that need to be addressed. The first problem is that most fundamental mining algorithms (rule-based learning systems, neural networks, decision trees, Bayesian networks, logistic regressions, and so on), which have been used with great success in medicine, assume that data sets contain only simple numeric and symbolic entries. It is important, therefore, to Data mining in brain imaging is proving to be an effective methodology for disease prognosis and prevention. This, together with the rapid accumulation of massive heterogeneous data sets, motivates the need for efficient methods that filter, clarify, assess, correlate and cluster brain-related information. Here, we present data mining methods that have been or could be employed in the analysis of brain images. These methods address two types of brain imaging data: structural and functional. We introduce statistical methods that aid the discovery of interesting associations and patterns between brain images and other clinical data. We consider several applications of these methods, such as the analysis of taskactivation, lesion-deficit, and structure morphological variability; the development of probabilistic atlases; and tumour analysis. We include examples of applications to real brain data. Several data mining issues, such as that of method validation or verification, are also discussed.

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Section: Other Methodsmentioning

confidence: 88%

Section: Other Methodsmentioning

confidence: 99%

Data mining in brain imaging

Megalooikonomou¹,

Ford²,

Shen³

et al. 2000

stat methods med res

View full text Add to dashboard Cite

show abstract

“…The key issues are how to design the psychological and physiological experiments for systematic obtaining various data from HIPS, as well as how to analyze and manage such data from multiple aspects for discovering new models of HIPS. Although several human-expert centric tools such as SPM (MEDx) have been developed for cleaning, normalizing and visualizing the fMRI images, researchers have also been studying how the fMRI images can be automatically analyzed and understood by using data mining and statistical learning techniques [43,45,66,70,96]. We are concerned with how to extract significant features from multiple brain data measured by using fMRI and EEG in preparation for multi-aspect data mining that uses various data mining techniques for analyzing multiple data sources.…”

Section: Fig 6 From Fmri/eeg Experiments To New Cognitive Wi Modelsmentioning

confidence: 99%

Web Intelligence Meets Brain Informatics

2007

Lecture Notes in Computer Science

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Abstract. In this chapter, we outline a vision of Web Intelligence (WI) research from the viewpoint of Brain Informatics (BI), a new interdisciplinary field that systematically studies the mechanisms of human information processing from both the macro and micro viewpoints by combining experimental cognitive neuroscience with advanced information technology. BI studies human brain from the viewpoint of informatics (i.e., human brain is an information processing system) and uses informatics (i.e., WI centric information technology) to support brain science study. Advances in instrumentation, e.g., based on fMRI and information technologies offer more opportunities for research in both Web intelligence and brain sciences. Further understanding of human intelligence through brain sciences fosters innovative Web intelligence research and development. WI portal techniques provide a powerful new platform for brain sciences. The synergy between WI and BI advances our ways of analyzing and understanding of data, knowledge, intelligence, and wisdom, as well as their interrelationships, organizations, and creation processes. Web intelligence is becoming a central field that revolutionizes information technologies and artificial intelligence to achieve human-level Web intelligence.

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“…Their method can deal with the morphological variability that exists between subjects, and is scalable so that large longitudinal studies can be performed. Tsukimoto and colleagues developed a novel method for mining classification rules by dividing brain images into meshes as condition attributes, and treating functions as classes (Tsukimoto and Morita 1998). Thus, such rules as "if mesh A and mesh B are active, then some function is positive" can be found.…”

Section: On Multiple Brain Data Analysismentioning

confidence: 99%

Building a Data‐mining Grid for Multiple Human Brain Data Analysis

Zhong

Jia

Motomura

et al. 2005

Computational Intelligence

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E-science is about global collaboration in key areas of science such as cognitive science and brain science, and the next generation of infrastructure such as the Wisdom Web and Knowledge Grids. As a case study, we investigate human multiperception mechanism by cooperatively using various psychological experiments, physiological measurements, and data mining techniques for developing artificial systems which match human ability in specific aspects. In particular, we observe fMRI (functional magnetic resonance imaging) and EEG (electroencephalogram) brain activations from the viewpoint of peculiarity oriented mining and propose a way of peculiarity oriented mining for knowledge discovery in multiple human brain data. Based on such experience and needs, we concentrate on the architectural aspect of a brain-informatics portal from the perspective of the Wisdom Web and Knowledge Grids. We describe how to build a data-mining grid on the Wisdom Web for multiaspect human brain data analysis. The proposed methodology attempts to change the perspective of cognitive scientists from a single type of experimental data analysis toward a holistic view at a long-term, global field of vision.

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The Discovery of Rules from Brain Images

Cited by 16 publications

References 2 publications

Data mining in brain imaging

Data mining in brain imaging

Web Intelligence Meets Brain Informatics

Building a Data‐mining Grid for Multiple Human Brain Data Analysis

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