Data mining in brain imaging

Abstract

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.

Graph-Based Shape Analysis for MRI Classification

“…The scale of the images in the OASIS dataset is not consistent, so although the approximate volume of the third and lateral ventricles in voxels is known at this point, it is only roughly correlated with the volume in physical units. This is similar to the "anchor points" described by Megalooikonomou et al (2000).…”

Independent component analysis of functional MRI: what is signal and what is noise?

“…ICA has also provided insight into artifacts caused by large vessel effects in perfusion imaging [68]. The versatility of the technique is such that ICA has been used as a preprocessing method for attempting to visualize color multichannel magnetic resonance data [69], and has been considered within the data mining framework used in statistics [70].…”

A Data Mining Approach To Gen Dynamic Behavioral Process

“…Neuroanatomy [9,10,11], and imaging. [12,13] Taxonomic thinking, involving anthologies and part-whole relations, require semantically-oriented tools. [14] Other areas of neuroscience, such as electrophysiology, are primarily numerical.…”