Name
Abstract | ||
|---|---|---|
Cortical analysis becomes increasingly important for brain research and clinical diagnosis. This problem involves a combinatorial search to find the essential modules among a large number of brain regions. Despite several statistical approaches, cortical analysis remains a formidable challenge due to high dimensionality and sparsity of data. Here we describe an evolutionary method for finding significant modules from cortical data. The method uses a hypernetwork which is encoded as a population of hyperedges, where hyperedges represent building blocks or potential modules. We develop an efficient method for evolving the hypernetwork using mutual information to generate essential hyperedges. We evaluate the method on predicting intelligence quotient (IQ) levels and finding potential significant modules on IQ from brain MRI data consisting of 62 healthy adults with over 80,000 measured points (variables). The experimental results show that our information-theoretic evolutionary hypernetworks improve the classification accuracy by 5-15%. Moreover, it extracts significant cortical modules that distinguish high IQ from low IQ groups. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CEC.2011.5949944 | IEEE Congress on Evolutionary Computation |
Keywords | DocType | ISSN |
cortical analysis,evolutionary computation,mutual information-based evolution,brain research,data analysis,information-theoretic evolutionary hypernetworks,hypernetworks,biology computing,cortical thickness,evolutionary method,brain mri data,classifier,mutual information,clinical diagnosis,human intelligence,brain data analysis,brain models,data consistency,intelligence quotient,accuracy,support vector machine,support vector machines,genetic algorithm,decision trees,genetic algorithms,decision tree,sampling methods | Conference | Pending |
ISBN | Citations | PageRank |
978-1-4244-7834-7 | 2 | 0.37 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eun-Sol Kim | 1 | 2 | 1.72 |
| Jung-Woo Ha | 2 | 216 | 25.36 |
| Wi Hoon Jung | 3 | 4 | 1.80 |
| Joon Hwan Jang | 4 | 7 | 1.81 |
| Jun Soo Kwon | 5 | 67 | 7.79 |
| Byoung-Tak Zhang | 6 | 1571 | 158.56 |