Publications

Abstract

Scene-selective regions (SSRs), including the parahippocampal place area (PPA), retrosplenial cortex (RSC), and transverse occipital sulcus (TOS), are among the most widely characterized functional regions in the human brain. However, previous studies have mostly focused on the commonality within each SSR, providing little information on different aspects of their variability. In a large group of healthy adults (N = 202), we used functional magnetic resonance imaging to investigate different aspects of topographical and functional variability within SSRs, including interindividual, interhemispheric, and sex differences. First, the PPA, RSC, and TOS were delineated manually for each individual. We then demonstrated that SSRs showed substantial interindividual variability in both spatial topography and functional selectivity. We further identified consistent interhemispheric differences in the spatial topography of all three SSRs, but distinct interhemispheric differences in scene selectivity. Moreover, we found that all three SSRs showed stronger scene selectivity in men than in women. In summary, our work thoroughly characterized the interindividual, interhemispheric, and sex variability of the SSRs and invites future work on the origin and functional significance of these variabilities. Additionally, we constructed the first probabilistic atlases for the SSRs, which provide the detailed anatomical reference for further investigations of the scene network.

Abstract

Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.