Statistical methods for analysing maps in the visual brain

本计画旨在应用高斯过程方法（一种贝叶斯数据分析方法）来分析脑成像实验的数据。发现功能性大脑结构的原理需要分析功能性成像技术的数据。然而，这些技术产生非常嘈杂的数据，难以解释。本计画将应用高斯过程方法来研究视觉脑的光学影像与功能性磁共振影像的资料。这有望揭示正常大脑结构如何随着发育和感官体验而变化的关键信息。开发的统计方法应该适用于神经科学内外，并最终有助于改善人类健康疾病的诊断。

This project aims to apply Gaussian process methods, a Bayesian approach for data analysis, to analyse data from brain imaging experiments. Discovering the principles of functional brain architecture requires analysing data from functional imaging technologies. However, these technologies produce very noisy data which is difficult to interpret. This project will apply Gaussian process methods to study data from optical imaging and functional magnetic resonance imaging of the visual brain. This is expected to reveal critical information about how normal brain structure changes with development and sensory experience. The statistical methods developed should be applicable within and beyond neuroscience, and may ultimately help improve the diagnosis of human health disorders.