Department of Imaging and Medical Informatics, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland,
Brain Topogr. 2014 May;27(3):329-37. doi: 10.1007/s10548-014-0360-z. Epub 2014 Mar 28.
Many diseases are associated with systematic modifications in brain morphometry and function. These alterations may be subtle, in particular at early stages of the disease progress, and thus not evident by visual inspection alone. Group-level statistical comparisons have dominated neuroimaging studies for many years, proving fascinating insight into brain regions involved in various diseases. However, such group-level results do not warrant diagnostic value for individual patients. Recently, pattern recognition approaches have led to a fundamental shift in paradigm, bringing multivariate analysis and predictive results, notably for the early diagnosis of individual patients. We review the state-of-the-art fundamentals of pattern recognition including feature selection, cross-validation and classification techniques, as well as limitations including inter-individual variation in normal brain anatomy and neurocognitive reserve. We conclude with the discussion of future trends including multi-modal pattern recognition, multi-center approaches with data-sharing and cloud-computing.
许多疾病都与大脑形态和功能的系统改变有关。这些改变可能很细微，特别是在疾病进展的早期阶段，仅凭肉眼观察是无法发现的。多年来，基于组水平的统计比较一直主导着神经影像学研究，为各种疾病涉及的脑区提供了引人入胜的见解。然而，这种组水平的结果并不能保证对个体患者的诊断价值。最近，模式识别方法带来了范式的根本转变，带来了多元分析和预测结果，特别是对个体患者的早期诊断。我们回顾了模式识别的最新基础，包括特征选择、交叉验证和分类技术，以及包括正常大脑解剖和神经认知储备的个体间变异性在内的局限性。最后，我们讨论了未来的趋势，包括多模态模式识别、具有数据共享和云计算的多中心方法。
