Dimitriadis Stavros I, Liparas Dimitris
Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University; Cardiff University Brain Research Imaging Centre, School of Psychology; School of Psychology; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology; Neuroscience and Mental Health Research Institute; MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff, UK.
High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Stuttgart, Germany; Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Neural Regen Res. 2018 Jun;13(6):962-970. doi: 10.4103/1673-5374.233433.
Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfunctions in brain diseases. Neuroinformaticians work in the intersection of neuroscience and informatics supporting the integration of various sub-disciplines (behavioural neuroscience, genetics, cognitive psychology, etc.) working on brain research. Neuroinformaticians are the pathway of information exchange between informaticians and clinicians for a better understanding of the outcome of computational models and the clinical interpretation of the analysis. Machine learning is one of the most significant computational developments in the last decade giving tools to neuroinformaticians and finally to radiologists and clinicians for an automatic and early diagnosis-prognosis of a brain disease. Random forest (RF) algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to correctly predict the Alzheimer's disease (AD), the conversion from mild cognitive impairment (MCI) and its robustness to overfitting, outliers and handling of non-linear data. Finally, we described our RF-based model that gave us the 1 position in an international challenge for automated prediction of MCI from MRI data.
神经信息学是一个引人入胜的研究领域，它将计算模型和分析工具应用于高维实验神经科学数据，以更好地理解大脑在脑部疾病中的功能或功能障碍。神经信息学家在神经科学和信息学的交叉领域工作，支持从事大脑研究的各个子学科（行为神经科学、遗传学、认知心理学等）的整合。神经信息学家是信息学家和临床医生之间信息交流的途径，以便更好地理解计算模型的结果和分析的临床解释。机器学习是过去十年中最重要的计算发展之一，它为神经信息学家提供了工具，最终也为放射科医生和临床医生提供了工具，用于脑部疾病的自动早期诊断和预后评估。随机森林（RF）算法已成功应用于高维神经影像数据的特征约简，也已应用于使用单模态或多模态神经影像数据集对受试者的临床标签进行分类。我们的目的是回顾那些应用RF来正确预测阿尔茨海默病（AD）、从轻度认知障碍（MCI）的转化及其对过拟合、异常值和非线性数据处理的稳健性的研究。最后，我们描述了我们基于RF的模型，该模型在一项从MRI数据自动预测MCI的国际挑战赛中使我们获得了第一名。
