摘要
由一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-关于人工智能的最新研究结果已经发表。根据NewsRx记者来自华盛顿大学的新闻，研究表明，"机器学习算法正越来越多地被用于识别与行为和临床结果相关的大脑连接生物标记物"。这项研究的财政支持者包括国家生物医学成像和生物工程研究所。新闻记者从华盛顿大学的研究中获得了一句话：“然而，研究往往以牺牲生物可解释性为代价优先考虑预测精度，ML方法的不一致实施可能会提高模型的准确性。为了解决这个问题，我们引入了一种网络级的丰富方法，该方法将脑系统组织整合在Conn全切统计分析的背景下，揭示了脑连接与行为之间的网络层次联系。我们使用li近支持向量回归(LSVR)模型来研究静息状态功能连接网络与年龄的关系。我们将基于原始LSVR权重的网络级关联与正向和反向模型产生的关联进行了分类。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news originating from Washington Un iversity by NewsRx correspondents, research stated, “Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linke d to behavioral and clinical outcomes.” Financial supporters for this research include National Institute of Biomedical Imaging And Bioengineering. The news journalists obtained a quote from the research from Washington Universi ty: “However, research often prioritizes prediction accuracy at the expense of b iological interpretability and inconsistent implementation of ML methods may hin der model accuracy. To address this, our paper introduces a network-level enrich ment approach, which integrates brain system organization in the context of conn ectome-wide statistical analysis to reveal network-level links between brain con nectivity and behavior. To demonstrate the efficacy of this approach, we used li near support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compar ed network-level associations based on raw LSVR weights to those produced from t he forward and inverse models.”
NETL
NSTL