Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging.
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging.
复制标题
使用功能连接对脑部疾病进行分类和预测：有希望但具有挑战性
DOI：
10.3389/fnins.2018.00525
复制
发表时间：
2018
影响因子：
4.3
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Du Y;Fu Z;Calhoun VD
脑功能成像数据，特别是功能磁共振成像（fMRI）数据，已被用来反映大脑的功能整合。脑功能连接（FC）的改变有望为脑疾病的分类或预测提供潜在的生物标志物。在本文中，我们提出了一个全面的审查，以提供指导有关可用的大脑FC措施和典型的分类策略。我们调查了最先进的FC分析方法，包括广泛使用的静态功能连接（SFC）和最近提出的动态功能连接（DFC）。感兴趣区域（ROI）、数据驱动的空间网络和功能网络连接（FNC）之间的时间相关性通常被计算以从不同角度反映SFC。SFC可以使用滑动窗口框架扩展到DFC，并且通常使用聚类或分解方法提取沿时变连接模式的内在连接状态沿着。我们还简要总结了无窗口DFC方法。随后，我们强调了各种策略的功能选择，包括过滤器，包装器和嵌入式方法。在模型构建方面，我们包括传统的分类器以及最近应用的深度学习方法。此外，我们回顾了具有代表性的应用程序，显着的分类准确性精神病和情绪障碍，神经发育障碍，神经系统疾病使用功能磁共振成像数据。精神分裂症，双相情感障碍，自闭症谱系障碍（ASD），注意缺陷多动障碍（ADHD），阿尔茨海默病和轻度认知障碍（MCI）进行了讨论。最后，在该领域的挑战指出，相对于不准确的诊断标记，丰富的可能的功能和验证的困难。并对今后的工作提出了一些建议。
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.
登录
查看更多内容
影响因子：
5.7
作者：
Bassett, Danielle S.;Nelson, Brent G.;Mueller, Bryon A.;Camchong, Jazmin;Lim, Kelvin O.
通讯作者：
Lim, Kelvin O.
神经影像学中脑部疾病的单受试者预测：前景和陷阱
影响因子：
5.7
作者：
Arbabshirani MR;Plis S;Sui J;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
5.4
作者：
Anderson, Matthew;Fu, Geng-Shen;Adali, Tulay
通讯作者：
Adali, Tulay
影响因子：
6.1
作者：
Bernas, Antoine;Aldenkamp, Albert P.;Zinger, Svitlana
通讯作者：
Zinger, Svitlana
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.