Promises and pitfalls of topological data analysis for brain connectivity analysis
Promises and pitfalls of topological data analysis for brain connectivity analysis
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DOI：
10.1016/j.neuroimage.2021.118245
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发表时间：
2021-06-19
期刊：
影响因子：
5.7
通讯作者：
Hlinka, Jaroslav
中科院分区：
文献类型：
--
作者：
Caputi, Luigi;Pidnebesna, Anna;Hlinka, Jaroslav
开发灵敏可靠的方法来区分正常和异常的大脑状态是神经科学的一个关键挑战。拓扑数据分析尽管相对新奇，但已经产生了许多有前途的应用，包括在神经科学中。我们猜想其突出的工具，持久的同源性可能会受益于超越分析结构和功能的连接，有效的连接图捕捉直接的因果关系的相互作用或信息流。因此，我们评估了潜在的持续同源性定向脑网络分析，通过测试其区别的权力，在两个不同的例子疾病相关的脑连接改变：癫痫和精神分裂症。我们估计连接功能磁共振成像和电生理数据，采用持久同源性和量化的能力，以区分健康的患病大脑状态，通过应用支持向量机的功能量化持久同源性结构。我们展示了这种新的方法相比，使用标准的无向方法和原始连接矩阵的分类。在精神分裂症分类中，拓扑数据分析通常表现得接近随机，而原始连接的分类表现得更好;可能是由于拓扑而不是拓扑的差异特异性。在从头皮脑电图数据中进行癫痫发作鉴别的更容易的任务中，基于持久同源性特征的分类通常达到与使用原始连接相当的性能，尽管与无向（功能）连接相比，定向（有效）连接获得的准确度通常较小。当连接矩阵的直接比较不合适时，例如对于具有个体数量和测量位置的颅内电生理学，可以打开拓扑数据分析的特定应用。虽然标准同源性总体上比定向同源性表现得更好，但这可能是由于精确有效连接性估计的臭名昭著的技术问题。
Developing sensitive and reliable methods to distinguish normal and abnormal brain states is a key neuroscientific challenge. Topological Data Analysis, despite its relative novelty, already generated many promising applications, including in neuroscience. We conjecture its prominent tool of persistent homology may benefit from going beyond analysing structural and functional connectivity to effective connectivity graphs capturing the direct causal interactions or information flows. Therefore, we assess the potential of persistent homology to directed brain network analysis by testing its discriminatory power in two distinctive examples of disease-related brain connectivity alterations: epilepsy and schizophrenia. We estimate connectivity from functional magnetic resonance imaging and electrophysiology data, employ Persistent Homology and quantify its ability to distinguish healthy from diseased brain states by applying a support vector machine to features quantifying persistent homology structure. We show how this novel approach compares to classification using standard undirected approaches and original connectivity matrices. In the schizophrenia classification, topological data analysis generally performs close to random, while classifications from raw connectivity perform substantially better; potentially due to topographical, rather than topological, specificity of the differences. In the easier task of seizure discrimination from scalp electroencephalography data, classification based on persistent homology features generally reached comparable performance to using raw connectivity, albeit with typically smaller accuracies obtained for the directed (effective) connectivity compared to the undirected (functional) connectivity. Specific applications for topological data analysis may open when direct comparison of connectivity matrices is unsuitable such as for intracranial electrophysiology with individual number and location of measurements. While standard homology performed overall better than directed homology, this could be due to notorious technical problems of accurate effective connectivity estimation.