Promises and Pitfalls of Topological Data Analysis for Brain Connectivity Analysis

0543460 - ÚI 2022 RIV US eng J - Journal Article
Caputi, Luigi - Pidnebesna, Anna - Hlinka, Jaroslav
Promises and Pitfalls of Topological Data Analysis for Brain Connectivity Analysis.
Neuroimage. Roč. 238, September 2021 (2021), č. článku 118245. ISSN 1053-8119. E-ISSN 1095-9572
R&D Projects: GA ČR(CZ) GA19-11753S; GA MZd(CZ) NV17-28427A; GA ČR(CZ) GA21-17211S; GA ČR(CZ) GA21-32608S; GA MZd(CZ) NU21-08-00432; GA ČR(CZ) GF21-14727K
Institutional support: RVO:67985807
Keywords : Persistent homology * Connectivity * fMRI * Electrophysiology * Epilepsy * Schizophrenia
OECD category: Neurosciences (including psychophysiology
Impact factor: 7.400, year: 2021
Method of publishing: Open access
http://dx.doi.org/10.1016/j.neuroimage.2021.118245

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.
Permanent Link: http://hdl.handle.net/11104/0320658