Thought Chart: tracking the thought with manifold learning during emotion regulation
Open Access
- 19 July 2018
- journal article
- research article
- Published by Springer Science and Business Media LLC in Brain Informatics
- Vol. 5 (2), 1-9
- https://doi.org/10.1186/s40708-018-0085-y
Abstract
The Nash embedding theorem demonstrates that any compact manifold can be isometrically embedded in a Euclidean space. Assuming the complex brain states form a high-dimensional manifold in a topological space, we propose a manifold learning framework, termed Thought Chart, to reconstruct and visualize the manifold in a low-dimensional space. Furthermore, it serves as a data-driven approach to discover the underlying dynamics when the brain is engaged in a series of emotion and cognitive regulation tasks. EEG-based temporal dynamic functional connectomes are created based on 20 psychiatrically healthy participants’ EEG recordings during resting state and an emotion regulation task. Graph dissimilarity space embedding was applied to all the dynamic EEG connectomes. In order to visualize the learned manifold in a lower dimensional space, local neighborhood information is reconstructed via k-nearest neighbor-based nonlinear dimensionality reduction (NDR) and epsilon distance-based NDR. We showed that two neighborhood constructing approaches of NDR embed the manifold in a two-dimensional space, which we named Thought Chart. In Thought Chart, different task conditions represent distinct trajectories. Properties such as the distribution or average length in the 2-D space may serve as useful parameters to explore the underlying cognitive load and emotion processing during the complex task. In sum, this framework is a novel data-driven approach to the learning and visualization of underlying neurophysiological dynamics of complex functional brain data.Keywords
Funding Information
- National Institute of Mental Health (K23MH093679 (HK), R01MH101497 (KLP))
- Center for Clinical and Translational Research (UL1RR029879)
- University of Illinois at Chicago Campus Research Board Award
This publication has 22 references indexed in Scilit:
- Resting-state theta band connectivity and graph analysis in generalized social anxiety disorderNeuroImage: Clinical, 2016
- Regional Manifold Learning for Deformable Registration of Brain MR ImagesLecture Notes in Computer Science, 2012
- A Combined Manifold Learning Analysis of Shape and Appearance to Characterize Neonatal Brain DevelopmentIEEE Transactions on Medical Imaging, 2011
- SEMI-SUPERVISED ANALYSIS OF HUMAN BRAIN TUMOURS FROM PARTIALLY LABELED MRS INFORMATION, USING MANIFOLD LEARNING MODELSInternational Journal of Neural Systems, 2011
- Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across IndividualsPsychological Science, 2009
- The dynamic architecture of emotion: Evidence for the component process modelCognition and Emotion, 2009
- Analyzing brain networks with PCA and conditional Granger causalityHuman Brain Mapping, 2008
- A Global Geometric Framework for Nonlinear Dimensionality ReductionScience, 2000
- The Imbedding Problem for Riemannian ManifoldsAnnals of Mathematics, 1956
- C 1 Isometric ImbeddingsAnnals of Mathematics, 1954