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Updated: Jan 22, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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Uncovering Dynamic Brain Reconfiguration in MEG Working Memory n-Back Task Using Topological Data Analysis.

Ali Nabi Duman1, Ahmet Emin Tatar2, Harun Pirim3

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Brain Sciences
|June 29, 2019
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Summary

Topological data analysis (TDA) using the Mapper method reveals novel neuroimaging markers in magnetoencephalography (MEG) data. This approach enhances understanding of brain dynamics during working memory tasks without losing critical spatiotemporal information.

Keywords:
MEGdynamic functional connectivitymappertopological data analysisworking memory

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Area of Science:

  • Neuroscience
  • Data Science
  • Computational Biology

Background:

  • High temporal resolution neuroimaging data, including functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and magnetoencephalography (MEG), is increasingly available, driving efforts to understand neural function dynamics.
  • Current state-of-the-art methods often collapse data spatially and temporally, leading to information loss during the analysis of complex neural systems.
  • Generative models for neural system classification and prediction are less explored compared to architectural descriptions.

Purpose of the Study:

  • To address the information loss in neuroimaging data analysis by applying a Topological Data Analysis (TDA) method, specifically Mapper.
  • To visualize evolving patterns of brain activity as a mathematical graph using MEG data.
  • To examine how variations in brain state dynamics, visualized through Mapper graphs, relate to performance measures like response time and accuracy during a working memory task.

Main Methods:

  • Analysis of preprocessed MEG data from 83 subjects from the Human Connectome Project (HCP) during an n-back task.
  • Application of the Mapper algorithm to visualize brain activity dynamics as a mathematical graph.
  • Correlation of Mapper graph features with behavioral measures such as response time and task performance.

Main Results:

  • Identification of a novel neuroimaging marker using the Mapper method on MEG data, which effectively explains participant performance and response times.
  • Distinction of two task-positive brain activations during 0-back and 2-back tasks, which are difficult to discern with traditional methods requiring data collapse.
  • Observation of a distinct group in Mapper graphs indicating high brain engagement with fine temporal resolution, suggesting potential for enhanced spatiotemporal resolution through multimodal imaging.

Conclusions:

  • Topological Data Analysis (TDA) methods, particularly Mapper, are effective for extracting subtle dynamic properties from high temporal resolution MEG data without temporal and spatial collapse.
  • The Mapper approach offers a valuable tool for understanding brain dynamics and identifying performance-related neuroimaging markers.
  • This study highlights the potential of TDA in advancing neuroimaging analysis and potentially merging different imaging modalities for improved spatiotemporal resolution.