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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Parallel Factorization to Implement Group Analysis in Brain Networks Estimation.

Andrea Ranieri1,2, Floriana Pichiorri2, Emma Colamarino1,2

  • 1Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto, 25, 00185 Rome, Italy.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Parallel Factor Analysis (PARAFAC) for analyzing complex brain networks. PARAFAC effectively extracts grand average connectivity, with performance influenced by data size, noise, and specific algorithm parameters.

Keywords:
EEGconnectivity estimationgroup analysisparallel factorizationpartial directed coherencetensor decomposition

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Area of Science:

  • Neuroscience
  • Network Analysis
  • Signal Processing

Background:

  • Group analysis of complex functional brain networks remains a challenge.
  • Extracting a reliable grand average connectivity matrix is crucial for understanding network dynamics.

Purpose of the Study:

  • To investigate the efficacy of Parallel Factor Analysis (PARAFAC) for extracting grand average connectivity matrices.
  • To evaluate the impact of data characteristics and algorithm parameters on PARAFAC performance.

Main Methods:

  • PARAFAC was applied to both simulated and real electroencephalography (EEG) datasets.
  • Simulated data were manipulated for network dimension, sample size, and noise levels.
  • The PARAFAC approach was solved using varying numbers of rank-one tensors (PAR-FACT).

Main Results:

  • PARAFAC demonstrated potential for grand average connectivity extraction in both synthetic and real data.
  • Optimal performance (low False Positive Rate, False Negative Rate, high Area Under the Curve) was associated with larger sample sizes and lower noise.
  • Increasing the number of rank-one tensors (PAR-FACT) in the PARAFAC solution negatively impacted estimation accuracy.

Conclusions:

  • PARAFAC is a promising tool for group analysis of functional brain networks.
  • Data quality (sample size, noise) and algorithm parameterization (PAR-FACT) are critical for accurate grand average estimation.