# High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

- EEG data were collected from three groups of patients (CNT, MCI, and AD) by a high-density acquisition system. Starting from that, three different electrodes configurations were considered. The signals preprocessing was performed by means of MATLAB (see Section 2.1);
- the preprocessed EEGs were used as input signals for the LORETA-KEY software. In order to quantify the functional connectivity, the Lagged Linear Connectivity (LLC) matrix was computed for each couple of regions of interest (ROIs), for a given frequency range (see Section 2.2);
- starting from the LLC, the small-world properties of the brain networks were measured by three parameters: $\lambda $, $CC$, and D, estimated through a MATLAB toolbox (see Section 2.3).

#### 2.1. Data Acquisition System and Preprocessing

#### 2.2. eLORETA and Lagged Linear Connectivity

**M**.

**M**) denotes the real part of

**M**.

#### 2.3. Complex Network Analysis

## 3. Results

## 4. Discussion and Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**(

**a**) Boxplot of the Characteristic Path Length of CNT, MCI, and AD; (

**b**) boxplot of the Clustering Coefficient of CNT, MCI, and AD. Both are computed for three electrode configurations. The bottom and the top edges of the boxes indicate the first and the third quartile, respectively; the segment inside the boxes represents the median and the “whiskers” below and above the boxes are the minimum and maximum values of the distribution. The stars outside the whiskers are considered outliers.

**Figure 3.**Mean values of the Connection Density (D) computed by thresholding the connectivity matrix of CNT, MCI, and AD for: (

**a**) 18 electrodes, (

**b**) 64 electrodes and (

**c**) 173 electrodes. The vertical segments represent the standard deviation of D.

Characteristic Path Length p-Value | Clustering Coefficient p-Value | Connection Density p-Value | ||||||
---|---|---|---|---|---|---|---|---|

18 | 64 | 173 | 18 | 64 | 173 | 18 | 64 | 173 |

0.4345 | 0.0130 | 1.38 × 10${}^{-12}$ | 6.24 × 10${}^{-10}$ | 0.0282 | 0.0481 | 0.8780 | 0.9870 | 0.9965 |

Subjects | Characteristic Path Length p-Value | Clustering Coefficient p-Value | ||||
---|---|---|---|---|---|---|

18 | 64 | 173 | 18 | 64 | 173 | |

CNT-MCI | 0.9974 | 0.1835 | 0.0101 | 2.8 × 10${}^{-4}$ | 0.0613 | 0.3000 |

MCI-AD | 0.4507 | 0.2054 | 8.6 × 10${}^{-8}$ | 3.0 × 10${}^{-9}$ | 0.7935 | 0.3353 |

CNT-AD | 0.5132 | 0.0091 | 9.5 × 10${}^{-10}$ | 0.0918 | 0.0377 | 0.0368 |

**Table 3.**Mean Connection Density values of CNT, MCI, and AD for all electrode configurations for three threshold values.

THRESHOLD | DENSITY | ||||||||
---|---|---|---|---|---|---|---|---|---|

18 | 64 | 173 | |||||||

CNT | MCI | AD | CNT | MCI | AD | CNT | MCI | AD | |

0.3 | 0.5549 | 0.5221 | 0.5761 | 0.4725 | 0.4572 | 0.4530 | 0.3312 | 0.3196 | 0.3101 |

0.5 | 0.1729 | 0.1551 | 0.1885 | 0.1164 | 0.1123 | 0.1114 | 0.0682 | 0.0651 | 0.0639 |

0.7 | 0.0290 | 0.0256 | 0.0327 | 0.0168 | 0.0165 | 0.0161 | 0.0101 | 0.0100 | 0.0099 |

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**MDPI and ACS Style**

La Foresta, F.; Morabito, F.C.; Marino, S.; Dattola, S.
High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease. *Electronics* **2019**, *8*, 1031.
https://doi.org/10.3390/electronics8091031

**AMA Style**

La Foresta F, Morabito FC, Marino S, Dattola S.
High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease. *Electronics*. 2019; 8(9):1031.
https://doi.org/10.3390/electronics8091031

**Chicago/Turabian Style**

La Foresta, Fabio, Francesco Carlo Morabito, Silvia Marino, and Serena Dattola.
2019. "High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease" *Electronics* 8, no. 9: 1031.
https://doi.org/10.3390/electronics8091031