Quantification of the robustness of functional neural networks: application to the characterization of Alzheimer's disease continuum.

Objective.Brain connectivity networks are usually characterized in terms of properties coming from the complex network theory. Using new measures to summarize the attributes of functional connectivity networks can be an important step for their better understanding and characterization, as well as to comprehend the alterations associated with neuropsychiatric and neurodegenerative disorders. In this context, the main objective of this study was to introduce a novel methodology to evaluate network robustness, which was subsequently applied to characterize the brain activity in the Alzheimer's disease (AD) continuum.Approach.Functional connectivity networks were built using 478 electroencephalographic and magnetoencephalographic resting-state recordings from three different databases. These functional connectivity networks computed in the conventional frequency bands were modified simulating an iterative attack procedure using six different strategies. The network changes caused by these attacks were evaluated by means of Spearman's correlation. The obtained results at the conventional frequency bands were aggregated in a correlation surface, which was characterized in terms of four gradient distribution properties: mean, variance, skewness, and kurtosis.Main results.The new proposed methodology was able to consistently quantify network robustness. Our results showed statistically significant differences in the inherent ability of the network to deal with attacks (i.e. differences in network robustness) between controls, mild cognitive impairment subjects, and AD patients for the three different databases. In addition, we found a significant correlation between mini-mental state examination scores and the changes in network robustness.Significance.To the best of our knowledge, this is the first study which assesses the robustness of the functional connectivity network in the AD continuum. Our findings consistently evidence the loss of network robustness as the AD progresses for the three databases. Furthermore, the changes in this complex network property may be related with the progressive deterioration in brain functioning due to AD.

Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity.

Volume Conduction Effects on Connectivity Metrics: Application of Network Parameters to Characterize Alzheimer's Disease Continuum.

This study had two main objectives: (i) to study the effects of volume conduction on different connectivity metrics (Amplitude Envelope Correlation AEC, Phase Lag Index PLI, and Magnitude Squared Coherence MSCOH), comparing the coupling patterns at electrode- and sensor-level; and (ii) to characterize spontaneous EEG activity during different stages of Alzheimer's disease (AD) continuum by means of three complementary network parameters: node degree (k), characteristic path length (L), and clustering coefficient (C). Our results revealed that PLI and AEC are weakly influenced by volume conduction compared to MSCOH, but they are not immune to it. Furthermore, network parameters obtained from PLI showed that AD continuum is characterized by an increase in L and C in low frequency bands, suggesting lower integration and higher segregation as the disease progresses. These network changes reflect the abnormalities during AD continuum and are mainly due to neuronal alterations, because PLI is slightly affected by volume conduction effects.

Consistency of local activation parameters at sensor- and source-level in neural signals.

OBJECTIVE: Although magnetoencephalography and electroencephalography (M/EEG) signals at sensor level are robust and reliable, they suffer from different degrees of distortion due to changes in brain tissue conductivities, known as field spread and volume conduction effects. To estimate original neural generators from M/EEG activity acquired at sensor level, diverse source localisation algorithms have been proposed; however, they are not exempt from limitations and usually involve time-consuming procedures. Connectivity and network-based M/EEG analyses have been found to be affected by field spread and volume conduction effects; nevertheless, the influence of the aforementioned effects on widely used local activation parameters has not been assessed yet. The goal of this study is to evaluate the consistency of various local activation parameters when they are computed at sensor- and source-level. APPROACH: Six spectral (relative power, median frequency, and individual alpha frequency) and non-linear parameters (Lempel-Ziv complexity, sample entropy, and central tendency measure) are computed from M/EEG signals at sensor- and source-level using four source inversion methods: weighted minimum norm estimate (wMNE), standardised low-resolution brain electromagnetic tomography (sLORETA), linear constrained minimum variance (LCMV), and dynamical statistical parametric mapping (dSPM). MAIN RESULTS: Our results show that the spectral and non-linear parameters yield similar results at sensor- and source-level, showing high correlation values between them for all the source inversion methods evaluated and both modalities of signal, EEG and MEG. Furthermore, the correlation values remain high when performing coarse-grained spatial analyses. SIGNIFICANCE: To the best of our knowledge, this is the first study analysing how field spread and volume conduction effects impact on local activation parameters computed from resting-state neural activity. Our findings evidence that local activation parameters are robust against field spread and volume conduction effects and provide equivalent information at sensor- and source-level even when performing regional analyses.