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Spatial characterization of functional neural activity during lower limb motion through functional connectivity

Espinoza-Valdez Aurora, Quiroz-Compean Griselda, González-Garrido Andrés A., Salido-Ruiz Ricardo A., Mercado Luis

Biocybernetics and Biomedical Engineering

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2024

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Vol. 44, no. 1

183--196

EN

Analyzing electroencephalographic signals (EEG) could provide valuable information about functional neural activity (FNA) during human motion. The hypothesis of this work is twofold: spatial patterns emerge in EEG signals from functional connectivity (FC) analysis during lower limb movements, and the spatial patterns are mosto robust in some frequency bands than in others. Accordingly, a set of human subjects without neuromotor pathologies participated in an experimental trial where EEG signals were recorded during lower limb movements. The FC was studied with coherence analysis (in δ, θ, and α) and graph theory was proposed to study the characteristics of spatial dynamics by means a set of metrics (degree, maximum connection, and closeness centrality) and two distances (Hamming distance and Jaccard). Finally, a statistical study of the metrics by frequency band was performed to analyze the significant differences between the phases of each stage and movement, considering the proposed metrics. The results of the study indicated that the frequency bands that showed greater statistical significance in the analysis were δ, θ, and α and that the major differences in graph dynamics were shown in degree, maximum connection, and closeness centrality in α band. Present findings portray leading underlying neural networks, implying that discernible spatial patterns exist in FNA during lower limb movements, and such patterns can be characterized with the proposed methodology.

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Disruptions in brain functional connectivity: The hidden risk for oxygen-intolerant professional divers in simulated deep water

Formaggio Emanuela, Pastena Lucio, Melucci Massimo, Ricciardi Lucio, Storti Silvia Francesca

Biocybernetics and Biomedical Engineering

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2024

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Vol. 44, no. 1

209--217

EN

In this study, we investigated the effects of oxygen toxicity on brain activity and functional connectivity (FC) in divers using a closed-circuit oxygen breathing apparatus. We acquired and analyzed electroencephalographic (EEG) signals from a group of normal professional divers (PD) and a group that developed oxygen intolerance, i.e., oxygen-intolerant professional divers (OPD), to evaluate the potential risk of a dive and understand the physiological mechanisms involved. The results highlighted a significant difference in the baseline levels of α rhythm between PD and OPD, with PD exhibiting a lower level to counteract the effects of increased O2 inhalation, while OPD showed a higher level that resulted in a pathological state. Connectivity analysis revealed a strong correlation between cognitive and motor regions, and high levels of α synchronization at rest in OPDs. Our findings suggest that a pathological condition may underlie the higher α levels observed in these individuals when facing the stress of high O2 inhalation. These findings support the hypothesis that oxygen modulates brain networks, and have important implications for understanding the neural mechanisms involved in oxygen toxicity. The study also provides a unique opportunity to investigate the impact of neurophysiological activity in simulated critical scenarios, and opens up new perspectives in the screening and monitoring of divers.

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Diffuse and Localized Functional Dysconnectivity in Schizophrenia: a Bootstrapped Top-Down Approach

Coluzzi Davide, Basell Giuseppe

Fundamenta Informaticae

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2022

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Vol. 189, nr 2

171--198

EN

Schizophrenia is a brain disorder leading to detached mind's normally integrated processes. Hence, the exploration of the symptoms in relation to functional connectivity (FC) had great relevance in the field. Connectivity can be investigated on different levels, going from global features to single edges between pairs of regions, revealing diffuse and localized dysconnection patterns. In this context, schizophrenia is characterized by a different global integration with reduced connectivity in specific areas of the brain, part of the Default Mode Network (DMN). However, the assessment of FC presents various sources of uncertainty. This study proposes a multi-level approach for more robust group-comparison. FC data between 74 AAL brain areas of 15 healthy controls (HC) and 12 subjects with chronic schizophrenia (SZ) were used. Multi-level analyses were carried out by the previously published SPIDER-NET tool. Graph topological indexes were evaluated to assess global abnormalities. Robustness was augmented by bootstrapped (BOOT) data and the stability was evaluated by removing one (RST1) or two subjects (RST2). The DMN subgraph was extracted and specifically evaluated. Changes relevant to the overall local indexes were also analyzed. Finally, the connection weights were explored to enhance common strongest activations/deactivations. At a global level, expected trends of the indexes were found and the significance of modularity (p = 0.043) was not confirmed by BOOT (p = 0.133). The robustness assessment tests (both RST1 and RST2) highlighted more stable results for BOOT compared to the direct data testing. Conversely, significant results were found in the analysis at lower levels. The DMN highlighted reduced connectivity and strength as well as increased deactivation in the SZ group. At local level, 13 areas were found to be significantly different (p < 0.05) in the groups, highlighting a greater divergence in the frontal lobe. These results were confirmed analyzing the single negative edges, suggesting inverted connectivity between prefronto-temporal areas. In conclusion, multi-level analysis supported by BOOT is highly recommended when analyzing FC, especially when diffuse and localized dysconnections must be investigated in limited samples.