TY - JOUR A1 - Alves, Caroline L. A1 - Ciba, Manuel A1 - de O Toutain, Thaise Graziele L A1 - Moura Porto, Joel Augusto A1 - de Sena, Eduardo Pondé A1 - Thielemann, Christiane A1 - Rodrigues, Francisco A. T1 - On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments JF - Journal of Physics: Complexity N2 - There is a growing interest in the medical use of psychedelic substances, as preliminary studies using them for psychiatric disorders have shown positive results. In particular, one of these substances is N, N-dimethyltryptamine (DMT), an agonist serotonergic psychedelic that can induce profound alterations in the state of consciousness. In this work, we use an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. We used a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine. We found that both approaches could detect changes in the brain's automatic activity, with case (B) showing the highest AUC (89%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In the second step, we ranked the features that contributed the most to this result. For case (A), we found that differences in the high alpha, low beta, and delta frequency bands were most important in distinguishing between the state before and after DMT inhalation, which is consistent with the results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (P8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical areas FC5 and P8 has not been found in the literature and is presumably related to the volunteers' emotional, visual, sensory, perceptual, and mystical experiences during DMT consumption. For case (B), closeness centrality was the most crucial complex network measure. Furthermore, we discovered larger communities and longer average path lengths when DMT was used and the converse when not, showing that the balance between functional segregation and integration had been disrupted. These findings support the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may help interpret EEG time series from patients who consumed other psychedelic drugs. KW - Neuronales Netz KW - Halluzinogen KW - Psychische Störung Y1 - 2024 U6 - https://doi.org/10.1088/2632-072X/ad1c68 VL - 2024 IS - 1 SP - 1 EP - 2 ER - TY - JOUR A1 - Alves, Caroline L. A1 - Toutain, Thaise A1 - Aguiar, Patricia A1 - Pineda, Aruane M. A1 - Roster, Kirstin A1 - Thielemann, Christiane A1 - Porto, Joel A1 - Rodrigues, Francisco A. T1 - Diagnosis of autism spectrum disorder based on functional brain networks and machine learning JF - Scientific Reports N2 - Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets. KW - Maschinelles Lernen KW - Gehirn KW - Neuronales Netz KW - Autismus Y1 - 2023 UR - https://rdcu.be/dcwIP U6 - https://doi.org/https://doi.org/10.1038/s41598-023-34650-6 VL - 2023 IS - 13/8072 SP - 1 EP - 20 ER - TY - JOUR A1 - Alves, Caroline L. A1 - Wissel, Lennart A1 - Capetian, Philipp A1 - Thielemann, Christiane T1 - Functional connectivity and convolutional neural networks for automatic classification of EEG data JF - Clinical Neurophysiology KW - Elektroencephalographie KW - Neuronales Netz Y1 - 2022 U6 - https://doi.org/https://doi.org/10.1016/j.clinph.2022.01.086 VL - 2022 IS - 137 SP - e37 EP - e47 ER - TY - JOUR A1 - Bouillet, Thomas A1 - Ciba, Manuel A1 - Alves, Caroline L. A1 - Rodrigues, Francisco A. A1 - Thielemann, Christiane A1 - Colin, Morvane A1 - Buée, Luc A1 - Halliez, Sophie T1 - Revisiting the involvement of tau in complex neural network remodeling: analysis of the extracellular neuronal activity in organotypic brain slice co-cultures JF - Journal of Neural Engineering N2 - Objective: Tau ablation has a protective effect in epilepsy due to inhibition of the hyperexcitability/hypersynchrony. Protection may also occur in transgenic models of Alzheimer's disease by reducing the epileptic activity and normalizing the excitation/inhibition imbalance. However, it is difficult to determine the exact functions of tau, because tau knockout (tauKO) brain networks exhibit elusive phenotypes. In this study, we aimed to further explore the physiological role of tau using brain network remodeling. Approach: The effect of tau ablation was investigated in hippocampal-entorhinal slice co-cultures during network remodeling. We recorded the spontaneous extracellular neuronal activity over two weeks in single-slice cultures and co-cultures from control and tauKO mice. We compared the burst parameters and applied concepts and analytical tools intended for the analysis of the network synchrony and connectivity. Main results: Comparison of the control and tauKO co-cultures revealed that tau ablation had an anti-synchrony effect on the hippocampal-entorhinal two-slice networks at late stages of culture, in line with the literature. Differences were also found between the single-slice and co-culture conditions, which indicated that tau ablation had differential effects at the sub-network scale. For instance, tau ablation was found to have an anti-synchrony effect on the co-cultured hippocampal slices throughout the culture, possibly due to a reduction in the excitation/inhibition ratio. Conversely, tau ablation led to increased synchrony in the entorhinal slices at early stages of the co-culture, possibly due to homogenization of the connectivity distribution. Significance: The new methodology presented here proved useful for investigating the role of tau in the remodeling of complex brain-derived neural networks. The results confirm previous findings and hypotheses concerning the effects of tau ablation on neural networks. Moreover, the results suggest, for the first time, that tau has multifaceted roles that vary in different brain sub-networks. KW - Neuronales Netz KW - Alzheimerkrankheit KW - Gehirn KW - Schnittpräparat Y1 - 2022 U6 - https://doi.org/DOI 10.1088/1741-2552/aca261 VL - 2022 IS - November SP - 1 EP - 2 ER -