Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

Alves, Caroline L.; Cury, Rubens Gisbert; Roster, Kirstin; Pineda, Aruane M.; Rodrigues, Francisco A.; Thielemann, Christiane; Ciba, Manuel

doi:10.1371/journal.pone.0277257

Cited by 10 publications

(27 citation statements)

References 135 publications

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“…Moreover, we determine which abstraction levels are most suited for EEG recording DMT-induced brain changes. In contrast to the previous study [81], which uses gamma-band frequencies in EEG from ayahuasca experiments. This study advances the methodology used before since different frequency bands are also considered to see the best frequencies to differentiate brain changes due to DMT.…”

Section: Introductionmentioning

confidence: 77%

“…In an earlier work of the authors [81], ML, in combination with complex network measures, was successfully applied to EEG data recorded after ayahuasca consumption to detect changes in brain activity. For this purpose, different levels of data abstraction were used as input: (a) the raw EEG time series, (b) the correlation of the EEG time series, and (c) the complex network measures calculated from (b).…”

Section: Methodsmentioning

confidence: 99%

“…It is a widely used and successfully approved measure to capture the correlation of EEG electrodes [95][96][97][98][99][100]. The choice of Pearson's correlation as the connectivity metric was based on our previous study [81] on ayahuasca experiments, in whose main compound is DMT, showcased that this metric successfully delineates the nuanced changes in brain activity associated with psychedelic substance consumption. Further, the same metric was usefully used in other psychedelic studies [101,102].…”

Section: Connectivity Matricesmentioning

confidence: 99%

“…The following complex network measures were calculated: Assortativity [106,107], average path length (APL) [108], betweenness centrality [109], CC [110], eigenvector centrality [111], diameter [112], hub score [113], average degree of nearest neighbors [114], mean degree [115], second moment degree [116], entropy degree (ED) [117], transitivity [38,118], complexity, k-core [119,120], eccentricity [121], density [122], and global efficiency [123]. In addition, newly developed metrics (described in detail in [81]) reflecting the number of communities in a complex network are applied. Community detection (also called clustering graph) is one of the fundamental analyses of complex networks aiming to decompose the network in order to find densely connected structures, so-called communities [124][125][126].…”

Section: Complex Network Measuresmentioning

confidence: 99%

“…To answer these questions, we use the same methodology implemented in [81], which proposes 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 (SVM) [82]. SVM has been used with excellent results for classifying complex network measures before [51,83,84].…”

Section: Introductionmentioning

confidence: 99%

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On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments

Alves,

Ciba,

de O. Toutain

et al. 2024

J. Phys. Complex.

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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.

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Section: Introductionmentioning

confidence: 77%

Section: Methodsmentioning

confidence: 99%

Section: Connectivity Matricesmentioning

confidence: 99%

Section: Complex Network Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments

Alves,

Ciba,

de O. Toutain

et al. 2024

J. Phys. Complex.

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Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Alves

Toutain

Aguiar

et al. 2023

Sci Rep

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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.

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Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia

Alves,

Toutain,

Porto

et al. 2023

J. Neural Eng.

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Schizophrenia is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization (WHO). Several studies encompass machine learning and deep learning algorithms to automate the diagnosis of this mental disorder. Others study schizophrenia brain networks to get new insights into the dynamics of information processing in patients suffering from the condition. In this paper, we offer a rigorous approach with machine learning and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in schizophrenia patients. For this purpose, we employed an fMRI and EEG dataset in a multimodal fashion. In addition, we combined EEG measures, i.e., Hjorth mobility and complexity, to complex network measurements to be analyzed in our model for the first time in the literature. When comparing the schizophrenia group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor.In terms of complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most important measure in a multimodal fashion. Furthermore, the schizophrenia brain networks exhibit less segregation and lower distribution of information. As a final result, EEG measures outperformed complex networks in capturing the brain alterations associated with schizophrenia. As a result, our model achieved anAUC of 100%, an accuracy of 98\% for the fMRI, an AUC of 95 %, and an accuracy of 95% for the EEG data set.

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Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

Cited by 10 publications

References 135 publications

On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments

On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia

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