Deficits of entropy modulation of the EEG: A biomarker for altered function in schizophrenia and bipolar disorder?

Molina, Vicente; Lubeiro, Alba; Luis‐García, Rodrigo de; Gómez‐Pilar, Javier; Martín-Santiago, Óscar; Iglesias-Tejedor, María; Holgado-Madera, Pedro; Segarra, Rafael; Recio-Barbero, María; Núñez, Pablo; Haidar, Karim; Fernández-Sevillano, Jéssica; Sanz-Fuentenebro, Javier

doi:10.1503/jpn.190032

Cited by 20 publications

(17 citation statements)

References 45 publications

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“…During the resting condition, we detected greater levels of spectral Entropy associated with SCZ, indicating more random frequencies. This conclusion contradicts previous studies [140][141][142] that reported spectral entropy deficiencies in schizophrenia patients executing the P300 activity. We assume this discrepancy because our data were obtained during the rest.…”

Section: B Complex Network and Measures Extracted From Eeg Time Seriescontrasting

confidence: 99%

Analysis of functional connectivity using machine learning and deep learning in multimodal data from patients with schizophrenia

Alves

Toutain

Porto

et al. 2022

Preprint

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Schizophrenia is a severe mental disorder associated with persistent or recurrent psychosis, hallu- cinations, 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. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures for these results, which helped us better describe changes in the diseased brain.

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Section: B Complex Network and Measures Extracted From Eeg Time Seriescontrasting

confidence: 99%

Analysis of functional connectivity using machine learning and deep learning in multimodal data from patients with schizophrenia

Alves

Toutain

Porto

et al. 2022

Preprint

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“…In support of such possibility, both clusters showed a decreased modulation of their EEG activity during an oddball task as measured with SE. We have described and replicated such a modulation deficit in schizophrenia and bipolar disorder as a possible biomarker for the altered function in this disorder (Molina et al., 2020, 2018). That deficit was associated with cognitive deficits and negative symptoms (Molina et al., 2020, 2018).…”

Section: Resultsmentioning

confidence: 81%

“…We have described and replicated such a modulation deficit in schizophrenia and bipolar disorder as a possible biomarker for the altered function in this disorder (Molina et al., 2020, 2018). That deficit was associated with cognitive deficits and negative symptoms (Molina et al., 2020, 2018). This could explain why clusters in the present study did not differ in these clinical dimensions, since SE modulation is similar between the clusters extracted in this study.…”

Section: Resultsmentioning

confidence: 81%

Search for schizophrenia and bipolar biotypes using functional network properties

Fernández-Linsenbarth

Planchuelo‐Gómez

Beño-Ruiz-de-la-Sierra

et al. 2021

Brain and Behavior

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Introduction:Recent studies support the identification of valid subtypes within schizophrenia and bipolar disorder using cluster analysis. Our aim was to identify meaningful biotypes of psychosis based on network properties of the electroencephalogram. We hypothesized that these parameters would be more altered in a subgroup of patients also characterized by more severe deficits in other clinical, cognitive, and biological measurements. Methods: A clustering analysis was performed using the electroencephalogram-based network parameters derived from graph-theory obtained during a P300 task of 137 schizophrenia (of them, 35 first episodes) and 46 bipolar patients. Both prestimulus and modulation of the electroencephalogram were included in the analysis. Demographic, clinical, cognitive, structural cerebral data, and the modulation of the spectral entropy of the electroencephalogram were compared between clusters. Data from 158 healthy controls were included for further comparisons. Results:We identified two clusters of patients. One cluster presented higher prestimulus connectivity strength, clustering coefficient, path-length, and lower small-worldThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…One of the most remarkable research efforts has been in the area of efficient neural network-based approaches to analysis of EEG signals for automatic assessment of mental disorders such as major depressive disorder (MDD) and bipolar disorder (BD). Indeed, EEG is a non-invasive, effective, and powerful tool for recording the brain's electrical activity and diagnosing various mental disorders such as MDD, BD, anxiety ( 17 ), schizophrenia ( 18 ), and sleep disorders ( 19 ). In the case of depression, the body releases signals into the brain that affect neuronal production and communication, which slows or otherwise changes some regions of the brain.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Depression Recognition Method Based on EEGNet

et al. 2022

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Major depressive disorder (MDD) is a common and highly debilitating condition that threatens the health of millions of people. However, current diagnosis of depression relies on questionnaires that are highly correlated with physician experience and hence not completely objective. Electroencephalography (EEG) signals combined with deep learning techniques may be an objective approach to effective diagnosis of MDD. This study proposes an end-to-end deep learning framework for MDD diagnosis based on EEG signals. We used EEG signals from 29 healthy subjects and 24 patients with severe depression to calculate Accuracy, Precision, Recall, F1-Score, and Kappa coefficient, which were 90.98%, 91.27%, 90.59%, and 81.68%, respectively. In addition, we found that these values were highest when happy-neutral face pairs were used as stimuli for detecting depression. Compared with exiting methods for EEG-based MDD classification, ours can maintain stable model performance without re-calibration. The present results suggest that the method is highly accurate for diagnosis of MDD and can be used to develop an automatic plug-and-play EEG-based system for diagnosing depression.

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Deficits of entropy modulation of the EEG: A biomarker for altered function in schizophrenia and bipolar disorder?

Cited by 20 publications

References 45 publications

Analysis of functional connectivity using machine learning and deep learning in multimodal data from patients with schizophrenia

Analysis of functional connectivity using machine learning and deep learning in multimodal data from patients with schizophrenia

Search for schizophrenia and bipolar biotypes using functional network properties

An End-to-End Depression Recognition Method Based on EEGNet

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