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Beño-Ruiz-de-la-Sierra RM, Arjona-Valladares A, Hernández-García M, Fernández-Linsenbarth I, Díez Á, Fondevila Estevez S, Castaño C, Muñoz F, Sanz-Fuentenebro J, Roig-Herrero A, Molina V. Corollary Discharge Dysfunction as a Possible Substrate of Anomalous Self-experiences in Schizophrenia. Schizophr Bull 2024; 50:1137-1146. [PMID: 37951230 PMCID: PMC11349017 DOI: 10.1093/schbul/sbad157] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2023]
Abstract
BACKGROUND AND HYPOTHESIS Corollary discharge mechanism suppresses the conscious auditory sensory perception of self-generated speech and attenuates electrophysiological markers such as the auditory N1 Event-Related Potential (ERP) during Electroencephalographic (EEG) recordings. This phenomenon contributes to self-identification and seems to be altered in people with schizophrenia. Therefore, its alteration could be related to the anomalous self-experiences (ASEs) frequently found in these patients. STUDY DESIGN To analyze corollary discharge dysfunction as a possible substrate of ASEs, we recorded EEG ERP from 43 participants with schizophrenia and 43 healthy controls and scored ASEs with the 'Inventory of Psychotic-Like Anomalous Self-Experiences' (IPASE). Positive and negative symptoms were also scored with the 'Positive and Negative Syndrome Scale for Schizophrenia' (PANSS) and with the 'Brief Negative Symptom Scale' (BNSS) respectively. The N1 components were elicited by two task conditions: (1) concurrent listening to self-pronounced vowels (talk condition) and (2) subsequent non-concurrent listening to the same previously self-uttered vowels (listen condition). STUDY RESULTS The amplitude of the N1 component elicited by the talk condition was lower compared to the listen condition in people with schizophrenia and healthy controls. However, the difference in N1 amplitude between both conditions was significantly higher in controls than in schizophrenia patients. The values of these differences in patients correlated significantly and negatively with the IPASE, PANSS, and BNSS scores. CONCLUSIONS These results corroborate previous data relating auditory N1 ERP amplitude with altered corollary discharge mechanisms in schizophrenia and support corollary discharge dysfunction as a possible underpinning of ASEs in this illness.
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Affiliation(s)
| | | | | | | | - Álvaro Díez
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
| | | | | | - Francisco Muñoz
- UCM-ISCIII Center for Human Evolution and Behaviour, Madrid, Spain
- Psychobiology and Behavioural Sciences Methods Department, Complutense University of Madrid, Madrid, Spain
| | | | - Alejandro Roig-Herrero
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
- Imaging Processing Laboratory, University of Valladolid, Valladolid, Spain
| | - Vicente Molina
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
- Psychiatry Service, University Clinical Hospital of Valladolid, Valladolid, Spain
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Redwan SM, Uddin MP, Ulhaq A, Sharif MI, Krishnamoorthy G. Power spectral density-based resting-state EEG classification of first-episode psychosis. Sci Rep 2024; 14:15154. [PMID: 38956297 PMCID: PMC11219808 DOI: 10.1038/s41598-024-66110-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 06/27/2024] [Indexed: 07/04/2024] Open
Abstract
Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders.
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Affiliation(s)
- Sadi Md Redwan
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Palash Uddin
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, 5200, Bangladesh
- School of Information Technology, Deakin University, Geelong, VIC, 3220, Australia
| | - Anwaar Ulhaq
- School of Engineering and Technology, Central Queensland University Australia, 400 Kent Street, Sydney, NSW, 2000, Australia.
| | | | - Govind Krishnamoorthy
- School of Psychology and Wellbeing, University of Southern Queensland, Ipswich, QLD, Australia
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Blair DS, Miller RL, Calhoun VD. A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia. ENTROPY (BASEL, SWITZERLAND) 2024; 26:545. [PMID: 39056908 PMCID: PMC11275472 DOI: 10.3390/e26070545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/07/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024]
Abstract
Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients' brain function.
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Affiliation(s)
- David Sutherland Blair
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA 30303, USA (V.D.C.)
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Díez Á, Gomez-Pilar J, Poza J, Beño-Ruiz-de-la-Sierra R, Fernández-Linsenbarth I, Recio-Barbero M, Núñez P, Holgado-Madera P, Molina V. Functional network properties in schizophrenia and bipolar disorder assessed with high-density electroencephalography. Prog Neuropsychopharmacol Biol Psychiatry 2024; 129:110902. [PMID: 38036032 DOI: 10.1016/j.pnpbp.2023.110902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/10/2023] [Accepted: 11/24/2023] [Indexed: 12/02/2023]
Abstract
BACKGROUND The study of the cortical functional network properties in schizophrenia (SZ) may benefit from the use of graph theory parameters applied to high-density electroencephalography (EEG). Connectivity Strength (CS) assesses global synchrony of the network, and Shannon Graph Complexity (SGC) summarizes the network distribution of link weights and allows distinguishing between primary and secondary pathways. Their joint use may help in understanding the underpinnings of the functional network hyperactivation and task-related hypomodulation previously described in psychoses. METHODS We used 64-sensor EEG recordings during a P300 oddball task in 128 SZ patients (96 chronic, CR, and 32 first episodes, FE), as well as 46 bipolar disorder (BD) patients, and 92 healthy controls (HC). Pre-stimulus and modulation (task-response minus pre-stimulus windows values) of CS and SGC were assessed in the theta band (4-8 Hz) and the broadband (4-70 Hz). RESULTS Compared to HC, SZ patients (CR and FE) showed significantly higher pre-stimulus CS values in the broadband, and both SZ and BD patients showed lower theta-band CS modulation. SGC modulation values, both theta-band and broadband, were also abnormally reduced in CR patients. Statistically significant relationships were found in the theta band between SGC modulation and both CS pre-stimulus and modulation values in patients. CS altered measures in patients were additionally related to their cognitive outcome and negative symptoms. A primary role of antipsychotics in these results was ruled out. CONCLUSIONS Our results linking SGC and CS alterations in psychotic patients supported a hyperactive and hypomodulatory network mainly involving connections in secondary pathways.
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Affiliation(s)
- Álvaro Díez
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | | | | | | | - Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain.; Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
| | | | - Vicente Molina
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain.; Psychiatry Service, Clinical University Hospital of Valladolid, Valladolid, Spain..
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Alves CL, Toutain TGLDO, Porto JAM, Aguiar PMDC, de Sena EP, Rodrigues FA, Pineda AM, Thielemann C. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia. J Neural Eng 2023; 20:056025. [PMID: 37673060 DOI: 10.1088/1741-2552/acf734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 09/06/2023] [Indexed: 09/08/2023]
Abstract
Objective. Schizophrenia(SCZ) 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. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML 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 SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ 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. Regarding 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 crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.
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Affiliation(s)
- Caroline L Alves
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
- BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany
| | | | | | - Patrícia Maria de Carvalho Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Federal University of São Paulo, Department of Neurology and Neurosurgery, São Paulo, Brazil
| | | | - Francisco A Rodrigues
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
| | - Aruane M Pineda
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
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Wu Z, Tang X, Wu J, Huang J, Shen J, Hong H. Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism. Med Biol Eng Comput 2023; 61:2391-2404. [PMID: 37095297 DOI: 10.1007/s11517-023-02840-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 04/13/2023] [Indexed: 04/26/2023]
Abstract
Due to high computational requirements, deep-learning decoders for motor imaginary (MI) electroencephalography (EEG) signals are usually implemented on bulky and heavy computing devices that are inconvenient for physical actions. To date, the application of deep-learning techniques in independent portable brain-computer-interface (BCI) devices has not been extensively explored. In this study, we proposed a high-accuracy MI EEG decoder by incorporating spatial-attention mechanism into convolution neural network (CNN), and deployed it on fully integrated single-chip microcontroller unit (MCU). After the CNN model was trained on workstation computer using GigaDB MI datasets (52 subjects), its parameters were then extracted and converted to build deep-learning architecture interpreter on MCU. For comparison, EEG-Inception model was also trained using the same dataset, and was deployed on MCU. The results indicate that our deep-learning model can independently decode imaginary left-/right-hand motions. The mean accuracy of the proposed compact CNN reaches 96.75 ± 2.41% (8 channels: Frontocentral3 (FC3), FC4, Central1 (C1), C2, Central-Parietal1 (CP1), CP2, C3, and C4), versus 76.96 ± 19.08% of EEG-Inception (6 channels: FC3, FC4, C1, C2, CP1, and CP2). To the best of our knowledge, this is the first portable deep-learning decoder for MI EEG signals. The findings demonstrate high-accuracy deep-learning decoding of MI EEG in a portable mode, which has great implications for hand-disabled patients. Our portable system can be used for developing artificial-intelligent wearable BCI devices, as it is less computationally expensive and convenient for real-life application.
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Affiliation(s)
- Zhanxiong Wu
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.
| | - Xudong Tang
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Jinhui Wu
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Jiye Huang
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Jian Shen
- Neurosurgery Department, The First Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China
| | - Hui Hong
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
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7
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Beño-Ruiz-de-la-Sierra RM, Fernández-Linsenbarth I, Roig-Herrero A, Díez-Revuelta Á. Electroencephalography for the Study of the Auditory P300 Evoked Potential and Derived Measurements. Methods Mol Biol 2023; 2687:93-106. [PMID: 37464165 DOI: 10.1007/978-1-0716-3307-6_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Electroencephalography (EEG) is a widely used tool in neuropsychiatry research. The most used measurements in EEG are the amplitude and latency of the cortical electrophysiological activity in response to stimulus, known as evoked potentials. Besides potentials, time/frequency analysis is also used to obtain information on global fluctuations of the recordings, which evoked potentials do not provide. Time/frequency analysis results in different values known as derived measures. In this work, a brief introduction to evoked potentials and time/frequency analyses in schizophrenia is given, focusing on P300, noise power, and spectral entropy. Finally, a detailed description is given on how to obtain EEG recordings, evoked potentials, and derived measures.
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Affiliation(s)
| | | | | | - Álvaro Díez-Revuelta
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
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8
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Iglesias-Tejedor M, Díez Á, Llorca-Bofí V, Núñez P, Castaño-Díaz C, Bote B, Segarra R, Sanz-Fuentenebro J, Molina V. Relation between EEG resting-state power and modulation of P300 task-related activity in theta band in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2022; 116:110541. [PMID: 35218880 DOI: 10.1016/j.pnpbp.2022.110541] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/25/2022] [Accepted: 02/21/2022] [Indexed: 10/19/2022]
Abstract
There is some consistency in previous EEG findings that patients with schizophrenia have increased resting-state cortical activity. Furthermore, in previous work, we have provided evidence that there is a deficit in the modulation of bioelectrical activity during the performance of a P300 task in schizophrenia. Our hypothesis here is that a basal hyperactivation would be related with altered ability to change or modulate cortical activity during a cognitive task. However, no study so far, to the best of our knowledge, has studied the association between resting-state activity and task-related modulation. With this aim, we used a dual EEG paradigm (resting state and oddball task for elicitation of the P300 evoked potential) in a sample of patients with schizophrenia (n = 100), which included a subgroup of patients with first episode psychosis (n = 30), as well as a group of healthy controls (n = 93). The study measures were absolute power for resting-state; and spectral entropy (SE) and connectivity strength (CS) for P300-task data, whose modulation had been previously found to be altered in schizophrenia. Following the literature on P300, we focused our study on the theta frequency band. As expected, our results showed an increase in resting state activity and altered task-related modulation. Moreover, we found an inverse relationship between the amount of resting-state activity and modulation of task-related activity. Our results confirm our hypothesis and support the idea that a greater amount of resting theta-band synchrony could hamper the modulation of signal regularity (quantified by SE) and activity density (measured by CS) during the P300 task performance. This association was found in both patients and controls, suggesting the existence of a common mechanism and a possible ceiling effect in schizophrenia patients in relation to a decreased inhibitory function that limits their cortical reactivity to the task.
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Affiliation(s)
- María Iglesias-Tejedor
- Clinical Neurophysiology Service, Clinical University Hospital of Valladolid, Valladolid, Spain.
| | - Álvaro Díez
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain.
| | - Vicent Llorca-Bofí
- Psychiatry Department, Hospital Universitari Santa Maria, Lleida, Spain.
| | - Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
| | | | - Berta Bote
- Psychiatry Service, University Hospital of Salamanca, Salamanca, Spain.
| | - Rafael Segarra
- Psychiatry Service, Cruces Hospital, Biocruces-Bizkaia, Bilbao, Spain.
| | | | - Vicente Molina
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain; Psychiatry Service, University Hospital of Valladolid, Valladolid, Spain; Neuroscience Institute of Castilla y León (INCYL), University of Salamanca, Salamanca, Spain.
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Determination of deferasirox (anti-thalassemia drug) in serum and urine: cyclic voltammetry study. Int J Health Sci (Qassim) 2022. [DOI: 10.53730/ijhs.v6ns1.5653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The purpose of this project is to examine cyclic voltammetry (CV) analytical technique for anti_thalassemia drug of deferasirox by modified multi-walled carbon nanotubes (MWCNTs) on glassy carbon electrod (GCE) was described. The electrochemical performance of deferasirox was studied by cyclic voltammetry technique. The ability of the electrode for the determination of deferasirox under Optimize condition in pH 13.8~14, scan rate 100mv/s, temperature 30 0C and interference that have been studied. Where found the results that calibration curve of deferasirox was linear in the concentrations 13.4×10-4-2.6×10-2 M, its detection limit was 8.46×10-11 M and LOQ was 2.82×10-10 M. The enthalpy ∆H was calculated to be (6.736 kJ. mol-1), and the entropy can be calculated to be (213.8 J. K‐1 mol‐1). The area of electrode was calculated to be 0.314 cm2 and also the diffusion coefficient was 3.154×10-4 cm2sec-1. RSD% for bulk and form was less than 0.3% while serum and urine less than 2.5% and recovery in all close to 100.1%. The voltammogram for deferasirox give irreversible process with diffusion– controlled process. Finally, this technique has been applied for deferasirox on pharmaceutical formulations and biological samples (serum and urine).
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Núñez P, Gomez C, Rodríguez-González V, Hillebrand A, Tewarie P, Gomez-Pilar J, Molina V, Hornero R, Poza J. Schizophrenia induces abnormal frequency-dependent patterns of dynamic brain network reconfiguration during an auditory oddball task. J Neural Eng 2022; 19. [PMID: 35108688 DOI: 10.1088/1741-2552/ac514e] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 02/02/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Schizophrenia is a psychiatric disorder that has been shown to disturb the dynamic top-down processing of sensory information. Various imaging techniques have revealed abnormalities in brain activity associated with this disorder, both locally and between cerebral regions. However, there is increasing interest in investigating dynamic network response to novel and relevant events at the network level during an attention-demanding task with high-temporal-resolution techniques. The aim of the work was: (i) to test the capacity of a novel algorithm to detect recurrent brain meta-states from auditory oddball task recordings; and (ii) to evaluate how the dynamic activation and behavior of the aforementioned meta-states were altered in schizophrenia, since it has been shown to impair top-down processing of sensory information. APPROACH A novel unsupervised method for the detection of brain meta-states based on recurrence plots and community detection algorithms, previously tested on resting-state data, was used on auditory oddball task recordings. Brain meta-states and several properties related to their activation during target trials in the task were extracted from electroencephalography (EEG) data from patients with schizophrenia and cognitively healthy controls. MAIN RESULTS The methodology successfully detected meta-states during an auditory oddball task, and they appeared to show both frequency-dependent time-locked and non-time-locked activity with respect to the stimulus onset. Moreover, patients with schizophrenia displayed higher network diversity, and showed more sluggish meta-state transitions, reflected in increased dwell times, less complex meta-state sequences, decreased meta-state space speed, and abnormal ratio of negative meta-state correlations. SIGNIFICANCE Abnormal cognition in schizophrenia is also reflected in decreased brain flexibility at the dynamic network level, which may hamper top-down processing, possibly indicating impaired decision-making linked to dysfunctional predictive coding. Moreover, the results showed the ability of the methodology to find meaningful and task-relevant changes in dynamic connectivity and pathology-related group differences.
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Affiliation(s)
- Pablo Núñez
- Teoría de la señal y comunicaciones e ingeniería telemática, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, 47002, SPAIN
| | - Carlos Gomez
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, E. T. S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, Valladolid, Valladolid, 47011, SPAIN
| | - Víctor Rodríguez-González
- Teoría de la señal y comunicaciones e ingeniería telemática, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, 47011, SPAIN
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Centre, VU University Medical Centre, VU University Medical Centre, 1081 HV Amsterdam, Netherlands, Amsterdam, 1081 HV, NETHERLANDS
| | - Prejaas Tewarie
- Department of Clinical Neurophysiology and MEG Centre, VU University Medical Centre Amsterdam, VU University Medical Centre, 1081 HV Amsterdam, Netherlands, Amsterdam, Noord-Holland, 1081 HV, NETHERLANDS
| | - Javier Gomez-Pilar
- Communications and Signal Theory, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, Valladolid, 47011, SPAIN
| | - Vicente Molina
- Universidad de Valladolid, School of Medicine, University of Valladolid, 47005 - Valladolid, Valladolid, 47002, SPAIN
| | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, ETSI Telecomunicacion, Paseo Belen 15, Valladolid, 47011, SPAIN
| | - Jesus Poza
- Communications and Signal Theory, University of Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, 47002, SPAIN
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Liu B, Chang H, Peng K, Wang X. An End-to-End Depression Recognition Method Based on EEGNet. Front Psychiatry 2022; 13:864393. [PMID: 35360138 PMCID: PMC8963113 DOI: 10.3389/fpsyt.2022.864393] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 02/14/2022] [Indexed: 11/18/2022] Open
Abstract
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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Affiliation(s)
- Bo Liu
- Department of Emergency, The Second Hospital of Shandong University, Jinan, China
| | - Hongli Chang
- School of Information Science and Engineering, Southeast University, Nanjing, China
| | - Kang Peng
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xuenan Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
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Northoff G, Gomez-Pilar J. Overcoming Rest-Task Divide-Abnormal Temporospatial Dynamics and Its Cognition in Schizophrenia. Schizophr Bull 2021; 47:751-765. [PMID: 33305324 PMCID: PMC8661394 DOI: 10.1093/schbul/sbaa178] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Schizophrenia is a complex psychiatric disorder exhibiting alterations in spontaneous and task-related cerebral activity whose relation (termed "state dependence") remains unclear. For unraveling their relationship, we review recent electroencephalographic (and a few functional magnetic resonance imaging) studies in schizophrenia that assess and compare both rest/prestimulus and task states, ie, rest/prestimulus-task modulation. Results report reduced neural differentiation of task-related activity from rest/prestimulus activity across different regions, neural measures, cognitive domains, and imaging modalities. Together, the findings show reduced rest/prestimulus-task modulation, which is mediated by abnormal temporospatial dynamics of the spontaneous activity. Abnormal temporospatial dynamics, in turn, may lead to abnormal prediction, ie, predictive coding, which mediates cognitive changes and psychopathological symptoms, including confusion of internally and externally oriented cognition. In conclusion, reduced rest/prestimulus-task modulation in schizophrenia provides novel insight into the neuronal mechanisms that connect task-related changes to cognitive abnormalities and psychopathological symptoms.
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Affiliation(s)
- Georg Northoff
- Mental Health Center/7th Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Mind, Brain Imaging and Neuroethics, Institute of Mental Health Research, Royal Ottawa Healthcare Group, University of Ottawa, Ottawa ON, Canada
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
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Fernández-Linsenbarth I, Planchuelo-Gómez Á, Díez Á, Arjona-Valladares A, de Luis R, Martín-Santiago Ó, Benito-Sánchez JA, Pérez-Laureano Á, González-Parra D, Montes-Gonzalo C, Melero-Lerma R, Morante SF, Sanz-Fuentenebro J, Gómez-Pilar J, Núñez-Novo P, Molina V. Neurobiological underpinnings of cognitive subtypes in psychoses: A cross-diagnostic cluster analysis. Schizophr Res 2021; 229:102-111. [PMID: 33221149 DOI: 10.1016/j.schres.2020.11.013] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 10/01/2020] [Accepted: 11/12/2020] [Indexed: 02/02/2023]
Abstract
Schizophrenia and bipolar disorder include patients with different characteristics, which may hamper the definition of biomarkers. One of the dimensions with greater heterogeneity among these patients is cognition. Recent studies support the identification of different patients' subgroups along the cognitive domain using cluster analysis. Our aim was to validate clusters defined on the basis of patients' cognitive status and to assess its relation with demographic, clinical and biological measurements. We hypothesized that subgroups characterized by different cognitive profiles would show differences in an array of biological data. Cognitive data from 198 patients (127 with chronic schizophrenia, 42 first episodes of schizophrenia and 29 bipolar patients) were analyzed by a K-means cluster approach and were compared on several clinical and biological variables. We also included 155 healthy controls for further comparisons. A two-cluster solution was selected, including a severely impaired group and a moderately impaired group. The severely impaired group was associated with higher illness duration and symptoms scores, lower thalamus and hippocampus volume, lower frontal connectivity and basal hypersynchrony in comparison to controls and the moderately impaired group. Moreover, both patients' groups showed lower cortical thickness and smaller functional connectivity modulation than healthy controls. This study supports the existence of different cognitive subgroups within the psychoses with different neurobiological underpinnings.
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Affiliation(s)
| | | | - Álvaro Díez
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
| | | | - Rodrigo de Luis
- Imaging Processing Laboratory, University of Valladolid, Valladolid, Spain
| | | | | | | | | | | | | | | | | | - Javier Gómez-Pilar
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain
| | - Pablo Núñez-Novo
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain
| | - Vicente Molina
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain; Psychiatry Service, Clinical Hospital of Valladolid, Valladolid, Spain; Neurosciences Institute of Castilla y León (INCYL), University of Salamanca, Salamanca, Spain.
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Sato J, Hirano Y, Hirakawa N, Takahashi J, Oribe N, Kuga H, Nakamura I, Hirano S, Ueno T, Togao O, Hiwatashi A, Nakao T, Onitsuka T. Lower Hippocampal Volume in Patients with Schizophrenia and Bipolar Disorder: A Quantitative MRI Study. J Pers Med 2021; 11:jpm11020121. [PMID: 33668432 PMCID: PMC7918861 DOI: 10.3390/jpm11020121] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/06/2021] [Accepted: 02/09/2021] [Indexed: 01/02/2023] Open
Abstract
Since patients with schizophrenia (SZ) and bipolar disorder (BD) share many biological features, detecting biomarkers that differentiate SZ and BD patients is crucial for optimized treatments. High-resolution magnetic resonance imaging (MRI) is suitable for detecting subtle brain structural differences in patients with psychiatric disorders. In the present study, we adopted a neuroanatomically defined and manually delineated region of interest (ROI) method to evaluate the amygdalae, hippocampi, Heschl’s gyrus (HG), and planum temporale (PT), because these regions are crucial in the development of SZ and BD. ROI volumes were measured using high resolution MRI in 31 healthy subjects (HS), 23 SZ patients, and 21 BD patients. Right hippocampal volumes differed significantly among groups (HS > BD > SZ), whereas left hippocampal volumes were lower in SZ patients than in HS and BD patients (HS = BD > SZ). Volumes of the amygdalae, HG, and PT did not differ among the three groups. For clinical correlations, there were no significant associations between ROI volumes and demographics/clinical symptoms. Our study revealed significant lower hippocampal volume in patients with SZ and BD, and we suggest that the right hippocampal volume is a potential biomarker for differentiation between SZ and BD.
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Affiliation(s)
- Jinya Sato
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
- Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
- Correspondence: (Y.H.); (T.O.); Tel.: +81-92-642-5627 (Y.H. & T.O.)
| | - Noriaki Hirakawa
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
| | - Junichi Takahashi
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
| | - Naoya Oribe
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
- Hizen Psychiatric Medical Center, Division of Clinical Research, National Hospital Organization, Saga 842-0192, Japan;
| | - Hironori Kuga
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
- Hizen Psychiatric Medical Center, Division of Clinical Research, National Hospital Organization, Saga 842-0192, Japan;
| | - Itta Nakamura
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
| | - Shogo Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
| | - Takefumi Ueno
- Hizen Psychiatric Medical Center, Division of Clinical Research, National Hospital Organization, Saga 842-0192, Japan;
| | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan;
| | - Akio Hiwatashi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan;
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
| | - Toshiaki Onitsuka
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
- Correspondence: (Y.H.); (T.O.); Tel.: +81-92-642-5627 (Y.H. & T.O.)
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