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Chang Y, Wang X, Liao J, Chen S, Liu X, Liu S, Ming D. Temporal hyper-connectivity and frontal hypo-connectivity within gamma band in schizophrenia: A resting state EEG study. Schizophr Res 2024; 264:220-230. [PMID: 38183959 DOI: 10.1016/j.schres.2023.12.017] [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: 12/27/2022] [Revised: 11/12/2023] [Accepted: 12/16/2023] [Indexed: 01/08/2024]
Abstract
OBJECTIVE The brain network serves as the physiological foundation for information processing of the brain. Many studies have reported abnormalities of gamma oscillations in Schizophrenia. The aim of this study was to investigate the gamma-band connectivity in Schizophrenia patients. METHODS We recorded the resting state electroencephalogram (EEG) for 15 schizophrenia patients with refractory auditory hallucinations and 14 healthy controls, with eyes open and closed. The brain network was constructed based on weighted phase lag index for gamma band. Whole scalp metrics (clustering coefficient, global efficiency and local efficiency) and local region metrics (degree and betweenness centrality) in the frontal and temporal lobes were computed. Correlation analyses between network metrics and symptom scales were examined to find associations with symptom severity. RESULTS Schizophrenia patients had larger global efficiency and local efficiency (p < 0.05) with eyes closed, probably representing greater brain activity and information exchange. For degree and betweenness centrality, schizophrenia patients showed an increase (p < 0.05) in the temporal lobe but a decrease (p < 0.05) in the frontal lobe with eyes closed and open, potentially account for the patients' symptoms such as hallucinations and thought disorders. Local efficiency and frontal lobe degree were positively and negatively correlated with the scales, respectively (both p < 0.05). CONCLUSIONS Altered connectivity of the resting state brain network has been revealed and may be associated with the core symptoms of schizophrenia. Our study provides promising evidence for the investigation of the pathological basis of Schizophrenia and could aid in objective diagnosis.
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Affiliation(s)
- Yuan Chang
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China
| | - Xiaojuan Wang
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China
| | - Jingmeng Liao
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China
| | - Sitong Chen
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China
| | - Xiaoya Liu
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China
| | - Shuang Liu
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China.
| | - Dong Ming
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China
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Zandbagleh A, Mirzakuchaki S, Daliri MR, Sumich A, Anderson JD, Sanei S. Graph-based analysis of EEG for schizotypy classification applying flicker Ganzfeld stimulation. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:64. [PMID: 37735164 PMCID: PMC10514040 DOI: 10.1038/s41537-023-00395-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023]
Abstract
Ganzfeld conditions induce alterations in brain function and pseudo-hallucinatory experiences, particularly in people with high positive schizotypy. The current study uses graph-based parameters to investigate and classify brain networks under Ganzfeld conditions as a function of positive schizotypy. Participants from the general population (14 high schizotypy (HS), 29 low schizotypy (LS)) had an electroencephalography assessment during Ganzfeld conditions, with varying visual activation (8 frequencies of random light flicker) and soundscape-induced mood (neutral, serenity, and anxiety). Weighted functional networks were computed in six frequency sub-bands (delta, theta, alpha-low, alpha-high, beta, and gamma) as a function of light-flicker frequency and mood. The brain network was analyzed using graph theory parameters, including clustering coefficient (CC), strength, and global efficiency (GE). It was found that the LS groups had higher CC and strength than the HS groups, especially in bilateral temporal and frontotemporal brain regions. Moreover, some decreases in CC and strength measures were found in LS groups among occipital and parieto-occipital brain regions. LS groups also had significantly higher GE in all Ganzfeld conditions compared to the HS groups. The random under-sampling boosting (RUSBoost) algorithm achieved the best classification performance with an accuracy of 95.34%, specificity of 96.55%, and sensitivity of 92.85% during an anxiety-induction Ganzfeld condition. This is the first exploration of the relationship between brain functional state changes under Ganzfeld conditions in individuals who vary in positive schizotypy. The accuracy of graph-based parameters in classifying brain states as a function of schizotypy is shown, particularly for brain activity during anxiety induction, and should be investigated in psychosis.
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Affiliation(s)
- Ahmad Zandbagleh
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Sattar Mirzakuchaki
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
| | - Mohammad Reza Daliri
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Alexander Sumich
- Division of Psychology, Nottingham Trent University, Main Campus, Nottingham, UK
| | - John D Anderson
- Division of Psychology, Nottingham Trent University, Main Campus, Nottingham, UK
| | - Saeid Sanei
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, UK
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Deng J, Chen S, Ou Y, Zhang Y, Lin Z, Shen Y, Ye Y. Auditory P300 in individuals with high schizotypy: associations of schizotypal traits with amplitude and latency under different oddball conditions. Front Hum Neurosci 2023; 17:1107858. [PMID: 37275344 PMCID: PMC10232759 DOI: 10.3389/fnhum.2023.1107858] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 05/02/2023] [Indexed: 06/07/2023] Open
Abstract
Background The aim of this study was to compare the characteristics of auditory P300 between non-clinical individuals with high and low schizotypal traits, and investigate the relationship between schizotypy and P300 under various oddball conditions. Methods An extreme-group design was adopted. After screening 1,519 young adults using the Schizotypal Personality Questionnaire (SPQ), sixty-three participants were chosen and divided into two groups (schizotypy group: 31 participants; control group: 32 participants). Basic demographic information was assessed and matched between groups. Depression and anxiety indexes were evaluated and controlled. The P300 component was evoked by an auditory oddball paradigm with different frequencies and durations. Results (1) The duration P300 amplitude at PZ site was significantly weaker in the schizotypy group than in the control group [F(1,54) = 7.455, p = 0.009, ηp2 = 0.121]. (2) In the schizotypy group, the latency of frequency P300 at PZ site under large-variant oddball condition was significantly correlated with total SPQ scores (rp = 0.451, p = 0.018) and disorganized dimension scores (rp = 0.381, p = 0.050). (3) In the control group, significantly negative correlations was found between the negative dimension score of SPQ and the frequency P300 amplitudes under small variant condition (PZ: rp = -0.393, p = 0.043; CPZ: rp = -0.406, p = 0.035). In addition, a significant negative relationship was found between disorganized dimension scores and the duration P300 latency at CPZ site under large-variant oddball condition (rp = -0.518, p = 0.006). Moreover, a significant negative association was found between the duration P300 amplitude at CPZ site under small-variant oddball condition and negative factor scores (rp = -0.410, p = 0.034). Conclusion Individuals with high schizotypal traits were likely to have deficient attention and hypoactive working memory for processing auditory information, especially the duration of sounds. P300 effects were correlated with negative and disorganized schizotypy, rather than positive schizotypy. There were diverse patterns of relationship between schizotypal traits and P300 under different oddball conditions, suggesting that characteristics and parameters of target stimuli should be considered cautiously when implementing an auditory oddball paradigm for individuals with schizophrenia spectrum.
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Affiliation(s)
- Jue Deng
- Cognitive Neuroscience and Abnormal Psychology Laboratory, Department of Penalty Execution, Fujian Police College, Fuzhou, China
| | - Siwei Chen
- School of Psychology, Fujian Normal University, Fuzhou, China
| | - Yuanhua Ou
- Cognitive Neuroscience and Abnormal Psychology Laboratory, Department of Penalty Execution, Fujian Police College, Fuzhou, China
| | - Yuanjun Zhang
- School of Psychology, Fujian Normal University, Fuzhou, China
| | - Ziyue Lin
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Yane Shen
- School of Psychology, Fujian Normal University, Fuzhou, China
| | - Yiduo Ye
- School of Psychology, Fujian Normal University, Fuzhou, China
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Romero Milà B, Remakanthakurup Sindhu K, Mytinger JR, Shrey DW, Lopour BA. EEG biomarkers for the diagnosis and treatment of infantile spasms. Front Neurol 2022; 13:960454. [PMID: 35968272 PMCID: PMC9366674 DOI: 10.3389/fneur.2022.960454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis and treatment are critical for young children with infantile spasms (IS), as this maximizes the possibility of the best possible child-specific outcome. However, there are major barriers to achieving this, including high rates of misdiagnosis or failure to recognize the seizures, medication failure, and relapse. There are currently no validated tools to aid clinicians in assessing objective diagnostic criteria, predicting or measuring medication response, or predicting the likelihood of relapse. However, the pivotal role of EEG in the clinical management of IS has prompted many recent studies of potential EEG biomarkers of the disease. These include both visual EEG biomarkers based on human visual interpretation of the EEG and computational EEG biomarkers in which computers calculate quantitative features of the EEG. Here, we review the literature on both types of biomarkers, organized based on the application (diagnosis, treatment response, prediction, etc.). Visual biomarkers include the assessment of hypsarrhythmia, epileptiform discharges, fast oscillations, and the Burden of AmplitudeS and Epileptiform Discharges (BASED) score. Computational markers include EEG amplitude and power spectrum, entropy, functional connectivity, high frequency oscillations (HFOs), long-range temporal correlations, and phase-amplitude coupling. We also introduce each of the computational measures and provide representative examples. Finally, we highlight remaining gaps in the literature, describe practical guidelines for future biomarker discovery and validation studies, and discuss remaining roadblocks to clinical implementation, with the goal of facilitating future work in this critical area.
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Affiliation(s)
- Blanca Romero Milà
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain
| | | | - John R. Mytinger
- Division of Pediatric Neurology, Department of Pediatrics, Nationwide Children's Hospital, The Ohio State University, Columbus, OH, United States
| | - Daniel W. Shrey
- Division of Neurology, Children's Hospital Orange County, Orange, CA, United States
- Department of Pediatrics, University of California, Irvine, Irvine, CA, United States
| | - Beth A. Lopour
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- *Correspondence: Beth A. Lopour
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Zandbagleh A, Mirzakuchaki S, Daliri MR, Premkumar P, Sanei S. Classification of Low and High Schizotypy Levels via Evaluation of Brain Connectivity. Int J Neural Syst 2022; 32:2250013. [PMID: 35236254 DOI: 10.1142/s0129065722500137] [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: 11/18/2022]
Abstract
Schizotypy is a latent cluster of personality traits that denote a vulnerability for schizophrenia or a type of spectrum disorder. The aim of the study is to investigate parametric effective brain connectivity features for classifying high versus low schizotypy (LS) status. Electroencephalography (EEG) signals are recorded from 13 high schizotypy (HS) and 11 LS participants during an emotional auditory odd-ball task. The brain connectivity signals for machine learning are taken after the settlement of event-related potentials. A multivariate autoregressive (MVAR)-based connectivity measure is estimated from the EEG signals using the directed transfer functions (DTFs) method. The values of DTF power in five standard frequency bands are used as features. The support vector machines (SVMs) revealed significant differences between HS and LS. The accuracy, specificity, and sensitivity of the results using SVM are as high as 89.21%, 90.3%, and 88.2%, respectively. Our results demonstrate that the effective brain connectivity in prefrontal/parietal and prefrontal/frontal brain regions considerably changes according to schizotypal status. These findings prove that the brain connectivity indices offer valuable biomarkers for detecting schizotypal personality. Further monitoring of the changes in DTF following the diagnosis of schizotypy may lead to the early identification of schizophrenia and other spectrum disorders.
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Affiliation(s)
- Ahmad Zandbagleh
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Sattar Mirzakuchaki
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Mohammad Reza Daliri
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Preethi Premkumar
- Division of Psychology, School of Applied Sciences, London Southbank University, London, UK
| | - Saeid Sanei
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, UK
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Tarasi L, Trajkovic J, Diciotti S, di Pellegrino G, Ferri F, Ursino M, Romei V. Predictive waves in the autism-schizophrenia continuum: A novel biobehavioral model. Neurosci Biobehav Rev 2021; 132:1-22. [PMID: 34774901 DOI: 10.1016/j.neubiorev.2021.11.006] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 10/29/2021] [Accepted: 11/07/2021] [Indexed: 12/14/2022]
Abstract
The brain is a predictive machine. Converging data suggests a diametric predictive strategy from autism spectrum disorders (ASD) to schizophrenic spectrum disorders (SSD). Whereas perceptual inference in ASD is rigidly shaped by incoming sensory information, the SSD population is prone to overestimate the precision of their priors' models. Growing evidence considers brain oscillations pivotal biomarkers to understand how top-down predictions integrate bottom-up input. Starting from the conceptualization of ASD and SSD as oscillopathies, we introduce an integrated perspective that ascribes the maladjustments of the predictive mechanism to dysregulation of neural synchronization. According to this proposal, disturbances in the oscillatory profile do not allow the appropriate trade-off between descending predictive signal, overweighted in SSD, and ascending prediction errors, overweighted in ASD. These opposing imbalances both result in an ill-adapted reaction to external challenges. This approach offers a neuro-computational model capable of linking predictive coding theories with electrophysiological findings, aiming to increase knowledge on the neuronal foundations of the two spectra features and stimulate hypothesis-driven rehabilitation/research perspectives.
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Affiliation(s)
- Luca Tarasi
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy.
| | - Jelena Trajkovic
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy; Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
| | - Giuseppe di Pellegrino
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
| | - Francesca Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Mauro Ursino
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
| | - Vincenzo Romei
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy; IRCCS Fondazione Santa Lucia, 00179 Rome, Italy.
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