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Hamilton HK, Mathalon DH, Ford JM. P300 in schizophrenia: Then and now. Biol Psychol 2024; 187:108757. [PMID: 38316196 DOI: 10.1016/j.biopsycho.2024.108757] [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: 08/24/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 02/07/2024]
Abstract
The 1965 discovery of the P300 component of the electroencephalography (EEG)-based event-related potential (ERP), along with the subsequent identification of its alteration in people with schizophrenia, initiated over 50 years of P300 research in schizophrenia. Here, we review what we now know about P300 in schizophrenia after nearly six decades of research. We describe recent efforts to expand our understanding of P300 beyond its sensitivity to schizophrenia itself to its potential role as a biomarker of risk for psychosis or a heritable endophenotype that bridges genetic risk and psychosis phenomenology. We also highlight efforts to move beyond a syndrome-based approach to understand P300 within the context of the clinical, cognitive, and presumed pathophysiological heterogeneity among people diagnosed with schizophrenia. Finally, we describe several recent approaches that extend beyond measuring the traditional P300 ERP component in people with schizophrenia, including time-frequency analyses and pharmacological challenge studies, that may help to clarify specific cognitive mechanisms that are disrupted in schizophrenia. Moreover, we discuss several promising areas for future research, including studies of animal models that can be used for treatment development.
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Affiliation(s)
- Holly K Hamilton
- University of Minnesota, Department of Psychiatry & Behavioral Sciences, Minneapolis, MN, USA; Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, USA; University of California, San Francisco, Department of Psychiatry & Behavioral Sciences, San Francisco, CA, USA; San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA.
| | - Daniel H Mathalon
- University of California, San Francisco, Department of Psychiatry & Behavioral Sciences, San Francisco, CA, USA; San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | - Judith M Ford
- University of California, San Francisco, Department of Psychiatry & Behavioral Sciences, San Francisco, CA, USA; San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
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Wu G, Tang X, Gan R, Zeng J, Hu Y, Xu L, Wei Y, Tang Y, Chen T, Li C, Wang J, Zhang T. Temporal and time-frequency features of auditory oddball response in distinct subtypes of patients at clinical high risk for psychosis. Eur Arch Psychiatry Clin Neurosci 2022; 272:449-459. [PMID: 34333669 DOI: 10.1007/s00406-021-01316-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 07/26/2021] [Indexed: 01/10/2023]
Abstract
Individuals at clinical high risk (CHR) for psychosis exhibit a reduced P300 oddball response, which indicates deficits in attention and working memory processes. Previous studies have mainly researched these responses in the temporal domain; hence, non-phase-locked or induced neural activities may have been ignored. Event-related potential (ERP) and time-frequency (TF) information, combined with clinical and cognitive profiles, may provide an insight into the pathophysiology and psychopathology of the CHR stage. The 104 CHR individuals who completed cognitive assessments and ERP tests were recruited and followed up between 2016 and 2018. Individuals with CHR were classified by three clinical subtypes demonstrated before, specifically 32 from Cluster-1 (characterized by extensive negative symptoms and cognitive deficits, at the highest risk for conversion to psychosis), 34 from Cluster-2 (characterized by thought and behavioral disorganization, with moderate cognitive impairment), and 38 from Cluster-3 (characterized by the mildest symptoms and cognitive deficits). Electroencephalograms were recorded during the auditory oddball paradigm. The P300 ERPs were analyzed in the temporal domain. The event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) were acquired by TF analysis. A reduced P300 response to target tones was noted in Cluster-1 relative to the other two clusters. Moreover, the P300 amplitude of Cluster-1 was associated with speed of processing (SoP) scores. Furthermore, the P300 amplitude of Cluster-3 was significantly correlated with verbal and visual learning scores. In the TF analysis, decreased delta ERSP and ITC were observed in Cluster-1; delta ITC was associated with SoP scores in Cluster-3. The results indicate relatively disrupted oddball responses in a certain CHR subtype and a close affinity between these electrophysiological indexes and attention, working memory, and declarative memory within different CHR clusters. These findings suggest that the auditory oddball response is a potential neurophysiological marker for distinct clinical subtypes of CHR.
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Affiliation(s)
- GuiSen Wu
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - XiaoChen Tang
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - RanPiao Gan
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - JiaHui Zeng
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - YeGang Hu
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - LiHua Xu
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - YanYan Wei
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - YingYing Tang
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada.,Senior Research Fellow, Labor and Worklife Program, Harvard University, Cambridge, MA, USA.,Niacin (Shanghai) Technology Co., Ltd., Shanghai, People's Republic of China
| | - ChunBo Li
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - JiJun Wang
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China. .,Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, People's Republic of China. .,Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, People's Republic of China. .,Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China.
| | - TianHong Zhang
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China.
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Raymond N, Lizano P, Kelly S, Hegde R, Keedy S, Pearlson GD, Gershon ES, Clementz BA, Tamminga CA, Keshavan M. What can clozapine’s effect on neural oscillations tell us about its therapeutic effects? A scoping review and synthesis. Biomark Neuropsychiatry 2022. [DOI: 10.1016/j.bionps.2022.100048] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
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Li H, Li N, Xing Y, Zhang S, Liu C, Cai W, Hong W, Zhang Q. P300 as a Potential Indicator in the Evaluation of Neurocognitive Disorders After Traumatic Brain Injury. Front Neurol 2021; 12:690792. [PMID: 34566838 PMCID: PMC8458648 DOI: 10.3389/fneur.2021.690792] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 08/12/2021] [Indexed: 11/29/2022] Open
Abstract
Few objective indices can be used when evaluating neurocognitive disorders after a traumatic brain injury (TBI). P300 has been widely studied in mental disorders, cognitive dysfunction, and brain injury. Daily life ability and social function are key indices in the assessment of neurocognitive disorders after a TBI. The present study focused on the correlation between P300 and impairment of daily living activity and social function. We enrolled 234 patients with neurocognitive disorders after a TBI according to ICD-10 and 277 age- and gender-matched healthy volunteers. The daily living activity and social function were assessed by the social disability screening schedule (SDSS) scale, activity of daily living (ADL) scale, and scale of personality change following a TBI. P300 was evoked by a visual oddball paradigm. The results showed that the scores of the ADL scale, SDSS scale, and scale of personality change in the patient group were significantly higher than those in the control group. The amplitudes of Fz, Cz, and Pz in the patient group were significantly lower than those in the control group and were negatively correlated with the scores of the ADL and SDSS scales. In conclusion, a lower P300 amplitude means a greater impairment of daily life ability and social function, which suggested more severity of neurocognitive disorders after a TBI. P300 could be a potential indicator in evaluating the severity of neurocognitive disorders after a TBI.
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Affiliation(s)
- Haozhe Li
- Shanghai Key Laboratory of Forensic Medicine, Key Lab of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Ningning Li
- Hongkou District Mental Health Center, Shanghai, China
| | - Yan Xing
- Shanghai Key Laboratory of Forensic Medicine, Key Lab of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Shengyu Zhang
- Shanghai Key Laboratory of Forensic Medicine, Key Lab of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Chao Liu
- Shanghai Key Laboratory of Forensic Medicine, Key Lab of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Weixiong Cai
- Shanghai Key Laboratory of Forensic Medicine, Key Lab of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Wu Hong
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qinting Zhang
- Shanghai Key Laboratory of Forensic Medicine, Key Lab of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
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Javitt DC, Siegel SJ, Spencer KM, Mathalon DH, Hong LE, Martinez A, Ehlers CL, Abbas AI, Teichert T, Lakatos P, Womelsdorf T. A roadmap for development of neuro-oscillations as translational biomarkers for treatment development in neuropsychopharmacology. Neuropsychopharmacology 2020; 45:1411-1422. [PMID: 32375159 PMCID: PMC7360555 DOI: 10.1038/s41386-020-0697-9] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 03/16/2020] [Accepted: 04/27/2020] [Indexed: 02/08/2023]
Abstract
New treatment development for psychiatric disorders depends critically upon the development of physiological measures that can accurately translate between preclinical animal models and clinical human studies. Such measures can be used both as stratification biomarkers to define pathophysiologically homogeneous patient populations and as target engagement biomarkers to verify similarity of effects across preclinical and clinical intervention. Traditional "time-domain" event-related potentials (ERP) have been used translationally to date but are limited by the significant differences in timing and distribution across rodent, monkey and human studies. By contrast, neuro-oscillatory responses, analyzed within the "time-frequency" domain, are relatively preserved across species permitting more precise translational comparisons. Moreover, neuro-oscillatory responses are increasingly being mapped to local circuit mechanisms and may be useful for investigating effects of both pharmacological and neuromodulatory interventions on excitatory/inhibitory balance. The present paper provides a roadmap for development of neuro-oscillatory responses as translational biomarkers in neuropsychiatric treatment development.
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Affiliation(s)
- Daniel C Javitt
- Department of Psychiatry, Columbia University Medical Center, New York, NY, 10032, USA.
- Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10954, USA.
| | - Steven J Siegel
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Kevin M Spencer
- Research Service, VA Boston Healthcare System, and Dept. of Psychiatry, Harvard Medical School, Boston, MA, 02130, USA
| | - Daniel H Mathalon
- VA San Francisco Healthcare System, University of California, San Francisco, San Francisco, CA, 94121, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Antigona Martinez
- Department of Psychiatry, Columbia University Medical Center, New York, NY, 10032, USA
- Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10954, USA
| | - Cindy L Ehlers
- Department of Neuroscience, The Scripps Research Institute, 10550 N Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Atheir I Abbas
- VA Portland Health Care System, Portland, OR, 97239, USA
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, 97239, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Tobias Teichert
- Departments of Psychiatry and Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Peter Lakatos
- Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10954, USA
| | - Thilo Womelsdorf
- Department of Psychology, Vanderbilt University, Nashville, TN, 37203, USA
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Decomposing P300 into correlates of genetic risk and current symptoms in schizophrenia: An inter-trial variability analysis. Schizophr Res 2018; 192:232-239. [PMID: 28400070 DOI: 10.1016/j.schres.2017.04.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 03/15/2017] [Accepted: 04/01/2017] [Indexed: 12/28/2022]
Abstract
BACKGROUND The P300 event-related potential (ERP) component, which reflects cognitive processing, is a candidate biomarker for schizophrenia. However, the role of P300 in the pathophysiology of schizophrenia remains unclear because averaged P300 amplitudes reflect both genetic predisposition and current clinical status. Thus, we sought to identify which aspects of P300 are associated with genetic risk versus symptomatic status via an inter-trial variability analysis. METHODS Auditory P300, clinical symptoms, and neurocognitive function assessments were obtained from forty-five patients with schizophrenia, thirty-two subjects at genetic high risk (GHR), thirty-two subjects at clinical high risk (CHR), and fifty-two healthy control (HC) participants. Both conventional averaging and inter-trial variability analyses were conducted for P300, and results were compared across groups using analysis of variance (ANOVA). Pearson's correlation was utilized to determine associations among inter-trial variability for P300, current symptoms and neurocognitive status. RESULTS Average P300 amplitude was reduced in the GHR, CHR, and schizophrenia groups compared with that in the HC group. P300 inter-trial variability was elevated in the CHR and schizophrenia groups but relatively normal in the GHR and HC groups. Furthermore, P300 inter-trial variability was significantly related to negative symptom severity and neurocognitive performance results in schizophrenia patients. CONCLUSIONS These results suggest that P300 amplitude is an endophenotype for schizophrenia and that greater inter-trial variability of P300 is associated with more severe negative and cognitive symptoms in schizophrenia patients.
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Vignapiano A, Mucci A, Ford J, Montefusco V, Plescia G, Bucci P, Galderisi S. Reward anticipation and trait anhedonia: An electrophysiological investigation in subjects with schizophrenia. Clin Neurophysiol 2016; 127:2149-60. [DOI: 10.1016/j.clinph.2016.01.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 01/03/2016] [Accepted: 01/11/2016] [Indexed: 11/29/2022]
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Almeida PR, Ferreira-Santos F, Chaves PL, Paiva TO, Barbosa F, Marques-Teixeira J. Perceived arousal of facial expressions of emotion modulates the N170, regardless of emotional category: Time domain and time–frequency dynamics. Int J Psychophysiol 2016; 99:48-56. [DOI: 10.1016/j.ijpsycho.2015.11.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Revised: 10/13/2015] [Accepted: 11/30/2015] [Indexed: 10/22/2022]
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Díez Á, Suazo V, Casado P, Martín-Loeches M, Molina V. Gamma power and cognition in patients with schizophrenia and their first-degree relatives. Neuropsychobiology 2014; 69:120-8. [PMID: 24732388 DOI: 10.1159/000356970] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2012] [Accepted: 11/02/2013] [Indexed: 11/19/2022]
Abstract
BACKGROUND Gamma oscillations are essential for functional neural assembly formation underlying higher cerebral functions. Previous studies concerning gamma band power in schizophrenia have yielded diverse results. METHODS In this study, we assessed gamma band power in minimally treated patients with schizophrenia, their first-degree relatives and healthy controls during an oddball paradigm performance, as well as the relation between gamma power and cognitive performance. RESULTS We found a higher gamma power in the patient group than in the healthy controls at the P3, P4, Fz, Pz and T5 sites. Compared with their relatives, gamma power in the patients was only marginally higher over P3 and P4. We found a nearly significant inverse association between gamma power at F4 and Tower of London performance in the patients, as well as a significant inverse association between gamma power at T5 and verbal memory and working memory scores in the relatives. CONCLUSION These results support higher total gamma power in association with schizophrenia and its inverse association with cognitive performance in patients and their first-degree relatives.
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Affiliation(s)
- Álvaro Díez
- Basic Psychology, Psychobiology and Methodology Department, School of Psychology, University of Salamanca, Salamanca, Spain
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Chang WH, Chen KC, Yang YK, Chen PS, Lu RB, Yeh TL, Wang CSM, Lee IH. Association between auditory P300, psychopathology, and memory function in drug-naïve schizophrenia. Kaohsiung J Med Sci 2014; 30:133-8. [DOI: 10.1016/j.kjms.2013.10.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Accepted: 08/05/2013] [Indexed: 11/25/2022] Open
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Díez A, Suazo V, Casado P, Martín-Loeches M, Molina V. Spatial distribution and cognitive correlates of gamma noise power in schizophrenia. Psychol Med 2013; 43:1175-1185. [PMID: 22963867 DOI: 10.1017/s0033291712002103] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Brain activity is less organized in patients with schizophrenia than in healthy controls (HC). Noise power (scalp-recorded electroencephalographic activity unlocked to stimuli) may be of use for studying this disorganization. Method Fifty-four patients with schizophrenia (29 minimally treated and 25 stable treated), 23 first-degree relatives and 27 HC underwent clinical and cognitive assessments and an electroencephalographic recording during an oddball P300 paradigm to calculate noise power magnitude in the gamma band. We used a principal component analysis (PCA) to determine the factor structure of gamma noise power values across electrodes and the clinical and cognitive correlates of the resulting factors. RESULTS The PCA revealed three noise power factors, roughly corresponding to the default mode network (DMN), frontal and occipital regions respectively. Patients showed higher gamma noise power loadings in the first factor when compared to HC and first-degree relatives. In the patients, frontal gamma noise factor scores related significantly and inversely to working memory and problem-solving performance. There were no associations with symptoms. CONCLUSIONS There is an elevated gamma activity unrelated to task processing over regions coherent with the DMN topography in patients with schizophrenia. The same type of gamma activity over frontal regions is inversely related to performance in tasks with high involvement in these frontal areas. The idea of gamma noise as a possible biological marker for schizophrenia seems promising. Gamma noise might be of use in the study of underlying neurophysiological mechanisms involved in this disease.
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Affiliation(s)
- A Díez
- Basic Psychology, Psychobiology and Methodology Department, School of Psychology, University of Salamanca, Spain
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