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Gong Y, Song P, Du X, Zhai Y, Xu H, Ye H, Bao X, Huang Q, Tu Z, Chen P, Zhao X, Pérez-González D, Malmierca MS, Yu X. Neural correlates of novelty detection in the primary auditory cortex of behaving monkeys. Cell Rep 2024; 43:113864. [PMID: 38421870 DOI: 10.1016/j.celrep.2024.113864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 01/11/2024] [Accepted: 02/08/2024] [Indexed: 03/02/2024] Open
Abstract
The neural mechanisms underlying novelty detection are not well understood, especially in relation to behavior. Here, we present single-unit responses from the primary auditory cortex (A1) from two monkeys trained to detect deviant tones amid repetitive ones. Results show that monkeys can detect deviant sounds, and there is a strong correlation between late neuronal responses (250-350 ms after deviant onset) and the monkeys' perceptual decisions. The magnitude and timing of both neuronal and behavioral responses are increased by larger frequency differences between the deviant and standard tones and by increasing the number of standard tones preceding the deviant. This suggests that A1 neurons encode novelty detection in behaving monkeys, influenced by stimulus relevance and expectations. This study provides evidence supporting aspects of predictive coding in the sensory cortex.
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Affiliation(s)
- Yumei Gong
- Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, Shanghai, China; Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Hangzhou Extremely Weak Magnetic Field Major Science and Technology, Infrastructure Research Institute, Hangzhou 310000, China; Interdisciplinary Institute of Neuroscience and Technology, College of Biomedical, Engineering, and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Peirun Song
- Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xinyu Du
- Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yuying Zhai
- Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haoxuan Xu
- Interdisciplinary Institute of Neuroscience and Technology, College of Biomedical, Engineering, and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hangting Ye
- Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xuehui Bao
- Interdisciplinary Institute of Neuroscience and Technology, College of Biomedical, Engineering, and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qianyue Huang
- Interdisciplinary Institute of Neuroscience and Technology, College of Biomedical, Engineering, and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhiyi Tu
- Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, Shanghai, China
| | - Pei Chen
- Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, Shanghai, China
| | - Xuan Zhao
- Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, Shanghai, China
| | - David Pérez-González
- Cognitive and Auditory Neuroscience Laboratory (Lab 1), Institute of Neuroscience of Castilla y León (INCYL), University of Salamanca, Salamanca, Spain; Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain; Department of Basic Psychology, Psychobiology, and Methodology of Behavioral Sciences, Faculty of Psychology, University of Salamanca, Salamanca, Spain
| | - Manuel S Malmierca
- Cognitive and Auditory Neuroscience Laboratory (Lab 1), Institute of Neuroscience of Castilla y León (INCYL), University of Salamanca, Salamanca, Spain; Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain; Department of Cell Biology and Pathology, Faculty of Medicine, University of Salamanca, Salamanca, Spain.
| | - Xiongjie Yu
- Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, Shanghai, China; Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Haigh SM, Berryhill ME, Kilgore-Gomez A, Dodd M. Working memory and sensory memory in subclinical high schizotypy: An avenue for understanding schizophrenia? Eur J Neurosci 2023; 57:1577-1596. [PMID: 36895099 PMCID: PMC10178355 DOI: 10.1111/ejn.15961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
The search for robust, reliable biomarkers of schizophrenia remains a high priority in psychiatry. Biomarkers are valuable because they can reveal the underlying mechanisms of symptoms and monitor treatment progress and may predict future risk of developing schizophrenia. Despite the existence of various promising biomarkers that relate to symptoms across the schizophrenia spectrum, and despite published recommendations encouraging multivariate metrics, they are rarely investigated simultaneously within the same individuals. In those with schizophrenia, the magnitude of purported biomarkers is complicated by comorbid diagnoses, medications and other treatments. Here, we argue three points. First, we reiterate the importance of assessing multiple biomarkers simultaneously. Second, we argue that investigating biomarkers in those with schizophrenia-related traits (schizotypy) in the general population can accelerate progress in understanding the mechanisms of schizophrenia. We focus on biomarkers of sensory and working memory in schizophrenia and their smaller effects in individuals with nonclinical schizotypy. Third, we note irregularities across research domains leading to the current situation in which there is a preponderance of data on auditory sensory memory and visual working memory, but markedly less in visual (iconic) memory and auditory working memory, particularly when focusing on schizotypy where data are either scarce or inconsistent. Together, this review highlights opportunities for researchers without access to clinical populations to address gaps in knowledge. We conclude by highlighting the theory that early sensory memory deficits contribute negatively to working memory and vice versa. This presents a mechanistic perspective where biomarkers may interact with one another and impact schizophrenia-related symptoms.
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Affiliation(s)
- Sarah M. Haigh
- Department of Psychology, Center for Integrative Neuroscience, Programs in Cognitive and Brain Sciences, and Neuroscience, University of Nevada, Reno, Nevada, USA
| | - Marian E. Berryhill
- Department of Psychology, Center for Integrative Neuroscience, Programs in Cognitive and Brain Sciences, and Neuroscience, University of Nevada, Reno, Nevada, USA
| | - Alexandrea Kilgore-Gomez
- Department of Psychology, Center for Integrative Neuroscience, Programs in Cognitive and Brain Sciences, and Neuroscience, University of Nevada, Reno, Nevada, USA
| | - Michael Dodd
- Department of Psychology, University of Nebraska, Lincoln, Nebraska, USA
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Liu XQ, Ji XY, Weng X, Zhang YF. Artificial intelligence ecosystem for computational psychiatry: Ideas to practice. World J Meta-Anal 2023; 11:79-91. [DOI: 10.13105/wjma.v11.i4.79] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/18/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms. One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable. This may help researchers develop more effective treatments and interventions for mental health problems. This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry. The artificial intelligence ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources, such as brain imaging, genetics, and behavioral experiments, to obtain a more complete understanding of mental health conditions. Through the process of data preprocessing, training, and testing, the data that are required for model building can be prepared. By using machine learning, neural networks, artificial intelligence, and other methods, researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection. As we move progress in this field, it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.
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Affiliation(s)
- Xin-Qiao Liu
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xin-Yu Ji
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xing Weng
- Huzhou Educational Science & Research Center, Huzhou 313000, Zhejiang Province, China
| | - Yi-Fan Zhang
- School of Education, Tianjin University, Tianjin 300350, China
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Haumann NT, Petersen B, Friis Andersen AS, Faulkner KF, Brattico E, Vuust P. Mismatch negativity as a marker of music perception in individual cochlear implant users: A spike density component analysis study. Clin Neurophysiol 2023; 148:76-92. [PMID: 36822119 DOI: 10.1016/j.clinph.2023.01.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 01/19/2023] [Accepted: 01/24/2023] [Indexed: 02/10/2023]
Abstract
OBJECTIVE Ninety percent of cochlear implant (CI) users are interested in improving their music perception. However, only few objective behavioral and neurophysiological tests have been developed for tracing the development of music discrimination skills in CI users. In this study, we aimed to obtain an accurate individual mismatch negativity (MMN) marker that could predict behavioral auditory discrimination thresholds. METHODS We measured the individual MMN response to four magnitudes of deviations in four different musical features (intensity, pitch, timbre, and rhythm) in a rare sample of experienced CI users and a control sample of normally hearing participants. We applied a recently developed spike density component analysis (SCA), which can suppress confounding alpha waves, and contrasted it with previously proposed methods. RESULTS Statistically detected individual MMN predicted attentive sound discrimination ability with high accuracy: for CI users 89.2% (278/312 cases) and for controls 90.5% (384/424 cases). As expected, MMN was detected for fewer CI users when the sound deviants were of smaller magnitude. CONCLUSIONS The findings support the use of MMN responses in individual CI users as a diagnostic tool for testing music perception. SIGNIFICANCE For CI users, the new SCA method provided more accurate and replicable diagnostic detections than preceding state-of-the-art.
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Affiliation(s)
- Niels Trusbak Haumann
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus/Aalborg, Universitetsbyen 3, 8000 Aarhus C, Denmark.
| | - Bjørn Petersen
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus/Aalborg, Universitetsbyen 3, 8000 Aarhus C, Denmark
| | - Anne Sofie Friis Andersen
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus/Aalborg, Universitetsbyen 3, 8000 Aarhus C, Denmark
| | | | - Elvira Brattico
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus/Aalborg, Universitetsbyen 3, 8000 Aarhus C, Denmark
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus/Aalborg, Universitetsbyen 3, 8000 Aarhus C, Denmark
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Balasubramanian K, Ramya K, Gayathri Devi K. Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals. Cogn Neurodyn 2023; 17:133-151. [PMID: 36704627 PMCID: PMC9871147 DOI: 10.1007/s11571-022-09817-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/23/2022] [Accepted: 04/27/2022] [Indexed: 02/01/2023] Open
Abstract
Schizophrenia is a chronic mental disorder that impairs a person's thinking capacity, feelings and emotions, behavioural traits, etc., Emotional distortions, delusions, hallucinations, and incoherent speech are all some of the symptoms of schizophrenia, and cause disruption of routine activities. Computer-assisted diagnosis of schizophrenia is significantly needed to give its patients a higher quality of life. Hence, an improved adaptive neuro-fuzzy inference system based on the Hybrid Grey Wolf-Bat Algorithm for accurate prediction of schizophrenia from multi-channel EEG signals is presented in this study. The EEG signals are pre-processed using a Butterworth band pass filter and wICA initially, from which statistical, time-domain, frequency-domain, and spectral features are extracted. Discriminating features are selected using the ReliefF algorithm and are then forwarded to ANFIS for classification into either schizophrenic or normal. ANFIS is optimized by the Hybrid Grey Wolf-Bat Algorithm (HWBO) for better efficiency. The method is experimented on two separate EEG datasets-1 and 2, demonstrating an accuracy of 99.54% and 99.35%, respectively, with appreciable F1-score and MCC. Further experiments reveal the efficiency of the Hybrid Wolf-Bat algorithm in optimizing the ANFIS parameters when compared with traditional ANFIS model and other proven algorithms like genetic algorithm-ANFIS, particle optimization-ANFIS, crow search optimization algorithm-ANFIS and ant colony optimization algorithm-ANFIS, showing high R2 value and low RSME value. To provide a bias free classification, tenfold cross validation is performed which produced an accuracy of 97.8% and 98.5% on the two datasets respectively. Experimental outcomes demonstrate the superiority of the Hybrid Grey Wolf-Bat Algorithm over the similar techniques in predicting schizophrenia.
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Affiliation(s)
| | - K. Ramya
- PA College of Engineering and Technology, Pollachi, India
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Montazeri M, Montazeri M, Bahaadinbeigy K, Montazeri M, Afraz A. Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review. Health Sci Rep 2022; 6:e962. [PMID: 36589632 PMCID: PMC9795991 DOI: 10.1002/hsr2.962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 12/29/2022] Open
Abstract
Background and Aim Schizophrenia and bipolar disorder (BD) are critical and high-risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease. Methods A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers. Results In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full-text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity. Conclusion ML can precisely predict results and assist in making clinical decisions-concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research.
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Affiliation(s)
- Mahdieh Montazeri
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran,Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Mitra Montazeri
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Kambiz Bahaadinbeigy
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Mohadeseh Montazeri
- Department of Computer, Faculty of FatimahKerman Branch Technical and Vocational UniversityKermanIran
| | - Ali Afraz
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
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Qi YY, Guo ZH, Guo XT, Guan RR, Luo B, Sun JW, Wang M, Li HW, Chen L. Evidence for predictions established by phantom sound. Neuroimage 2022; 264:119766. [PMID: 36435344 DOI: 10.1016/j.neuroimage.2022.119766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/24/2022] [Accepted: 11/22/2022] [Indexed: 11/24/2022] Open
Abstract
Predictions, the bridge between the internal and external worlds, are established by prior experience and updated by sensory stimuli. Responses to omitted but unexpected stimuli, known as omission responses, can break the one-to-one mapping of stimulus-response and can expose predictions established by the preceding stimulus built up. While research into exogenous predictions (driven by external stimuli) is often reported, that into endogenous predictions (driven by internal percepts) is rarely available in the literature. Here, we report evidence for endogenous predictions established by the Zwicker tone illusion, a phantom pure-tone-like auditory percept following notch noises. We found that MMN, P300, and theta oscillations could be recorded using an omission paradigm in subjects who can perceive Zwicker tone illusions, but could not in those who cannot. The MMN and P300 responses relied on attention, but theta oscillations did not. In-depth analysis shows that an increase in single-trial theta power, including total and induced theta, with the endogenous prediction, is lateralized to the left frontal brain areas. Our study depicts that the brain automatically analyzes internal perception, progressively establishes predictions and yields prediction errors in the left frontal region when a violation occurs.
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O'Reilly JA. Recurrent Neural Network Model of Human Event-related Potentials in Response to Intensity Oddball Stimulation. Neuroscience 2022; 504:63-74. [DOI: 10.1016/j.neuroscience.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/27/2022] [Accepted: 10/03/2022] [Indexed: 10/31/2022]
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Huang J, Zhao Y, Qu W, Tian Z, Tan Y, Wang Z, Tan S. Automatic recognition of schizophrenia from facial videos using 3D convolutional neural network. Asian J Psychiatr 2022; 77:103263. [PMID: 36152565 DOI: 10.1016/j.ajp.2022.103263] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/22/2022] [Accepted: 09/14/2022] [Indexed: 11/17/2022]
Abstract
Schizophrenia affects patients and their families and society because of chronic impairments in cognition, behavior, and emotion. However, its clinical diagnosis mainly depends on the clinicians' knowledge of the patients' symptoms. Other auxiliary diagnostic methods such as MRI and EEG are cumbersome and time-consuming. Recently, the convolutional neural network (CNN) has been applied to the auxiliary diagnosis of psychiatry. Hence, in this study, a method based on deep learning and facial videos is proposed for the rapid detection of schizophrenia. Herein, 125 videos from 125 schizophrenic patients and 75 videos from 75 healthy controls based on emotional stimulation tasks were obtained. The video preprocessing included the experiment clips extraction, face detection, facial region cropping, resizing to 500 × 500 pixel size, and uniform sampling of 100 frames. The preprocessed facial videos were used to train the Resnet18_3D. We utilized ten-fold cross-validation, and held-out testing set to evaluate the model with the accuracy, the precision, the sensitivity, the specificity, the balanced accuracy, and the AUC. The Resnet18_3D trained on Film_order achieved the best performance with accuracy, sensitivity, specificity, balanced accuracy, and AUC of 89.00%, 96.80%, 76.00%, 86.40% and 0.9397. The neural network model indeed recognizes healthy controls and schizophrenic patients through the changes in the area of the face. The results show that facial video under emotional stimulation can be used to classify schizophrenic patients and help clinicians with diagnosis in the clinical environment. Among the different types of stimuli, the video stimuli with fixed emotional order showed the best classification performance.
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Affiliation(s)
- Jie Huang
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Yanli Zhao
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Wei Qu
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Zhanxiao Tian
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Yunlong Tan
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Zhiren Wang
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Shuping Tan
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China.
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Barros C, Roach B, Ford JM, Pinheiro AP, Silva CA. From Sound Perception to Automatic Detection of Schizophrenia: An EEG-Based Deep Learning Approach. Front Psychiatry 2022; 12:813460. [PMID: 35250651 PMCID: PMC8892210 DOI: 10.3389/fpsyt.2021.813460] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 12/31/2021] [Indexed: 12/27/2022] Open
Abstract
Deep learning techniques have been applied to electroencephalogram (EEG) signals, with promising applications in the field of psychiatry. Schizophrenia is one of the most disabling neuropsychiatric disorders, often characterized by the presence of auditory hallucinations. Auditory processing impairments have been studied using EEG-derived event-related potentials and have been associated with clinical symptoms and cognitive dysfunction in schizophrenia. Due to consistent changes in the amplitude of ERP components, such as the auditory N100, some have been proposed as biomarkers of schizophrenia. In this paper, we examine altered patterns in electrical brain activity during auditory processing and their potential to discriminate schizophrenia and healthy subjects. Using deep convolutional neural networks, we propose an architecture to perform the classification based on multi-channels auditory-related EEG single-trials, recorded during a passive listening task. We analyzed the effect of the number of electrodes used, as well as the laterality and distribution of the electrical activity over the scalp. Results show that the proposed model is able to classify schizophrenia and healthy subjects with an average accuracy of 78% using only 5 midline channels (Fz, FCz, Cz, CPz, and Pz). The present study shows the potential of deep learning methods in the study of impaired auditory processing in schizophrenia with implications for diagnosis. The proposed design can provide a base model for future developments in schizophrenia research.
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Affiliation(s)
- Carla Barros
- Psychological Neurosciences Lab, Psychology Research Center (CIPsi), School of Psychology, University of Minho, Braga, Portugal
| | - Brian Roach
- Psychiatry Service, San Francisco Veteran Affairs Medical Center (VAMC), San Francisco, CA, United States
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Judith M. Ford
- Psychiatry Service, San Francisco Veteran Affairs Medical Center (VAMC), San Francisco, CA, United States
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Ana P. Pinheiro
- Psychological Neurosciences Lab, Psychology Research Center (CIPsi), School of Psychology, University of Minho, Braga, Portugal
- Research Center for Psychological Science (CICPSI), Faculdade de Psicologia, Universidade de Lisboa, Lisbon, Portugal
| | - Carlos A. Silva
- Center for MicroElectromechanical Systems (CMEMS-UMinho), University of Minho, Guimarães, Portugal
- LABBELS - Associate Laboratory, Guimarães, Portugal
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Khare SK, Bajaj V. A hybrid decision support system for automatic detection of Schizophrenia using EEG signals. Comput Biol Med 2021; 141:105028. [PMID: 34836626 DOI: 10.1016/j.compbiomed.2021.105028] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 11/09/2021] [Accepted: 11/10/2021] [Indexed: 11/03/2022]
Abstract
BACKGROUND Schizophrenia (SCZ) is a serious neurological condition in which people suffer with distorted perception of reality. SCZ may result in a combination of delusions, hallucinations, disordered thinking, and behavior. This causes permanent disability and hampers routine functioning. Trained neurologists use interviewing and visual inspection techniques for the detection and diagnosis of SCZ. These techniques are manual, time-consuming, subjective, and error-prone. Therefore, there is a need to develop an automatic model for SCZ classification. The aim of this study is to develop an automated SCZ classification model using electroencephalogram (EEG) signals. The EEG signals can capture the changes in neural dynamics of human cognition during SCZ. METHOD Based on the nature of the SCZ condition, the EEG signals must be examined. For accurate interpretation of EEG signals during SCZ, an automated model integrating a robust variational mode decomposition (RVMD) and an optimized extreme learning machine (OELM) classifier is developed. Traditional VMD suffers from noisy mode generation, mode duplication, under segmentation, and mode discarding. These problems are suppressed in RVMD by automating the selection of quadratic penalty factor (α) and a number of modes (L). The hyperparameters (HPM) of the OELM classifier are automatically selected to ensure maximum accuracy for each mode without overfitting or underfitting. For the selection of α and L in RVMD and HPM in the OELM classifier, a whale optimization algorithm is used. The root mean square error is minimized for RVMD and classification accuracy of each mode is maximized for the OELM classifier. The EEG signals of three conditions performing basic sensory tasks have been analyzed to detect SCZ. RESULTS The Kruskal Wallis test is used to select different features extracted from the modes produced by RVMD. An OELM classifier is tested using a ten-fold cross-validation technique. An accuracy, precision, specificity, F-1 measure, sensitivity, and Cohen's Kappa of 92.93%, 93.94%, 91.06% 94.07%, 97.15%, and 85.32% are obtained. CONCLUSION The third mode's chaotic features helped to capture the significant changes that occurred during the SCZ state. The findings of the RVMD-OELM-based hybrid decision support system can help neuro-experts for the accurate identification of SCZ in real-time scenarios.
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Affiliation(s)
- Smith K Khare
- Electronics and Communication Discipline, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP, 482005, India
| | - Varun Bajaj
- Electronics and Communication Discipline, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP, 482005, India.
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Najafzadeh H, Esmaeili M, Farhang S, Sarbaz Y, Rasta SH. Automatic classification of schizophrenia patients using resting-state EEG signals. Phys Eng Sci Med 2021; 44:855-870. [PMID: 34370274 DOI: 10.1007/s13246-021-01038-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 07/26/2021] [Indexed: 12/17/2022]
Abstract
Schizophrenia is one of the serious mental disorders, which can suspend the patient from all aspects of life. In this paper we introduced a new method based on the adaptive neuro fuzzy inference system (ANFIS) to classify recorded electroencephalogram (EEG) signals from 14 schizophrenia patients and 14 age-matched control participants. Sixteen EEG channels from 19 main channels that had the most discriminatory information were selected. Possible artifacts of these channels were eliminated with the second-order Butterworth filter. Four features, Shannon entropy, spectral entropy, approximate entropy, and the absolute value of the highest slope of autoregressive coefficients (AVLSAC) were extracted from each selected EEG channel in 5 frequency sub-bands, Delta, Theta, Alpha, Beta, and Gamma. Forty-six features were introduced among the 640 possible ones, and the results included accuracies of near 100%, 98.89%, and 95.59% for classifiers of ANFIS, support vector machine (SVM), and artificial neural network (ANN), respectively. Also, our results show that channels of alpha of O1, theta and delta of Fz and F8, and gamma of Fp1 have the most discriminatory information between the two groups. The performance of our proposed model was also compared with the recently published approaches. This study led to presenting a new decision support system (DSS) that can receive a person's EEG signal and separates the schizophrenia patient and healthy subjects with high accuracy.
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Affiliation(s)
- Hossein Najafzadeh
- Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Golgasht Ave, 51666, Tabriz, Iran
| | - Mahdad Esmaeili
- Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Golgasht Ave, 51666, Tabriz, Iran
| | - Sara Farhang
- Department of Psychiatry, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Yashar Sarbaz
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Daneshgah St., Tabriz, Iran.
| | - Seyed Hossein Rasta
- Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Golgasht Ave, 51666, Tabriz, Iran. .,Department of Medical Physics, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran. .,Department of Biomedical Physics, School of Medical Sciences, University of Aberdeen, Aberdeen, AB25 ZD, UK.
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13
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Lai JW, Ang CKE, Acharya UR, Cheong KH. Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification. Int J Environ Res Public Health 2021; 18:6099. [PMID: 34198829 PMCID: PMC8201065 DOI: 10.3390/ijerph18116099] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.
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Affiliation(s)
- Joel Weijia Lai
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
| | - Candice Ke En Ang
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
- MOH Holdings Pte Ltd, 1 Maritime Square, Singapore 099253, Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Clementi 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Kang Hao Cheong
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
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14
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Masoumi PM, Sadjedi H. Trial-Specific Feature Performance on Single-Channel Auditory Mismatch Negativity Detection. IEEE J Biomed Health Inform 2021; 25:1062-1069. [PMID: 33108302 DOI: 10.1109/jbhi.2020.3034295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Successful detection of uncommon events is vital in the survival of an organism. Specifically, the study of neuro-sensory detection lends itself widely to understanding the human brain. Mismatch Negativity (MMN) is an important Event-Related Potential (ERP) response to an oddball stimulus which is preceded by repeated homogeneous stimulation. MMN is associated with perceptual learning and medical diagnostics among other applications. Currently, MMN detection relies on visual inspection of ERPs by skilled clinicians which makes for a costly, slow and subjective tool. In this paper, we use MMN to quantify the discriminative abilities of healthy or diagnosed subjects. We introduce a novel algorithmic method to extract and select important trial-specific features for discriminating standard from deviant responses. We utilize machine learning and classification approaches to evaluate our novel model using single-subject trial data while minimizing the number of necessary selection features provided by statistical test parameters and Genetic Algorithm (GA). In this work, a large variety of methods with 27 subjects, hundreds of trials and electrode counts compete for the definitive discrimination of MMN events. Our model requires only one EEG channel, a single subject and as low as five deviant tones. The results show statistically significant detection improvement over the traditional methods while maximizing resource economy.
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15
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Takahashi J, Miura K, Morita K, Fujimoto M, Miyata S, Okazaki K, Matsumoto J, Hasegawa N, Hirano Y, Yamamori H, Yasuda Y, Makinodan M, Kasai K, Ozaki N, Onitsuka T, Hashimoto R. Effects of age and sex on eye movement characteristics. Neuropsychopharmacol Rep 2021; 41:152-158. [PMID: 33615745 PMCID: PMC8340818 DOI: 10.1002/npr2.12163] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 01/20/2021] [Accepted: 01/21/2021] [Indexed: 02/02/2023] Open
Abstract
Abnormal eye movements are often associated with psychiatric disorders. Eye movements are sensorimotor functions of the brain, and aging and sex would affect their characteristics. A precise understanding of normal eye movements is required to distinguish disease-related abnormalities from natural differences associated with aging or sex. To date, there is no multicohort study examining age-related dependency and sex effects of eye movements in healthy, normal individuals using large samples to ensure the robustness and reproducibility of the results. In this study, we aimed to provide findings showing the impact of age and sex on eye movement measures. The present study used eye movement measures of more than seven hundred healthy individuals from three large independent cohorts. We herein evaluated eye movement measures quantified by using a set of standard eye movement tests that have been utilized for the examination of patients with schizophrenia. We assessed the statistical significance of the effects of age and sex and its reproducibility across cohorts. We found that 4-18 out of 35 eye movement measures were significantly correlated with age, depending on the cohort, and that 10 of those, which are related to the fixation and motor control of smooth pursuit and saccades, showed high reproducibility. On the other hand, the effects of sex, if any, were less reproducible. The present results suggest that we should take age into account when we evaluate abnormalities in eye movements.
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Affiliation(s)
- Junichi Takahashi
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Kentaro Morita
- Department of Rehabilitation, University of Tokyo Hospital, Tokyo, Japan.,Department of Neuropsychiatry, University of Tokyo, Tokyo, Japan
| | - Michiko Fujimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan.,Department of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Seiko Miyata
- Department of Psychiatry, Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Kosuke Okazaki
- Department of Psychiatry, Nara Medical University, Kashihara, Japan
| | - Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Naomi Hasegawa
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hidenaga Yamamori
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan.,Department of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Japan.,Japan Community Health Care Organization Osaka Hospital, Osaka, Japan
| | - Yuka Yasuda
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan.,Medical Corporation Foster, Osaka, Japan
| | - Manabu Makinodan
- Department of Psychiatry, Nara Medical University, Kashihara, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, University of Tokyo, Tokyo, Japan.,The International Research Center for Neurointelligence (WPI-IRCN) at University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Norio Ozaki
- Department of Psychiatry, Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Toshiaki Onitsuka
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan.,Department of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Japan
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16
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Barros C, Silva CA, Pinheiro AP. Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls. Artif Intell Med 2021; 114:102039. [PMID: 33875158 DOI: 10.1016/j.artmed.2021.102039] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 12/11/2020] [Accepted: 02/16/2021] [Indexed: 01/10/2023]
Abstract
The complexity and heterogeneity of schizophrenia symptoms challenge an objective diagnosis, which is typically based on behavioral and clinical manifestations. Moreover, the boundaries of schizophrenia are not precisely demarcated from other nosologic categories, such as bipolar disorder. The early detection of schizophrenia can lead to a more effective treatment, improving patients' quality of life. Over the last decades, hundreds of studies aimed at specifying the neurobiological mechanisms that underpin clinical manifestations of schizophrenia, using techniques such as electroencephalography (EEG). Changes in event-related potentials of the EEG have been associated with sensory and cognitive deficits and proposed as biomarkers of schizophrenia. Besides contributing to a more effective diagnosis, biomarkers can be crucial to schizophrenia onset prediction and prognosis. However, any proposed biomarker requires substantial clinical research to prove its validity and cost-effectiveness. Fueled by developments in computational neuroscience, automatic classification of schizophrenia at different stages (prodromal, first episode, chronic) has been attempted, using brain imaging pattern recognition methods to capture differences in functional brain activity. Advanced learning techniques have been studied for this purpose, with promising results. This review provides an overview of recent machine learning-based methods for schizophrenia classification using EEG data, discussing their potentialities and limitations. This review is intended to serve as a starting point for future developments of effective EEG-based models that might predict the onset of schizophrenia, identify subjects at high-risk of psychosis conversion or differentiate schizophrenia from other disorders, promoting more effective early interventions.
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Affiliation(s)
- Carla Barros
- Center for Research in Psychology (CIPsi), School of Psychology, University of Minho, Braga, Portugal
| | - Carlos A Silva
- Center for Microelectromechanical Systems (CMEMS), School of Engineering, University of Minho, Guimarães, Portugal
| | - Ana P Pinheiro
- Center for Research in Psychology (CIPsi), School of Psychology, University of Minho, Braga, Portugal; CICPSI, Faculdade de Psicologia, Universidade de Lisboa, Lisboa, Portugal.
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17
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Singh K, Singh S, Malhotra J. Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients. Proc Inst Mech Eng H 2020; 235:167-184. [PMID: 33124526 DOI: 10.1177/0954411920966937] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Schizophrenia is a fatal mental disorder, which affects millions of people globally by the disturbance in their thinking, feeling and behaviour. In the age of the internet of things assisted with cloud computing and machine learning techniques, the computer-aided diagnosis of schizophrenia is essentially required to provide its patients with an opportunity to own a better quality of life. In this context, the present paper proposes a spectral features based convolutional neural network (CNN) model for accurate identification of schizophrenic patients using spectral analysis of multichannel EEG signals in real-time. This model processes acquired EEG signals with filtering, segmentation and conversion into frequency domain. Then, given frequency domain segments are divided into six distinct spectral bands like delta, theta-1, theta-2, alpha, beta and gamma. The spectral features including mean spectral amplitude, spectral power and Hjorth descriptors (Activity, Mobility and Complexity) are extracted from each band. These features are independently fed to the proposed spectral features-based CNN and long short-term memory network (LSTM) models for classification. This work also makes use of raw time-domain and frequency-domain EEG segments for classification using temporal CNN and spectral CNN models of same architectures respectively. The overall analysis of simulation results of all models exhibits that the proposed spectral features based CNN model is an efficient technique for accurate and prompt identification of schizophrenic patients among healthy individuals with average classification accuracies of 94.08% and 98.56% for two different datasets with optimally small classification time.
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Affiliation(s)
- Kuldeep Singh
- Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India
| | - Sukhjeet Singh
- Machinery Fault Diagnostics & Signal Processing Laboratory, Department of Mechanical Engineering, University Institute of Technology, Guru Nanak Dev University, Amritsar, Punjab, India
| | - Jyoteesh Malhotra
- Department of Electronics and Communication Engineering, Guru Nanak Dev University, Jalandhar, Punjab, India
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18
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Johnson EL, Kam JWY, Tzovara A, Knight RT. Insights into human cognition from intracranial EEG: A review of audition, memory, internal cognition, and causality. J Neural Eng 2020; 17:051001. [PMID: 32916678 PMCID: PMC7731730 DOI: 10.1088/1741-2552/abb7a5] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
By recording neural activity directly from the human brain, researchers gain unprecedented insight into how neurocognitive processes unfold in real time. We first briefly discuss how intracranial electroencephalography (iEEG) recordings, performed for clinical practice, are used to study human cognition with the spatiotemporal and single-trial precision traditionally limited to non-human animal research. We then delineate how studies using iEEG have informed our understanding of issues fundamental to human cognition: auditory prediction, working and episodic memory, and internal cognition. We also discuss the potential of iEEG to infer causality through the manipulation or 'engineering' of neurocognitive processes via spatiotemporally precise electrical stimulation. We close by highlighting limitations of iEEG, potential of burgeoning techniques to further increase spatiotemporal precision, and implications for future research using intracranial approaches to understand, restore, and enhance human cognition.
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Affiliation(s)
- Elizabeth L Johnson
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
- Life-Span Cognitive Neuroscience Program, Institute of Gerontology, Wayne State University, United States of America
| | - Julia W Y Kam
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
- Department of Psychology, University of Calgary, Canada
- Hotchkiss Brain Institute, University of Calgary, Canada
| | - Athina Tzovara
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
- Institute for Computer Science, University of Bern, Switzerland
- Sleep Wake Epilepsy Center | NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
- Department of Psychology, University of California, Berkeley, United States of America
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19
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Dark F, Newman E, Gore-Jones V, De Monte V, Garrido MI, Dzafic I. Randomised controlled trial of Compensatory Cognitive Training and a Computerised Cognitive Remediation programme. Trials 2020; 21:810. [PMID: 32993754 PMCID: PMC7526389 DOI: 10.1186/s13063-020-04743-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Accepted: 09/16/2020] [Indexed: 11/25/2022] Open
Abstract
Background Compensation and adaptation therapies have been developed to improve community functioning via improving neurocognitive abilities in people with schizophrenia. Various modes of delivering compensation and adaptation therapies have been found to be effective. The aim of this trial is to compare two different cognitive interventions, Compensatory Cognitive Training (CCT) and Computerised Interactive Remediation of Cognition–Training for Schizophrenia (CIRCuiTS). The trial also aims to identify if mismatch negativity (MMN) can predict an individual’s response to the compensation and adaptation programmes. Methods This study will use a randomised, controlled trial of two cognitive interventions to compare the impact of these programmes on measures of neurocognition and function. One hundred clinically stable patients aged between 18 and 65 years with a diagnosis of a schizophrenia spectrum disorder will be recruited. Participants will be randomised to either the CCT or the CIRCuiTS therapy groups. The outcome measures are neurocognition (BACS), subjective sense of cognitive impairment (SSTICS), social functioning (SFS), and MMN (measured by EEG) in people with schizophrenia spectrum disorders. Discussion This trial will determine whether different approaches to addressing the cognitive deficits found in schizophrenia spectrum disorders are of comparable benefit using the outcome measures chosen. This has implications for services where cost and lack of computer technology limit the implementation and dissemination of interventions to address cognitive impairment in routine practice. The trial will contribute to the emerging evidence of MMN as a predictor of response to cognitive interventions. Trial registration Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12618000161224. Registered on 2 February 2018. Protocol version: 4.0, 18 June 2018.
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Affiliation(s)
- Frances Dark
- Metro South Addiction and Mental Health Services, 228 Logan Road, Woolloongabba, Queensland, Australia
| | - Ellie Newman
- St Kilda Road Clinic, Community Adult Mental Health, Alfred Psychiatry, Melbourne, Australia
| | - Victoria Gore-Jones
- Metro South Addiction and Mental Health Services, 228 Logan Road, Woolloongabba, Queensland, Australia.
| | - Veronica De Monte
- Metro South Addiction and Mental Health Services, 228 Logan Road, Woolloongabba, Queensland, Australia
| | - Marta I Garrido
- Queensland Brain Institute, Centre for Advanced Imaging, Centre of Excellence for Integrative Brain Function, The University of Queensland, Brisbane, Queensland, Australia.,Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
| | - Ilvana Dzafic
- Queensland Brain Institute, Centre for Advanced Imaging, Centre of Excellence for Integrative Brain Function, The University of Queensland, Brisbane, Queensland, Australia.,Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
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20
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Taylor JA, Larsen KM, Garrido MI. Multi-dimensional predictions of psychotic symptoms via machine learning. Hum Brain Mapp 2020; 41:5151-5163. [PMID: 32870535 PMCID: PMC7670649 DOI: 10.1002/hbm.25181] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/09/2020] [Accepted: 08/09/2020] [Indexed: 11/10/2022] Open
Abstract
The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have primarily focused on dichotomous patient-control classification, we predict the severity of each individual symptom on a continuum. We applied machine learning regression within a multi-modal fusion framework to fMRI and behavioural data acquired during an auditory oddball task in 80 schizophrenia patients. Brain activity was highly predictive of some, but not all symptoms, namely hallucinations, avolition, anhedonia and attention. Critically, each of these symptoms was associated with specific functional alterations across different brain regions. We also found that modelling symptoms as an ensemble of subscales was more accurate, specific and informative than models which predict compound scores directly. In principle, this approach is transferrable to any psychiatric condition or multi-dimensional diagnosis.
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Affiliation(s)
- Jeremy A Taylor
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia.,Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia
| | - Kit M Larsen
- Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Victoria, Australia.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark.,Child and Adolescent Mental Health Care, Mental Health Services Capital Region Copenhagen, University of Copenhagen, Copenhagen, Denmark
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia.,Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Victoria, Australia.,Centre for Advanced Imaging, University of Queensland, St Lucia, Queensland, Australia
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21
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Baradits M, Bitter I, Czobor P. Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls. Psychiatry Res 2020; 288:112938. [PMID: 32315875 DOI: 10.1016/j.psychres.2020.112938] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 03/18/2020] [Accepted: 03/22/2020] [Indexed: 12/21/2022]
Abstract
Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning techniques, we explored whether abnormalities in microstates can be used to classify patients with schizophrenia and healthy controls. We applied multivariate pattern analysis of microstate features to create a specified feature set to represent microstate characteristics. Machine learning approaches using these features for classification of patients with schizophrenia were compared with prior EEG based machine learning studies. Our microstate segmentation in both patients with schizophrenia and healthy controls yielded topographies that were similar to the normative database established earlier by Koenig et al. Our machine learning model was based on large sample size, low number of features and state-of-art K-fold cross-validation technique. The multivariate analysis revealed three patterns of correlated features, which yielded an AUC of 0.84 for the group separation (accuracy: 82.7%, sensitivity/specificity: 83.5%/85.3%). Microstate segmentation of resting state EEG results in informative features to discriminate patients with schizophrenia from healthy individuals. Moreover, alteration in microstate measures may represent disturbed activity of networks in patients with schizophrenia.
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Affiliation(s)
- Máté Baradits
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary.
| | - István Bitter
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Pál Czobor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
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22
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Schwartz F, Epinat-Duclos J, Léone J, Poisson A, Prado J. Neural representations of transitive relations predict current and future math calculation skills in children. Neuropsychologia 2020; 141:107410. [PMID: 32097661 DOI: 10.1016/j.neuropsychologia.2020.107410] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 01/14/2020] [Accepted: 02/21/2020] [Indexed: 12/27/2022]
Abstract
A large body of evidence suggests that math learning in children is built upon innate mechanisms for representing numerical quantities in the intraparietal sulcus (IPS). Learning math, however, is about more than processing quantitative information. It is also about understanding relations between quantities and making inferences based on these relations. Consistent with this idea, recent behavioral studies suggest that the ability to process transitive relations (A > B, B > C, therefore A > C) may contribute to math skills in children. Here we used fMRI coupled with a longitudinal design to determine whether the neural processing of transitive relations in children could predict their current and future math skills. At baseline (T1), children (n = 31) processed transitive relations in an MRI scanner. Math skills were measured at T1 and again 1.5 years later (T2). Using a machine learning approach with cross-validation, we found that activity associated with the representation of transitive relations in the IPS predicted math calculation skills at both T1 and T2. Our study highlights the potential of neurobiological measures of transitive reasoning for forecasting math skills in children, providing additional evidence for a link between this type of reasoning and math learning.
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23
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Abstract
BACKGROUND This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. METHODS We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review. RESULTS Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering. CONCLUSIONS Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.
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Affiliation(s)
- Adrian B R Shatte
- Federation University, School of Science, Engineering & Information Technology,Melbourne,Australia
| | - Delyse M Hutchinson
- Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health,Geelong,Australia
| | - Samantha J Teague
- Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health,Geelong,Australia
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24
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Luo Y, Zhang J, Wang C, Zhao X, Chang Q, Wang H, Wang C. Discriminating schizophrenia disease progression using a P50 sensory gating task with dense-array EEG, clinical assessments, and cognitive tests. Expert Rev Neurother 2019; 19:459-470. [DOI: 10.1080/14737175.2019.1601558] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Yu Luo
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100083, China
- Hefei Innovation Research Institute, Beihang University, Hefei, 100083, Anhui, China
| | - Jicong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100083, China
- Hefei Innovation Research Institute, Beihang University, Hefei, 100083, Anhui, China
| | - Changming Wang
- Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Xiaohui Zhao
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100083, China
- Hefei Innovation Research Institute, Beihang University, Hefei, 100083, Anhui, China
| | - Qi Chang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Hua Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100083, China
- Hefei Innovation Research Institute, Beihang University, Hefei, 100083, Anhui, China
| | - Chuanyue Wang
- Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
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Chen X, Zhang Q, Wang J, Xin Z, Chen J, Luo W. Combined machine learning and functional magnetic resonance imaging allows individualized prediction of high-altitude induced psychomotor impairment: The role of neural functionality in putamen and pallidum. Biosci Trends 2019; 13:98-104. [PMID: 30814403 DOI: 10.5582/bst.2019.01002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Hypoxia exposure during high-altitude expedition cause psychomotor impairment. Neuroimaging studies indicated that the impairment may be significantly associated with neuron loss and decreased regional homogeneity (ReHo) in several brain regions, suggesting the neural functionality in these regions may be utilized to predict psychomotor impairment under exposure. In this study, 69 subjects come from Shaanxi-Tibet immigrant cohort. Reaction time (RT) tasks were performed to measure the subject's psychomotor function before and after 2-year high-altitude exposure. For each individual, the RT differences between pre-exposure and post-exposure were calculated, which were referred to as "targets" in model establishment. Rs-fMRI data were acquired at the same time with RT tasks. For each individual, the map of ReHo alteration was generated, from which the patterns would be recognized. A pattern recognition procedure was utilized to train and test the predictive models. Two different cross-validation strategies were utilized to evaluate the model performance: leave-one-out cross-validation and four-fold cross-validation. For the models displaying significant R2 and MSE, weight maps were built. As a result, the predictive models were able to decode the changes of simple and recognition reaction time from the alterations of brain activation under the exposure. The regions with highest contributions to the predictions were bilateral putamen and bilateral pallidum, suggesting that predictions were mainly based on the patterns concentrated in these regions. This study was a proof of concept study designed to examine whether individual-level psychomotor impairment under high-altitude exposure could be predicted by a combination of pattern recognition approach and neuroimaging data.
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Affiliation(s)
- Xiaoming Chen
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University
| | - Qian Zhang
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University
| | - Jiye Wang
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University
| | - Zhenlong Xin
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University
| | - Jingyuan Chen
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University
| | - Wenjing Luo
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University
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Brown CA, Almarzouki AF, Brown RJ, Jones AKP. Neural representations of aversive value encoding in pain catastrophizers. Neuroimage 2018; 184:508-519. [PMID: 30243959 DOI: 10.1016/j.neuroimage.2018.09.052] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 08/14/2018] [Accepted: 09/18/2018] [Indexed: 02/08/2023] Open
Abstract
Chronic pain is exacerbated by maladaptive cognition such as pain catastrophizing (PC). Biomarkers of PC mechanisms may aid precision medicine for chronic pain. Here, we investigate EEG biomarkers using mass univariate and multivariate (machine learning) approaches. We test theoretical notions that PC results from a combination of augmented aversive-value encoding ("magnification") and persistent expectations of pain ("rumination"). Healthy individuals with high or low levels of PC underwent an experimental pain model involving nociceptive laser stimuli preceded by cues predicting forthcoming pain intensity. Analysis of EEG acquired during the cue and laser stimulation provided event-related potentials (ERPs) identifying spatially and temporally-extended neural representations associated with pain catastrophizing. Specifically, differential neural responses to cues predicting high vs. low intensity pain (i.e. aversive value encoding) were larger in the high PC group, largely originating from mid-cingulate and superior parietal cortex. Multivariate spatiotemporal EEG patterns evoked from cues with high aversive value selectively and significantly differentiated the high PC from low PC group (64.6% classification accuracy). Regression analyses revealed that neural patterns classifying groups could be partially predicted (R2 = 28%) from those neural patterns classifying the aversive value of cues. In contrast, behavioural and EEG analyses did not provide evidence that PC modifies more persistent effects of prior expectation on pain perception and nociceptive responses. These findings support the hypothesis of magnification of aversive value encoding but not persistent expression of expectation in pain catastrophizers. Multivariate patterns of aversive value encoding provide promising biomarkers of maladaptive cognitive responses to chronic pain that have future potential for psychological treatment development and clinical stratification.
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Affiliation(s)
- Christopher A Brown
- Department of Psychological Sciences, Institute of Psychology, Health and Society, University of Liverpool, Liverpool, United Kingdom; Human Pain Research Group, School of Biological Sciences, University of Manchester, Salford Royal NHS Foundation Trust, Salford, United Kingdom.
| | - Abeer F Almarzouki
- Physiology Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Richard J Brown
- School of Psychological Sciences, University of Manchester, Manchester, United Kingdom
| | - Anthony K P Jones
- Human Pain Research Group, School of Biological Sciences, University of Manchester, Salford Royal NHS Foundation Trust, Salford, United Kingdom
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