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Popiołek AK, Niznikiewicz MA, Borkowska A, Bieliński MK. Evaluation of Event-Related Potentials in Somatic Diseases - Systematic Review. Appl Psychophysiol Biofeedback 2024:10.1007/s10484-024-09642-5. [PMID: 38564137 DOI: 10.1007/s10484-024-09642-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Many somatic illnesses (e.g. hypertension, diabetes, pulmonary and cardiac diseases, hepatitis C, kidney and heart failure, HIV infection, Sjogren's disease) may impact central nervous system functions resulting in emotional, sensory, cognitive or even personality impairments. Event-related potential (ERP) methodology allows for monitoring neurocognitive processes and thus can provide a valuable window into these cognitive processes that are influenced, or brought about, by somatic disorders. The current review aims to present published studies on the relationships between somatic illness and brain function as assessed with ERP methodology, with the goal to discuss where this field of study is right now and suggest future directions.
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Affiliation(s)
- Alicja K Popiołek
- Department of Clinical Neuropsychology, Nicolaus Copernicus University in Toruń, Collegium Medicum in Bydgoszcz, Curie Sklodowskiej 9, 85-094, Bydgoszcz, Poland.
| | - Margaret A Niznikiewicz
- Medical Center, Harvard Medical School and Boston VA Healthcare System, Psychiatry 116a C/O R. McCarly 940 Belmont St, Brockton, MA, 02301, USA
| | - Alina Borkowska
- Department of Clinical Neuropsychology, Nicolaus Copernicus University in Toruń, Collegium Medicum in Bydgoszcz, Curie Sklodowskiej 9, 85-094, Bydgoszcz, Poland
| | - Maciej K Bieliński
- Department of Clinical Neuropsychology, Nicolaus Copernicus University in Toruń, Collegium Medicum in Bydgoszcz, Curie Sklodowskiej 9, 85-094, Bydgoszcz, Poland
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Rabiller G, Ip Z, Zarrabian S, Zhang H, Sato Y, Yazdan-Shahmorad A, Liu J. Type-2 Diabetes Alters Hippocampal Neural Oscillations and Disrupts Synchrony between the Hippocampus and Cortex. Aging Dis 2023:AD.2023.1106. [PMID: 38029397 DOI: 10.14336/ad.2023.1106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/06/2023] [Indexed: 12/01/2023] Open
Abstract
Type 2 diabetes mellitus (T2DM) increases the risk of neurological diseases, yet how brain oscillations change as age and T2DM interact is not well characterized. To delineate the age and diabetic effect on neurophysiology, we recorded local field potentials with multichannel electrodes spanning the somatosensory cortex and hippocampus (HPC) under urethane anesthesia in diabetic and normoglycemic control mice, at 200 and 400 days of age. We analyzed the signal power of brain oscillations, brain state, sharp wave associate ripples (SPW-Rs), and functional connectivity between the cortex and HPC. We found that while both age and T2DM were correlated with a breakdown in long-range functional connectivity and reduced neurogenesis in the dentate gyrus and subventricular zone, T2DM further slowed brain oscillations and reduced theta-gamma coupling. Age and T2DM also prolonged the duration of SPW-Rs and increased gamma power during SPW-R phase. Our results have identified potential electrophysiological substrates of hippocampal changes associated with T2DM and age. The perturbed brain oscillation features and diminished neurogenesis may underlie T2DM-accelerated cognitive impairment.
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Affiliation(s)
- Gratianne Rabiller
- Department of Neurological Surgery, University of California at San Francisco, San Francisco, CA, USA
- San Francisco VA medical Center, San Francisco, CA, USA
| | - Zachary Ip
- Departments of Bioengineering, University of Washington, Seattle, WA, USA
| | - Shahram Zarrabian
- Department of Neurological Surgery, University of California at San Francisco, San Francisco, CA, USA
- San Francisco VA medical Center, San Francisco, CA, USA
| | - Hongxia Zhang
- Department of Neurological Surgery, University of California at San Francisco, San Francisco, CA, USA
- San Francisco VA medical Center, San Francisco, CA, USA
| | - Yoshimichi Sato
- Department of Neurological Surgery, University of California at San Francisco, San Francisco, CA, USA
- San Francisco VA medical Center, San Francisco, CA, USA
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Azadeh Yazdan-Shahmorad
- Departments of Bioengineering, University of Washington, Seattle, WA, USA
- Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Jialing Liu
- Department of Neurological Surgery, University of California at San Francisco, San Francisco, CA, USA
- San Francisco VA medical Center, San Francisco, CA, USA
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Rabiller G, Ip Z, Zarrabian S, Zhang H, Sato Y, Yazdan-Shahmorad A, Liu J. Type-2 diabetes alters hippocampal neural oscillations and disrupts synchrony between hippocampus and cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.25.542288. [PMID: 37292743 PMCID: PMC10245872 DOI: 10.1101/2023.05.25.542288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Type 2 diabetes mellitus (T2DM) increases the risk of neurological diseases, yet how brain oscillations change as age and T2DM interact is not well characterized. To delineate the age and diabetic effect on neurophysiology, we recorded local field potentials with multichannel electrodes spanning the somatosensory cortex and hippocampus (HPC) under urethane anesthesia in diabetic and normoglycemic control mice, at 200 and 400 days of age. We analyzed the signal power of brain oscillations, brain state, sharp wave associate ripples (SPW-Rs), and functional connectivity between the cortex and HPC. We found that while both age and T2DM were correlated with a breakdown in long-range functional connectivity and reduced neurogenesis in the dentate gyrus and subventricular zone, T2DM further slowed brain oscillations and reduced theta-gamma coupling. Age and T2DM also prolonged the duration of SPW-Rs and increased gamma power during SPW-R phase. Our results have identified potential electrophysiological substrates of hippocampal changes associated with T2DM and age. The perturbed brain oscillation features and diminished neurogenesis may underlie T2DM-accelerated cognitive impairment.
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Wen D, Li R, Jiang M, Li J, Liu Y, Dong X, Saripan MI, Song H, Han W, Zhou Y. Multi-dimensional conditional mutual information with application on the EEG signal analysis for spatial cognitive ability evaluation. Neural Netw 2021; 148:23-36. [PMID: 35051867 DOI: 10.1016/j.neunet.2021.12.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 12/16/2021] [Accepted: 12/17/2021] [Indexed: 10/19/2022]
Abstract
This study aims to explore an effective method to evaluate spatial cognitive ability, which can effectively extract and classify the feature of EEG signals collected from subjects participating in the virtual reality (VR) environment; and evaluate the training effect objectively and quantitatively to ensure the objectivity and accuracy of spatial cognition evaluation, according to the classification results. Therefore, a multi-dimensional conditional mutual information (MCMI) method is proposed, which could calculate the coupling strength of two channels considering the influence of other channels. The coupled characteristics of the multi-frequency combination were transformed into multi-spectral images, and the image data were classified employing the convolutional neural networks (CNN) model. The experimental results showed that the multi-spectral image transform features based on MCMI are better in classification than other methods, and among the classification results of six band combinations, the best classification accuracy of Beta1-Beta2-Gamma combination is 98.3%. The MCMI characteristics on the Beta1-Beta2-Gamma band combination can be a biological marker for the evaluation of spatial cognition. The proposed feature extraction method based on MCMI provides a new perspective for spatial cognitive ability assessment and analysis.
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Affiliation(s)
- Dong Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
| | - Rou Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Mengmeng Jiang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Jingjing Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Yijun Liu
- School of Science, Yanshan University, Qinhuangdao, China
| | - Xianling Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde, China
| | - M Iqbal Saripan
- Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia
| | - Haiqing Song
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Wei Han
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Yanhong Zhou
- School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China.
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Liu Y, Xu X, Zhou Y, Xu J, Dong X, Li X, Yin S, Wen D. Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information. Cogn Neurodyn 2021; 15:987-997. [PMID: 34790266 PMCID: PMC8572246 DOI: 10.1007/s11571-021-09682-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/28/2021] [Accepted: 04/19/2021] [Indexed: 01/06/2023] Open
Abstract
This study aimed to find a good coupling feature extraction method to effectively analyze resting state EEG signals (rsEEG) of amnestic mild cognitive impairment(aMCI) with type 2 diabetes mellitus(T2DM) and normal control (NC) with T2DM. A method of EEG signal coupling feature extraction based on weight permutation conditional mutual information (WPCMI) was proposed in this research. With the WPCMI method, coupling feature strength of two time series in Alpha1, Alpha2, Beta1, Beta2 and Gamma bands for aMCI with T2DM and NC with T2DM could be extracted respectively. Then selected three frequency bands coupling feature matrix with the help of multi-spectral image transformation method to map it as spectral image characteristics. And finally classified these characteristics through the convolution neural network method(CNN). For aMCI with T2DM and NC with T2DM, the highest classification accuracy of 96%, 95%, 95% could be achieved respectively in the combination of three frequency bands (Alpha1, Alpha2, Gamma), (Beta1, Beta2 and Gamma) and (Alpha2, Beta1, Beta2). This WPCMI method highlighted the coupling dynamic characteristics of EEG signals, and its classification performance was better than all previous methods in aMCI with T2DM diagnosis field. WPCMI method could be used as an effective biomarker to distinguish EEG signals of aMCI with T2DM and NC with T2DM. The coupling feature extraction method used in this paper provided a new perspective for the EEG analysis of aMCI with T2DM.
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Affiliation(s)
- Yijun Liu
- School of Science, Yanshan University, Qinhuangdao, China
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Xiaodong Xu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Yanhong Zhou
- School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China
| | - Jian Xu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Xianling Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde, China
| | - Xiaoli Li
- The National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Shimin Yin
- Department of Neurology, The Rocket Force Hospital of Chinese People’s Liberation Army, Beijing, China
| | - Dong Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
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Jang KI, Kim S, Kim SY, Lee C, Chae JH. Machine Learning-Based Electroencephalographic Phenotypes of Schizophrenia and Major Depressive Disorder. Front Psychiatry 2021; 12:745458. [PMID: 34721112 PMCID: PMC8549692 DOI: 10.3389/fpsyt.2021.745458] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 09/14/2021] [Indexed: 12/03/2022] Open
Abstract
Background: Psychiatric diagnosis is formulated by symptomatic classification; disease-specific neurophysiological phenotyping could help with its fundamental treatment. Here, we investigated brain phenotyping in patients with schizophrenia (SZ) and major depressive disorder (MDD) by using electroencephalography (EEG) and conducted machine-learning-based classification of the two diseases by using EEG components. Materials and Methods: We enrolled healthy controls (HCs) (n = 30) and patients with SZ (n = 34) and MDD (n = 33). An auditory P300 (AP300) task was performed, and the N1 and P3 components were extracted. Two-group classification was conducted using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Positive and negative symptoms and depression and/or anxiety symptoms were evaluated. Results: Considering both the results of statistical comparisons and machine learning-based classifications, patients and HCs showed significant differences in AP300, with SZ and MDD showing lower N1 and P3 than HCs. In the sum of amplitudes and cortical sources, the findings for LDA with classification accuracy (SZ vs. HCs: 71.31%, MDD vs. HCs: 74.55%), sensitivity (SZ vs. HCs: 77.67%, MDD vs. HCs: 79.00%), and specificity (SZ vs. HCs: 64.00%, MDD vs. HCs: 69.67%) supported these results. The SVM classifier showed reasonable scores between SZ and HCs and/or MDD and HCs. The comparison between SZ and MDD showed low classification accuracy (59.71%), sensitivity (65.08%), and specificity (54.83%). Conclusions: Patients with SZ and MDD showed deficiencies in N1 and P3 components in the sum of amplitudes and cortical sources, indicating attentional dysfunction in both early and late sensory/cognitive gating input. The LDA and SVM classifiers in the AP300 are useful to distinguish patients with SZ and HCs and/or MDD and HCs.
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Affiliation(s)
- Kuk-In Jang
- Department of Cognitive Science Research, Korea Brain Research Institute (KBRI), Daegu, South Korea
| | - Sungkean Kim
- Department of Human-Computer Interaction, Hanyang University, Ansan, South Korea
| | - Soo Young Kim
- Department of Psychiatry, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Chany Lee
- Department of Cognitive Science Research, Korea Brain Research Institute (KBRI), Daegu, South Korea
| | - Jeong-Ho Chae
- Department of Psychiatry, College of Medicine, The Catholic University of Korea, Seoul, South Korea
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Wen D, Li P, Zhou Y, Sun Y, Xu J, Liu Y, Li X, Li J, Bian Z, Wang L. Feature Classification Method of Resting-State EEG Signals From Amnestic Mild Cognitive Impairment With Type 2 Diabetes Mellitus Based on Multi-View Convolutional Neural Network. IEEE Trans Neural Syst Rehabil Eng 2020; 28:1702-1709. [PMID: 32746302 DOI: 10.1109/tnsre.2020.3004462] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The convolutional neural network (CNN) model is an active research topic in the field of EEG signals analysis. However, the classification effect of CNN on EEG signals of amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) is not ideal. Even if EEG signals are transformed into multispectral images that are more closely matched with the model, the best classification performance can not be achieved. Therefore, to improve the performance of CNN toward EEG multispectral image classification, a multi-view convolutional neural network (MVCNN) classification model based on inceptionV1 is designed in this study. This model mainly improves and optimizes the convolutional layers and stochastic gradient descent (SGD) in the convolutional architecture model. Firstly, based on the discreteness of EEG multispectral image features, the multi-view convolutional layer structure was proposed. Then the learning rate change function of the SGD was optimized to increase the classification performance. The multi-view convolutional nerve was used in an EEG multispectral classification task involving 19 aMCI with T2DM and 20 normal controls. The results showed that compared with the traditional classification models, MVCNN had a better stability and accuracy. Therefore, MVCNN could be used as an effective feature classification method for aMCI with T2DM.
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Wen D, Zhou Y, Li P, Zhang P, Li J, Wang Y, Li X, Bian Z, Yin S, Xu Y. Resting-state EEG signal classification of amnestic mild cognitive impairment with type 2 diabetes mellitus based on multispectral image and convolutional neural network. J Neural Eng 2020; 17:036005. [PMID: 32315997 DOI: 10.1088/1741-2552/ab8b7b] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE The purpose of this study is to judge whether this combination method of multispectral image and convolutional neural network (CNN) method can be used to distinguish amnestic mild cognitive impairment (aMCI) with Type 2 diabetes mellitus (T2DM) and normal controls (NC) with T2DM effectively. APPROACH In this study, the authors first combined EEG signals from aMCI patients with T2DM and NC with T2DM on five different frequency bands, including Theta, Alpha1, Alpha2, Beta1, and Beta2. Then, the authors converted these time series into a series of multispectral images. Finally, the images data were classified with the CNN method. MAIN RESULTS The classification effects of up to 89%, 91%, and 92% are obtained on the three combinations of frequency bands: Theta, Alpha1, and Alpha2; Alpha1, Alpha2, and Beta1; and Alpha2, Beta1, and Beta2. The spatial properties of EEG signals are highlighted, and its classification performance is found to be better than all the previous methods in the field of aMCI and T2DM diagnosis. The combination of multispectral images and CNN can be used as an effective biomarker for distinguishing the EEG signals in patients with aMCI and T2DM and in patients with NC with T2DM. SIGNIFICANCE The combined approach used in this paper provides a new perspective for the analysis of EEG signals in patients with aMCI and T2DM.
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Affiliation(s)
- Dong Wen
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, People's Republic of China. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, People's Republic of China. These authors contributed equally to this paper
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Manduca JD, Thériault RK, Perreault ML. Glycogen synthase kinase-3: The missing link to aberrant circuit function in disorders of cognitive dysfunction? Pharmacol Res 2020; 157:104819. [PMID: 32305493 DOI: 10.1016/j.phrs.2020.104819] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 02/10/2020] [Accepted: 04/07/2020] [Indexed: 12/15/2022]
Abstract
Elevated GSK-3 activity has been implicated in cognitive dysfunction associated with various disorders including Alzheimer's disease, schizophrenia, type 2 diabetes, traumatic brain injury, major depressive disorder and bipolar disorder. Further, aberrant neural oscillatory activity in, and between, cortical regions and the hippocampus is consistently present within these same cognitive disorders. In this review, we will put forth the idea that increased GSK-3 activity serves as a pathological convergence point across cognitive disorders, inducing similar consequent impacts on downstream signaling mechanisms implicated in the maintenance of processes critical to brain systems communication and normal cognitive functioning. In this regard we suggest that increased activation of GSK-3 and neuronal oscillatory dysfunction are early pathological changes that may be functionally linked. Mechanistic commonalities between these disorders of cognitive dysfunction will be discussed and potential downstream targets of GSK-3 that may contribute to neuronal oscillatory dysfunction identified.
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Affiliation(s)
- Joshua D Manduca
- Department of Molecular and Cellular Biology, University of Guelph, ON, Canada
| | | | - Melissa L Perreault
- Department of Molecular and Cellular Biology, University of Guelph, ON, Canada.
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The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method. Neural Netw 2020; 124:373-382. [PMID: 32058892 DOI: 10.1016/j.neunet.2020.01.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 10/06/2019] [Accepted: 01/21/2020] [Indexed: 11/23/2022]
Abstract
Recently, combining feature extraction and classification method of electroencephalogram (EEG) signals has been widely used in identifying mild cognitive impairment. However, it remains unclear which feature of EEG signals is best effective in assessing amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) when combining one classifier. This study proposed a novel feature extraction method of EEG signals named feature-fusion multispectral image method (FMIM) for diagnosis of aMCI with T2DM. The FMIM was integrated with convolutional neural network (CNN) to classify the processed multispectral image data. The results showed that FMIM could effectively identify aMCI with T2DM from the control group compared to existing multispectral image method (MIM), with improvements including the type and quantity of feature extraction. Meanwhile, part of the invalid calculation could be avoided during the classification process. In addition, the classification evaluation indexes were best under the combination of Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-1, and were also best under the combination of the Theta-Alpha1-Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-2. Therefore, FMIM can be used as an effective feature extraction method of aMCI with T2DM, and as a valuable biomarker in clinical applications.
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Grigolon RB, Brietzke E, Mansur RB, Idzikowski MA, Gerchman F, De Felice FG, McIntyre RS. Association between diabetes and mood disorders and the potential use of anti-hyperglycemic agents as antidepressants. Prog Neuropsychopharmacol Biol Psychiatry 2019; 95:109720. [PMID: 31352032 DOI: 10.1016/j.pnpbp.2019.109720] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Revised: 07/08/2019] [Accepted: 07/25/2019] [Indexed: 12/13/2022]
Abstract
Epidemiological and mechanistic studies support the association between Diabetes Mellitus and mood disorders, such as Major Depressive Disorder and Bipolar Disorder. This association is especially relevant in specific domains of depressive psychopathology, such as disturbances in reward systems and cognitive functions. Several anti-hyperglycemic agents have demonstrated effects on depressive symptoms and cognitive decline and this efficacy is probably the result of an action in shared brain targets between these two groups of conditions. These medications include subcutaneous insulin, intranasal insulin, metformin, and liraglutide. The study of the mechanisms involved in the relationship between Diabetes Mellitus and mood disorders offers a new avenue of investigation, and this understanding can be applied when examining whether antidiabetic agents can be repurposed as antidepressants and mood stabilizers. The objective of this narrative review is to critically appraise the literature surrounding drugs commonly used as anti-hyperglycemic agents and their effects on the brain, while discussing their potential as a new treatment for mental illnesses, and specifically, mood disorders.
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Affiliation(s)
- Ruth B Grigolon
- Post-Graduation Program in Psychiatry and Medical Psychology, Universidade Federal de São Paulo (Unifesp), São Paulo, SP, Brazil; Research Group in Molecular and Behavioral Neuroscience of Mood Disorders, Universidade Federal de São Paulo (Unifesp), São Paulo, SP, Brazil
| | - Elisa Brietzke
- Post-Graduation Program in Psychiatry and Medical Psychology, Universidade Federal de São Paulo (Unifesp), São Paulo, SP, Brazil; Research Group in Molecular and Behavioral Neuroscience of Mood Disorders, Universidade Federal de São Paulo (Unifesp), São Paulo, SP, Brazil; Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada.
| | - Rodrigo B Mansur
- Mood Disorders Psychopharmacology Unit (MDPU), Toronto Western Hospital, University Health Network (UHN), Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Maia A Idzikowski
- Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada
| | - Fernando Gerchman
- Department of Internal Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Unit of Endocrinology and Metabolism, Hospital de Clinicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Fernanda G De Felice
- Institute of Medical Biochemistry Leopoldo de Meis, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Department of Psychiatry and Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Roger S McIntyre
- Mood Disorders Psychopharmacology Unit (MDPU), Toronto Western Hospital, University Health Network (UHN), Toronto, ON, Canada; Brain and Cognition Discovery Foundation (BCDF), Toronto, ON, Canada
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Abstract
We present clinical, electroencephalographic and low-resolution electromagnetic tomography data that support combined treatment with insulin and a monoamine oxidase inhibitor in a patient with type 1 diabetes. We suggest that brain imaging data can identify a subgroup of patients who are likely to benefit from an insulin regimen and monoamine oxidase inhibition to improve glycaemic control, cardiovascular function, normalize the circadian rhythm and restore perception of glycaemic awareness.
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Affiliation(s)
- Hamlin Emory
- Emory Neurophysiologic Institute, Los Angeles, CA, USA
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13
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Sejling AS, Kjaer TW, Pedersen-Bjergaard U, Remvig LS, Frandsen CS, Hilsted L, Faber J, Holst JJ, Tarnow L, Møller JS, Nielsen MN, Thorsteinsson B, Juhl CB. Hypoglycemia-Associated EEG Changes Following Antecedent Hypoglycemia in Type 1 Diabetes Mellitus. Diabetes Technol Ther 2017; 19:85-90. [PMID: 28118048 DOI: 10.1089/dia.2016.0331] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND Recurrent hypoglycemia has been shown to blunt hypoglycemia symptom scores and counterregulatory hormonal responses during subsequent hypoglycemia. We therefore studied whether hypoglycemia-associated electroencephalogram (EEG) changes are affected by an antecedent episode of hypoglycemia. METHODS Twenty-four patients with type 1 diabetes mellitus (10 with normal hypoglycemia awareness, 14 with hypoglycemia unawareness) were studied on 2 consecutive days by hyperinsulinemic glucose clamp at hypoglycemia (2.0-2.5 mmol/L) during a 1-h period. EEG was recorded, cognitive function assessed, and hypoglycemia symptom scores and counterregulatory hormonal responses were obtained. RESULTS Twenty-one patients completed the study. Hypoglycemia-associated EEG changes were identified on both days with no differences in power or frequency distribution in the theta, alpha, or the combined theta-alpha band during hypoglycemia on the 2 days. Similar degree of cognitive dysfunction was also present during hypoglycemia on both days. When comparing the aware and unaware group, there were no differences in the hypoglycemia-associated EEG changes. There were very subtle differences in cognitive function between the two groups on day 2. The symptom response was higher in the aware group on both days, while only subtle differences were seen in the counterregulatory hormonal response. CONCLUSION Antecedent hypoglycemia does not affect hypoglycemia-associated EEG changes in patients with type 1 diabetes mellitus.
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Affiliation(s)
- Anne-Sophie Sejling
- 1 Faculty of Health, University of Southern Denmark , Odense, Denmark
- 2 Department of Cardiology, Nephrology and Endocrinology, Nordsjællands Hospital , Hillerød, Denmark
| | - Troels W Kjaer
- 3 Department of Neurology, Roskilde Hospital , Roskilde, Denmark
- 4 Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark
- 5 Department of Neurophysiology, Rigshospitalet , Copenhagen, Denmark
| | - Ulrik Pedersen-Bjergaard
- 2 Department of Cardiology, Nephrology and Endocrinology, Nordsjællands Hospital , Hillerød, Denmark
- 4 Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark
| | | | - Christian S Frandsen
- 2 Department of Cardiology, Nephrology and Endocrinology, Nordsjællands Hospital , Hillerød, Denmark
- 7 Department of Endocrinology, Hvidovre Hospital , Hvidovre, Denmark
| | - Linda Hilsted
- 8 Department of Clinical Biochemistry, Rigshospitalet , Copenhagen, Denmark
| | - Jens Faber
- 4 Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark
- 9 Department of Endocrinology, Herlev Hospital , Herlev, Denmark
| | - Jens Juul Holst
- 10 NNF Center for Basic Metabolic Research, University of Copenhagen , Copenhagen, Denmark
| | - Lise Tarnow
- 11 Health, Aarhus University , Aarhus, Denmark
- 12 Steno Diabetes Center , Gentofte, Denmark
- 13 The Research Unit, Nordsjællands Hospital , Hillerød, Denmark
| | | | - Martin N Nielsen
- 5 Department of Neurophysiology, Rigshospitalet , Copenhagen, Denmark
| | - Birger Thorsteinsson
- 2 Department of Cardiology, Nephrology and Endocrinology, Nordsjællands Hospital , Hillerød, Denmark
- 4 Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark
| | - Claus B Juhl
- 1 Faculty of Health, University of Southern Denmark , Odense, Denmark
- 6 HypoSafe A/S , Lyngby, Denmark
- 14 Department of Medicine, Hospital of South West Jutland , Esbjerg, Denmark
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14
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Affiliation(s)
- Maria Rubega
- Department of Information Engineering, University of Padova , Padova, Italy
| | - Giovanni Sparacino
- Department of Information Engineering, University of Padova , Padova, Italy
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15
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Sejling AS, Kjær TW, Pedersen-Bjergaard U, Diemar SS, Frandsen CSS, Hilsted L, Faber J, Holst JJ, Tarnow L, Nielsen MN, Remvig LS, Thorsteinsson B, Juhl CB. Hypoglycemia-associated changes in the electroencephalogram in patients with type 1 diabetes and normal hypoglycemia awareness or unawareness. Diabetes 2015; 64:1760-9. [PMID: 25488900 DOI: 10.2337/db14-1359] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2014] [Accepted: 12/02/2014] [Indexed: 11/13/2022]
Abstract
Hypoglycemia is associated with increased activity in the low-frequency bands in the electroencephalogram (EEG). We investigated whether hypoglycemia awareness and unawareness are associated with different hypoglycemia-associated EEG changes in patients with type 1 diabetes. Twenty-four patients participated in the study: 10 with normal hypoglycemia awareness and 14 with hypoglycemia unawareness. The patients were studied at normoglycemia (5-6 mmol/L) and hypoglycemia (2.0-2.5 mmol/L), and during recovery (5-6 mmol/L) by hyperinsulinemic glucose clamp. During each 1-h period, EEG, cognitive function, and hypoglycemia symptom scores were recorded, and the counterregulatory hormonal response was measured. Quantitative EEG analysis showed that the absolute amplitude of the θ band and α-θ band up to doubled during hypoglycemia with no difference between the two groups. In the recovery period, the θ amplitude remained increased. Cognitive function declined equally during hypoglycemia in both groups and during recovery reaction time was still prolonged in a subset of tests. The aware group reported higher hypoglycemia symptom scores and had higher epinephrine and cortisol responses compared with the unaware group. In patients with type 1 diabetes, EEG changes and cognitive performance during hypoglycemia are not affected by awareness status during a single insulin-induced episode with hypoglycemia.
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Affiliation(s)
- Anne-Sophie Sejling
- Faculty of Health, University of Southern Denmark, Odense, Denmark Nordsjællands Hospital Hillerød, Hillerød, Denmark
| | - Troels W Kjær
- Roskilde Hospital, Roskilde, Denmark Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Rigshospitalet, Copenhagen, Denmark
| | | | - Sarah S Diemar
- Nordsjællands Hospital Hillerød, Hillerød, Denmark Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christian S S Frandsen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Hvidovre Hospital, Hvidovre, Denmark
| | | | - Jens Faber
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Herlev Hospital, Herlev, Denmark
| | - Jens J Holst
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lise Tarnow
- Nordsjællands Hospital Hillerød, Hillerød, Denmark Health, Aarhus University, Aarhus, Denmark
| | | | | | - Birger Thorsteinsson
- Nordsjællands Hospital Hillerød, Hillerød, Denmark Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Claus B Juhl
- Faculty of Health, University of Southern Denmark, Odense, Denmark HypoSafe A/S, Lyngby, Denmark Sydvestjysk Sygehus, Esbjerg, Denmark
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