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Genkel V, Domozhirova E, Malinina E. Multimorbidity in Severe Mental Illness as Part of the Neurodevelopmental Continuum: Physical Health-Related Endophenotypes of Schizophrenia-A Narrative Review. Brain Sci 2024; 14:725. [PMID: 39061465 PMCID: PMC11274495 DOI: 10.3390/brainsci14070725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 07/14/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND The majority of deaths in patients with schizophrenia and other severe mental illnesses (SMIs) are caused by natural causes, such as cardiovascular diseases (CVDs). The increased risk of CVD and other somatic diseases in SMIs cannot be fully explained by the contribution of traditional risk factors, behavioral risk factors, patients' lifestyle peculiarities, and the influence of antipsychotics. The present review has the following main objectives: (1) to aggregate evidence that neurodevelopmental disorders are the basis of SMIs; (2) to provide a review of studies that have addressed the shared genetic architecture of SMI and cardiovascular disease; and (3) to propose and substantiate the consideration of somatic diseases as independent endophenotypes of SMIs, which will make it possible to place the research of somatic diseases in SMIs within the framework of the concepts of the "neurodevelopmental continuum and gradient" and "endophenotype". METHODS A comprehensive literature search was performed on 1 July 2024. The search was performed using PubMed and Google Scholar databases up to June 2024. RESULTS The current literature reveals considerable overlap between the genetic susceptibility loci for SMIs and CVDs. We propose that somatic diseases observed in SMIs that have a shared genetic architecture with SMIs can be considered distinct physical health-related endophenotypes. CONCLUSIONS In this narrative review, the results of recent studies of CVDs in SMIs are summarized. Reframing schizophrenia as a multisystem disease should contribute to the activation of new research on somatic diseases in SMIs.
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Affiliation(s)
- Vadim Genkel
- Department of Internal Medicine, South-Ural State Medical University, Chelyabinsk 454092, Russia
| | - Elena Domozhirova
- Department of Psychiatry, South-Ural State Medical University, Chelyabinsk 454092, Russia; (E.D.); (E.M.)
| | - Elena Malinina
- Department of Psychiatry, South-Ural State Medical University, Chelyabinsk 454092, Russia; (E.D.); (E.M.)
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Arakaki X, Arechavala RJ, Choy EH, Bautista J, Bliss B, Molloy C, Wu DA, Shimojo S, Jiang Y, Kleinman MT, Kloner RA. The connection between heart rate variability (HRV), neurological health, and cognition: A literature review. Front Neurosci 2023; 17:1055445. [PMID: 36937689 PMCID: PMC10014754 DOI: 10.3389/fnins.2023.1055445] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 02/06/2023] [Indexed: 03/05/2023] Open
Abstract
The heart and brain have bi-directional influences on each other, including autonomic regulation and hemodynamic connections. Heart rate variability (HRV) measures variation in beat-to-beat intervals. New findings about disorganized sinus rhythm (erratic rhythm, quantified as heart rate fragmentation, HRF) are discussed and suggest overestimation of autonomic activities in HRV changes, especially during aging or cardiovascular events. When excluding HRF, HRV is regulated via the central autonomic network (CAN). HRV acts as a proxy of autonomic activity and is associated with executive functions, decision-making, and emotional regulation in our health and wellbeing. Abnormal changes of HRV (e.g., decreased vagal functioning) are observed in various neurological conditions including mild cognitive impairments, dementia, mild traumatic brain injury, migraine, COVID-19, stroke, epilepsy, and psychological conditions (e.g., anxiety, stress, and schizophrenia). Efforts are needed to improve the dynamic and intriguing heart-brain interactions.
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Affiliation(s)
- Xianghong Arakaki
- Cognition and Brain Integration Laboratory, Department of Neurosciences, Huntington Medical Research Institutes, Pasadena, CA, United States
| | - Rebecca J. Arechavala
- Department of Environmental and Occupational Health, University of California, Irvine, Irvine, CA, United States
| | - Elizabeth H. Choy
- Department of Environmental and Occupational Health, University of California, Irvine, Irvine, CA, United States
| | - Jayveeritz Bautista
- Department of Environmental and Occupational Health, University of California, Irvine, Irvine, CA, United States
| | - Bishop Bliss
- Department of Environmental and Occupational Health, University of California, Irvine, Irvine, CA, United States
| | - Cathleen Molloy
- Cognition and Brain Integration Laboratory, Department of Neurosciences, Huntington Medical Research Institutes, Pasadena, CA, United States
| | - Daw-An Wu
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Shinsuke Shimojo
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Yang Jiang
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Michael T. Kleinman
- Department of Environmental and Occupational Health, University of California, Irvine, Irvine, CA, United States
| | - Robert A. Kloner
- Cardiovascular Research, Huntington Medical Research Institutes, Pasadena, CA, United States
- Division of Cardiovascular Medicine, Department of Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States
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Zhang J, Yang H, Li W, Li Y, Qin J, He L. Automatic Schizophrenia Detection Using Multimodality Media via a Text Reading Task. Front Neurosci 2022; 16:933049. [PMID: 35911987 PMCID: PMC9331283 DOI: 10.3389/fnins.2022.933049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia is a crippling chronic mental disease that affects people worldwide. In this work, an automatic schizophrenia detection algorithm is proposed based on the reading deficit of schizophrenic patients. From speech and video modalities, the automatic schizophrenia detection algorithm illustrates abnormal speech, head movement, and reading fluency during the reading task. In the speech modality, an acoustic model of speech emotional flatness in schizophrenia is established to reflect the emotional expression flatness of schizophrenic speech from the perspective of speech production and perception. In the video modality, the head-movement-related features are proposed to illustrate the spontaneous head movement caused by repeated reading and unconscious movement, and the reading-fluency-related features are proposed to convey the damaged degree of schizophrenic patients' reading fluency. The experimental data of this work are 160 segments of speech and video data recorded by 40 participants (20 schizophrenic patients and 20 normal controls). Combined with support vector machines and random forest, the accuracy of the proposed acoustic model, the head-movement-related features, and the reading-fluency-related features range from 94.38 to 96.50%, 73.38 to 83.38%, and 79.50 to 83.63%, respectively. The average accuracy of the proposed automatic schizophrenia detection algorithm reaches 97.50%. The experimental results indicate the effectiveness of the proposed automatic detection algorithm as an auxiliary diagnostic method for schizophrenia.
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Affiliation(s)
- Jing Zhang
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Hui Yang
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Wen Li
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Yuanyuan Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jing Qin
- Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Ling He
- College of Biomedical Engineering, Sichuan University, Chengdu, China
- *Correspondence: Ling He
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González C, Garcia-Hernando G, Jensen EW, Vallverdú-Ferrer M. Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:912733. [PMID: 36926077 PMCID: PMC10013012 DOI: 10.3389/fnetp.2022.912733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022]
Abstract
Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing evidence of the causal coupling between hemodynamics and brain activity. The provided alternative of REG signal processing confirmed the hypothesis that REG signals carry information on CBF. The simplicity of the technology, together with its low cost and easily interpretable outcomes, should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity were assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use to prevent unwanted CBF alterations during anesthetic procedures.
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Affiliation(s)
- Carmen González
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain.,Research and Development Department, Quantium Medical, Mataró, Spain
| | - Gabriel Garcia-Hernando
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain.,Research and Development Department, Quantium Medical, Mataró, Spain
| | - Erik W Jensen
- Research and Development Department, Quantium Medical, Mataró, Spain
| | - Montserrat Vallverdú-Ferrer
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain
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