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Morozova A, Ushakova V, Pavlova O, Bairamova S, Andryshenko N, Ochneva A, Abramova O, Zorkina Y, Spektor VA, Gadisov T, Ukhov A, Zubkov E, Solovieva K, Alexeeva P, Khobta E, Nebogina K, Kozlov A, Klimenko T, Gurina O, Shport S, Kostuyk G, Chekhonin V, Pavlov K. BDNF, DRD4, and HTR2A Gene Allele Frequency Distribution and Association with Mental Illnesses in the European Part of Russia. Genes (Basel) 2024; 15:240. [PMID: 38397229 PMCID: PMC10887670 DOI: 10.3390/genes15020240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/31/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
The prevalence of mental disorders and how they are diagnosed represent some of the major problems in psychiatry. Modern genetic tools offer the potential to reduce the complications concerning diagnosis. However, the vast genetic diversity in the world population requires a closer investigation of any selected populations. In the current research, four polymorphisms, namely rs6265 in BDNF, rs10835210 in BDNF, rs6313 in HTR2A, and rs1800955 in DRD4, were analyzed in a case-control study of 2393 individuals (1639 patients with mental disorders (F20-F29, F30-F48) and 754 controls) from the European part of Russia using the TaqMan SNP genotyping method. Significant associations between rs6265 BDNF and rs1800955 DRD4 and mental impairments were detected when comparing the general group of patients with mental disorders (without separation into diagnoses) to the control group. Associations of rs6265 in BDNF, rs1800955 in DRD4, and rs6313 in HTR2A with schizophrenia in patients from the schizophrenia group separately compared to the control group were also found. The obtained results can extend the concept of a genetic basis for mental disorders in the Russian population and provide a basis for the future improvement in psychiatric diagnostics.
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Affiliation(s)
- Anna Morozova
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia
| | - Valeriya Ushakova
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia
- Department of Neurobiology, M.V. Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Olga Pavlova
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
| | - Sakeena Bairamova
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
| | - Nika Andryshenko
- Department of Biology, MSU-BIT Shenzhen University, Shenzhen 518172, China;
| | - Aleksandra Ochneva
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia
| | - Olga Abramova
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia
| | - Yana Zorkina
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia
| | - Valery A. Spektor
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
| | - Timur Gadisov
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
| | - Andrey Ukhov
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
| | - Eugene Zubkov
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
| | - Kristina Solovieva
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia
| | - Polina Alexeeva
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia
| | - Elena Khobta
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia
| | - Kira Nebogina
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
| | - Alexander Kozlov
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
| | - Tatyana Klimenko
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
| | - Olga Gurina
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
| | - Svetlana Shport
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
| | - George Kostuyk
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia
| | - Vladimir Chekhonin
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
- Department of Medical Nanobiotechnologies, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Konstantin Pavlov
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia; (V.U.); (O.P.); (S.B.); (A.O.); (O.A.); (Y.Z.); (T.G.); (A.U.); (E.Z.); (O.G.); (K.P.)
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia
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Caudwell KM, Baldini S, Calvezzi G, Graham A, Jackson K, Johansson I, Sines M, Lim LW, Aquili L. Learning bias predicts fear acquisition under stress but not cognitive flexibility. Physiol Behav 2023; 272:114384. [PMID: 37866645 DOI: 10.1016/j.physbeh.2023.114384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 10/24/2023]
Abstract
Individuals differ in their ability to learn from reinforcement and in avoiding punishment, which can be measured by the Probabilistic Selection Task (PST). Recently, some studies have demonstrated that this learning bias is regulated by the dopaminergic system, and that stress can differentially affect the use of positive (i.e., reinforcement) and negative (i.e., avoiding punishment) feedback. The current two studies examined whether performance on the PST can predict measures of goal-directed behaviour as assessed by a cognitive flexibility task (Wisconsin Card Sorting Test) and the acquisition of fear responses, when individuals are exposed to a stressor (Socially Evaluated Cold Pressor Test). A total of 26 and 59 healthy participants completed Experiments I and II, respectively. In those who were best at learning from reinforcement, stress increased the processing (i.e., higher skin conductance responses) of non-threatening stimuli during fear acquisition compared to the non-stressful condition, which was not recapitulated in those who were best at avoiding punishment. Additionally, PST performance did not interact with stress to modulate cognitive flexibility, although stress negatively impaired this domain, consistent with previous findings. Furthermore, independent of stress, both positive and negative learning biases were correlated with cognitive flexibility errors. Our results demonstrate that the PST has predictive value for better understanding the determinants of reinforcement and avoidance learning.
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Affiliation(s)
- Kim M Caudwell
- Faculty of Health, Charles Darwin University, Darwin, Australia
| | - Sara Baldini
- College of Health and Education, School of Psychology, Murdoch University, Perth, Australia
| | - Gemma Calvezzi
- College of Health and Education, School of Psychology, Murdoch University, Perth, Australia
| | - Aidan Graham
- College of Health and Education, School of Psychology, Murdoch University, Perth, Australia
| | - Kasie Jackson
- College of Health and Education, School of Psychology, Murdoch University, Perth, Australia
| | - Isabella Johansson
- College of Health and Education, School of Psychology, Murdoch University, Perth, Australia
| | - Madeline Sines
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China
| | - Lee Wei Lim
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China
| | - Luca Aquili
- College of Health and Education, School of Psychology, Murdoch University, Perth, Australia; School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China.
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Rissardo JP, Vora N, Mathew B, Kashyap V, Muhammad S, Fornari Caprara AL. Overview of Movement Disorders Secondary to Drugs. Clin Pract 2023; 13:959-976. [PMID: 37623268 PMCID: PMC10453030 DOI: 10.3390/clinpract13040087] [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: 07/16/2023] [Revised: 08/11/2023] [Accepted: 08/17/2023] [Indexed: 08/26/2023] Open
Abstract
Drug-induced movement disorders affect a significant percentage of individuals, and they are commonly overlooked and underdiagnosed in clinical practice. Many comorbidities can affect these individuals, making the diagnosis even more challenging. Several variables, including genetics, environmental factors, and aging, can play a role in the pathophysiology of these conditions. The Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Statistical Classification of Diseases and Related Health Problems (ICD) are the most commonly used classification systems in categorizing drug-induced movement disorders. This literature review aims to describe the abnormal movements associated with some medications and illicit drugs. Myoclonus is probably the most poorly described movement disorder, in which most of the reports do not describe electrodiagnostic studies. Therefore, the information available is insufficient for the diagnosis of the neuroanatomical source of myoclonus. Drug-induced parkinsonism is rarely adequately evaluated but should be assessed with radiotracers when these techniques are available. Tardive dyskinesias and dyskinesias encompass various abnormal movements, including chorea, athetosis, and ballism. Some authors include a temporal relationship to define tardive syndromes for other movement disorders, such as dystonia, tremor, and ataxia. Antiseizure medications and antipsychotics are among the most thoroughly described drug classes associated with movement disorders.
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Affiliation(s)
| | - Nilofar Vora
- Medicine Department, Terna Speciality Hospital and Research Centre, Navi Mumbai 400706, India;
| | - Bejoi Mathew
- Medicine Department, Sri Devaraj Urs Medical College, Kolar Karnataka 563101, India;
| | - Vikas Kashyap
- Medicine Department, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi 110029, India;
| | - Sara Muhammad
- Neurology Department, Mayo Clinic, Rochester, MN 55906, USA;
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Coarse-Grained Neural Network Model of the Basal Ganglia to Simulate Reinforcement Learning Tasks. Brain Sci 2022; 12:brainsci12020262. [PMID: 35204025 PMCID: PMC8870197 DOI: 10.3390/brainsci12020262] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/05/2022] [Accepted: 02/11/2022] [Indexed: 01/27/2023] Open
Abstract
Computational models of the basal ganglia (BG) provide a mechanistic account of different phenomena observed during reinforcement learning tasks performed by healthy individuals, as well as by patients with various nervous or mental disorders. The aim of the present work was to develop a BG model that could represent a good compromise between simplicity and completeness. Based on more complex (fine-grained neural network, FGNN) models, we developed a new (coarse-grained neural network, CGNN) model by replacing layers of neurons with single nodes that represent the collective behavior of a given layer while preserving the fundamental anatomical structures of BG. We then compared the functionality of both the FGNN and CGNN models with respect to several reinforcement learning tasks that are based on BG circuitry, such as the Probabilistic Selection Task, Probabilistic Reversal Learning Task and Instructed Probabilistic Selection Task. We showed that CGNN still has a functionality that mirrors the behavior of the most often used reinforcement learning tasks in human studies. The simplification of the CGNN model reduces its flexibility but improves the readability of the signal flow in comparison to more detailed FGNN models and, thus, can help to a greater extent in the translation between clinical neuroscience and computational modeling.
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