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Jornkokgoud K, Baggio T, Bakiaj R, Wongupparaj P, Job R, Grecucci A. Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits. Eur J Neurosci 2024; 59:3273-3291. [PMID: 38649337 DOI: 10.1111/ejn.16345] [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: 12/13/2023] [Revised: 03/11/2024] [Accepted: 03/24/2024] [Indexed: 04/25/2024]
Abstract
Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto-temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism.
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Affiliation(s)
- Khanitin Jornkokgoud
- Department of Research and Applied Psychology, Faculty of Education, Burapha University, Chonburi, Thailand
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
| | - Teresa Baggio
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
| | - Richard Bakiaj
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
| | - Peera Wongupparaj
- Department of Psychology, Faculty of Humanities and Social Sciences, Burapha University, Chonburi, Thailand
| | - Remo Job
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
| | - Alessandro Grecucci
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
- Centre for Medical Sciences (CISMed), University of Trento, Trento, Italy
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2
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Khan Y, Davis CN, Jinwala Z, Feuer KL, Toikumo S, Hartwell EE, Sanchez-Roige S, Peterson RE, Hatoum AS, Kranzler HR, Kember RL. Combining Transdiagnostic and Disorder-Level GWAS Enhances Precision of Psychiatric Genetic Risk Profiles in a Multi-Ancestry Sample. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.09.24307111. [PMID: 38766259 PMCID: PMC11100926 DOI: 10.1101/2024.05.09.24307111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
The etiology of substance use disorders (SUDs) and psychiatric disorders reflects a combination of both transdiagnostic (i.e., common) and disorder-level (i.e., independent) genetic risk factors. We applied genomic structural equation modeling to examine these genetic factors across SUDs, psychotic, mood, and anxiety disorders using genome-wide association studies (GWAS) of European- (EUR) and African-ancestry (AFR) individuals. In EUR individuals, transdiagnostic genetic factors represented SUDs (143 lead single nucleotide polymorphisms [SNPs]), psychotic (162 lead SNPs), and mood/anxiety disorders (112 lead SNPs). We identified two novel SNPs for mood/anxiety disorders that have probable regulatory roles on FOXP1, NECTIN3, and BTLA genes. In AFR individuals, genetic factors represented SUDs (1 lead SNP) and psychiatric disorders (no significant SNPs). The SUD factor lead SNP, although previously significant in EUR- and cross-ancestry GWAS, is a novel finding in AFR individuals. Shared genetic variance accounted for overlap between SUDs and their psychiatric comorbidities, with second-order GWAS identifying up to 12 SNPs not significantly associated with either first-order factor in EUR individuals. Finally, common and independent genetic effects showed different associations with psychiatric, sociodemographic, and medical phenotypes. For example, the independent components of schizophrenia and bipolar disorder had distinct associations with affective and risk-taking behaviors, and phenome-wide association studies identified medical conditions associated with tobacco use disorder independent of the broader SUDs factor. Thus, combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for psychiatric disorders that demonstrate considerable symptom and etiological overlap.
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Affiliation(s)
- Yousef Khan
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Christal N. Davis
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Kyra L. Feuer
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Sylvanus Toikumo
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Emily E. Hartwell
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, United States
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37235, United States
- Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Roseann E. Peterson
- Institute for Department of Psychiatry and Behavioral Sciences, Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, United States
| | - Alexander S. Hatoum
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Henry R. Kranzler
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Rachel L. Kember
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
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Miola A, Trevisan N, Salvucci M, Minerva M, Valeggia S, Manara R, Sambataro F. Network dysfunction of sadness facial expression processing and morphometry in euthymic bipolar disorder. Eur Arch Psychiatry Clin Neurosci 2024; 274:525-536. [PMID: 37498325 PMCID: PMC10995000 DOI: 10.1007/s00406-023-01649-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 07/07/2023] [Indexed: 07/28/2023]
Abstract
Facial emotion recognition (FER), including sadness, is altered in bipolar disorder (BD). However, the relationship between this impairment and the brain structure in BD is relatively unexplored. Furthermore, its association with clinical variables and with the subtypes of BD remains to be clarified. Twenty euthymic patients with BD type I (BD-I), 28 BD type II (BD-II), and 45 healthy controls completed a FER test and a 3D-T1-weighted magnetic resonance imaging. Gray matter volume (GMV) of the cortico-limbic regions implicated in emotional processing was estimated and their relationship with FER performance was investigated using network analysis. Patients with BD-I had worse total and sadness-related FER performance relative to the other groups. Total FER performance was significantly negatively associated with illness duration and positively associated with global functioning in patients with BD-I. Sadness-related FER performance was also significantly negatively associated with the number of previous manic episodes. Network analysis showed a reduced association of the GMV of the frontal-insular-occipital areas in patients with BD-I, with a greater edge strength between sadness-related FER performance and amygdala GMV relative to controls. Our results suggest that FER performance, particularly for facial sadness, may be distinctively impaired in patients with BD-I. The pattern of reduced interrelationship in the frontal-insular-occipital regions and a stronger positive relationship between facial sadness recognition and the amygdala GMV in BD may reflect altered cortical modulation of limbic structures that ultimately predisposes to emotional dysregulation. Future longitudinal studies investigating the effect of mood state on FER performance in BD are warranted.
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Affiliation(s)
- Alessandro Miola
- Department of Neuroscience (DNS), University of Padova, Via Giustiniani 5, Padua, Italy
| | - Nicolò Trevisan
- Department of Neuroscience (DNS), University of Padova, Via Giustiniani 5, Padua, Italy
| | - Margherita Salvucci
- Department of Neuroscience (DNS), University of Padova, Via Giustiniani 5, Padua, Italy
| | - Matteo Minerva
- Department of Neuroscience (DNS), University of Padova, Via Giustiniani 5, Padua, Italy
| | - Silvia Valeggia
- Department of Neuroscience (DNS), University of Padova, Via Giustiniani 5, Padua, Italy
| | - Renzo Manara
- Department of Neuroscience (DNS), University of Padova, Via Giustiniani 5, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Via Giustiniani 5, Padua, Italy.
- Padova Neuroscience Center, University of Padova, Padua, Italy.
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Li J, Cao Y, Huang M, Qin Z, Lang J. Progressive increase of brain gray matter volume in individuals with regular soccer training. Sci Rep 2024; 14:7023. [PMID: 38528027 DOI: 10.1038/s41598-024-57501-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 03/19/2024] [Indexed: 03/27/2024] Open
Abstract
The study aimed to investigate alterations in gray matter volume in individuals undergoing regular soccer training, using high-resolution structural data, while also examining the temporal precedence of such structural alterations. Both voxel-based morphometry and source-based morphometry (SBM) methods were employed to analyze volumetric changes in gray matter between the soccer and control groups. Additionally, a causal network of structural covariance (CaSCN) was built using granger causality analysis on brain structural data ordering by training duration. Significant increases in gray matter volume were observed in the cerebellum in the soccer group. Additionally, the results of the SBM analysis revealed significant increases in gray matter volume in the calcarine and thalamus of the soccer group. The analysis of CaSCN demonstrated that the thalamus had a prominent influence on other brain regions in the soccer group, while the calcarine served as a transitional node, and the cerebellum acted as a prominent node that could be easily influenced by other brain regions. In conclusion, our study identified widely affected regions with increased gray matter volume in individuals with regular soccer training. Furthermore, a temporal precedence relationship among these regions was observed.
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Affiliation(s)
- Ju Li
- College of P.E. and Sports, Beijing Normal University, Beijing, 100875, China
| | - Yaping Cao
- College of P.E. and Sports, Beijing Normal University, Beijing, 100875, China
| | - Minghao Huang
- College of P.E. and Sports, Beijing Normal University, Beijing, 100875, China
| | - Zhe Qin
- College of P.E. and Sports, Northwest Normal University, Gansu, 730070, China
| | - Jian Lang
- College of P.E. and Sports, Beijing Normal University, Beijing, 100875, China.
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Saha DK, Silva RF, Baker BT, Saha R, Calhoun VD. dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping. Hum Brain Mapp 2023; 44:5892-5905. [PMID: 37837630 PMCID: PMC10619413 DOI: 10.1002/hbm.26483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/09/2023] [Accepted: 08/29/2023] [Indexed: 10/16/2023] Open
Abstract
The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data-driven source-based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi-blind extension of SBM, called constrained source-based morphometry (constrained SBM), which enables the extraction of maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source-based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.
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Affiliation(s)
- Debbrata K. Saha
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Rogers F. Silva
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Bradley T. Baker
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Rekha Saha
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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Langerbeck M, Baggio T, Messina I, Bhat S, Grecucci A. Borderline shades: Morphometric features predict borderline personality traits but not histrionic traits. Neuroimage Clin 2023; 40:103530. [PMID: 37879232 PMCID: PMC10618757 DOI: 10.1016/j.nicl.2023.103530] [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: 06/16/2023] [Revised: 10/09/2023] [Accepted: 10/12/2023] [Indexed: 10/27/2023]
Abstract
Borderline personality disorder (BPD) is one of the most diagnosed disorders in clinical settings. Besides the fully diagnosed disorder, borderline personality traits (BPT) are quite common in the general population. Prior studies have investigated the neural correlates of BPD but not of BPT. This paper investigates the neural correlates of BPT in a subclinical population using a supervised machine learning method known as Kernel Ridge Regression (KRR) to build predictive models. Additionally, we want to determine whether the same brain areas involved in BPD are also involved in subclinical BPT. Recent attempts to characterize the specific role of resting state-derived macro networks in BPD have highlighted the role of the default mode network. However, it is not known if this extends to the subclinical population. Finally, we wanted to test the hypothesis that the same circuitry that predicts BPT can also predict histrionic personality traits. Histrionic personality is sometimes considered a milder form of BPD, and making a differential diagnosis between the two may be difficult. For the first time KRR was applied to structural images of 135 individuals to predict BPT, based on the whole brain, on a circuit previously found to correctly classify BPD, and on the five macro-networks. At a whole brain level, results show that frontal and parietal regions, as well as the Heschl's area, the thalamus, the cingulum, and the insula, are able to predict borderline traits. BPT predictions increase when considering only the regions limited to the brain circuit derived from a study on BPD, confirming a certain overlap in brain structure between subclinical and clinical samples. Of all the five macro networks, only the DMN successfully predicts BPD, confirming previous observations on its role in the BPD. Histrionic traits could not be predicted by the BPT circuit. The results have implications for the diagnosis of BPD and a dimensional model of personality.
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Affiliation(s)
- Miriam Langerbeck
- Faculty of Psychology and Neuroscience (FPN), Maastricht University, Netherlands
| | - Teresa Baggio
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy.
| | - Irene Messina
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy; Universitas Mercatorum, Rome, Italy.
| | - Salil Bhat
- Department of Cognitive Neuroscience, Faculty of Psychology and Cognitive Neuroscience (FPN), Maastricht University, Netherlands.
| | - Alessandro Grecucci
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy; Centre for Medical Sciences (CISMed), University of Trento, Italy.
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Aas M, Andreassen OA, Gjerstad J, Rødevand L, Hjell G, Johansen IT, Lunding SH, Ormerod MBEG, Lagerverg TV, Steen NE, Djurovic S, Akkouh I. Expression of ANK3 moderates the association between childhood trauma and affective traits in severe mental disorders. Sci Rep 2023; 13:13845. [PMID: 37620394 PMCID: PMC10449847 DOI: 10.1038/s41598-023-40310-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 08/08/2023] [Indexed: 08/26/2023] Open
Abstract
Exposure to early life trauma increases the risk of psychopathology later in life. Here we investigated if ANK3 mRNA levels influence the relationship between childhood trauma experiences and clinical characteristics in mental disorders. A sample of 174 patients with bipolar disorder and 291 patients with schizophrenia spectrum disorder were included. Patients were diagnosed using the Structured Clinical Interview for DSM-IV, and childhood trauma was assessed using the childhood trauma questionnaire. Age at illness onset and number of psychotic and affective episodes were assessed from interview and medical records. Current depressive symptoms were measured using the calgary depression scale for schizophrenia and the inventory for depressive symptomatology. ANK3 expression was analyzed in whole blood using the Illumina HumanHT-12 v4 Expression BeadChip. Analyses were carried out with the Process adjusted for confounders. Within the total sample, patients with both high ANK3 expression and with the most severe childhood sexual abuse had more manic/hypomanic episodes and an earlier age at onset of the first episode. ANK3 mRNA levels also moderated the relationship between emotional neglect and manic/hypomanic episodes. Our results suggest that ANK3 expression levels moderate the association between specific types of childhood trauma and affective traits in mental disorders.
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Affiliation(s)
- Monica Aas
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Department of Behavioural Sciences, OsloMet - Oslo Metropolitan University, Oslo, Norway.
| | - Ole A Andreassen
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Johannes Gjerstad
- Department of Behavioural Sciences, OsloMet - Oslo Metropolitan University, Oslo, Norway
| | - Linn Rødevand
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Gabriela Hjell
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Oslo, Norway
- Department of Psychiatry, Østfold Hospital, Grålum, Norway
| | - Ingrid Torp Johansen
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Synve Hoffart Lunding
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Monica B E G Ormerod
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Trine V Lagerverg
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Nils Eiel Steen
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Clinical Science, NORMENT, University of Bergen, Bergen, Norway
| | - Ibrahim Akkouh
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
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Cao P, Chen C, Si Q, Li Y, Ren F, Han C, Zhao J, Wang X, Xu G, Sui Y. Volumes of hippocampal subfields suggest a continuum between schizophrenia, major depressive disorder and bipolar disorder. Front Psychiatry 2023; 14:1191170. [PMID: 37547217 PMCID: PMC10400724 DOI: 10.3389/fpsyt.2023.1191170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/03/2023] [Indexed: 08/08/2023] Open
Abstract
Objective There is considerable debate as to whether the continuum of major psychiatric disorders exists and to what extent the boundaries extend. Converging evidence suggests that alterations in hippocampal volume are a common sign in psychiatric disorders; however, there is still no consensus on the nature and extent of hippocampal atrophy in schizophrenia (SZ), major depressive disorder (MDD) and bipolar disorder (BD). The aim of this study was to verify the continuum of SZ - BD - MDD at the level of hippocampal subfield volume and to compare the volume differences in hippocampal subfields in the continuum. Methods A total of 412 participants (204 SZ, 98 MDD, and 110 BD) underwent 3 T MRI scans, structured clinical interviews, and clinical scales. We segmented the hippocampal subfields with FreeSurfer 7.1.1 and compared subfields volumes across the three diagnostic groups by controlling for age, gender, education, and intracranial volumes. Results The results showed a gradual increase in hippocampal subfield volumes from SZ to MDD to BD. Significant volume differences in the total hippocampus and 13 of 26 hippocampal subfields, including CA1, CA3, CA4, GC-ML-DG, molecular layer and the whole hippocampus, bilaterally, and parasubiculum in the right hemisphere, were observed among diagnostic groups. Medication treatment had the most effect on subfields of MDD compared to SZ and BD. Subfield volumes were negatively correlated with illness duration of MDD. Positive correlations were found between subfield volumes and drug dose in SZ and MDD. There was no significant difference in laterality between diagnostic groups. Conclusion The pattern of hippocampal volume reduction in SZ, MDD and BD suggests that there may be a continuum of the three disorders at the hippocampal level. The hippocampus represents a phenotype that is distinct from traditional diagnostic strategies. Combined with illness duration and drug intervention, it may better reflect shared pathophysiology and mechanisms across psychiatric disorders.
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Affiliation(s)
- Peiyu Cao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Congxin Chen
- Women’s Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Qi Si
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
- Huai’an No. 3 People’s Hospital, Huai’an, China
| | - Yuting Li
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Fangfang Ren
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Chongyang Han
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Jingjing Zhao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Xiying Wang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Guoxin Xu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Yuxiu Sui
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
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Romeo Z, Marino M, Mantini D, Angrilli A, Spironelli C. Language Network Connectivity of Euthymic Bipolar Patients Is Altered at Rest and during a Verbal Fluency Task. Biomedicines 2023; 11:1647. [PMID: 37371743 DOI: 10.3390/biomedicines11061647] [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: 03/02/2023] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 06/29/2023] Open
Abstract
Abnormalities of the Language Network (LN) have been found in different psychiatric conditions (e.g., schizophrenia and bipolar disorder), supporting the hypothesis that language plays a central role in a high-level integration/connectivity of second-level cognitive processes and the underlying cortical regions. This view implies a continuum of shared neural alterations along the psychotic disorder spectrum. In particular, bipolar disorder (BD) patients were recently documented to have an altered LN asymmetry during resting state. The extent to which the LN architecture is altered and stable also during a language task has yet to be investigated. To address this question, we analyzed fMRI data recorded during an open-eyes resting state session and a silent verbal fluency task in 16 euthymic BD patients and 16 matched healthy controls (HC). Functional connectivity in the LN of both groups was computed using spatial independent component analysis, and group comparisons were carried out to assess the network organization during both rest and active linguistic task conditions. The LN of BD patients involved left and right brain areas during both resting state and linguistic task. Compared to the left-lateralized network found in HC, the BD group was characterized by two anterior clusters (in left frontal and right temporo-insular regions) and the disengagement of the posterior language areas, especially during the verbal fluency task. Our findings support the hypothesis that reduced language lateralization may represent a biological marker across different psychotic disorders and that the altered language network connectivity found at rest in bipolar patients is stable and pervasive as it is also impaired during a verbal fluency task.
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Affiliation(s)
- Zaira Romeo
- Department of General Psychology, University of Padova, 35131 Padova, Italy
| | - Marco Marino
- Department of General Psychology, University of Padova, 35131 Padova, Italy
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001 Leuven, Belgium
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001 Leuven, Belgium
| | - Alessandro Angrilli
- Department of General Psychology, University of Padova, 35131 Padova, Italy
- Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
| | - Chiara Spironelli
- Department of General Psychology, University of Padova, 35131 Padova, Italy
- Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
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10
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Ghomroudi PA, Scaltritti M, Grecucci A. Decoding reappraisal and suppression from neural circuits: A combined supervised and unsupervised machine learning approach. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023:10.3758/s13415-023-01076-6. [PMID: 36977965 PMCID: PMC10400700 DOI: 10.3758/s13415-023-01076-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/06/2023] [Indexed: 03/30/2023]
Abstract
Emotion regulation is a core construct of mental health and deficits in emotion regulation abilities lead to psychological disorders. Reappraisal and suppression are two widely studied emotion regulation strategies but, possibly due to methodological limitations in previous studies, a consistent picture of the neural correlates related to the individual differences in their habitual use remains elusive. To address these issues, the present study applied a combination of unsupervised and supervised machine learning algorithms to the structural MRI scans of 128 individuals. First, unsupervised machine learning was used to separate the brain into naturally grouping grey matter circuits. Then, supervised machine learning was applied to predict individual differences in the use of different strategies of emotion regulation. Two predictive models, including structural brain features and psychological ones, were tested. Results showed that a temporo-parahippocampal-orbitofrontal network successfully predicted the individual differences in the use of reappraisal. Differently, insular and fronto-temporo-cerebellar networks successfully predicted suppression. In both predictive models, anxiety, the opposite strategy, and specific emotional intelligence factors played a role in predicting the use of reappraisal and suppression. This work provides new insights regarding the decoding of individual differences from structural features and other psychologically relevant variables while extending previous observations on the neural bases of emotion regulation strategies.
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Affiliation(s)
- Parisa Ahmadi Ghomroudi
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences - DiPSCo, University of Trento, Rovereto, Italy.
| | - Michele Scaltritti
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences - DiPSCo, University of Trento, Rovereto, Italy
| | - Alessandro Grecucci
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences - DiPSCo, University of Trento, Rovereto, Italy
- Center for Medical Sciences - CISMed, University of Trento, Trento, Italy
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11
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Grecucci A, Dadomo H, Salvato G, Lapomarda G, Sorella S, Messina I. Abnormal Brain Circuits Characterize Borderline Personality and Mediate the Relationship between Childhood Traumas and Symptoms: A mCCA+jICA and Random Forest Approach. SENSORS (BASEL, SWITZERLAND) 2023; 23:2862. [PMID: 36905064 PMCID: PMC10006907 DOI: 10.3390/s23052862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/01/2023] [Accepted: 03/02/2023] [Indexed: 06/18/2023]
Abstract
Borderline personality disorder (BPD) is a severe personality disorder whose neural bases are still unclear. Indeed, previous studies reported inconsistent findings concerning alterations in cortical and subcortical areas. In the present study, we applied for the first time a combination of an unsupervised machine learning approach known as multimodal canonical correlation analysis plus joint independent component analysis (mCCA+jICA), in combination with a supervised machine learning approach known as random forest, to possibly find covarying gray matter and white matter (GM-WM) circuits that separate BPD from controls and that are also predictive of this diagnosis. The first analysis was used to decompose the brain into independent circuits of covarying grey and white matter concentrations. The second method was used to develop a predictive model able to correctly classify new unobserved BPD cases based on one or more circuits derived from the first analysis. To this aim, we analyzed the structural images of patients with BPD and matched healthy controls (HCs). The results showed that two GM-WM covarying circuits, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC. Notably, these circuits are affected by specific child traumatic experiences (emotional and physical neglect, and physical abuse) and predict symptoms severity in the interpersonal and impulsivity domains. These results support that BPD is characterized by anomalies in both GM and WM circuits related to early traumatic experiences and specific symptoms.
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Affiliation(s)
- Alessandro Grecucci
- Clinical and Affective Neuroscience Lab (CL.I.A.N. Lab), Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, 38068 Rovereto, Italy
- Centre for Medical Sciences (CISMed), University of Trento, 38122 Trento, Italy
| | - Harold Dadomo
- Unit of Neuroscience, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
| | - Gerardo Salvato
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
- Cognitive Neuropsychology Centre, ASST “Grande Ospedale Metropolitano” Niguarda, 20162 Milan, Italy
- Milan Centre for Neuroscience (NeuroMI), 20126 Milan, Italy
| | - Gaia Lapomarda
- Department of Psychology, Science Division, New York University of Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates
| | - Sara Sorella
- Clinical and Affective Neuroscience Lab (CL.I.A.N. Lab), Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, 38068 Rovereto, Italy
| | - Irene Messina
- Clinical and Affective Neuroscience Lab (CL.I.A.N. Lab), Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, 38068 Rovereto, Italy
- Universitas Mercatorum, 00186 Rome, Italy
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12
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Grecucci A, Sorella S, Consolini J. Decoding individual differences in expressing and suppressing anger from structural brain networks: A supervised machine learning approach. Behav Brain Res 2023; 439:114245. [PMID: 36470420 DOI: 10.1016/j.bbr.2022.114245] [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: 05/19/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022]
Abstract
Anger can be broken down into different elements: a transitory state (state anger), a stable personality feature (trait anger), a tendency to express it (anger-out), or to suppress it (anger-in), and the ability to regulate it (anger control). These elements are characterized by individual differences that vary across a continuum. Among them, the abilities to express and suppress anger are of particular relevance as they determine outcomes and enable successful anger management in daily situations. The aim of this study was to demonstrate that anger suppression and expression can be decoded by patterns of grey matter of specific well-known brain networks. To this aim, a supervised machine learning technique, known as Kernel Ridge Regression, was used to predict anger expression and suppression scores of 212 healthy subjects from the grey matter concentration. Results show that individual differences in anger suppression were predicted by two grey matter patterns associated with the Default-Mode Network and the Salience Network. Additionally, individual differences in anger expression were predicted by a circuit mainly involving subcortical and fronto-temporal regions when considering whole brain grey matter features. These results expand previous findings regarding the neural bases of anger by showing that individual differences in specific anger-related components can be predicted by the grey matter features of specific networks.
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Affiliation(s)
- Alessandro Grecucci
- Clinical and Affective Neuroscience Lab, Cli.A.N. Lab, Department of Psychology and Cognitive Sciences - DiPSCo, University of Trento, Rovereto, Italy; Center for Medical Sciences, CISMed, University of Trento, Trento, Italy.
| | - Sara Sorella
- Clinical and Affective Neuroscience Lab, Cli.A.N. Lab, Department of Psychology and Cognitive Sciences - DiPSCo, University of Trento, Rovereto, Italy.
| | - Jennifer Consolini
- Clinical and Affective Neuroscience Lab, Cli.A.N. Lab, Department of Psychology and Cognitive Sciences - DiPSCo, University of Trento, Rovereto, Italy.
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Aas M, Ueland T, Lagerberg TV, Melle I, Aminoff SR, Hoegh MC, Lunding SH, Laskemoen JF, Steen NE, Andreassen OA. Retrospectively assessed childhood trauma experiences are associated with illness severity in mental disorders adjusted for symptom state. Psychiatry Res 2023; 320:115045. [PMID: 36621206 DOI: 10.1016/j.psychres.2022.115045] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 12/13/2022] [Accepted: 12/27/2022] [Indexed: 12/30/2022]
Abstract
Converging evidence suggests that childhood trauma is a causal factor in schizophrenia (SZ) and in bipolar disorders (BD). Here, we investigated whether retrospective reports are associated with severity of illness, independent of current symptom state in a large sample of participants with SZ or BD. We included 1260 individuals (SZ [n = 461], BD [n = 352]), and healthy controls; HC [n = 447]) recruited from the same catchment area. A history of childhood trauma was obtained with the Childhood Trauma Questionnaire (CTQ). Diagnosis and episodes were obtained with the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I). Clinical symptoms (state) were assessed with the Positive and Negative Syndrome scale (PANSS), the Calgary Depression Scale (CDSS). Trait related illness characteristics were assessed with age at illness onset, number of episodes, and lifetime suicide attempts. Patients who reported multiple types of childhood trauma experiences had significantly more severe illness course including an earlier illness onset, more mood episodes, and increased risk of at least one suicide attempt, also after adjusting for current symptom state. Retrospective assessed childhood trauma experiences are associated with illness severity in mental disorders adjusted for symptom state. Our results strengthen the role of childhood trauma in development of psychopathology.
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Affiliation(s)
- Monica Aas
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Norway; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Torill Ueland
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Norway
| | - Trine V Lagerberg
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Norway
| | - Ingrid Melle
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Norway
| | - Sofie R Aminoff
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Norway; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Early Intervention in Psychosis Advisory Unit for South East Norway, Division of Mental Health and Addiction, Oslo University Hospital, Norway
| | - Margrethe C Hoegh
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Norway
| | - Synve Hoffart Lunding
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Norway
| | - Jannicke F Laskemoen
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Norway
| | - Nils Eiel Steen
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre for Psychosis Research, Oslo University Hospital and University of Oslo, Norway
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Baggio T, Grecucci A, Meconi F, Messina I. Anxious Brains: A Combined Data Fusion Machine Learning Approach to Predict Trait Anxiety from Morphometric Features. SENSORS (BASEL, SWITZERLAND) 2023; 23:610. [PMID: 36679404 PMCID: PMC9863274 DOI: 10.3390/s23020610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 12/30/2022] [Accepted: 01/01/2023] [Indexed: 06/17/2023]
Abstract
Trait anxiety relates to the steady propensity to experience and report negative emotions and thoughts such as fear and worries across different situations, along with a stable perception of the environment as characterized by threatening stimuli. Previous studies have tried to investigate neuroanatomical features related to anxiety mostly using univariate analyses and thus giving rise to contrasting results. The aim of this study is to build a predictive model of individual differences in trait anxiety from brain morphometric features, by taking advantage of a combined data fusion machine learning approach to allow generalization to new cases. Additionally, we aimed to perform a network analysis to test the hypothesis that anxiety-related networks have a central role in modulating other networks not strictly associated with anxiety. Finally, we wanted to test the hypothesis that trait anxiety was associated with specific cognitive emotion regulation strategies, and whether anxiety may decrease with ageing. Structural brain images of 158 participants were first decomposed into independent covarying gray and white matter networks with a data fusion unsupervised machine learning approach (Parallel ICA). Then, supervised machine learning (decision tree) and backward regression were used to extract and test the generalizability of a predictive model of trait anxiety. Two covarying gray and white matter independent networks successfully predicted trait anxiety. The first network included mainly parietal and temporal regions such as the postcentral gyrus, the precuneus, and the middle and superior temporal gyrus, while the second network included frontal and parietal regions such as the superior and middle temporal gyrus, the anterior cingulate, and the precuneus. We also found that trait anxiety was positively associated with catastrophizing, rumination, other- and self-blame, and negatively associated with positive refocusing and reappraisal. Moreover, trait anxiety was negatively associated with age. This paper provides new insights regarding the prediction of individual differences in trait anxiety from brain and psychological features and can pave the way for future diagnostic predictive models of anxiety.
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Affiliation(s)
- Teresa Baggio
- Clinical and Affective Neuroscience Lab (CLI.A.N. Lab), Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, 38068 Rovereto, Italy
| | - Alessandro Grecucci
- Clinical and Affective Neuroscience Lab (CLI.A.N. Lab), Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, 38068 Rovereto, Italy
- Centre for Medical Sciences, CISMed, University of Trento, 38122 Trento, Italy
| | - Federica Meconi
- Clinical and Affective Neuroscience Lab (CLI.A.N. Lab), Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, 38068 Rovereto, Italy
| | - Irene Messina
- Clinical and Affective Neuroscience Lab (CLI.A.N. Lab), Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, 38068 Rovereto, Italy
- Department of Economics, Universitas Mercatorum, 00186 Rome, Italy
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de Sousa TR, Dt C, Novais F. Exploring the Hypothesis of a Schizophrenia and Bipolar Disorder Continuum: Biological, Genetic and Pharmacologic Data. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2023; 22:161-171. [PMID: 34477537 DOI: 10.2174/1871527320666210902164235] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 07/19/2021] [Accepted: 08/08/2021] [Indexed: 12/16/2022]
Abstract
Present time nosology has its roots in Kraepelin's demarcation of schizophrenia and bipolar disorder. However, accumulating evidence has shed light on several commonalities between the two disorders, and some authors have advocated for the consideration of a disease continuum. Here, we review previous genetic, biological and pharmacological findings that provide the basis for this conceptualization. There is a cross-disease heritability, and they share single-nucleotide polymorphisms in some common genes. EEG and imaging patterns have a number of similarities, namely reduced white matter integrity and abnormal connectivity. Dopamine, serotonin, GABA and glutamate systems have dysfunctional features, some of which are identical among the disorders. Finally, cellular calcium regulation and mitochondrial function are, also, impaired in the two.
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Affiliation(s)
- Teresa Reynolds de Sousa
- Department of Neurosciences and Mental Health, Centro Hospitalar Universitário Lisboa Norte (CHULN), Hospital de Santa Maria, Lisbon, Portugal
| | - Correia Dt
- Department of Neurosciences and Mental Health, Centro Hospitalar Universitário Lisboa Norte (CHULN), Hospital de Santa Maria, Lisbon, Portugal
- Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
- ISAMB - Instituto de Saúde Ambiental, Lisboa, Portugal
| | - Filipa Novais
- Department of Neurosciences and Mental Health, Centro Hospitalar Universitário Lisboa Norte (CHULN), Hospital de Santa Maria, Lisbon, Portugal
- Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
- ISAMB - Instituto de Saúde Ambiental, Lisboa, Portugal
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16
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Rootes-Murdy K, Edmond JT, Jiang W, Rahaman MA, Chen J, Perrone-Bizzozero NI, Calhoun VD, van Erp TGM, Ehrlich S, Agartz I, Jönsson EG, Andreassen OA, Westlye LT, Wang L, Pearlson GD, Glahn DC, Hong E, Buchanan RW, Kochunov P, Voineskos A, Malhotra A, Tamminga CA, Liu J, Turner JA. Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach. Front Hum Neurosci 2022; 16:1001692. [PMID: 36438633 PMCID: PMC9684186 DOI: 10.3389/fnhum.2022.1001692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 10/17/2022] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles. MATERIALS AND METHODS We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP. RESULTS Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences. CONCLUSION These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.
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Affiliation(s)
- Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jesse T. Edmond
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, Medical School, Zhongda Hospital, Institute of Psychosomatics, Southeast University, Nanjing, China
| | - Md A. Rahaman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | | | - Vince D. Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Theo G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Ingrid Agartz
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Erik G. Jönsson
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden
| | - Ole A. Andreassen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States
| | - Godfrey D. Pearlson
- Department of Psychiatry, Yale University, New Haven, CT, United States
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States
| | - David C. Glahn
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States
- Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Robert W. Buchanan
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Aristotle Voineskos
- Department of Psychiatry, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Anil Malhotra
- Division of Psychiatry Research, Zucker Hillside Hospital, Queens, NY, United States
| | - Carol A. Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX, United States
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jessica A. Turner
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States
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Altered language network lateralization in euthymic bipolar patients: a pilot study. Transl Psychiatry 2022; 12:435. [PMID: 36202786 PMCID: PMC9537562 DOI: 10.1038/s41398-022-02202-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 11/09/2022] Open
Abstract
Bipolar patients (BD) in the euthymic phase show almost no symptoms, nevertheless possibility of relapse is still present. We expected to find a psychobiological trace of their vulnerability by analyzing a specific network-the Language Network (LN)-connecting many high-level processes and brain regions measured at rest. According to Crow's hypothesis on the key role of language in the origin of psychoses, we expected an altered asymmetry of the LN in euthymic BDs. Eighteen euthymic BD patients (10 females; age = 54.50 ± 11.38 years) and 16 healthy controls (HC) (8 females; age = 51.16 ± 11.44 years) underwent a functional magnetic resonance imaging scan at rest. The LN was extracted through independent component analysis. Then, LN time series was used to compute the fractional amplitude of the low-frequency fluctuation (fALFF) index, which was then correlated with clinical scales. Compared with HC, euthymic patients showed an altered LN with greater activation of Broca's area right homologous and anterior insula together with reduced activation of left middle temporal gyrus. The normalized fALFF analysis on BD patients' LN time series revealed that the Slow-5 fALFF band was positively correlated with residual mania symptoms but negatively associated with depression scores. In line with Crow's hypothesis postulating an altered language hemispheric asymmetry in psychoses, we revealed, in euthymic BD patients, a right shift involving both the temporal and frontal linguistic hubs. The fALFF applied to LN allowed us to highlight a number of significant correlations of this measure with residual mania and depression psychiatric symptoms.
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Tong W, Dong Z, Guo W, Zhang M, Zhang Y, Du Y, Zhao J, Lv L, Liu Y, Wang X, Kou Y, Zhang H, Zhang H. Progressive Changes in Brain Regional Homogeneity Induced by Electroconvulsive Therapy Among Patients With Schizophrenia. J ECT 2022; 38:117-123. [PMID: 35613010 DOI: 10.1097/yct.0000000000000815] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Electroconvulsive therapy (ECT) has significant effects on improving psychotic symptoms in schizophrenia (SZ), but the changes of brain function induced by it are unclear. The purpose of the study was to explore progressive ECT-induced changes in regional homogeneity (ReHo) at multiple time points before, during, and after a course of ECT. METHODS The 27 in-patients with SZ (SZ group) who met the recruitment criteria accepted clinical evaluations and resting-state functional magnetic resonance imaging scans before the first ECT (pre-ECT), after the first ECT (ECT1), and after the eighth ECT (ECT8), all conducted within 10 to 12 hours. Forty-three healthy controls (HCs; HC group) who matched well with the patients for age, sex, and years of education were recruited. For Positive and Negative Syndrome Scale (PANSS) and ReHo, progressive changes were examined. RESULTS Pair-wise comparisons of patient pre-ECT, ECT1, and ECT8 ReHo values with HC ReHo values revealed that ECT normalized the ReHo values in bilateral superior occipital gyrus (SOG), right lingual gyrus (LG), left medial prefrontal cortex. Furthermore, improved ReHo in bilateral SOG and right LG appeared after the first ECT application. The ReHo values in right middle occipital gyrus, right middle temporal gyrus, and right inferior parietal lobule were not significantly altered by ECT. The total PANSS score was lower even after the first ECT application (mean ΔPANSSECT1, 11.7%; range, 2%-32.8%) and markedly reduced after the eighth application (mean ΔPANSSECT8, 86.3%; range, 72.5%-97.9%). CONCLUSIONS The antipsychotic effects of ECT may be achieved through regulating synchronization of some regions such as bilateral SOG, right LG, and left medial prefrontal cortex. Furthermore, the enhanced synchronizations also take place in other regions.
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Affiliation(s)
- Wenjing Tong
- From the School of Psychology of Xinxiang Medical University
| | | | - Wenbin Guo
- Mental Health Institute, Second Xiangya Hospital of Central South University, Changsha
| | - Meng Zhang
- From the School of Psychology of Xinxiang Medical University
| | - Yujuan Zhang
- Department of Psychiatry of the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang
| | - Yunhong Du
- Department of Psychiatry of the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang
| | - Jingping Zhao
- Mental Health Institute, Second Xiangya Hospital of Central South University, Changsha
| | - Luxian Lv
- Department of Psychiatry of the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang
| | - Yahui Liu
- From the School of Psychology of Xinxiang Medical University
| | - Xueke Wang
- From the School of Psychology of Xinxiang Medical University
| | - Yanna Kou
- Department of Psychiatry of the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang
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Yan W, Palaniyappan L, Liddle PF, Rangaprakash D, Wei W, Deshpande G. Characterization of Hemodynamic Alterations in Schizophrenia and Bipolar Disorder and Their Effect on Resting-State fMRI Functional Connectivity. Schizophr Bull 2022; 48:695-711. [PMID: 34951473 PMCID: PMC9077436 DOI: 10.1093/schbul/sbab140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Common and distinct neural bases of Schizophrenia (SZ) and bipolar disorder (BP) have been explored using resting-state fMRI (rs-fMRI) functional connectivity (FC). However, fMRI is an indirect measure of neural activity, which is a convolution of the hemodynamic response function (HRF) and latent neural activity. The HRF, which models neurovascular coupling, varies across the brain within and across individuals, and is altered in many psychiatric disorders. Given this background, this study had three aims: quantifying HRF aberrations in SZ and BP, measuring the impact of such HRF aberrations on FC group differences, and exploring the genetic basis of HRF aberrations. We estimated voxel-level HRFs by deconvolving rs-fMRI data obtained from SZ (N = 38), BP (N = 19), and matched healthy controls (N = 35). We identified HRF group differences (P < .05, FDR corrected) in many regions previously implicated in SZ/BP, with mediodorsal, habenular, and central lateral nuclei of the thalamus exhibiting HRF differences in all pairwise group comparisons. Thalamus seed-based FC analysis revealed that ignoring HRF variability results in false-positive and false-negative FC group differences, especially in insula, superior frontal, and lingual gyri. HRF was associated with DRD2 gene expression (P < .05, 1.62 < |Z| < 2.0), as well as with medication dose (P < .05, 1.75 < |Z| < 3.25). In this first study to report HRF aberrations in SZ and BP, we report the possible modulatory effect of dopaminergic signalling on HRF, and the impact that HRF variability can have on FC studies in clinical samples. To mitigate the impact of HRF variability on FC group differences, we suggest deconvolution during data preprocessing.
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Affiliation(s)
- Wenjing Yan
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, USA
- Department of Information Management, School of E-business and Logistics, Beijing Technology and Business University, Beijing, China
| | - Lena Palaniyappan
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Department of Medical Biophysics, University of Western Ontario, London, ON, Canada
| | - Peter F Liddle
- Centre for Translational Neuroimaging, Division of Mental Health and Clinical Neuroscience, Institute of Mental Health, University of Nottingham, UK
| | - D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Wei Wei
- Department of Information Management, School of E-business and Logistics, Beijing Technology and Business University, Beijing, China
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, USA
- Department of Psychological Sciences, Auburn University, Auburn, AL
- Alabama Advanced Imaging Consortium, Birmingham, AL
- Center for Neuroscience, Auburn University, AL, USA
- School of Psychology, Capital Normal University, Beijing, China
- Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Centre for Brain Research, Indian Institute of Science, Bangalore, India
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20
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Fernandes TP, Shoshina II, Oliveira MEC, Andreevna VE, Silva GM, Santos NA. Correlates of clinical variables on early-stage visual processing in schizophrenia and bipolar disorder. J Psychiatr Res 2022; 149:323-330. [PMID: 35339912 DOI: 10.1016/j.jpsychires.2022.03.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/27/2022] [Accepted: 03/07/2022] [Indexed: 12/15/2022]
Abstract
The use of noninvasive tools can help understand mental states and changes that are caused by medications, symptom severity, and other clinical variables. We investigated low-level visual processing using the contrast sensitivity function (CSF), a reliable, robust, and widely used approach. Our main purpose was (1) to evaluate visual impairments in schizophrenia (SCZ) and bipolar disorder (BPD) patients and (2) to investigate associations between clinical variables and visual function in both diseases. Fifty-six healthy controls (HCs; mean age = 31.04 years), 42 BPD patients (mean age = 32.84 years) who took only lithium, and 39 SCZ patients who took only olanzapine (mean age = 32.80 years) were recruited for this study. CSF differed between groups. Both groups of patients exhibited lower discrimination at low, mid-, and high spatial frequencies compared with HCs. No differences were observed between patients, with the exception of high spatial frequency. These impairments were also related to clinical variables, revealed by a strong effect in the mediation analyses. These findings may aid investigations of other clinical variables and the role of state- and trait-like effects on visual and cognitive processing in these patient populations. This study underscores the need for visual remediation interventions.
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Affiliation(s)
- Thiago P Fernandes
- Department of Psychology, Federal University of Paraiba, Joao Pessoa, Brazil; Perception, Neuroscience and Behaviour Laboratory, Federal University of Paraiba, Brazil.
| | - Irina I Shoshina
- Pavlov Institute of Physiology, Russian Academy of Sciences, St. Petersburg, Russia
| | - Milena E C Oliveira
- Department of Psychology, Federal University of Paraiba, Joao Pessoa, Brazil; Perception, Neuroscience and Behaviour Laboratory, Federal University of Paraiba, Brazil
| | | | - Gabriella M Silva
- Department of Psychology, Federal University of Paraiba, Joao Pessoa, Brazil; Perception, Neuroscience and Behaviour Laboratory, Federal University of Paraiba, Brazil
| | - Natanael A Santos
- Department of Psychology, Federal University of Paraiba, Joao Pessoa, Brazil; Perception, Neuroscience and Behaviour Laboratory, Federal University of Paraiba, Brazil
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21
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Grecucci A, Lapomarda G, Messina I, Monachesi B, Sorella S, Siugzdaite R. Structural Features Related to Affective Instability Correctly Classify Patients With Borderline Personality Disorder. A Supervised Machine Learning Approach. Front Psychiatry 2022; 13:804440. [PMID: 35295769 PMCID: PMC8918568 DOI: 10.3389/fpsyt.2022.804440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/03/2022] [Indexed: 11/13/2022] Open
Abstract
Previous morphometric studies of Borderline Personality Disorder (BPD) reported inconsistent alterations in cortical and subcortical areas. However, these studies have investigated the brain at the voxel level using mass univariate methods or region of interest approaches, which are subject to several artifacts and do not enable detection of more complex patterns of structural alterations that may separate BPD from other clinical populations and healthy controls (HC). Multiple Kernel Learning (MKL) is a whole-brain multivariate supervised machine learning method able to classify individuals and predict an objective diagnosis based on structural features. As such, this method can help identifying objective biomarkers related to BPD pathophysiology and predict new cases. To this aim, we applied MKL to structural images of patients with BPD and matched HCs. Moreover, to ensure that results are specific for BPD and not for general psychological disorders, we also applied MKL to BPD against a group of patients with bipolar disorder, for their similarities in affective instability. Results showed that a circuit, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC (80%). Notably, this circuit positively correlates with the affective sector of the Zanarini questionnaire, thus indicating an involvement of this circuit with affective disturbances. Moreover, by contrasting BPD with BD, the spurious regions were excluded, and a specific circuit for BPD was outlined. These results support that BPD is characterized by anomalies in a cortico-subcortical circuit related to affective instability and that this circuit discriminates BPD from controls and from other clinical populations.
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Affiliation(s)
- Alessandro Grecucci
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
- Center for Medical Sciences - CISMed, University of Trento, Trento, Italy
| | - Gaia Lapomarda
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
- Department of Psychology, Science Division, New York University of Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Irene Messina
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
- Universitas Mercatorum, Rome, Italy
| | - Bianca Monachesi
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
| | - Sara Sorella
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
| | - Roma Siugzdaite
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
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22
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Dadomo H, Salvato G, Lapomarda G, Ciftci Z, Messina I, Grecucci A. Structural Features Predict Sexual Trauma and Interpersonal Problems in Borderline Personality Disorder but Not in Controls: A Multi-Voxel Pattern Analysis. Front Hum Neurosci 2022; 16:773593. [PMID: 35280205 PMCID: PMC8904389 DOI: 10.3389/fnhum.2022.773593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 01/17/2022] [Indexed: 12/01/2022] Open
Abstract
Child trauma plays an important role in the etiology of Bordeline Personality Disorder (BPD). Of all traumas, sexual trauma is the most common, severe and most associated with receiving a BPD diagnosis when adult. Etiologic models posit sexual abuse as a prognostic factor in BPD. Here we apply machine learning using Multiple Kernel Regression to the Magnetic Resonance Structural Images of 20 BPD and 13 healthy control (HC) to see whether their brain predicts five sources of traumas: sex abuse, emotion neglect, emotional abuse, physical neglect, physical abuse (Child Trauma Questionnaire; CTQ). We also applied the same analysis to predict symptom severity in five domains: affective, cognitive, impulsivity, interpersonal (Zanarini Rating Scale for Borderline Personality Disorder; Zan-BPD) for BPD patients only. Results indicate that CTQ sexual trauma is predicted by a set of areas including the amygdala, the Heschl area, the Caudate, the Putamen, and portions of the Cerebellum in BPD patients only. Importantly, interpersonal problems only in BPD patients were predicted by a set of areas including temporal lobe and cerebellar regions. Notably, sexual trauma and interpersonal problems were not predicted by structural features in matched healthy controls. This finding may help elucidate the brain circuit affected by traumatic experiences and connected with interpersonal problems BPD suffer from.
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Affiliation(s)
- Harold Dadomo
- Unit of Neuroscience, Department of Medicine and Surgery, University of Parma, Parma, Italy
- *Correspondence: Harold Dadomo,
| | - Gerardo Salvato
- Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Milan Center for Neuroscience, School of Medicine and Surgery, University of Milano Bicocca, Milan, Italy
- Cognitive Neuropsychology Centre, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Gaia Lapomarda
- Clinical and Affective Neuroscience Lab – Cli.A.N. Lab, Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
| | - Zafer Ciftci
- Clinical and Affective Neuroscience Lab – Cli.A.N. Lab, Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
| | | | - Alessandro Grecucci
- Clinical and Affective Neuroscience Lab – Cli.A.N. Lab, Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
- Centre for Medical Sciences, CISMed, University of Trento, Trento, Italy
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23
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Sorella S, Vellani V, Siugzdaite R, Feraco P, Grecucci A. Structural and functional brain networks of individual differences in trait anger and anger control: An unsupervised machine learning study. Eur J Neurosci 2022; 55:510-527. [PMID: 34797003 PMCID: PMC9303475 DOI: 10.1111/ejn.15537] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 10/25/2021] [Accepted: 11/08/2021] [Indexed: 12/03/2022]
Abstract
The ability to experience, use and eventually control anger is crucial to maintain well-being and build healthy relationships. Despite its relevance, the neural mechanisms behind individual differences in experiencing and controlling anger are poorly understood. To elucidate these points, we employed an unsupervised machine learning approach based on independent component analysis to test the hypothesis that specific functional and structural networks are associated with individual differences in trait anger and anger control. Structural and functional resting state images of 71 subjects as well as their scores from the State-Trait Anger Expression Inventory entered the analyses. At a structural level, the concentration of grey matter in a network including ventromedial temporal areas, posterior cingulate, fusiform gyrus and cerebellum was associated with trait anger. The higher the concentration, the higher the proneness to experience anger in daily life due to the greater tendency to orient attention towards aversive events and interpret them with higher hostility. At a functional level, the activity of the default mode network (DMN) was associated with anger control. The higher the DMN temporal frequency, the stronger the exerted control over anger, thus extending previous evidence on the role of the DMN in regulating cognitive and emotional functions in the domain of anger. Taken together, these results show, for the first time, two specialized brain networks for encoding individual differences in trait anger and anger control.
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Affiliation(s)
- Sara Sorella
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo)University of TrentoRoveretoItaly
| | - Valentina Vellani
- Affective Brain Lab, Department of Experimental PsychologyUniversity College LondonLondonUK
| | | | - Paola Feraco
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES)University of BolognaBolognaItaly
| | - Alessandro Grecucci
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo)University of TrentoRoveretoItaly,Centre for Medical Sciences (CISMed)University of TrentoTrentoItaly
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24
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Kandilarova S, Stoyanov DS, Paunova R, Todeva-Radneva A, Aryutova K, Maes M. Effective Connectivity between Major Nodes of the Limbic System, Salience and Frontoparietal Networks Differentiates Schizophrenia and Mood Disorders from Healthy Controls. J Pers Med 2021; 11:1110. [PMID: 34834462 PMCID: PMC8623155 DOI: 10.3390/jpm11111110] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/24/2021] [Accepted: 10/26/2021] [Indexed: 12/18/2022] Open
Abstract
This study was conducted to examine whether there are quantitative or qualitative differences in the connectome between psychiatric patients and healthy controls and to delineate the connectome features of major depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BD), as well as the severity of these disorders. Toward this end, we performed an effective connectivity analysis of resting state functional MRI data in these three patient groups and healthy controls. We used spectral Dynamic Causal Modeling (spDCM), and the derived connectome features were further subjected to machine learning. The results outlined a model of five connections, which discriminated patients from controls, comprising major nodes of the limbic system (amygdala (AMY), hippocampus (HPC) and anterior cingulate cortex (ACC)), the salience network (anterior insula (AI), and the frontoparietal and dorsal attention network (middle frontal gyrus (MFG), corresponding to the dorsolateral prefrontal cortex, and frontal eye field (FEF)). Notably, the alterations in the self-inhibitory connection of the anterior insula emerged as a feature of both mood disorders and SCZ. Moreover, four out of the five connectome features that discriminate mental illness from controls are features of mood disorders (both MDD and BD), namely the MFG→FEF, HPC→FEF, AI→AMY, and MFG→AMY connections, whereas one connection is a feature of SCZ, namely the AMY→SPL connectivity. A large part of the variance in the severity of depression (31.6%) and SCZ (40.6%) was explained by connectivity features. In conclusion, dysfunctions in the self-regulation of the salience network may underpin major mental disorders, while other key connectome features shape differences between mood disorders and SCZ, and can be used as potential imaging biomarkers.
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Affiliation(s)
- Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology and Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (D.S.S.); (R.P.); (A.T.-R.); (K.A.); (M.M.)
| | - Drozdstoy St. Stoyanov
- Department of Psychiatry and Medical Psychology and Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (D.S.S.); (R.P.); (A.T.-R.); (K.A.); (M.M.)
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology and Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (D.S.S.); (R.P.); (A.T.-R.); (K.A.); (M.M.)
| | - Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology and Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (D.S.S.); (R.P.); (A.T.-R.); (K.A.); (M.M.)
| | - Katrin Aryutova
- Department of Psychiatry and Medical Psychology and Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (D.S.S.); (R.P.); (A.T.-R.); (K.A.); (M.M.)
| | - Michael Maes
- Department of Psychiatry and Medical Psychology and Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (D.S.S.); (R.P.); (A.T.-R.); (K.A.); (M.M.)
- Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
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25
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Zhao G, Lau WKW, Wang C, Yan H, Zhang C, Lin K, Qiu S, Huang R, Zhang R. A Comparative Multimodal Meta-analysis of Anisotropy and Volume Abnormalities in White Matter in People Suffering From Bipolar Disorder or Schizophrenia. Schizophr Bull 2021; 48:69-79. [PMID: 34374427 PMCID: PMC8781378 DOI: 10.1093/schbul/sbab093] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Schizophrenia (SZ) and bipolar disorder (BD) share some similarities in terms of genetic-risk genes and abnormalities of gray-matter structure in the brain, but white matter (WM) abnormalities have not been studied in depth. We undertook a comparative multimodal meta-analysis to identify common and disorder-specific abnormalities in WM structure between SZ and BD. Anisotropic effect size-signed differential mapping software was used to conduct a comparative meta-analysis of 68 diffusion tensor imaging (DTI) and 34 voxel-based morphometry (VBM) studies comparing fractional anisotropy (FA) and white matter volume (WMV), respectively, between patients with SZ (DTI: N = 1543; VBM: N = 1068) and BD (DTI: N = 983; VBM: N = 518) and healthy controls (HCs). The bilateral corpus callosum (extending to the anterior and superior corona radiata) showed shared decreased WMV and FA in SZ and BD. Compared with BD patients, SZ patients showed remarkable disorder-specific WM abnormalities: decreased FA and increased WMV in the left cingulum, and increased FA plus decreased WMV in the right anterior limb of the internal capsule. SZ patients showed more extensive alterations in WM than BD cases, which may be the pathophysiological basis for the clinical continuity of both disorders. The disorder-specific regions in the left cingulum and right anterior limb of the internal capsule provided novel insights into both disorders. Our study adds value to further understanding of the pathophysiology, classification, and differential diagnosis of SZ and BD.
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Affiliation(s)
- Guorui Zhao
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Way K W Lau
- Department of Special Education and Counselling, The Education University of Hong Kong, Hong Kong, China
| | - Chanyu Wang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Haifeng Yan
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Chichen Zhang
- School of Management, Southern Medical University, Guangzhou, China
| | - Kangguang Lin
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Chinese traditional Medical University, Guangzhou, China
| | - Ruiwang Huang
- School of Psychology, South China Normal University, Guangzhou, China
| | - Ruibin Zhang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China,To whom correspondence should be addressed; Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, tel/fax:020-62789234, e-mail:
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26
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Lapomarda G, Pappaianni E, Siugzdaite R, Sanfey AG, Rumiati RI, Grecucci A. Out of control: An altered parieto-occipital-cerebellar network for impulsivity in bipolar disorder. Behav Brain Res 2021; 406:113228. [PMID: 33684426 DOI: 10.1016/j.bbr.2021.113228] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 01/28/2021] [Accepted: 03/01/2021] [Indexed: 12/21/2022]
Abstract
Bipolar disorder is an affective disorder characterized by rapid fluctuations in mood ranging from episodes of depression to mania, as well as by increased impulsivity. Previous studies investigated the neural substrates of bipolar disorder mainly using univariate methods, with a particular focus on the neural circuitry underlying emotion regulation difficulties. In the present study, capitalizing on an innovative whole-brain multivariate method to structural analysis known as Source-based Morphometry, we investigated the neural substrates of bipolar disorder and their relation with impulsivity, assessed with both self-report measures and performance-based tasks. Structural images from 46 patients with diagnosis of bipolar disorder and 60 healthy controls were analysed. Compared to healthy controls, patients showed decreased gray matter concentration in a parietal-occipital-cerebellar network. Notably, the lower the gray matter concentration in this circuit, the higher the self-reported impulsivity. In conclusion, we provided new evidence of an altered brain network in bipolar disorder patients related to their abnormal impulsivity. Taken together, these findings extend our understanding of the neural and symptomatic characterization of bipolar disorder.
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Affiliation(s)
- Gaia Lapomarda
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences, University of Trento, Rovereto, Italy.
| | - Edoardo Pappaianni
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Roma Siugzdaite
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Alan G Sanfey
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Raffaella I Rumiati
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), University of Trieste, Trieste, Italy
| | - Alessandro Grecucci
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences, University of Trento, Rovereto, Italy
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27
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Reviewing applications of structural and functional MRI for bipolar disorder. Jpn J Radiol 2021; 39:414-423. [PMID: 33389525 DOI: 10.1007/s11604-020-01074-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 11/19/2020] [Indexed: 02/07/2023]
Abstract
Bipolar disorders (BDs) represent one of the leading causes of disability and morbidity globally. The use of functional magnetic resonance imaging (fMRI) is being increasingly studied as a tool to improve the diagnosis and treatment of BDs. While morphological biomarkers can be identified through the use of structural magnetic resonance imaging (sMRI), recent studies have demonstrated that varying degrees of both structural and functional impairments indicate differing bipolar subtypes. Within fMRI, resting-state fMRI has specifically drawn increased interest for its capability to detect different neuronal activation patterns compared to task-based fMRI. This study aims to review recently published literature regarding the use of fMRI to investigate structural-functional relationships in BD diagnosis and specifically resting-state fMRI to provide an opinion on fMRI's modern clinical application. All sources in this literature review were collected through searches on both PubMed and Google Scholar databases for terms such as 'resting-state fMRI' and 'functional neuroimaging biomarkers of bipolar disorder'. While there are promising results supporting the use of fMRI for improving differential accuracy and establishing clinically relevant biomarkers, additional evidence will be required before fMRI is considered a dependable component of the overall BD diagnostic process.
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28
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Zangani C, Casetta C, Saunders AS, Donati F, Maggioni E, D’Agostino A. Sleep abnormalities across different clinical stages of Bipolar Disorder: A review of EEG studies. Neurosci Biobehav Rev 2020; 118:247-257. [DOI: 10.1016/j.neubiorev.2020.07.031] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/20/2020] [Accepted: 07/27/2020] [Indexed: 12/17/2022]
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29
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Sulpizio S, Grecucci A, Job R. Tune in to the right frequency: Theta changes when distancing from emotions elicited by unpleasant images and words. Eur J Neurosci 2020; 53:916-928. [PMID: 33091188 DOI: 10.1111/ejn.15013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 09/24/2020] [Accepted: 10/13/2020] [Indexed: 11/29/2022]
Abstract
Emotion regulation allows humans to successfully modulate their reactions to life events. Whether regulation strategies may alter brain oscillatory activity and how they are influenced by format and emotional dimensions is still under debate. We investigated oscillatory brain dynamics during the implementation of the strategy of Distancing and during the regulation of the emotions elicited by neutral and unpleasant pictures and, for the first time, words. When implementing the strategy, an early increase in theta band in posterior regions was observed (Effect of Strategy). We interpret this effect as a marker of emotion regulation, and we suggest an integrative framework of the role of theta on regulatory processes. When regulating the emotional impact elicited by stimuli, a decrease in the theta and beta bands in posterior regions for pictures, but not for words, was observed (Effect of Regulation). Behaviorally, the Effect of Regulation was evident for both pictures and words and more pronounced for Valence than for Arousal. These results contribute to better understand the neural and behavioral features of Distancing (both Effect of Strategy and of Regulation), and open up the possibility to clarify which strategy works better to modulate specific stimulus types and emotional dimensions.
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Affiliation(s)
- Simone Sulpizio
- Department of Psychology, University of Milano-Bicocca, Milano, Italy
| | - Alessandro Grecucci
- Department of Psychology and Cognitive Sciences, University of Trento, Trento, Italy
| | - Remo Job
- Department of Psychology and Cognitive Sciences, University of Trento, Trento, Italy
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30
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Saviola F, Pappaianni E, Monti A, Grecucci A, Jovicich J, De Pisapia N. Trait and state anxiety are mapped differently in the human brain. Sci Rep 2020; 10:11112. [PMID: 32632158 PMCID: PMC7338355 DOI: 10.1038/s41598-020-68008-z] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 06/12/2020] [Indexed: 12/20/2022] Open
Abstract
Anxiety is a mental state characterized by an intense sense of tension, worry or apprehension, relative to something adverse that might happen in the future. Researchers differentiate aspects of anxiety into state and trait, respectively defined as a more transient reaction to an adverse situation, and as a more stable personality attribute in experiencing events. It is yet unclear whether brain structural and functional features may distinguish these aspects of anxiety. To study this, we assessed 42 healthy participants with the State-Trait Anxiety Inventory and then investigated with MRI to characterize structural grey matter covariance and resting-state functional connectivity (rs-FC). We found several differences in the structural-functional patterns across anxiety types: (1) trait anxiety was associated to both structural covariance of Default Mode Network (DMN), with an increase in dorsal nodes and a decrease in its ventral part, and to rs-FC of DMN within frontal regions; (2) state anxiety, instead, was widely related to rs-FC of Salience Network and of DMN, specifically in its ventral nodes, but not associated with any structural pattern. In conclusion, our study provides evidence of a neuroanatomical and functional distinction between state and trait anxiety. These neural features may be additional markers in future studies evaluating early diagnosis or treatment effects.
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Affiliation(s)
- Francesca Saviola
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, TN, Italy
| | - Edoardo Pappaianni
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Corso Bettini 31, 38068, Rovereto, TN, Italy
| | - Alessia Monti
- Department of Neurorehabilitation Sciences, Casa Di Cura Privata del Policlinico, Milan, Italy
| | - Alessandro Grecucci
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Corso Bettini 31, 38068, Rovereto, TN, Italy
| | - Jorge Jovicich
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, TN, Italy
| | - Nicola De Pisapia
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Corso Bettini 31, 38068, Rovereto, TN, Italy.
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31
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Grecucci A, Messina I, Amodeo L, Lapomarda G, Crescentini C, Dadomo H, Panzeri M, Theuninck A, Frederickson J. A Dual Route Model for Regulating Emotions: Comparing Models, Techniques and Biological Mechanisms. Front Psychol 2020; 11:930. [PMID: 32581903 PMCID: PMC7287186 DOI: 10.3389/fpsyg.2020.00930] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 04/14/2020] [Indexed: 11/13/2022] Open
Abstract
The aim of this article is to present recent applications of emotion regulation theory and methods to the field of psychotherapy. The term Emotion Regulation refers to the neurocognitive mechanisms by which we regulate the onset, strength, and the eventual expression of our emotions. Deficits in the regulation of emotions have been linked to most, if not all, psychiatric disorders, with patients presenting either dysregulated emotions, or dysfunctional regulatory strategies. We discuss the implications of regulating emotions from two different theoretical perspectives: the Cognitive Emotion Regulation (CER), and the Experiential-Dynamic Emotion Regulation (EDER) model. Each proposes different views on how emotions are generated, dysregulated and regulated. These perspectives directly influence the way clinicians treat such problems. The CER model views emotional dysregulation as due to a deficit in regulation mechanisms that prioritizes modifying or developing cognitive skills, whilst the EDER model posits emotional dysregulation as due to the presence of dysregulatory mechanisms that prioritizes restoring natural regulatory processes. Examples of relevant techniques for each model are presented including a range of cognitive-behavioral, and experiential (including both dynamic and cognitive) techniques. The aim of the paper is to provide a toolbox from which clinician may gain different techniques to enhance and maintain their patient’s capacity for emotional regulation. Finally, the biological mechanisms behind the two models of emotion regulation are discussed as well as a proposal of a dual route model of emotion regulation.
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Affiliation(s)
- Alessandro Grecucci
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences, University of Trento, Rovereto, Italy
| | - Irene Messina
- Department of Psychiatry and Psychotherapy III, Ulm University, Ulm, Germany
| | - Letizia Amodeo
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences, University of Trento, Rovereto, Italy
| | - Gaia Lapomarda
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences, University of Trento, Rovereto, Italy
| | - Cristiano Crescentini
- Department of Languages and Literatures, Communication, Education and Society, University of Udine, Udine, Italy
| | - Harold Dadomo
- Department of Neuroscience, University of Parma, Parma, Italy.,Parma Schema Therapy Center, Parma, Italy
| | - Marta Panzeri
- Department of Developmental Psychology and Socialisation, University of Padua, Padua, Italy
| | | | - Jon Frederickson
- Washington School of Psychiatry, Washington, DC, WA, United States
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32
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Du Y, Hao H, Wang S, Pearlson GD, Calhoun VD. Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis. Neuroimage Clin 2020; 27:102284. [PMID: 32563920 PMCID: PMC7306624 DOI: 10.1016/j.nicl.2020.102284] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/29/2020] [Accepted: 05/19/2020] [Indexed: 12/23/2022]
Abstract
It is difficult to distinguish schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BPP) as their clinical diagnoses rely on symptoms that overlap. In this paper, we investigate if there is biological evidence to support the symptom-based clinical categories by looking across the three disorders using dynamic connectivity measures, and provide meaningful characteristics on which brain functional connectivity measures are commonly or uniquely impaired. Large-sample functional magnetic resonance image (fMRI) datasets from 623 subjects including 238 healthy controls (HCs), 113 SZ patients, 132 SAD patients, and 140 BPP patients were analyzed. First, we computed whole-brain dynamic functional connectivity (DFC) using a sliding-window technique, and then extracted the individual connectivity states by applying our previously proposed decomposition-based DFC analysis method. Next, with the features from the dominant connectivity state, we assessed the clinical categories by performing both four-group (SZ, SAD, BPP and healthy control groups) and pair-wise classification using a support vector machine within cross-validation. Furthermore, we comprehensively summarized the shared and unique connectivity alterations among the disorders. In terms of the classification performance, our method achieved 69% in the four-group classification and >80% in the between-group classifications for the mean overall accuracy; and yielded 66% in the four-group classification and >80% in the between-group classifications for the mean balanced accuracy. Through summarizing the features that were automatically selected in the classifications, we found that among the three symptom-related disorders, their disorder-common impairments primarily included the decreased connectivity strength between thalamus and cerebellum and the increased strength between postcentral gyrus and thalamus. The disorder-unique changes included more various brain regions, mainly in the temporal and frontal gyrus. Our work demonstrates that dynamic functional connectivity provides biological evidence that both common and unique impairments exist in psychosis sub-groups.
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Affiliation(s)
- Yuhui Du
- School of Computer & Information Technology, Shanxi University, Taiyuan, China; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
| | - Hui Hao
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Shuhua Wang
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | | | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
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Long Y, Liu Z, Chan CKY, Wu G, Xue Z, Pan Y, Chen X, Huang X, Li D, Pu W. Altered Temporal Variability of Local and Large-Scale Resting-State Brain Functional Connectivity Patterns in Schizophrenia and Bipolar Disorder. Front Psychiatry 2020; 11:422. [PMID: 32477194 PMCID: PMC7235354 DOI: 10.3389/fpsyt.2020.00422] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 04/24/2020] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia and bipolar disorder share some common clinical features and are both characterized by aberrant resting-state functional connectivity (FC). However, little is known about the common and specific aberrant features of the dynamic FC patterns in these two disorders. In this study, we explored the differences in dynamic FC among schizophrenia patients (n = 66), type I bipolar disorder patients (n = 53), and healthy controls (n = 66), by comparing temporal variabilities of FC patterns involved in specific brain regions and large-scale brain networks. Compared with healthy controls, both patient groups showed significantly increased regional FC variabilities in subcortical areas including the thalamus and basal ganglia, as well as increased inter-network FC variability between the thalamus and sensorimotor areas. Specifically, more widespread changes were found in the schizophrenia group, involving increased FC variabilities in sensorimotor, visual, attention, limbic and subcortical areas at both regional and network levels, as well as decreased regional FC variabilities in the default-mode areas. The observed alterations shared by schizophrenia and bipolar disorder may help to explain their overlapped clinical features; meanwhile, the schizophrenia-specific abnormalities in a wider range may support that schizophrenia is associated with more severe functional brain deficits than bipolar disorder. Together, these findings highlight the potentials of using dynamic FC as an objective biomarker for the monitoring and diagnosis of either schizophrenia or bipolar disorder.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | | | - Guowei Wu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Zhimin Xue
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Yunzhi Pan
- Mental Health Institute of Central South University, Changsha, China
| | - Xudong Chen
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Xiaojun Huang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Dan Li
- Department of Geriatrics, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Weidan Pu
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
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