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Liu H, Chen D, Liu C, Liu P, Yang H, Lu H. Brain structural changes and molecular analyses in children with benign epilepsy with centrotemporal spikes. Pediatr Res 2024; 96:184-189. [PMID: 38431664 DOI: 10.1038/s41390-024-03118-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/29/2024] [Accepted: 02/15/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND Benign epilepsy with centrotemporal spikes (BECTS) is a common childhood epilepsy syndrome, accompanied by behavioral problems and cognitive impairments. Previous studies of BECTS-related brain structures applied univariate analysis and showed inconsistent results. And neurotransmitter patterns associated with brain structural alterations were still unclear. METHODS Structural images of twenty-one drug-naïve children with BECTS and thirty-five healthy controls (HCs) were scanned. Segmented gray matter volume (GMV) images were decomposed into independent components (ICs) using the source-based morphometry method. Then spatial correlation analyses were applied to examine possible relationships between GMV changes and neurotransmitter systems. RESULTS Compared with HCs, drug-naïve children with BECTS showed increased volume in one GMV component (IC7), including bilateral precentral gyrus, bilateral supplementary motor area, left superior frontal cortex, bilateral middle/ inferior frontal cortex and bilateral anterior/ middle cingulate cortex. A positive correlation was observed between one GMV component (IC6) and seizure frequency. There were significantly positive correlations between abnormal GMV in IC7 and serotonergic, GABAergic and glutamatergic systems. CONCLUSION These findings provided further evidence of changed GMV in drug-naïve children with BECTS related to their behavioral problems and cognitive impairments, and associated neurotransmitters which could help to better understand neurobiological mechanisms and underlying molecular mechanisms of BECTS. IMPACT The article provides further evidence of changed gray matter volume in drug-naïve children with BECTS related to their behavioral problems and cognitive impairments as well as associated neurotransmitters. Most literature to date has applied univariate analysis and showed inconsistent results, and neurotransmitter patterns associated with brain structural alterations were still unclear. Therefore, this article uses multivariate method and JuSpace toolbox to fill the gap. Significantly increased gray matter volume was found in drug-naïve children with BECTS compared with healthy controls. Abnormal gray matter volume was significantly correlated with clinical data and specific neurotransmitters.
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Affiliation(s)
- Heng Liu
- Department of Radiology, The Seventh People's Hospital of Chongqing, The Central Hospital Affiliated to Chongqing University of Technology, Chongqing, China.
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Zunyi, China.
| | - Duoli Chen
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Chengxiang Liu
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Peng Liu
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Hua Yang
- Department of Medical Imaging, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China.
| | - Hong Lu
- Department of Radiology, The Seventh People's Hospital of Chongqing, The Central Hospital Affiliated to Chongqing University of Technology, Chongqing, China.
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Tai H, Kandeel N, Menon M, Ibrahim A, Choo B, Santana R, Jolayemi A. Role of the Cerebellum in Bipolar Disorder: A Systematic Literature Review. Cureus 2024; 16:e56044. [PMID: 38606213 PMCID: PMC11008919 DOI: 10.7759/cureus.56044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
The aim of this systematic literature review was to investigate the role of the cerebellum in the affective symptoms observed in patients with bipolar disorder. The present systematic literature review included clinical studies conducted from 2013-2023 among adult populations with bipolar I and II disorders, along with their specifiers. With regard to cerebellar pathology, it was found that those with bipolar disorder performed worse than their healthy counterparts in their ability to comprehend the mental states of others and in identifying negative mental states. Additionally, individuals with bipolar disorder had reduced gray matter loss in regions such as lobules I-IX, crus I, and crus II, different functional activation patterns of the thalamus, striatum, and hippocampus on functional magnetic resonance imaging (fMRI), and increased cortical thickness. Cerebro-cerebellar functional connectivities were altered in patients with bipolar disorder. The effects of lamotrigine and lithium on cerebellar volume and abnormalities are also discussed in this paper. The present systematic literature review illustrates the emerging involvement of the cerebellum in bipolar disorder and its affective symptoms and paves the way for future research and a better understanding of bipolar disorder.
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Affiliation(s)
- Hina Tai
- Medicine, St. George's University School of Medicine, St. George's, GRD
| | - Nermien Kandeel
- Medicine, American University of Antigua, New York City, USA
| | - Maya Menon
- Medicine, American University of Antigua, New York City, USA
| | - Andrew Ibrahim
- Medicine, Saba University School of Medicine, The Bottom, NLD
| | - Byeongyeon Choo
- Medicine, American University of Antigua, New York City, USA
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Teh WL, Liu J, Chandwani N, Lee YW, Tor PC, Subramaniam M, Ho RC. Emotional urgency predicts bipolar symptoms, severity, and suicide attempt better than non-emotional impulsivity: a cross-sectional study. Front Psychol 2023; 14:1277655. [PMID: 38106393 PMCID: PMC10722176 DOI: 10.3389/fpsyg.2023.1277655] [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: 08/15/2023] [Accepted: 10/30/2023] [Indexed: 12/19/2023] Open
Abstract
Introduction Emotional urgency is an emotion-based subdimension of trait impulsivity that is more clinically relevant to psychopathology and disorders of emotion dysfunction than non-emotional subdimensions (i.e., lack of perseverance, sensation seeking, lack of premeditation). However, few studies have examined the relative effects of emotional urgency in bipolar disorder. This cross-sectional study aimed to establish the clinical relevance of emotional urgency in bipolar disorders by (1) explicating clinically relevant correlates of emotional urgency and (2) comparing its effects against non-emotional impulsivity subdimensions. Methods and results A total of 150 individuals with bipolar disorder were recruited between October 2021 and January 2023. Zero-order correlations found that emotional urgency had the greatest effect on bipolar symptoms (r = 0.37 to 0.44). Multiple two-step hierarchical regression models showed that (1) positive urgency predicted past manic symptomology and dysfunction severity (b = 1.94, p < 0.001 and 0.35 p < 0.05, respectively), (2) negative urgency predicted current depression severity, and (3) non-emotional facets of impulsivity had smaller effects on bipolar symptoms and dysfunction by contrast, and were non-significant factors in the final step of all regression models (b < 0.30, ns); Those who had a history of attempted suicide had significantly greater levels of emotional urgency (Cohen's d = -0.63). Discussion Notwithstanding the study's limitations, our findings expand status quo knowledge beyond the perennial relationship between non-emotion-based impulsivity and bipolar disorder and its implications.
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Affiliation(s)
- Wen Lin Teh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Mental Health, Singapore, Singapore
| | - Jianlin Liu
- Institute of Mental Health, Singapore, Singapore
| | | | - Yu Wei Lee
- Institute of Mental Health, Singapore, Singapore
| | | | | | - Roger C. Ho
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Psychological Medicine, National University of Singapore, Singapore, Singapore
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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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Wang Y, Li C, Qi X. The effect of acupuncture at the Yuji point on resting-state brain function in anxiety. Medicine (Baltimore) 2023; 102:e33094. [PMID: 36827004 PMCID: PMC11309646 DOI: 10.1097/md.0000000000033094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/17/2023] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND The COVID-19 epidemic has placed a lot of mental burdens on school students, causing anxiety. Clinically, it has been found that the Yuji point (LU10) can relieve anxiety by regulating Qi. METHODS Thirty-six volunteers with anxiety disorders were divided into 3 groups, all of whom underwent 2 MRI examinations. The Yuji and nonacupoint groups received acupuncture between functional magnetic resonance imagings. We used the amplitude of low-frequency fluctuation to analyze regional brain activity, and seed-based functional connectivity (FC) to analyze changes in brain networks. RESULTS After acupuncture, the LU10 was able to activate the frontal lobe, medial frontal gyrus, anterior cingulate gyrus, temporal lobe, hippocampus, etc in the left brain compared to the control group. The frontal lobe, medial frontal gyrus, cingulate gyrus, and anterior cingulate gyrus in the left brain were activated compared to those in the nonacupoint group. Compared with the control group, LU10 showed increased FC in the right parietal lobe, right precuneus, left temporal lobe, left superior temporal gyrus, and with cingulate gyrus. FC was enhanced among the hippocampus with the left temporal lobe and the superior temporal gyrus and reduced in the right lingual gyrus and right occipital lobe. CONCLUSION Acupuncture at LU10s can regulate anxiety by upregulating or downregulating the relevant brain regions and networks. LU10s can be used to treat not only lung disorders but also related mental disorders.
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Affiliation(s)
- Yuangeng Wang
- First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Chunlin Li
- First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
- Department of Encephalopathy, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xianghua Qi
- Department of Encephalopathy, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
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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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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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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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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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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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Cerebellum and Neurorehabilitation in Emotion with a Focus on Neuromodulation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1378:285-299. [DOI: 10.1007/978-3-030-99550-8_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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