|
1
|
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.
Collapse
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
| |
Collapse
|
|
2
|
Türk Y, Devecioğlu İ, Küskün A, Öge C, Beyazyüz E, Albayrak Y. ROI-based analysis of diffusion indices in healthy subjects and subjects with deficit or non-deficit syndrome schizophrenia. Psychiatry Res Neuroimaging 2023; 336:111726. [PMID: 37925764 DOI: 10.1016/j.pscychresns.2023.111726] [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: 01/19/2023] [Revised: 09/29/2023] [Accepted: 10/14/2023] [Indexed: 11/07/2023]
Abstract
We analyzed DTI data involving 22 healthy subjects (HC), 15 patients with deficit syndrome schizophrenia (DSZ), and 25 patients with non-deficit syndrome schizophrenia (NDSZ). We used a 1.5-T MRI scanner to collect diffusion-weighted images and T1 images, which were employed to correct distortions and deformations within the diffusion-weighted images. For 156 regions of interest (ROI), we calculated the average fractional anisotropy (FA), mean diffusion (MD), and radial diffusion (RD). Each ROI underwent a group-wise comparison using permutation F-test, followed by post hoc pairwise comparisons with Bonferroni correction. In general, we observed lower FA in both schizophrenia groups compared to HC (i.e., HC>(DSZ=NDSZ)), while MD and RD showed the opposite pattern. Notably, specific ROIs with reduced FA in schizophrenia patients included bilateral nucleus accumbens, left fusiform area, brain stem, anterior corpus callosum, left rostral and caudal anterior cingulate, right posterior cingulate, left thalamus, left hippocampus, left inferior temporal cortex, right superior temporal cortex, left pars triangularis and right lingual gyrus. Significantly, the right cuneus exhibited lower FA in the DSZ group compared to other groups ((HC=NDSZ)>DSZ), without affecting MD and RD. These results indicate that compromised neural integrity in the cuneus may contribute to the pathophysiological distinctions between DSZ and NDSZ.
Collapse
Affiliation(s)
- Yaşar Türk
- Radiology Department, Medical Faculty, Tekirdağ Namık Kemal University. Namik Kemal Mh., Kampus Cd., Suleymanpasa, Tekirdag 59100, Turkey; Radiology Department, İstanbul Health and Technology University Hospital, Kaptanpasa Mh., Darulaceze Cd., Sisli, İstanbul 34384, Turkey
| | - İsmail Devecioğlu
- Biomedical Engineering Department, Çorlu Faculty of Engineering, Tekirdağ Namık Kemal University, NKU Corlu Muhendislik Fakultesi, Silahtaraga Mh., Çorlu, Tekirdağ 59860, Turkey.
| | - Atakan Küskün
- Radiology Department, Medical Faculty, Kırklareli University, Cumhuriyet Mh., Kofcaz Yolu, Kayali Yerleskesi, Merkezi Derslikler 2, No 39/L, Merkez, Kırklareli, Turkey
| | - Cem Öge
- Psychiatry Department, Çorlu State Hospital, Zafer, Mah. Bülent Ecevit Blv. No:33, Çorlu, Tekirdağ 59850, Turkey
| | - Elmas Beyazyüz
- Psychiatry Department, Medical Faculty, Tekirdağ Namık Kemal University. Namik Kemal Mh., Kampus Cd., Suleymanpasa, Tekirdag 59100, Turkey
| | - Yakup Albayrak
- Psychiatry Department, Medical Faculty, Tekirdağ Namık Kemal University. Namik Kemal Mh., Kampus Cd., Suleymanpasa, Tekirdag 59100, Turkey
| |
Collapse
|
|
3
|
He K, Hua Q, Li Q, Zhang Y, Yao X, Yang Y, Xu W, Sun J, Wang L, Wang A, Ji GJ, Wang K. Abnormal interhemispheric functional cooperation in schizophrenia follows the neurotransmitter profiles. J Psychiatry Neurosci 2023; 48:E452-E460. [PMID: 38123242 PMCID: PMC10743641 DOI: 10.1503/jpn.230037] [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: 03/02/2023] [Revised: 06/26/2023] [Accepted: 09/05/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Interhemispheric cooperation is one of the most prominent functional architectures of the human brain. In patients with schizophrenia, interhemispheric cooperation deficits have been reported using increasingly powerful neurobehavioural and neuroimaging measures. However, these methods rely in part on the assumption of anatomic symmetry between hemispheres. In the present study, we explored interhemispheric cooperation deficits in schizophrenia using a newly developed index, connectivity between functionally homotopic voxels (CFH), which is unbiased by hemispheric asymmetry. METHODS Patients with schizophrenia and age- and sexmatched healthy controls underwent multimodal MRI, and whole-brain CFH maps were constructed for comparison between groups. We examined the correlations of differing CFH values between the schizophrenia and control groups using various neurotransmitter receptor and transporter densities. RESULTS We included 86 patients with schizophrenia and 86 matched controls in our analysis. Patients with schizophrenia showed significantly lower CFH values in the frontal lobes, left postcentral gyrus and right inferior temporal gyrus, and significantly greater CFH values in the right caudate nucleus than healthy controls. Moreover, the differing CFH values in patients with schizophrenia were significantly correlated with positive symptom score and illness duration. Functional connectivity within frontal lobes was significantly reduced at the voxel cluster level compared with healthy controls. Finally, the abnormal CFH map of patients with schizophrenia was spatially associated with the densities of the dopamine D1 and D2 receptors, fluorodopa, dopamine transporter, serotonin transporter and acetylcholine transporter. CONCLUSION Regional abnormalities in interhemispheric cooperation may contribute to the clinical symptoms of schizophrenia. These CFH abnormalities may be associated with dysfunction in neurotransmitter systems strongly implicated in schizophrenia.
Collapse
Affiliation(s)
- Kongliang He
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Qiang Hua
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Qianqian Li
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Yan Zhang
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Xiaoqing Yao
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Yinian Yang
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Wenqiang Xu
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Jinmei Sun
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Lu Wang
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Anzhen Wang
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Gong-Jun Ji
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Kai Wang
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| |
Collapse
|
|
4
|
Pinto D, Martins R, Macedo A, Castelo Branco M, Valente Duarte J, Madeira N. Brain Hemispheric Asymmetry in Schizophrenia and Bipolar Disorder. J Clin Med 2023; 12:jcm12103421. [PMID: 37240527 DOI: 10.3390/jcm12103421] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/01/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND This study aimed to compare brain asymmetry in patients with schizophrenia (SCZ), bipolar disorder (BPD), and healthy controls to test whether asymmetry patterns could discriminate and set boundaries between two partially overlapping severe mental disorders. METHODS We applied a fully automated voxel-based morphometry (VBM) approach to assess structural brain hemispheric asymmetry in magnetic resonance imaging (MRI) anatomical scans in 60 participants (SCZ = 20; BP = 20; healthy controls = 20), all right-handed and matched for gender, age, and education. RESULTS Significant differences in gray matter asymmetry were found between patients with SCZ and BPD, between SCZ patients and healthy controls (HC), and between BPD patients and HC. We found a higher asymmetry index (AI) in BPD patients when compared to SCZ in Brodmann areas 6, 11, and 37 and anterior cingulate cortex and an AI higher in SCZ patients when compared to BPD in the cerebellum. CONCLUSION Our study found significant differences in brain asymmetry between patients with SCZ and BPD. These promising results could be translated to clinical practice, given that structural brain changes detected by MRI are good candidates for exploration as biological markers for differential diagnosis, besides helping to understand disease-specific abnormalities.
Collapse
Affiliation(s)
- Diogo Pinto
- Faculty of Medicine, University of Coimbra (UC), 3004-504 Coimbra, Portugal
| | - Ricardo Martins
- Faculty of Medicine, University of Coimbra (UC), 3004-504 Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal
| | - António Macedo
- Faculty of Medicine, University of Coimbra (UC), 3004-504 Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal
- Department of Psychiatry, Centro Hospitalar e Universitário de Coimbra (CHUC), 3000-075 Coimbra, Portugal
| | - Miguel Castelo Branco
- Faculty of Medicine, University of Coimbra (UC), 3004-504 Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal
| | - João Valente Duarte
- Faculty of Medicine, University of Coimbra (UC), 3004-504 Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal
| | - Nuno Madeira
- Faculty of Medicine, University of Coimbra (UC), 3004-504 Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal
- Department of Psychiatry, Centro Hospitalar e Universitário de Coimbra (CHUC), 3000-075 Coimbra, Portugal
| |
Collapse
|
|
5
|
Mao Q, Lin X, Yin Q, Liu P, Zhang Y, Qu S, Xu J, Cheng W, Luo X, Kang L, Taximaimaiti R, Zheng C, Zhang H, Wang X, Ren H, Cao Y, Lin J, Luo X. A significant, functional and replicable risk KTN1 variant block for schizophrenia. Sci Rep 2023; 13:3890. [PMID: 36890161 PMCID: PMC9995530 DOI: 10.1038/s41598-023-27448-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] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 01/02/2023] [Indexed: 03/10/2023] Open
Abstract
Cortical and subcortical structural alteration has been extensively reported in schizophrenia, including the unusual expansion of gray matter volumes (GMVs) of basal ganglia (BG), especially putamen. Previous genome-wide association studies pinpointed kinectin 1 gene (KTN1) as the most significant gene regulating the GMV of putamen. In this study, the role of KTN1 variants in risk and pathogenesis of schizophrenia was explored. A dense set of SNPs (n = 849) covering entire KTN1 was analyzed in three independent European- or African-American samples (n = 6704) and one mixed European and Asian Psychiatric Genomics Consortium sample (n = 56,418 cases vs. 78,818 controls), to identify replicable SNP-schizophrenia associations. The regulatory effects of schizophrenia-associated variants on the KTN1 mRNA expression in 16 cortical or subcortical regions in two European cohorts (n = 138 and 210, respectively), the total intracranial volume (ICV) in 46 European cohorts (n = 18,713), the GMVs of seven subcortical structures in 50 European cohorts (n = 38,258), and the surface areas (SA) and thickness (TH) of whole cortex and 34 cortical regions in 50 European cohorts (n = 33,992) and eight non-European cohorts (n = 2944) were carefully explored. We found that across entire KTN1, only 26 SNPs within the same block (r2 > 0.85) were associated with schizophrenia across ≥ 2 independent samples (7.5 × 10-5 ≤ p ≤ 0.048). The schizophrenia-risk alleles, which increased significantly risk for schizophrenia in Europeans (q < 0.05), were all minor alleles (f < 0.5), consistently increased (1) the KTN1 mRNA expression in 12 brain regions significantly (5.9 × 10-12 ≤ p ≤ 0.050; q < 0.05), (2) the ICV significantly (6.1 × 10-4 ≤ p ≤ 0.008; q < 0.05), (3) the SA of whole (9.6 × 10-3 ≤ p ≤ 0.047) and two regional cortices potentially (2.5 × 10-3 ≤ p ≤ 0.042; q > 0.05), and (4) the TH of eight regional cortices potentially (0.006 ≤ p ≤ 0.050; q > 0.05), and consistently decreased (1) the BG GMVs significantly (1.8 × 10-19 ≤ p ≤ 0.050; q < 0.05), especially putamen GMV (1.8 × 10-19 ≤ p ≤ 1.0 × 10-4; q < 0.05, (2) the SA of four regional cortices potentially (0.010 ≤ p ≤ 0.048), and (3) the TH of four regional cortices potentially (0.015 ≤ p ≤ 0.049) in Europeans. We concluded that we identified a significant, functional, and robust risk variant block covering entire KTN1 that might play a critical role in the risk and pathogenesis of schizophrenia.
Collapse
Affiliation(s)
- Qiao Mao
- Department of Psychosomatic Medicine, People's Hospital of Deyang City, Deyang, 618000, Sichuan, China
| | - Xiandong Lin
- Laboratory of Radiation Oncology and Radiobiology, Fujian Provincial Cancer Hospital, the Teaching Hospital of Fujian Medical University, Fuzhou, 350014, Fujian, China
| | - Qin Yin
- Department of Respiratory and Critical Care Medicine, Hubei Provincial Hospital of Integrated Chinese and Western Medicine, Wuhan, 430000, Hubei, China
| | - Ping Liu
- Department of Psychosomatic Medicine, People's Hospital of Deyang City, Deyang, 618000, Sichuan, China
| | - Yong Zhang
- Tianjin Mental Health Center, Tianjin, 300222, China
| | - Shihao Qu
- Zhuhai Center for Maternal and Child Health Care, Zhuhai, Guangdong, 519001, China
| | - Jianying Xu
- Zhuhai Center for Maternal and Child Health Care, Zhuhai, Guangdong, 519001, China
| | - Wenhong Cheng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Xinqun Luo
- Department of Neurosurgery, The First Hospital, Fujian Medical University, Fuzhou, 350004, Fujian, China
| | - Longli Kang
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research On High Altitude Diseases of Tibet Autonomous Region, Xizang Minzu University School of Medicine, Xiangyang, 712082, Shaanxi, China
| | - Reyisha Taximaimaiti
- Department of Neurology, Shanghai Tongren Hospital, Shanghai Jiao Tong University, Shanghai, 200080, China
| | - Chengchou Zheng
- Minqing Psychiatric Hospital, Minqing, 350800, Fujian, China
| | - Huihao Zhang
- The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350001, China
| | - Xiaoping Wang
- Department of Neurology, The 1st People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 201620, USA
| | - Honggang Ren
- Department of Internal Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuping Cao
- Department of Psychiatry, Second Xiangya Hospital, Central South University, China National Clinical Research Center On Mental Disorders, China National Technology Institute On Mental Disorders, Changsha, 410011, Hunan, China.
| | - Jie Lin
- Fujian Center for Disease Control and Prevention, Fuzhou, 350012, Fujian, China.
- Fujian Institute of Preventive Medicine, Fuzhou, 350012, Fujian, China.
| | - Xingguang Luo
- Beijing Huilongguan Hospital, Peking University Huilongguan School of Clinical Medicine, Beijing, 100096, China.
| |
Collapse
|
|
6
|
Lang X, Wang D, Zhou H, Wang L, Kosten TR, Zhang XY. P50 inhibition defects, psychopathology and gray matter volume in patients with first-episode drug-naive schizophrenia. Asian J Psychiatr 2023; 80:103421. [PMID: 36563611 DOI: 10.1016/j.ajp.2022.103421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 12/08/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Sensory gating deficits and gray matter volume (GMV) abnormalities have been found to be associated with the pathogenesis and psychopathology of patients with schizophrenia (SCZ). However, no studies have investigated their interrelationship in first-episode treatment-naive (FETN) SCZ patients. METHODS We recruited 52 FETN SCZ patients and 57 healthy controls. The Positive and Negative Syndrome Scale (PANSS) was used to measure the psychopathology of the patients. We collected magnetic resonance imaging and P50 inhibition data from all participants. RESULTS Compared to healthy controls, patients had shorter S1 and S2 latencies but larger S2 amplitudes and P50 ratio (Bonferroni adjusted all p < 0.01). In patients, S2 latency was independently associated with PANSS total score, negative symptoms and general psychopathology (t = 2.26-2.58, both P < 0.05), whereas S1 (t = 2.44, P < 0.05) and S2 latencies (t = 2.13, P < 0.05) were associated with PANSS cognitive factor. Moreover, GMV in the left inferior temporal gyrus, left lingual gyrus and right superior occipital gyrus, and bilateral dorsolateral superior frontal gyrus were each associated with the P50 components (all p < 0.05). In addition, GMV associated with S2 latency was negatively correlated with PANSS general psychopathology (t = -2.46, p < 0.05) and total score (t = -2.34, p < 0.05). CONCLUSIONS Our findings indicate that FETN SCZ patients exhibit deficits in P50 inhibition and GMV of brain regions associated with these deficits may be associated with their psychopathological symptoms, suggesting that brain structures associated with P50 components may be important biomarkers of SCZ psychopathology. Future studies could use a prospective longitudinal design to investigate the potential causal relationship of brain structures associated with P50 components in the psychopathological symptoms of SCZ patients.
Collapse
Affiliation(s)
- XiaoE Lang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.
| | - Dongmei Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Huixia Zhou
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Li Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Thomas R Kosten
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, USA
| | - Xiang-Yang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
| |
Collapse
|
|
7
|
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.
Collapse
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
| |
Collapse
|
|
8
|
Du Y, He X, Kochunov P, Pearlson G, Hong LE, van Erp TGM, Belger A, Calhoun VD. A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder. Hum Brain Mapp 2022; 43:3887-3903. [PMID: 35484969 PMCID: PMC9294304 DOI: 10.1002/hbm.25890] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/24/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.
Collapse
Affiliation(s)
- Yuhui Du
- School of Computer and Information TechnologyShanxi UniversityTaiyuanShanxiChina
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Xingyu He
- School of Computer and Information TechnologyShanxi UniversityTaiyuanShanxiChina
| | - Peter Kochunov
- Center for Brain Imaging ResearchUniversity of MarylandBaltimoreMarylandUSA
| | | | - L. Elliot Hong
- Center for Brain Imaging ResearchUniversity of MarylandBaltimoreMarylandUSA
| | - Theo G. M. van Erp
- Department of Psychiatry and Human BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of CaliforniaIrvineCaliforniaUSA
| | - Aysenil Belger
- Department of PsychiatryUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| |
Collapse
|
|
9
|
Garg S, Pant K, Tikka S, Mishra P, Tyagi P. Laterality of cognitive dysfunction in schizophrenia: A cross-sectional study. ARCHIVES OF MENTAL HEALTH 2022. [DOI: 10.4103/amh.amh_66_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
|
|
10
|
Stein F, Meller T, Brosch K, Schmitt S, Ringwald K, Pfarr JK, Meinert S, Thiel K, Lemke H, Waltemate L, Grotegerd D, Opel N, Jansen A, Nenadić I, Dannlowski U, Krug A, Kircher T. Psychopathological Syndromes Across Affective and Psychotic Disorders Correlate With Gray Matter Volumes. Schizophr Bull 2021; 47:1740-1750. [PMID: 33860786 PMCID: PMC8530386 DOI: 10.1093/schbul/sbab037] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
INTRODUCTION More than a century of research on the neurobiological underpinnings of major psychiatric disorders (major depressive disorder [MDD], bipolar disorder [BD], schizophrenia [SZ], and schizoaffective disorder [SZA]) has been unable to identify diagnostic markers. An alternative approach is to study dimensional psychopathological syndromes that cut across categorical diagnoses. The aim of the current study was to identify gray matter volume (GMV) correlates of transdiagnostic symptom dimensions. METHODS We tested the association of 5 psychopathological factors with GMV using multiple regression models in a sample of N = 1069 patients meeting Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for MDD (n = 818), BD (n = 132), and SZ/SZA (n = 119). T1-weighted brain images were acquired with 3-Tesla magnetic resonance imaging and preprocessed with CAT12. Interactions analyses (diagnosis × psychopathological factor) were performed to test whether local GMV associations were driven by DSM-IV diagnosis. We further tested syndrome specific regions of interest (ROIs). RESULTS Whole brain analysis showed a significant negative association of the positive formal thought disorder factor with GMV in the right middle frontal gyrus, the paranoid-hallucinatory syndrome in the right fusiform, and the left middle frontal gyri. ROI analyses further showed additional negative associations, including the negative syndrome with bilateral frontal opercula, positive formal thought disorder with the left amygdala-hippocampus complex, and the paranoid-hallucinatory syndrome with the left angular gyrus. None of the GMV associations interacted with DSM-IV diagnosis. CONCLUSIONS We found associations between psychopathological syndromes and regional GMV independent of diagnosis. Our findings open a new avenue for neurobiological research across disorders, using syndrome-based approaches rather than categorical diagnoses.
Collapse
Affiliation(s)
- Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind Brain and Behavior, University of Marburg, Marburg, Germany,To whom correspondence should be addressed; Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; tel: +49-6421-58-63831, fax: +49-6421-58-68939, e-mail:
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind Brain and Behavior, University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind Brain and Behavior, University of Marburg, Marburg, Germany
| | - Simon Schmitt
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind Brain and Behavior, University of Marburg, Marburg, Germany
| | - Kai Ringwald
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind Brain and Behavior, University of Marburg, Marburg, Germany
| | - Julia Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind Brain and Behavior, University of Marburg, Marburg, Germany
| | - Susanne Meinert
- Department of Psychiatry University of Münster, Münster, Germany
| | - Katharina Thiel
- Department of Psychiatry University of Münster, Münster, Germany
| | - Hannah Lemke
- Department of Psychiatry University of Münster, Münster, Germany
| | - Lena Waltemate
- Department of Psychiatry University of Münster, Münster, Germany
| | | | - Nils Opel
- Department of Psychiatry University of Münster, Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind Brain and Behavior, University of Marburg, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind Brain and Behavior, University of Marburg, Marburg, Germany
| | - Udo Dannlowski
- Department of Psychiatry University of Münster, Münster, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind Brain and Behavior, University of Marburg, Marburg, Germany,Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind Brain and Behavior, University of Marburg, Marburg, Germany
| |
Collapse
|
|
11
|
Clementz BA, Trotti RL, Pearlson GD, Keshavan MS, Gershon ES, Keedy SK, Ivleva EI, McDowell JE, Tamminga CA. Testing Psychosis Phenotypes From Bipolar-Schizophrenia Network for Intermediate Phenotypes for Clinical Application: Biotype Characteristics and Targets. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:808-818. [PMID: 32600898 DOI: 10.1016/j.bpsc.2020.03.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 03/30/2020] [Accepted: 03/31/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Psychiatry aspires to the molecular understanding of its disorders and, with that knowledge, to precision medicine. Research supporting such goals in the dimension of psychosis has been compromised, in part, by using phenomenology alone to estimate disease entities. To this end, we are proponents of a deep phenotyping approach in psychosis, using computational strategies to discover the most informative phenotypic fingerprint as a promising strategy to uncover mechanisms in psychosis. METHODS Doing this, the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has used biomarkers to identify distinct subtypes of psychosis with replicable biomarker characteristics. While we have presented these entities as relevant, their potential utility in clinical practice has not yet been demonstrated. RESULTS Here we carried out an analysis of clinical features that characterize biotypes. We found that biotypes have unique and defining clinical characteristics that could be used as initial screens in the clinical and research settings. Differences in these clinical features appear to be consistent with biotype biomarker profiles, indicating a link between biological features and clinical presentation. Clinical features associated with biotypes differ from those associated with DSM diagnoses, indicating that biotypes and DSM syndromes are not redundant and are likely to yield different treatment predictions. We highlight 3 predictions based on biotype that are derived from individual biomarker features and cannot be obtained from DSM psychosis syndromes. CONCLUSIONS In the future, biotypes may prove to be useful for targeting distinct molecular, circuit, cognitive, and psychosocial therapies for improved functional outcomes.
Collapse
Affiliation(s)
- Brett A Clementz
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, Georgia
| | - Rebekah L Trotti
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, Georgia
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess, Harvard Medical School, Boston, Massachusetts
| | - Elliot S Gershon
- Department of Psychiatry, University of Chicago, Chicago, Illinois
| | - Sarah K Keedy
- Department of Psychiatry, University of Chicago, Chicago, Illinois
| | - Elena I Ivleva
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Jennifer E McDowell
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, Georgia
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas.
| |
Collapse
|
|
12
|
Zhu J, Zhang S, Cai H, Wang C, Yu Y. Common and distinct functional stability abnormalities across three major psychiatric disorders. NEUROIMAGE-CLINICAL 2020; 27:102352. [PMID: 32721869 PMCID: PMC7393318 DOI: 10.1016/j.nicl.2020.102352] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/10/2020] [Accepted: 07/15/2020] [Indexed: 11/14/2022]
Abstract
Functional stability is a recently developed dynamic functional connectivity approach. Schizophrenia individuals had a distributed pattern of higher and lower stability. Individuals with bipolar disorder only manifested local higher stability. Individuals with attention deficit/hyperactivity disorder exhibited no stability differences. Psychiatric disorders show common and distinct functional stability abnormalities.
Delineating the neuropathological characteristics across psychiatric disorders is critical for understanding their pathophysiology. The purpose of this study was to investigate common and distinct brain functional abnormalities across psychiatric disorders by using functional stability, a recently developed dynamic functional connectivity approach. Resting-state functional magnetic resonance imaging data were collected from a transdisease sample of healthy controls (n = 115) and individuals with schizophrenia (SZ) (n = 47), bipolar disorder (BD) (n = 44), and attention deficit/hyperactivity disorder (ADHD) (n = 40). Functional stability of each voxel was calculated by measuring the concordance of dynamic functional connectivity over time. Differences in functional stability among the four groups were assessed voxel-wisely. Compared to healthy controls, individuals with SZ demonstrated a distributed pattern of higher functional stability in the bilateral inferior temporal gyrus yet lower stability in the bilateral calcarine sulcus and left insula; individuals with BD only manifested local higher stability in the left inferior temporal gyrus; no differences were found between ADHD and healthy individuals. Notably, individuals with SZ and BD had common higher functional stability in the left inferior temporal gyrus, whereas higher functional stability in the right inferior temporal gyrus and lower stability in the bilateral calcarine sulcus and left insula were unique abnormalities in individuals with SZ. Additionally, direct comparisons between disorders revealed that individuals with SZ showed lower functional stability in the right calcarine sulcus compared to those with BD and higher stability in the left inferior temporal gyrus compared to those with ADHD. However, no significant associations between functional stability and clinical symptoms were observed. Our findings suggest that the functional stability approach has the potential to be extended to the domain of psychiatry and encourage further investigations of shared and unique neuropathology of psychiatric disorders.
Collapse
Affiliation(s)
- Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Shujun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Chunli Wang
- Department of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.
| |
Collapse
|
|
13
|
Wei Q, Zhao L, Zou Y, Wang J, Qiu Y, Niu M, Kang Z, Liu X, Tang Y, Li C, Zhang J, Fan X, Huang R, Han Z. The role of altered brain structural connectivity in resilience, vulnerability, and disease expression to schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2020; 101:109917. [PMID: 32169560 DOI: 10.1016/j.pnpbp.2020.109917] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 02/05/2020] [Accepted: 03/09/2020] [Indexed: 01/06/2023]
Abstract
BACKGROUND Schizophrenia (SCZ) is a highly heritable disorder associated with brain connectivity changes. Although the mechanism of disease expression and vulnerability of SCZ have been reported by previous studies, the mechanism of resilience to SCZ based on the brain structural connectivity is poorly understood. The goal of the present study was to identify the structural brain connectivity related with the resilience to SCZ, which is defined here as the capacity to avoid or delay the onset of SCZ in unaffected siblings of SCZ probands. METHOD We collected diffusion tensor imaging (DTI) data of 49 medication-naive, first-episode SCZ (FE-SCZ) patients, 56 unaffected siblings of SCZ probands (SIB-SCZ), and 90 healthy controls. Then we used graph theoretical approach to calculate the topological properties of the brain structural network, including global, subnetwork, and regional parameters. Finally, we compared the parameters between the three groups, and identified the brain structural network related to the resilience, vulnerability and disease expression to SCZ. RESULTS With respect to resilience, only the SIB-SCZ showed significantly increased connectivity in the subnetworks of the left cuneus-precuneus and left posterior cingulate gyrus-precuneus, and in brain areas of right supramarginal gyrus and right inferior temporal gyrus. With respect to vulnerability, both the FE-SCZ and SIB-SCZ had decreased cluster coefficients and local efficiency, and decreased nodal efficiency in the right medial superior frontal gyrus and right medial orbital superior frontal gyrus compared with the healthy controls. With respect to disease expression, only the FE-SCZ group showed decreased or increased global, subnetwork, and nodal connectivity in broader brain regions compared with the healthy controls. CONCLUSION Difference in the topological properties of brain structural connectivity not only reflect the underlying mechanism of vulnerability but also that of resilience to schizophrenia. Alteration in the brain structural connectivity associating with resilience and disease expression may contribute to the onset of SCZ.
Collapse
Affiliation(s)
- Qinling Wei
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China
| | - Ling Zhao
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, 55 Zhongshan Avenue West, Guangzhou, Guangdong Province, China; Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH University, Aachen, Germany
| | - Yan Zou
- Department of Radiology, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China
| | - Junjing Wang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, 55 Zhongshan Avenue West, Guangzhou, Guangdong Province, China; Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou 510006, China
| | - Yong Qiu
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China
| | - Meiqi Niu
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, 55 Zhongshan Avenue West, Guangzhou, Guangdong Province, China
| | - Zhuang Kang
- Department of Radiology, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China
| | - Xiaojin Liu
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, 55 Zhongshan Avenue West, Guangzhou, Guangdong Province, China
| | - Yanxia Tang
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China; Department of Neurology, Yiyang Central Hospital,118 Kangfu Road,Yiyang, Hunan Province 413000, China
| | - Changhong Li
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, 55 Zhongshan Avenue West, Guangzhou, Guangdong Province, China
| | - Jinbei Zhang
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China
| | - Xiaoduo Fan
- UMass Memorial Medical Center, University of Massachusetts Medical School, One Biotech, Suite 100, 365 Plantation Street, Worcester, MA 01605, United States
| | - Ruiwang Huang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, 55 Zhongshan Avenue West, Guangzhou, Guangdong Province, China.
| | - Zili Han
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China.
| |
Collapse
|
|
14
|
A Systematic Characterization of Structural Brain Changes in Schizophrenia. Neurosci Bull 2020; 36:1107-1122. [PMID: 32495122 DOI: 10.1007/s12264-020-00520-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 02/13/2020] [Indexed: 01/10/2023] Open
Abstract
A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia, such as voxel-based morphometry (VBM), tensor-based morphometry (TBM), and projection-based thickness (PBT), is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia. However, such studies are still lacking. Here, we performed VBM, TBM, and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls. We found that, although all methods detected wide-spread structural changes, different methods captured different information - only 10.35% of the grey matter changes in cortex were detected by all three methods, and VBM only detected 11.36% of the white matter changes detected by TBM. Further, pattern classification between patients and controls revealed that combining different measures improved the classification accuracy (81.9%), indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.
Collapse
|
|
15
|
Distinct structural brain circuits indicate mood and apathy profiles in bipolar disorder. NEUROIMAGE-CLINICAL 2019; 26:101989. [PMID: 31451406 PMCID: PMC7229320 DOI: 10.1016/j.nicl.2019.101989] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 08/01/2019] [Accepted: 08/16/2019] [Indexed: 11/22/2022]
Abstract
Bipolar disorder (BD) is a severe manic-depressive illness. Patients with BD have been shown to have gray matter (GM) deficits in prefrontal, frontal, parietal, and temporal regions; however, the relationship between structural effects and clinical profiles has proved elusive when considered on a region by region or voxel by voxel basis. In this study, we applied parallel independent component analysis (pICA) to structural neuroimaging measures and the positive and negative syndrome scale (PANSS) in 110 patients (mean age 34.9 ± 11.65) with bipolar disorder, to examine networks of brain regions that relate to symptom profiles. The pICA revealed two distinct symptom profiles and associated GM concentration alteration circuits. The first PANSS pICA profile mainly involved anxiety, depression and guilty feelings, reflecting mood symptoms. Reduced GM concentration in right temporal regions predicted worse mood symptoms in this profile. The second PANSS pICA profile generally covered blunted affect, emotional withdrawal, passive/apathetic social withdrawal, depression and active social avoidance, exhibiting a withdrawal or apathy dominating component. Lower GM concentration in bilateral parietal and frontal regions showed worse symptom severity in this profile. In summary, a pICA decomposition suggested BD patients showed distinct mood and apathy profiles differing from the original PANSS subscales, relating to distinct brain structural networks. Structural relationships with symptoms in bipolar disorder are complex. A parallel ICA analysis of PANSS questions and structural images finds two correlated profiles. The first pair links mood symptoms with right temporal regions. The second pair highlights social withdrawal and apathy symptoms linked to bilateral frontal and parietal regions.
Collapse
|