1
|
Perez-Rando M, García-Martí G, Escarti MJ, Salgado-Pineda P, McKenna PJ, Pomarol-Clotet E, Grasa E, Postiguillo A, Corripio I, Nacher J. Alterations in the volume and shape of the basal ganglia and thalamus in schizophrenia with auditory hallucinations. Prog Neuropsychopharmacol Biol Psychiatry 2024; 131:110960. [PMID: 38325744 DOI: 10.1016/j.pnpbp.2024.110960] [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: 11/03/2023] [Revised: 01/31/2024] [Accepted: 02/03/2024] [Indexed: 02/09/2024]
Abstract
Different lines of evidence indicate that the structure and physiology of the basal ganglia and the thalamus is disturbed in schizophrenia. However, it is unknown whether the volume and shape of these subcortical structures are affected in schizophrenia with auditory hallucinations (AH), a core positive symptom of the disorder. We took structural MRI from 63 patients with schizophrenia, including 36 patients with AH and 27 patients who had never experienced AH (NAH), and 51 matched healthy controls. We extracted volumes for the left and right thalamus, globus pallidus, putamen, caudate and nucleus accumbens. Shape analysis was also carried out. When comparing to controls, the volume of the right globus pallidus, thalamus, and putamen, was only affected in AH patients. The volume of the left putamen was also increased in individuals with AH, whereas the left globus pallidus was affected in both groups of patients. The shapes of right and left putamen and thalamus were also affected in both groups. The shape of the left globus pallidus was only altered in patients lacking AH, both in comparison to controls and to cases with AH. Lastly, the general PANSS subscale was correlated with the volume of the right thalamus, and the right and left putamen, in patients with AH. We have found volume and shape alterations of many basal ganglia and thalamus in patients with and without AH, suggesting in some cases a possible relationship between this positive symptom and these morphometric alterations.
Collapse
Affiliation(s)
- Marta Perez-Rando
- Institute of Biotechnology and Biomedicine (BIOTECMED), Universitat de València, Burjassot, Spain; CIBERSAM, ISCIII Spanish National Network for Research in Mental Health, Madrid, Spain; Biomedical Research Institute of Valencia (INCLIVA), Valencia, Spain.
| | - Gracián García-Martí
- CIBERSAM, ISCIII Spanish National Network for Research in Mental Health, Madrid, Spain; Quironsalud Hospital, Valencia, Spain
| | - Maria J Escarti
- CIBERSAM, ISCIII Spanish National Network for Research in Mental Health, Madrid, Spain; Biomedical Research Institute of Valencia (INCLIVA), Valencia, Spain; Servicio de Psiquiatría, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Pilar Salgado-Pineda
- CIBERSAM, ISCIII Spanish National Network for Research in Mental Health, Madrid, Spain; FIDMAG Germanes Hospitalàries Research Foundation, Spain
| | - Peter J McKenna
- CIBERSAM, ISCIII Spanish National Network for Research in Mental Health, Madrid, Spain; FIDMAG Germanes Hospitalàries Research Foundation, Spain
| | - Edith Pomarol-Clotet
- CIBERSAM, ISCIII Spanish National Network for Research in Mental Health, Madrid, Spain; FIDMAG Germanes Hospitalàries Research Foundation, Spain
| | - Eva Grasa
- CIBERSAM, ISCIII Spanish National Network for Research in Mental Health, Madrid, Spain; Mental Health, Institut d'Investigació Biomèdica Sant Pau (IIB SANT PAU), Sant Quintí, Barcelona, Spain
| | - Alba Postiguillo
- Biomedical Research Institute of Valencia (INCLIVA), Valencia, Spain
| | - Iluminada Corripio
- CIBERSAM, ISCIII Spanish National Network for Research in Mental Health, Madrid, Spain; Mental Health and Psychiatry Department, Vic Hospital Consortium, Francesc Pla, Vic, Spain
| | - Juan Nacher
- Institute of Biotechnology and Biomedicine (BIOTECMED), Universitat de València, Burjassot, Spain; CIBERSAM, ISCIII Spanish National Network for Research in Mental Health, Madrid, Spain; Biomedical Research Institute of Valencia (INCLIVA), Valencia, Spain.
| |
Collapse
|
2
|
Zhao Q, Cao H, Zhang W, Li S, Xiao Y, Tamminga CA, Keshavan MS, Pearlson GD, Clementz BA, Gershon ES, Hill SK, Keedy SK, Ivleva EI, Lencer R, Sweeney JA, Gong Q, Lui S. A subtype of institutionalized patients with schizophrenia characterized by pronounced subcortical and cognitive deficits. Neuropsychopharmacology 2022; 47:2024-2032. [PMID: 35260788 PMCID: PMC9556672 DOI: 10.1038/s41386-022-01300-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 01/28/2022] [Accepted: 02/19/2022] [Indexed: 02/05/2023]
Abstract
Some patients with schizophrenia have severe cognitive impairment and functional deficits that require long-term institutional care. The patterns of brain-behavior alterations in these individuals, and their differences from patients living successfully in the community, remain poorly understood. Previous cognition-based studies for stratifying schizophrenia patients highlight the importance of subcortical structures in the context of illness heterogeneity. In the present study, subcortical volumes from 96 institutionalized patients with long-term schizophrenia were evaluated using cluster analysis to test for heterogeneity. These data were compared to those from two groups of community-dwelling individuals with schizophrenia for comparison purposes, including 68 long-term ill and 126 first-episode individuals. A total of 290 demographically matched healthy participants were included as normative references at a 1:1 ratio for each patient sample. A subtype of institutionalized patients was identified based on their pattern of subcortical alterations. Using a machine learning algorithm developed to discriminate the two groups of institutionalized patients, all three patient samples were found to have similar rates of patients assigned to the two subtypes (approximately 50% each). In institutionalized patients, only the subtype with the identified pattern of subcortical alterations had greater neocortical and cognitive abnormalities than those in the similarity classified community-dwelling patients with long-term illness. Thus, for the subtype of patients with a distinctive pattern of subcortical alterations, when the distinct pattern of subcortical alterations is present and particularly severe, it is associated with cognitive impairments that may contribute to persistent disability and institutionalization.
Collapse
Affiliation(s)
- Qiannan Zhao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Hengyi Cao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Wenjing Zhang
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Siyi Li
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Yuan Xiao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neurobiology, Yale University and Olin Neuropsychiatric Research Center, Hartford, CT, USA
| | - Brett A Clementz
- Department of Psychology, University of Georgia, Athens, GA, USA
| | - Elliot S Gershon
- Department of Psychiatry, University of Chicago, Chicago, IL, USA
| | - Scot Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Sarah K Keedy
- Department of Psychiatry, University of Chicago, Chicago, IL, USA
| | - Elena I Ivleva
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - John A Sweeney
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
| | - Su Lui
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
| |
Collapse
|
3
|
Huang Q, Qiao C, Jing K, Zhu X, Ren K. Biomarkers identification for Schizophrenia via VAE and GSDAE-based data augmentation. Comput Biol Med 2022; 146:105603. [PMID: 35588680 DOI: 10.1016/j.compbiomed.2022.105603] [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: 02/22/2022] [Revised: 04/13/2022] [Accepted: 05/07/2022] [Indexed: 11/24/2022]
Abstract
Deep learning has made great progress in analyzing MRI data, while the MRI data with high dimensional but small sample size (HDSSS) brings many limitations to biomarkers identification. Few-shot learning has been proposed to solve such problems and data augmentation is a typical method of it. The variational auto-encoder (VAE) is a generative method based on variational Bayesian inference that is used for data augmentation. Graph regularized sparse deep autoencoder (GSDAE) can reconstruct sparse samples and keep the manifold structure of data which will facilitate biomarkers selection greatly. To generate better HDSSS data for biomarkers identification, a data augmentation method based on VAE and GSDAE is proposed in this paper, termed GS-VDAE. Instead of utilizing the final products of GSDAE, our proposed model embeds the generation procedure into GSDAE for augmentation. In this way, the augmented samples will be rooted in the significant features extracted from the original samples, which can ensure the newly formed samples contain the most significant characteristics of the original samples. The classification accuracy of the samples generated directly from VAE is 0.74, while the classification accuracy of the samples generated from GS-VDAE is 0.84, which proves the validity of our model. Additionally, a regression feature selection method with truncated nuclear norm regularization is chosen for biomarkers selection. The biomarkers selection results of schizophrenia data reveal that the augmented samples obtained by our proposed method can get higher classification accuracy with less ranked features compared with original samples, which proves the validation of our model.
Collapse
Affiliation(s)
- Qi Huang
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Chen Qiao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Kaili Jing
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China; Department of Mathematics and Statistics, University of Ottawa, Ottawa, K7L 3P7, Canada.
| | - Xu Zhu
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Kai Ren
- Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China.
| |
Collapse
|
4
|
Zhang L, Zhang R, Han S, Womer FY, Wei Y, Duan J, Chang M, Li C, Feng R, Liu J, Zhao P, Jiang X, Wei S, Yin Z, Zhang Y, Zhang Y, Zhang X, Tang Y, Wang F. Three major psychiatric disorders share specific dynamic alterations of intrinsic brain activity. Schizophr Res 2022; 243:322-329. [PMID: 34244046 DOI: 10.1016/j.schres.2021.06.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 05/21/2021] [Accepted: 06/18/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Increasing evidence suggests that major psychiatric disorders, including major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ) share biological, neuropsychological and clinical features, despite the criteria for their respective diagnoses being different. Neuroimaging studies have shown disrupted 'static' neural connectivity in these disorders. However, the changes in brain dynamics across the three psychiatric disorders remain unknown. METHODS We aim to examine the connections and divergencies of the dynamic amplitude of low-frequency fluctuation (dALFF) in MDD, BD and SZ. In total, 901 participants [MDD, 229; BD, 146; SZ, 142; and healthy controls (HCs), 384] received resting-state functional magnetic resonance imaging. The dALFF was calculated using sliding-window analysis and compared across three psychiatric disorders. RESULTS We found significant increases of dALFF in the right fusiform, right hippocampus, right parahippocampal in participants with MDD, BD and SZ compared to HC. We also found specific increased dALFF changes in caudate and left thalamus for SZ and BD and decreased dALFF changes in calcarine and lingual for SZ and MDD. CONCLUSION Our study found significant intrinsic brain activity changes in the limbic system and primary visual area in MDD, BD, and SZ, which indicates these areas disruptions are core neurobiological features shared among three psychiatric disorders. Meanwhile, our findings also indicate that specific alterations in MDD, BD, and SZ provide neuroimaging evidence for the differential diagnosis of the three mental disorders.
Collapse
Affiliation(s)
- Luheng Zhang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Ran Zhang
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China
| | - Shaoqiang Han
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, PR China
| | - Fay Y Womer
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Yange Wei
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China
| | - Jia Duan
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Miao Chang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Chao Li
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Ruiqi Feng
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Juan Liu
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Pengfei Zhao
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Xiaowei Jiang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Shengnan Wei
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Zhiyang Yin
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Yifan Zhang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Yanbo Zhang
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Canada
| | - Xizhe Zhang
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China; School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, PR China.
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China.
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China; Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China.
| |
Collapse
|