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Phulara NR, Rege A, Bieberich CJ, Seneviratne HK. Mass Spectrometry Imaging Reveals Region-Specific Lipid Alterations in the Mouse Brain in Response to Efavirenz Treatment. ACS Pharmacol Transl Sci 2024; 7:2379-2390. [PMID: 39156742 PMCID: PMC11326009 DOI: 10.1021/acsptsci.4c00228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/27/2024] [Accepted: 07/01/2024] [Indexed: 08/20/2024]
Abstract
Efavirenz (EFV) is a commonly used drug to treat human immunodeficiency virus infection and is known to exert adverse effects on the brain. Although it is known that EFV is associated with abnormal plasma lipid levels, the changes in the spatial localization of individual lipid molecules in brain tissue following EFV treatment are yet to be explored. In this study, we employed a matrix-assisted laser desorption/ionization mass spectrometry imaging approach to determine region-specific lipid alterations in mouse brains following EFV treatment. We detected unique spatial localization patterns of phosphatidylcholine (PC), sphingomyelin (SM), ceramide phosphoinositol (PI-Cer), and hexosylceramide (HexCer) molecules in the mouse brain. Interestingly, PC(32:0), PC(38:5), and SM(36:1;O2) showed high abundance in the hippocampus region, whereas PI-Cer(38:8) exhibited low abundance in the hippocampus region of the EFV-treated mouse brains. Additionally, we observed low abundance of PC(38:6), PC(40:6), and PI-Cer(40:3) in the thalamus region of the EFV-treated mouse brains. Furthermore, SM(40:1;O2), SM(42:2;O2), SM(42:1;O2), SM(43:2;O2), and SM(43:1;O2) exhibited their accumulation in the corpus callosum region of the EFV-treated mouse brains as compared to controls. However, HexCer(42:1;O3) exhibited depletion in the corpus callosum region in response to EFV treatment. To characterize the expression patterns of proteins, including lipid metabolizing enzymes, in response to EFV treatment, mass spectrometry-based proteomics was utilized. From these, the expression levels of 12 brain proteins were found to be significantly decreased following EFV treatment. Taken together, these multiomics data provide important insights into the effects of EFV on brain lipid metabolism.
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Affiliation(s)
- Nav Raj Phulara
- Department
of Chemistry and Biochemistry, University
of Maryland, Baltimore County, Baltimore, Maryland 21250, United States
| | - Apurv Rege
- Department
of Biological Sciences, University of Maryland,
Baltimore County, Baltimore, Maryland 21250, United States
| | - Charles J. Bieberich
- Department
of Biological Sciences, University of Maryland,
Baltimore County, Baltimore, Maryland 21250, United States
| | - Herana Kamal Seneviratne
- Department
of Chemistry and Biochemistry, University
of Maryland, Baltimore County, Baltimore, Maryland 21250, United States
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2
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Ge YJ, Fu Y, Gong W, Cheng W, Yu JT. Genetic architecture of brain morphology and overlap with neuropsychiatric traits. Trends Genet 2024; 40:706-717. [PMID: 38702264 DOI: 10.1016/j.tig.2024.04.005] [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: 02/12/2024] [Revised: 04/05/2024] [Accepted: 04/12/2024] [Indexed: 05/06/2024]
Abstract
Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.
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Affiliation(s)
- Yi-Jun Ge
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, 266071, China
| | - Weikang Gong
- School of Data Science, Fudan University, Shanghai, China; Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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3
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Stout JJ, George AE, Kim S, Hallock HL, Griffin AL. Using synchronized brain rhythms to bias memory-guided decisions. eLife 2024; 12:RP92033. [PMID: 39037771 PMCID: PMC11262798 DOI: 10.7554/elife.92033] [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] [Indexed: 07/23/2024] Open
Abstract
Functional interactions between the prefrontal cortex and hippocampus, as revealed by strong oscillatory synchronization in the theta (6-11 Hz) frequency range, correlate with memory-guided decision-making. However, the degree to which this form of long-range synchronization influences memory-guided choice remains unclear. We developed a brain-machine interface that initiated task trials based on the magnitude of prefrontal-hippocampal theta synchronization, then measured choice outcomes. Trials initiated based on strong prefrontal-hippocampal theta synchrony were more likely to be correct compared to control trials on both working memory-dependent and -independent tasks. Prefrontal-thalamic neural interactions increased with prefrontal-hippocampal synchrony and optogenetic activation of the ventral midline thalamus primarily entrained prefrontal theta rhythms, but dynamically modulated synchrony. Together, our results show that prefrontal-hippocampal theta synchronization leads to a higher probability of a correct choice and strengthens prefrontal-thalamic dialogue. Our findings reveal new insights into the neural circuit dynamics underlying memory-guided choices and highlight a promising technique to potentiate cognitive processes or behavior via brain-machine interfacing.
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Affiliation(s)
- John J Stout
- Department of Psychological and Brain Sciences, University of DelawareNewarkUnited States
| | | | - Suhyeong Kim
- Department of Psychological and Brain Sciences, University of DelawareNewarkUnited States
| | | | - Amy L Griffin
- Department of Psychological and Brain Sciences, University of DelawareNewarkUnited States
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4
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Zhang Z, Wei W, Wang S, Li M, Li X, Li X, Wang Q, Yu H, Zhang Y, Guo W, Ma X, Zhao L, Deng W, Sham PC, Sun Y, Li T. Dynamic structure-function coupling across three major psychiatric disorders. Psychol Med 2024; 54:1629-1640. [PMID: 38084608 DOI: 10.1017/s0033291723003525] [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] [Indexed: 05/29/2024]
Abstract
BACKGROUND Convergent evidence has suggested atypical relationships between brain structure and function in major psychiatric disorders, yet how the abnormal patterns coincide and/or differ across different disorders remains largely unknown. Here, we aim to investigate the common and/or unique dynamic structure-function coupling patterns across major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). METHODS We quantified the dynamic structure-function coupling in 452 patients with psychiatric disorders (MDD/BD/SZ = 166/168/118) and 205 unaffected controls at three distinct brain network levels, such as global, meso-, and local levels. We also correlated dynamic structure-function coupling with the topological features of functional networks to examine how the structure-function relationship facilitates brain information communication over time. RESULTS The dynamic structure-function coupling is preserved for the three disorders at the global network level. Similar abnormalities in the rich-club organization are found in two distinct functional configuration states at the meso-level and are associated with the disease severity of MDD, BD, and SZ. At the local level, shared and unique alterations are observed in the brain regions involving the visual, cognitive control, and default mode networks. In addition, the relationships between structure-function coupling and the topological features of functional networks are altered in a manner indicative of state specificity. CONCLUSIONS These findings suggest both transdiagnostic and illness-specific alterations in the dynamic structure-function relationship of large-scale brain networks across MDD, BD, and SZ, providing new insights and potential biomarkers into the neurodevelopmental basis underlying the behavioral and cognitive deficits observed in these disorders.
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Affiliation(s)
- Zhe Zhang
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- School of Physics, Hangzhou Normal University, Hangzhou, China
- Institute of Brain Science, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China
| | - Wei Wei
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Sujie Wang
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
| | - Mingli Li
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaojing Li
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Xiaoyu Li
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Hua Yu
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Yamin Zhang
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Wanjun Guo
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Wei Deng
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Pak C Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Yu Sun
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Li
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
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5
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Thalhammer M, Schulz J, Scheulen F, Oubaggi MEM, Kirschner M, Kaiser S, Schmidt A, Borgwardt S, Avram M, Brandl F, Sorg C. Distinct Volume Alterations of Thalamic Nuclei Across the Schizophrenia Spectrum. Schizophr Bull 2024:sbae037. [PMID: 38577901 DOI: 10.1093/schbul/sbae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
BACKGROUND AND HYPOTHESIS Abnormal thalamic nuclei volumes and their link to cognitive impairments have been observed in schizophrenia. However, whether and how this finding extends to the schizophrenia spectrum is unknown. We hypothesized a distinct pattern of aberrant thalamic nuclei volume across the spectrum and examined its potential associations with cognitive symptoms. STUDY DESIGN We performed a FreeSurfer-based volumetry of T1-weighted brain MRIs from 137 healthy controls, 66 at-risk mental state (ARMS) subjects, 89 first-episode psychosis (FEP) individuals, and 126 patients with schizophrenia to estimate thalamic nuclei volumes of six nuclei groups (anterior, lateral, ventral, intralaminar, medial, and pulvinar). We used linear regression models, controlling for sex, age, and estimated total intracranial volume, both to compare thalamic nuclei volumes across groups and to investigate their associations with positive, negative, and cognitive symptoms. STUDY RESULTS We observed significant volume alterations in medial and lateral thalamic nuclei. Medial nuclei displayed consistently reduced volumes across the spectrum compared to controls, while lower lateral nuclei volumes were only observed in schizophrenia. Whereas positive and negative symptoms were not associated with reduced nuclei volumes across all groups, higher cognitive scores were linked to lower volumes of medial nuclei in ARMS. In FEP, cognition was not linked to nuclei volumes. In schizophrenia, lower cognitive performance was associated with lower medial volumes. CONCLUSIONS Results demonstrate distinct thalamic nuclei volume reductions across the schizophrenia spectrum, with lower medial nuclei volumes linked to cognitive deficits in ARMS and schizophrenia. Data suggest a distinctive trajectory of thalamic nuclei abnormalities along the course of schizophrenia.
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Affiliation(s)
- Melissa Thalhammer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julia Schulz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Felicitas Scheulen
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Mohamed El Mehdi Oubaggi
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Matthias Kirschner
- Department of Psychiatry, University Hospital of Geneva, Geneva, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Stefan Kaiser
- Department of Psychiatry, University Hospital of Geneva, Geneva, Switzerland
| | - André Schmidt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Stefan Borgwardt
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Mihai Avram
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Felix Brandl
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Sorg
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany
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6
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Patel K, Xie Z, Yuan H, Islam SMS, Xie Y, He W, Zhang W, Gottlieb A, Chen H, Giancardo L, Knaack A, Fletcher E, Fornage M, Ji S, Zhi D. Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging. Commun Biol 2024; 7:414. [PMID: 38580839 PMCID: PMC10997628 DOI: 10.1038/s42003-024-06096-7] [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: 01/31/2024] [Accepted: 03/22/2024] [Indexed: 04/07/2024] Open
Abstract
Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.
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Affiliation(s)
- Khush Patel
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Ziqian Xie
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Hao Yuan
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | | | - Yaochen Xie
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Wei He
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Wanheng Zhang
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Assaf Gottlieb
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Han Chen
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Luca Giancardo
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Alexander Knaack
- Department of Neurology and Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, 95618, USA
| | - Evan Fletcher
- Department of Neurology and Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, 95618, USA
| | - Myriam Fornage
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
- McGovern Medical School, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Shuiwang Ji
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Degui Zhi
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
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7
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Nordengen K, Cappelletti C, Bahrami S, Frei O, Pihlstrøm L, Henriksen SP, Geut H, Rozemuller AJM, van de Berg WDJ, Andreassen OA, Toft M. Pleiotropy with sex-specific traits reveals genetic aspects of sex differences in Parkinson's disease. Brain 2024; 147:858-870. [PMID: 37671566 PMCID: PMC10907091 DOI: 10.1093/brain/awad297] [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: 03/01/2023] [Revised: 08/01/2023] [Accepted: 08/22/2023] [Indexed: 09/07/2023] Open
Abstract
Parkinson's disease is an age-related neurodegenerative disorder with a higher incidence in males than females. The causes for this sex difference are unknown. Genome-wide association studies (GWAS) have identified 90 Parkinson's disease risk loci, but the genetic studies have not found sex-specific differences in allele frequency on autosomal chromosomes or sex chromosomes. Genetic variants, however, could exert sex-specific effects on gene function and regulation of gene expression. To identify genetic loci that might have sex-specific effects, we studied pleiotropy between Parkinson's disease and sex-specific traits. Summary statistics from GWASs were acquired from large-scale consortia for Parkinson's disease (n cases = 13 708; n controls = 95 282), age at menarche (n = 368 888 females) and age at menopause (n = 69 360 females). We applied the conditional/conjunctional false discovery rate (FDR) method to identify shared loci between Parkinson's disease and these sex-specific traits. Next, we investigated sex-specific gene expression differences in the superior frontal cortex of both neuropathologically healthy individuals and Parkinson's disease patients (n cases = 61; n controls = 23). To provide biological insights to the genetic pleiotropy, we performed sex-specific expression quantitative trait locus (eQTL) analysis and sex-specific age-related differential expression analysis for genes mapped to Parkinson's disease risk loci. Through conditional/conjunctional FDR analysis we found 11 loci shared between Parkinson's disease and the sex-specific traits age at menarche and age at menopause. Gene-set and pathway analysis of the genes mapped to these loci highlighted the importance of the immune response in determining an increased disease incidence in the male population. Moreover, we highlighted a total of nine genes whose expression or age-related expression in the human brain is influenced by genetic variants in a sex-specific manner. With these analyses we demonstrated that the lack of clear sex-specific differences in allele frequencies for Parkinson's disease loci does not exclude a genetic contribution to differences in disease incidence. Moreover, further studies are needed to elucidate the role that the candidate genes identified here could have in determining a higher incidence of Parkinson's disease in the male population.
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Affiliation(s)
- Kaja Nordengen
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
| | - Chiara Cappelletti
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
- Department of Mechanical, Electronics and Chemical Engineering, Faculty of Technology, Art and Design, OsloMet—Oslo Metropolitan University, 0130 Oslo, Norway
- Department of Research, Innovation and Education, Oslo University Hospital, 0424 Oslo, Norway
| | - Shahram Bahrami
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0450 Oslo, Norway
| | - Oleksandr Frei
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0450 Oslo, Norway
| | - Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | | | - Hanneke Geut
- Section of Clinical Neuroanatomy and Biobanking, Department of Anatomy and Neurosciences, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 Amsterdam, The Netherlands
| | - Annemieke J M Rozemuller
- Department of Pathology, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 Amsterdam, The Netherlands
| | - Wilma D J van de Berg
- Section of Clinical Neuroanatomy and Biobanking, Department of Anatomy and Neurosciences, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 Amsterdam, The Netherlands
| | - Ole A Andreassen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0450 Oslo, Norway
| | - Mathias Toft
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
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8
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Mufford MS, van der Meer D, Kaufmann T, Frei O, Ramesar R, Thompson PM, Jahanshad N, Morey RA, Andreassen OA, Stein DJ, Dalvie S. The Genetic Architecture of Amygdala Nuclei. Biol Psychiatry 2024; 95:72-84. [PMID: 37391117 DOI: 10.1016/j.biopsych.2023.06.022] [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: 03/08/2022] [Revised: 06/02/2023] [Accepted: 06/02/2023] [Indexed: 07/02/2023]
Abstract
BACKGROUND Whereas genetic variants influencing total amygdala volume have been identified, the genetic architecture of its distinct nuclei has yet to be explored. We aimed to investigate whether increased phenotypic specificity through nuclei segmentation aids genetic discoverability and elucidates the extent of shared genetic architecture and biological pathways with related disorders. METHODS T1-weighted brain magnetic resonance imaging scans (N = 36,352, 52% female) from the UK Biobank were segmented into 9 amygdala nuclei with FreeSurfer (version 6.1). Genome-wide association analyses were performed on the entire sample, a European-only subset (n = 31,690), and a generalization (transancestry) subset (n = 4662). We estimated single nucleotide polymorphism-based heritability; derived polygenicity, discoverability, and power estimates; and investigated genetic correlations and shared loci with psychiatric disorders. RESULTS The heritability of the nuclei ranged from 0.17 to 0.33. Across the whole amygdala and the nuclei volumes, we identified 28 novel genome-wide significant (padj < 5 × 10-9) loci in the European analysis, with significant en masse replication for the whole amygdala and central nucleus volumes in the generalization analysis, and we identified 10 additional candidate loci in the combined analysis. The central nucleus had the highest statistical power for discovery. The significantly associated genes and pathways showed unique and shared effects across the nuclei, including immune-related pathways. Shared variants were identified between specific nuclei and autism spectrum disorder, Alzheimer's disease, Parkinson's disease, bipolar disorder, and schizophrenia. CONCLUSIONS Through investigation of amygdala nuclei volumes, we have identified novel candidate loci in the neurobiology of amygdala volume. These nuclei volumes have unique associations with biological pathways and genetic overlap with psychiatric disorders.
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Affiliation(s)
- Mary S Mufford
- South African Medical Research Council Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa; Global Initiative for Neuropsychiatric Genetics Education in Research program, Harvard T.H. Chan School of Public Health and the Stanley Center for Psychiatric Research at the Broad Institute of Harvard and MIT, Boston, Massachusetts; South African Medical Research Council Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Raj Ramesar
- South African Medical Research Council Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, California
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, California
| | - Rajendra A Morey
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, North Carolina
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dan J Stein
- South African Medical Research Council Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Shareefa Dalvie
- South African Medical Research Council Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
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9
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Kiral FR, Choe M, Park IH. Diencephalic organoids - A key to unraveling development, connectivity, and pathology of the human diencephalon. Front Cell Neurosci 2023; 17:1308479. [PMID: 38130869 PMCID: PMC10733522 DOI: 10.3389/fncel.2023.1308479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
The diencephalon, an integral component of the forebrain, governs a spectrum of crucial functions, ranging from sensory processing to emotional regulation. Yet, unraveling its unique development, intricate connectivity, and its role in neurodevelopmental disorders has long been hampered by the scarcity of human brain tissue and ethical constraints. Recent advancements in stem cell technology, particularly the emergence of brain organoids, have heralded a new era in neuroscience research. Although most brain organoid methodologies have hitherto concentrated on directing stem cells toward telencephalic fates, novel techniques now permit the generation of region-specific brain organoids that faithfully replicate precise diencephalic identities. These models mirror the complexity of the human diencephalon, providing unprecedented opportunities for investigating diencephalic development, functionality, connectivity, and pathophysiology in vitro. This review summarizes the development, function, and connectivity of diencephalic structures and touches upon developmental brain disorders linked to diencephalic abnormalities. Furthermore, it presents current diencephalic organoid models and their applications in unraveling the intricacies of diencephalic development, function, and pathology in humans. Lastly, it highlights thalamocortical assembloid models, adept at capturing human-specific aspects of thalamocortical connections, along with their relevance in neurodevelopmental disorders.
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Affiliation(s)
| | | | - In-Hyun Park
- Interdepartmental Neuroscience Program, Department of Genetics, Yale Stem Cell Center, Yale Child Study Center, Wu Tsai Institute, Yale School of Medicine, New Haven, CT, United States
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10
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Xue H, Xu X, Yan Z, Cheng J, Zhang L, Zhu W, Cui G, Zhang Q, Qiu S, Yao Z, Qin W, Liu F, Liang M, Fu J, Xu Q, Xu J, Xie Y, Zhang P, Li W, Wang C, Shen W, Zhang X, Xu K, Zuo XN, Ye Z, Yu Y, Xian J, Yu C. Genome-wide association study of hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation in Chinese Han population. iScience 2023; 26:108005. [PMID: 37822511 PMCID: PMC10562876 DOI: 10.1016/j.isci.2023.108005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/29/2023] [Accepted: 09/18/2023] [Indexed: 10/13/2023] Open
Abstract
Correlation between blood-oxygen-level-dependent (BOLD) and cerebral blood flow (CBF) has been used as an index of neurovascular coupling. Hippocampal BOLD-CBF correlation is associated with neurocognition, and the reduced correlation is associated with neuropsychiatric disorders. We conducted the first genome-wide association study of the hippocampal BOLD-CBF correlation in 4,832 Chinese Han subjects. The hippocampal BOLD-CBF correlation had an estimated heritability of 16.2-23.9% and showed reliable genome-wide significant association with a locus at 3q28, in which many variants have been linked to neuroimaging and cerebrospinal fluid markers of Alzheimer's disease. Gene-based association analyses showed four significant genes (GMNC, CRTC2, DENND4B, and GATAD2B) and revealed enrichment for mast cell calcium mobilization, microglial cell proliferation, and ubiquitin-related proteolysis pathways that regulate different cellular components of the neurovascular unit. This is the first unbiased identification of the association of hippocampal BOLD-CBF correlation, providing fresh insights into the genetic architecture of hippocampal neurovascular coupling.
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Affiliation(s)
- Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou 325027, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Guangbin Cui
- Functional and Molecular Imaging Key Lab of Shaanxi Province & Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an 710038, China
| | - Quan Zhang
- Department of Radiology, Characteristic Medical Center of Chinese People’s Armed Police Force, Tianjin 300162, China
| | - Shijun Qiu
- Department of Medical Imaging, the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou 510405, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin 300203, China
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jiayuan Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Peng Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Wei Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, Tianjin 300192, China
| | - Xiaochu Zhang
- Division of Life Science and Medicine, University of Science & Technology of China, Hefei 230027, China
| | - Kai Xu
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, China
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center at IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
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11
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Oldham S, Ball G. A phylogenetically-conserved axis of thalamocortical connectivity in the human brain. Nat Commun 2023; 14:6032. [PMID: 37758726 PMCID: PMC10533558 DOI: 10.1038/s41467-023-41722-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
The thalamus enables key sensory, motor, emotive, and cognitive processes via connections to the cortex. These projection patterns are traditionally considered to originate from discrete thalamic nuclei, however recent work showing gradients of molecular and connectivity features in the thalamus suggests the organisation of thalamocortical connections occurs along a continuous dimension. By performing a joint decomposition of densely sampled gene expression and non-invasive diffusion tractography in the adult human thalamus, we define a principal axis of genetic and connectomic variation along a medial-lateral thalamic gradient. Projections along this axis correspond to an anterior-posterior cortical pattern and are aligned with electrophysiological properties of the cortex. The medial-lateral axis demonstrates phylogenetic conservation, reflects transitions in neuronal subtypes, and shows associations with neurodevelopment and common brain disorders. This study provides evidence for a supra-nuclear axis of thalamocortical organisation characterised by a graded transition in molecular properties and anatomical connectivity.
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Affiliation(s)
- Stuart Oldham
- Developmental Imaging, Murdoch Children's Research Institute, Parkville, VIC, Australia.
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia.
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
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12
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Zhang Y, Lu Z, Sun Y, Zhang X, Li Q, Li M, Liao Y, Kang Z, Feng X, Zhao G, Sun J, Yang Y, Yan H, Zhang D, Yue W. Predictive role of pulvinar in social functional outcome of schizophrenia. Psychiatry Res 2023; 327:115419. [PMID: 37598626 DOI: 10.1016/j.psychres.2023.115419] [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: 07/08/2023] [Revised: 08/11/2023] [Accepted: 08/13/2023] [Indexed: 08/22/2023]
Abstract
Identifying objective biological subtypes that predict long-term functional outcomes is crucial for understanding neurobiological mechanisms and identifying potential targets. Using resting-state functional magnetic resonance imaging data from 178 patients and 70 controls, we explored social function patterns using latent profile analysis. Long-term outcomes were compared among the biological subtypes using K-means clustering. Partial least squares regression (PLSR) was used to identify gene expression profiles associated with alterations in activity by leveraging transcriptional data from the Allen Human Brain Atlas. In patients with more functional impairment, left medial pulvinar (PM) exhibited significantly lower regional homogeneity of brain activity (ReHo, [95% CI (0.06-0.27), P = 0.002), a finding validated in the independent cohort. Functional connectivity between PM and secondary visual cortex displayed a suggestive decrease. Patients belonging to "higher pulvinar ReHo - better information processing" demonstrated better long-term outcomes and acute treatment response [95% CI (11.2-34.4), P < 0.001]. The PLSR component of imaging-transcriptomic associations partly explained the ReHo differences among patients with varying levels of functional impairment. It revealed enrichment of genes in the synaptic signaling pathway. Pathological changes in the pulvinar may affect social functioning. Higher pulvinar ReHo and better information processing, two objective biomarkers, have a predictive value for better long-term functional outcomes.
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Affiliation(s)
- Yuyanan Zhang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Zhe Lu
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Yaoyao Sun
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Xiao Zhang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Qianqian Li
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Mingzhu Li
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Yundan Liao
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Zhewei Kang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Xiaoyang Feng
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Guorui Zhao
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Junyuan Sun
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Yang Yang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Hao Yan
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Dai Zhang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, Institute for Brain Research and Rehabilitation (IBRR), South China Normal University, Guangzhou 510631, China
| | - Weihua Yue
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China; Chinese Institute for Brain Research, Beijing 102206, China; PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China; Research Unit of Diagnosis and Treatment of Mood Cognitive Disorder (2018RU006), Chinese Academy of Medical Sciences, Beijing 100191, China.
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13
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Wang H, Makowski C, Zhang Y, Qi A, Kaufmann T, Smeland OB, Fiecas M, Yang J, Visscher PM, Chen CH. Chromosomal inversion polymorphisms shape human brain morphology. Cell Rep 2023; 42:112896. [PMID: 37505983 PMCID: PMC10508191 DOI: 10.1016/j.celrep.2023.112896] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 06/27/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
The impact of chromosomal inversions on human brain morphology remains underexplored. We studied 35 common inversions classified from genotypes of 33,018 adults with European ancestry. The inversions at 2p22.3, 16p11.2, and 17q21.31 reach genome-wide significance, followed by 8p23.1 and 6p21.33, in their association with cortical and subcortical morphology. The 17q21.31, 8p23.1, and 16p11.2 regions comprise the LRRC37, OR7E, and NPIP duplicated gene families. We find the 17q21.31 MAPT inversion region, known for harboring neurological risk, to be the most salient locus among common variants for shaping and patterning the cortex. Overall, we observe the inverted orientations decreasing brain size, with the exception that the 2p22.3 inversion is associated with increased subcortical volume and the 8p23.1 inversion is associated with increased motor cortex. These significant inversions are in the genomic hotspots of neuropsychiatric loci. Our findings are generalizable to 3,472 children and demonstrate inversions as essential genetic variation to understand human brain phenotypes.
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Affiliation(s)
- Hao Wang
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Carolina Makowski
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Yanxiao Zhang
- Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Anna Qi
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72076 Tübingen, Germany; Norwegian Centre for Mental Disorders Research, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway
| | - Olav B Smeland
- Norwegian Centre for Mental Disorders Research, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway
| | - Mark Fiecas
- Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN 55455, USA
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Chi-Hua Chen
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA.
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14
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Mao Y, Zhang P, Sun R, Zhang X, He Y, Li S, Yin T, Zeng F. Altered resting-state brain activity in functional dyspepsia patients: a coordinate-based meta-analysis. Front Neurosci 2023; 17:1174287. [PMID: 37250423 PMCID: PMC10213416 DOI: 10.3389/fnins.2023.1174287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 04/14/2023] [Indexed: 05/31/2023] Open
Abstract
Background Neuroimaging studies have identified aberrant activity patterns in multiple brain regions in functional dyspepsia (FD) patients. However, due to the differences in study design, these previous findings are inconsistent, and the underlying neuropathological characteristics of FD remain unclear. Methods Eight databases were systematically searched for literature from inception to October 2022 with the keywords "Functional dyspepsia" and "Neuroimaging." Thereafter, the anisotropic effect size signed the differential mapping (AES-SDM) approach that was applied to meta-analyze the aberrant brain activity pattern of FD patients. Results A total of 11 articles with 260 FD patients and 202 healthy controls (HCs) were included. The AES-SDM meta-analysis demonstrated that FD patients manifested increased activity in the bilateral insula, left anterior cingulate gyrus, bilateral thalamus, right precentral gyrus, left supplementary motor area, right putamen, and left rectus gyrus and decreased functional activity in the right cerebellum compared to the HCs. Sensitivity analysis showed that all these above regions were highly reproducible, and no significant publication bias was detected. Conclusion The current study demonstrated that FD patients had significantly abnormal activity patterns in several brain regions involved in visceral sensation perception, pain modulation, and emotion regulation, which provided an integrated insight into the neuropathological characteristics of FD.
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Affiliation(s)
- Yangke Mao
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Pan Zhang
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ruirui Sun
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xinyue Zhang
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yuqi He
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Siyang Li
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Tao Yin
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Fang Zeng
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Key Laboratory of Sichuan Province for Acupuncture and Chronobiology, Chengdu, China
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15
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Ou YN, Wu BS, Ge YJ, Zhang Y, Jiang YC, Kuo K, Yang L, Tan L, Feng JF, Cheng W, Yu JT. The genetic architecture of human amygdala volumes and their overlap with common brain disorders. Transl Psychiatry 2023; 13:90. [PMID: 36906575 PMCID: PMC10008562 DOI: 10.1038/s41398-023-02387-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/26/2023] [Accepted: 02/28/2023] [Indexed: 03/13/2023] Open
Abstract
The amygdala is a crucial interconnecting structure in the brain that performs several regulatory functions, yet its genetic architectures and involvement in brain disorders remain largely unknown. We carried out the first multivariate genome-wide association study (GWAS) of amygdala subfield volumes in 27,866 UK Biobank individuals. The whole amygdala was segmented into nine nuclei groups using Bayesian amygdala segmentation. The post-GWAS analysis allowed us to identify causal genetic variants in phenotypes at the SNP, locus, and gene levels, as well as genetic overlap with brain health-related traits. We further generalized our GWAS in Adolescent Brain Cognitive Development (ABCD) cohort. The multivariate GWAS identified 98 independent significant variants within 32 genomic loci associated (P < 5 × 10-8) with amygdala volume and its nine nuclei. The univariate GWAS identified significant hits for eight of the ten volumes, tagging 14 independent genomic loci. Overall, 13 of the 14 loci identified in the univariate GWAS were replicated in the multivariate GWAS. The generalization in ABCD cohort supported the GWAS results with the 12q23.2 (RNA gene RP11-210L7.1) being discovered. All of these imaging phenotypes are heritable, with heritability ranging from 15% to 27%. Gene-based analyses revealed pathways relating to cell differentiation/development and ion transporter/homeostasis, with the astrocytes found to be significantly enriched. Pleiotropy analyses revealed shared variants with neurological and psychiatric disorders under the conjFDR threshold of 0.05. These findings advance our understanding of the complex genetic architectures of amygdala and their relevance in neurological and psychiatric disorders.
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Affiliation(s)
- Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Bang-Sheng Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yi Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yu-Chao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.,Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China. .,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. .,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China. .,Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China.
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16
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Andreassen OA, Hindley GFL, Frei O, Smeland OB. New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications. World Psychiatry 2023; 22:4-24. [PMID: 36640404 PMCID: PMC9840515 DOI: 10.1002/wps.21034] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 01/15/2023] Open
Abstract
Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.
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Affiliation(s)
- Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Guy F L Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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Zhang P, He Z, Mao Y, Sun R, Qu Y, Chen L, Ma P, Yin S, Yin T, Zeng F. Aberrant resting-state functional connectivity and topological properties of the subcortical network in functional dyspepsia patients. Front Mol Neurosci 2022; 15:1001557. [PMCID: PMC9606653 DOI: 10.3389/fnmol.2022.1001557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
Functional dyspepsia (FD) is a disorder of gut-brain interaction. Previous studies have demonstrated a wide range of abnormalities in functional brain activity and connectivity patterns in FD. However, the connectivity pattern of the subcortical network (SCN), which is a hub of visceral information transmission and processing, remains unclear in FD patients. The study compared the resting-state functional connectivity (rsFC) and the global and nodal topological properties of SCN between 109 FD patients and 98 healthy controls, and then explored the correlations between the connectivity metrics and clinical symptoms in FD patients. The results demonstrated that FD patients manifested the increased rsFC in seventeen edges among the SCN, decreased small-worldness and local efficiency in SCN, as well as increased nodal efficiency and nodal degree centrality in the anterior thalamus than healthy controls (p < 0.05, false discovery rate corrected). Moreover, the rsFC of the right anterior thalamus-left nucleus accumbens edge was significantly correlated with the NDSI scores (r = 0.255, p = 0.008, uncorrected) and NDLQI scores (r = −0.241, p = 0.013, uncorrected), the nodal efficiency of right anterior thalamus was significantly correlated with NDLQI scores (r = 0.204, p = 0.036, uncorrected) in FD patients. This study indicated the abnormal rsFC pattern, as well as global and nodal topological properties of the SCN, especially the bilateral anterior thalamus in FD patients, which enhanced our understanding of the central pathophysiology of FD and will lay the foundation for the objective diagnosis of FD and the development of new therapies.
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Affiliation(s)
- Pan Zhang
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zhaoxuan He
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yangke Mao
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ruirui Sun
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yuzhu Qu
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Li Chen
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Peihong Ma
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
| | - Shuai Yin
- First Affiliated Hospital, Henan University of Traditional Chinese Medicine, Zhengzhou, Henan, China
| | - Tao Yin
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- *Correspondence: Tao Yin,
| | - Fang Zeng
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Fang Zeng,
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18
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Li Y, Wang J, Wang X, Chen Q, Qin B, Chen J. Reconfiguration of static and dynamic thalamo-cortical network functional connectivity of epileptic children with generalized tonic-clonic seizures. Front Neurosci 2022; 16:953356. [PMID: 35937891 PMCID: PMC9353948 DOI: 10.3389/fnins.2022.953356] [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: 05/26/2022] [Accepted: 06/24/2022] [Indexed: 12/05/2022] Open
Abstract
Objective A number of studies in adults and children with generalized tonic-clonic seizure (GTCS) have reported the alterations in morphometry, functional activity, and functional connectivity (FC) in the thalamus. However, the neural mechanisms underlying the alterations in the thalamus of patients with GTCS are not well understood, particularly in children. The aim of the current study was to explore the temporal properties of functional pathways connecting thalamus in children with GTCS. Methods Here, we recruited 24 children with GTCS and 36 age-matched healthy controls. Static and dynamic FC approaches were used to evaluate alterations in the temporal variability of thalamo-cortical networks in children with GTCS. The dynamic effective connectivity (dEC) method was also used to evaluate the directions of the fluctuations in effective connectivity. In addition, the relationships between the dynamic properties and clinical features were assessed. Results The static FC analysis presented significantly decreased connectivity patterns between the bilateral thalamus and between the thalamus and right inferior temporal gyrus. The dynamic connectivity analysis found decreased FC variability in the thalamo-cortical network of children with epilepsy. Dynamic EC analyses identified increased connectivity variability from the frontal gyrus to the bilateral thalamus, and decreased connectivity variability from the right thalamus to the left thalamus and from the right thalamus to the right superior parietal lobe. In addition, correlation analysis revealed that both static FC and connectivity temporal variability in the thalamo-cortical network related to the clinical features (epilepsy duration and epilepsy onset time). Significance Our findings of both increased and decreased connectivity variability in the thalamo-cortical network imply a dynamic restructuring of the functional pathways connecting the thalamus in children with GTCS. These alterations in static and temporal dynamic pathways connecting the bilateral thalamus may extend our understanding of the neural mechanisms underlying the GTCS in children.
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Affiliation(s)
- Yongxin Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
- *Correspondence: Yongxin Li,
| | - Jianping Wang
- The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiao Wang
- Epilepsy Center and Department of Neurosurgery, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children’s Hospital, Shenzhen, China
| | - Bing Qin
- Epilepsy Center and Department of Neurosurgery, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Jiaxu Chen
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
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Williams B, Roesch E, Christakou A. Systematic validation of an automated thalamic parcellation technique using anatomical data at 3T. Neuroimage 2022; 258:119340. [DOI: 10.1016/j.neuroimage.2022.119340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 05/20/2022] [Accepted: 05/28/2022] [Indexed: 11/24/2022] Open
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The anterior thalamic nuclei: core components of a tripartite episodic memory system. Nat Rev Neurosci 2022; 23:505-516. [PMID: 35478245 DOI: 10.1038/s41583-022-00591-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2022] [Indexed: 12/13/2022]
Abstract
Standard models of episodic memory focus on hippocampal-parahippocampal interactions, with the neocortex supplying sensory information and providing a final repository of mnemonic representations. However, recent advances have shown that other regions make distinct and equally critical contributions to memory. In particular, there is growing evidence that the anterior thalamic nuclei have a number of key cognitive functions that support episodic memory. In this article, we describe these findings and argue for a core, tripartite memory system, comprising a 'temporal lobe' stream (centred on the hippocampus) and a 'medial diencephalic' stream (centred on the anterior thalamic nuclei) that together act on shared cortical areas. We demonstrate how these distributed brain regions form complementary and necessary partnerships in episodic memory formation.
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21
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Sadeghi I, Gispert JD, Palumbo E, Muñoz-Aguirre M, Wucher V, D'Argenio V, Santpere G, Navarro A, Guigo R, Vilor-Tejedor N. Brain transcriptomic profiling reveals common alterations across neurodegenerative and psychiatric disorders. Comput Struct Biotechnol J 2022; 20:4549-4561. [PMID: 36090817 PMCID: PMC9428860 DOI: 10.1016/j.csbj.2022.08.037] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/16/2022] [Accepted: 08/16/2022] [Indexed: 11/29/2022] Open
Abstract
Neurodegenerative and neuropsychiatric disorders (ND-NPs) are multifactorial, polygenic and complex behavioral phenotypes caused by brain abnormalities. Large-scale collaborative efforts have tried to identify the genetic architecture of these conditions. However, the specific and shared underlying molecular pathobiology of brain illnesses is not clear. Here, we examine transcriptome-wide characterization of eight conditions, using a total of 2,633 post-mortem brain samples from patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), Progressive Supranuclear Palsy (PSP), Pathological Aging (PA), Autism Spectrum Disorder (ASD), Schizophrenia (Scz), Major Depressive Disorder (MDD), and Bipolar Disorder (BP)–in comparison with 2,078 brain samples from matched control subjects. Similar transcriptome alterations were observed between NDs and NPs with the top correlations obtained between Scz-BP, ASD-PD, AD-PD, and Scz-ASD. Region-specific comparisons also revealed shared transcriptome alterations in frontal and temporal lobes across NPs and NDs. Co-expression network analysis identified coordinated dysregulations of cell-type-specific modules across NDs and NPs. This study provides a transcriptomic framework to understand the molecular alterations of NPs and NDs through their shared- and specific gene expression in the brain.
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22
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Kondratyev NV, Alfimova MV, Golov AK, Golimbet VE. Bench Research Informed by GWAS Results. Cells 2021; 10:3184. [PMID: 34831407 PMCID: PMC8623533 DOI: 10.3390/cells10113184] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/11/2021] [Accepted: 11/11/2021] [Indexed: 12/15/2022] Open
Abstract
Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually 'highly polygenic'. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise 'wet biologists' with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.
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Affiliation(s)
| | | | - Arkadiy K. Golov
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
- Institute of Gene Biology, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Vera E. Golimbet
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
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Thalamic connectivity system across psychiatric disorders: Current status and clinical implications. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 2:332-340. [PMID: 36324665 PMCID: PMC9616255 DOI: 10.1016/j.bpsgos.2021.09.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 09/23/2021] [Accepted: 09/25/2021] [Indexed: 12/20/2022] Open
Abstract
The thalamic connectivity system, with the thalamus as the central node, enables transmission of the brain’s neural computations via extensive connections to cortical, subcortical, and cerebellar regions. Emerging reports suggest deficits in this system across multiple psychiatric disorders, making it a unique network of high translational and transdiagnostic utility in mapping neural alterations that potentially contribute to symptoms and disturbances in psychiatric patients. However, despite considerable research effort, it is still debated how this system contributes to psychiatric disorders. This review characterizes current knowledge regarding thalamic connectivity system deficits in psychiatric disorders, including schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder, across multiple levels of the system. We identify the presence of common and distinct patterns of deficits in the thalamic connectivity system in major psychiatric disorders and assess their nature and characteristics. Specifically, this review assembles evidence for the hypotheses of 1) thalamic microstructure, particularly in the mediodorsal nucleus, as a state marker of psychosis; 2) thalamo-prefrontal connectivity as a trait marker of psychosis; and 3) thalamo-somatosensory/parietal connectivity as a possible marker of general psychiatric illness. Furthermore, possible mechanisms contributing to thalamocortical dysconnectivity are explored. We discuss current views on the contributions of cerebellar-thalamic connectivity to the thalamic connectivity system and propose future studies to examine its effects at multiple levels, from the molecular (e.g., glutamatergic) to the behavioral (e.g., cognition), to gain a deeper understanding of the mechanisms that underlie the disturbances observed in psychiatric disorders.
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