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Wang J, Cheng Y. Mediating role of right superior corona microstructural changes in linking attentional control and trait anxiety among youth with childhood maltreatment. Neuroreport 2024:00001756-990000000-00251. [PMID: 38829957 DOI: 10.1097/wnr.0000000000002053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
This study explores the neural correlates between attentional control and trait anxiety among youth with a history of childhood maltreatment. Using diffusion tensor imaging, we investigated the microstructural integrity of brain white matter, particularly focusing on the right superior corona radiata (SCA-R). A total of 173 university students with experiences of childhood maltreatment underwent behavioral assessments using the Attentional Control Scale and trait anxiety measurements via the Spielberger State-Trait Anxiety Inventory. Our analysis found significant correlations between fractional anisotropy values in the SCA-R and trait anxiety levels, controlled for age and sex. Notably, SCA-R fractional anisotropy values partially mediated the relationship between attentional control and trait anxiety, suggesting a potential pathway through which attentional control could mitigate trait anxiety. These insights highlight attentional control as a potential mitigating factor against trait anxiety, particularly noting the partial mediation role of the SCA-R. Importantly, this study is descriptive and correlative, highlighting associations rather than causal relationships among the variables studied. These findings enhance our understanding of the neural mechanisms underlying anxiety in individuals with a history of childhood maltreatment.
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Affiliation(s)
- Junyi Wang
- School of Teacher Education, Nanjing University of Information Science & Technology, Nanjing
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2
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Belov V, Erwin-Grabner T, Aghajani M, Aleman A, Amod AR, Basgoze Z, Benedetti F, Besteher B, Bülow R, Ching CRK, Connolly CG, Cullen K, Davey CG, Dima D, Dols A, Evans JW, Fu CHY, Gonul AS, Gotlib IH, Grabe HJ, Groenewold N, Hamilton JP, Harrison BJ, Ho TC, Mwangi B, Jaworska N, Jahanshad N, Klimes-Dougan B, Koopowitz SM, Lancaster T, Li M, Linden DEJ, MacMaster FP, Mehler DMA, Melloni E, Mueller BA, Ojha A, Oudega ML, Penninx BWJH, Poletti S, Pomarol-Clotet E, Portella MJ, Pozzi E, Reneman L, Sacchet MD, Sämann PG, Schrantee A, Sim K, Soares JC, Stein DJ, Thomopoulos SI, Uyar-Demir A, van der Wee NJA, van der Werff SJA, Völzke H, Whittle S, Wittfeld K, Wright MJ, Wu MJ, Yang TT, Zarate C, Veltman DJ, Schmaal L, Thompson PM, Goya-Maldonado R. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures. Sci Rep 2024; 14:1084. [PMID: 38212349 PMCID: PMC10784593 DOI: 10.1038/s41598-023-47934-8] [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: 01/23/2023] [Accepted: 11/19/2023] [Indexed: 01/13/2024] Open
Abstract
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
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Affiliation(s)
- Vladimir Belov
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Tracy Erwin-Grabner
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Moji Aghajani
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Institute of Education and Child Studies, Section Forensic Family and Youth Care, Leiden University, Leiden, The Netherlands
| | - Andre Aleman
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alyssa R Amod
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Zeynep Basgoze
- Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Francesco Benedetti
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Bianca Besteher
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Robin Bülow
- Institute for Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Colm G Connolly
- Department of Biomedical Sciences, Florida State University, Tallahassee, FL, USA
| | - Kathryn Cullen
- Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Christopher G Davey
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia
| | - Danai Dima
- Department of Psychology, School of Arts and Social Sciences, City, University of London, London, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Annemiek Dols
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jennifer W Evans
- Experimental Therapeutics and Pathophysiology Branch, National Institute for Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Cynthia H Y Fu
- School of Psychology, University of East London, London, UK
- Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ali Saffet Gonul
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Nynke Groenewold
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - J Paul Hamilton
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Center for Medical Imaging and Visualization, Linköping University, Linköping, Sweden
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia
| | - Tiffany C Ho
- Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Benson Mwangi
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center Of Excellence On Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Natalia Jaworska
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | | | - Thomas Lancaster
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK
- MRC Center for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - David E J Linden
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK
- MRC Center for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Frank P MacMaster
- Departments of Psychiatry and Pediatrics, University of Calgary, Calgary, AB, Canada
| | - David M A Mehler
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK
- MRC Center for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Elisa Melloni
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Amar Ojha
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mardien L Oudega
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sara Poletti
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Catalonia, Spain
| | - Maria J Portella
- Sant Pau Mental Health Research Group, Institut de Recerca de L'Hospital de La Santa Creu I Sant Pau, Barcelona, Catalonia, Spain
| | - Elena Pozzi
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Matthew D Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Jair C Soares
- Center Of Excellence On Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Dan J Stein
- SA MRC Research Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Aslihan Uyar-Demir
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey
| | - Nic J A van der Wee
- Leiden Institute for Brain and Cognition, Leiden University Medical Center, Leiden, The Netherlands
| | - Steven J A van der Werff
- Leiden Institute for Brain and Cognition, Leiden University Medical Center, Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Sarah Whittle
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/ Greifswald, Greifswald, Germany
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Mon-Ju Wu
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center Of Excellence On Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Tony T Yang
- Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Carlos Zarate
- Section on the Neurobiology and Treatment of Mood Disorders, National Institute of Mental Health, Bethesda, MD, USA
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Roberto Goya-Maldonado
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Von-Siebold-Str. 5, 37075, Göttingen, Germany.
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3
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Liang S, Zhao L, Ni P, Wang Q, Guo W, Xu Y, Cai J, Tao S, Li X, Deng W, Palaniyappan L, Li T. Frontostriatal circuitry and the tryptophan kynurenine pathway in major psychiatric disorders. Psychopharmacology (Berl) 2024; 241:97-107. [PMID: 37735237 DOI: 10.1007/s00213-023-06466-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 09/09/2023] [Indexed: 09/23/2023]
Abstract
RATIONALE An imbalance of the tryptophan kynurenine pathway (KP) commonly occurs in psychiatric disorders, though the neurocognitive and network-level effects of this aberration are unclear. OBJECTIVES In this study, we examined the connection between dysfunction in the frontostriatal brain circuits, imbalances in the tryptophan kynurenine pathway (KP), and neurocognition in major psychiatric disorders. METHODS Forty first-episode medication-naive patients with schizophrenia (SCZ), fifty patients with bipolar disorder (BD), fifty patients with major depressive disorder (MDD), and forty-two healthy controls underwent resting-state functional magnetic resonance imaging. Plasma levels of KP metabolites were measured, and neurocognitive function was evaluated. Frontostriatal connectivity and KP metabolites were compared between groups while controlling for demographic and clinical characteristics. Canonical correlation analyses were conducted to explore multidimensional relationships between frontostriatal circuits-KP and KP-cognitive features. RESULTS Patient groups shared hypoconnectivity between bilateral ventrolateral prefrontal cortex (vlPFC) and left insula, with disorder-specific dysconnectivity in SCZ related to PFC, left dorsal striatum hypoconnectivity. The BD group had higher anthranilic acid and lower xanthurenic acid levels than the other groups. KP metabolites and ratios related to disrupted frontostriatal dysconnectivity in a transdiagnostic manner. The SCZ group and MDD group separately had high-dimensional associations between KP metabolites and cognitive measures. CONCLUSIONS The findings suggest that KP may influence cognitive performance across psychiatric conditions via frontostriatal dysfunction.
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Affiliation(s)
- Sugai Liang
- Department of Neurobiology, Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, Zhejiang, China
| | - Liansheng Zhao
- Mental Health Centre & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Peiyan Ni
- Mental Health Centre & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Qiang Wang
- Mental Health Centre & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Wanjun Guo
- Department of Neurobiology, Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, Zhejiang, China
| | - Yan Xu
- Department of Neurobiology, Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, Zhejiang, China
| | - Jia Cai
- Mental Health Centre & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Shiwan Tao
- Mental Health Centre & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Xiaojing Li
- Department of Neurobiology, Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, Zhejiang, China
| | - Wei Deng
- Department of Neurobiology, Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, Zhejiang, China
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, H4H1R3, Canada.
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario, N6A5K8, Canada.
| | - Tao Li
- Department of Neurobiology, Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, Zhejiang, China.
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Zhejiang, 310000, Hangzhou, China.
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Zhejiang, 310063, Hangzhou, China.
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4
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Zhang B, Li Y, Shen Y, Zhao W, Yu Y, Tang J. Dimensional subtyping of first-episode drug-naïve major depressive disorder: A multisite resting-state fMRI study. Psychiatry Res 2023; 330:115598. [PMID: 37979320 DOI: 10.1016/j.psychres.2023.115598] [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: 05/21/2023] [Revised: 11/01/2023] [Accepted: 11/06/2023] [Indexed: 11/20/2023]
Abstract
Major depressive disorder (MDD) is a heterogeneous syndrome, and understanding its neural mechanisms is crucial for the advancement of personalized medicine. However, conventional subtyping studies often categorize MDD patients into a single subgroup, neglecting the continuous interindividual variations. This implies a pressing need for a dimensional approach. 230 first-episode drug-naïve MDD patients and 395 healthy controls were obtained from 5 sites via the Rest-meta-MDD project. A Bayesian model was used to decompose the resting-state functional connectivity (RSFC) into multiple distinct RSFC patterns (refer to as "factors"), and each individual was allowed to express multiple factors to varying degrees (dimensional subtyping). The associations between demographic and clinical variables with the identified factors were calculated. We identified three latent factors with distinct but partially overlapping hypo- and hyper-RSFC patterns. Most participants co-expressed multiple latent factors. All factors shared abnormal RSFC involving the default mode network and frontoparietal network, but the directionality partially differed across factors. All factors were not significantly associated with demographic and clinical variables. These findings shed light on the interindividual variability in MDD and could form the basis for developing novel therapeutic approaches that capitalize on the heterogeneity of MDD.
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Affiliation(s)
- Biao Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230026, China
| | - Yating Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Yuhao Shen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China.
| | - Jin Tang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230026, China.
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Sun W, Wu Q, Gao L, Zheng Z, Xiang H, Yang K, Yu B, Yao J. Advancements in Transcranial Magnetic Stimulation Research and the Path to Precision. Neuropsychiatr Dis Treat 2023; 19:1841-1851. [PMID: 37641588 PMCID: PMC10460597 DOI: 10.2147/ndt.s414782] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) has become increasingly popular in clinical practice in recent years, and there have been significant advances in the principles and stimulation modes of TMS. With the development of multi-mode and precise stimulation technology, it is crucial to have a comprehensive understanding of TMS. The neuroregulatory effects of TMS can vary depending on the specific mode of stimulation, highlighting the importance of exploring these effects through multimodal application. Additionally, the use of precise TMS therapy can help enhance our understanding of the neural mechanisms underlying these effects, providing us with a more comprehensive perspective. This article aims to review the mechanism of action, stimulation mode, multimodal application, and precision of TMS.
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Affiliation(s)
- Wei Sun
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Qiao Wu
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Li Gao
- Department of Neurology, The Third People’s Hospital of Chengdu, Chengdu Institute of Neurological Diseases, Chengdu City, Sichuan Province, People’s Republic of China
| | - Zhong Zheng
- Neurobiological Detection Center, West China Hospital Affiliated to Sichuan University, Chengdu City, Sichuan Province, People’s Republic of China
| | - Hu Xiang
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Kun Yang
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Bo Yu
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Jing Yao
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
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6
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Constable PA, Lim JKH, Thompson DA. Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies. Front Neurosci 2023; 17:1215097. [PMID: 37600004 PMCID: PMC10433210 DOI: 10.3389/fnins.2023.1215097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
The retina and brain share similar neurochemistry and neurodevelopmental origins, with the retina, often viewed as a "window to the brain." With retinal measures of structure and function becoming easier to obtain in clinical populations there is a growing interest in using retinal findings as potential biomarkers for disorders affecting the central nervous system. Functional retinal biomarkers, such as the electroretinogram, show promise in neurological disorders, despite having limitations imposed by the existence of overlapping genetic markers, clinical traits or the effects of medications that may reduce their specificity in some conditions. This narrative review summarizes the principal functional retinal findings in central nervous system disorders and related mouse models and provides a background to the main excitatory and inhibitory retinal neurotransmitters that have been implicated to explain the visual electrophysiological findings. These changes in retinal neurochemistry may contribute to our understanding of these conditions based on the findings of retinal electrophysiological tests such as the flash, pattern, multifocal electroretinograms, and electro-oculogram. It is likely that future applications of signal analysis and machine learning algorithms will offer new insights into the pathophysiology, classification, and progression of these clinical disorders including autism, attention deficit/hyperactivity disorder, bipolar disorder, schizophrenia, depression, Parkinson's, and Alzheimer's disease. New clinical applications of visual electrophysiology to this field may lead to earlier, more accurate diagnoses and better targeted therapeutic interventions benefiting individual patients and clinicians managing these individuals and their families.
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Affiliation(s)
- Paul A. Constable
- College of Nursing and Health Sciences, Caring Futures Institute, Flinders University, Adelaide, SA, Australia
| | - Jeremiah K. H. Lim
- Discipline of Optometry, School of Allied Health, University of Western Australia, Perth, WA, Australia
| | - Dorothy A. Thompson
- The Tony Kriss Visual Electrophysiology Unit, Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Trust, London, United Kingdom
- UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
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7
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Xu EP, Nguyen L, Leibenluft E, Stange JP, Linke JO. A meta-analysis on the uncinate fasciculus in depression. Psychol Med 2023; 53:2721-2731. [PMID: 37051913 PMCID: PMC10235669 DOI: 10.1017/s0033291723000107] [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: 08/22/2022] [Revised: 12/29/2022] [Accepted: 01/09/2023] [Indexed: 04/14/2023]
Abstract
Aberrant microstructure of the uncinate fasciculus (UNC), a white matter (WM) tract implicated in emotion regulation, has been hypothesized as a neurobiological mechanism of depression. However, studies testing this hypothesis have yielded inconsistent results. The present meta-analysis consolidates evidence from 44 studies comparing fractional anisotropy (FA) and radial diffusivity (RD), two metrics characterizing WM microstructure, of the UNC in individuals with depression (n = 5016) to healthy individuals (n = 18 425). We conduct meta-regressions to identify demographic and clinical characteristics that contribute to cross-study heterogeneity in UNC findings. UNC FA was reduced in individuals with depression compared to healthy individuals. UNC RD was comparable between individuals with depression and healthy individuals. Comorbid anxiety explained inter-study heterogeneity in UNC findings. Depression is associated with perturbations in UNC microstructure, specifically with respect to UNC FA and not UNC RD. The association between depression and UNC microstructure appears to be moderated by anxiety. Future work should unravel the cellular mechanisms contributing to aberrant UNC microstructure in depression; clarify the relationship between UNC microstructure, depression, and anxiety; and link UNC microstructure to psychological processes, such as emotion regulation.
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Affiliation(s)
- Ellie P. Xu
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Lynn Nguyen
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Ellen Leibenluft
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Jonathan P. Stange
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, CA, USA
| | - Julia O. Linke
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA
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8
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Hu W, Liuyang Z, Tian Y, Liang J, Zhang X, Zhang H, Wang G, Huo Y, Shentu Y, Wang J, Wang X, Lu Y, Westermarck J, Man H, Liu R. CIP2A deficiency promotes depression-like behaviors in mice through inhibition of dendritic arborization. EMBO Rep 2022; 23:e54911. [PMID: 36305233 PMCID: PMC9724669 DOI: 10.15252/embr.202254911] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 08/25/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022] Open
Abstract
Major depressive disorder (MDD) is a severe mental illness. Decreased brain plasticity and dendritic fields have been consistently found in MDD patients and animal models; however, the underlying molecular mechanisms remain to be clarified. Here, we demonstrate that the deletion of cancerous inhibitor of PP2A (CIP2A), an endogenous inhibitor of protein phosphatase 2A (PP2A), leads to depression-like behaviors in mice. Hippocampal RNA sequencing analysis of CIP2A knockout mice shows alterations in the PI3K-AKT pathway and central nervous system development. In primary neurons, CIP2A stimulates AKT activity and promotes dendritic development. Further analysis reveals that the effect of CIP2A in promoting dendritic development is dependent on PP2A-AKT signaling. In vivo, CIP2A deficiency-induced depression-like behaviors and impaired dendritic arborization are rescued by AKT activation. Decreased CIP2A expression and impaired dendrite branching are observed in a mouse model of chronic unpredictable mild stress (CUMS). Indicative of clinical relevance to humans, CIP2A expression is found decreased in transcriptomes from MDD patients. In conclusion, we discover a novel mechanism that CIP2A deficiency promotes depression through the regulation of PP2A-AKT signaling and dendritic arborization.
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Affiliation(s)
- Wen‐Ting Hu
- Department of Pathophysiology, Key Laboratory of Ministry of Education for Neurological Disorders, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
- Department of PathologyPeking University Shenzhen HospitalShenzhenChina
| | - Zhen‐Yu Liuyang
- Department of Pathophysiology, Key Laboratory of Ministry of Education for Neurological Disorders, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
- Department of General Surgery, Sir Run Run Shaw Hospital, School of MedicineZhejiang UniversityHangzhouChina
| | - Yuan Tian
- Department of BiologyBoston UniversityBostonUSA
| | - Jia‐Wei Liang
- Department of Pathophysiology, Key Laboratory of Ministry of Education for Neurological Disorders, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Xiao‐Lin Zhang
- Department of Pathophysiology, Key Laboratory of Ministry of Education for Neurological Disorders, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Hui‐Liang Zhang
- Department of Pathophysiology, Key Laboratory of Ministry of Education for Neurological Disorders, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Guan Wang
- Department of BiologyBoston UniversityBostonUSA
| | - Yuda Huo
- Department of BiologyBoston UniversityBostonUSA
| | - Yang‐Ping Shentu
- Department of NephrologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Jian‐Zhi Wang
- Department of Pathophysiology, Key Laboratory of Ministry of Education for Neurological Disorders, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Xiao‐Chuan Wang
- Department of Pathophysiology, Key Laboratory of Ministry of Education for Neurological Disorders, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - You‐ming Lu
- Department of Pathophysiology, Key Laboratory of Ministry of Education for Neurological Disorders, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
- The Institute of Brain Research, Collaborative Innovation Center for Brain ScienceHuazhong University of Science and TechnologyWuhanChina
| | - Jukka Westermarck
- Turku Bioscience CentreUniversity of TurkuTurkuFinland
- Åbo Akademi UniversityTurkuFinland
- Institute of BiomedicineUniversity of TurkuTurkuFinland
| | - Heng‐Ye Man
- Department of BiologyBoston UniversityBostonUSA
| | - Rong Liu
- Department of Pathophysiology, Key Laboratory of Ministry of Education for Neurological Disorders, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
- The Institute of Brain Research, Collaborative Innovation Center for Brain ScienceHuazhong University of Science and TechnologyWuhanChina
- Department of Pediatrics, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
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9
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Miskowiak KW, Yalin N, Seeberg I, Burdick KE, Balanzá‐Martínez V, Bonnin CDM, Bowie CR, Carvalho AF, Dols A, Douglas K, Gallagher P, Hasler G, Kessing LV, Lafer B, Lewandowski KE, López‐Jaramillo C, Martinez‐Aran A, McIntyre RS, Porter RJ, Purdon SE, Schaffer A, Sumiyoshi T, Torres IJ, Van Rheenen TE, Yatham LN, Young AH, Vieta E, Stokes PRA. Can magnetic resonance imaging enhance the assessment of potential new treatments for cognitive impairment in mood disorders? A systematic review and position paper by the International Society for Bipolar Disorders Targeting Cognition Task Force. Bipolar Disord 2022; 24:615-636. [PMID: 35950925 PMCID: PMC9826389 DOI: 10.1111/bdi.13247] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
BACKGROUND Developing treatments for cognitive impairment is key to improving the functioning of people with mood disorders. Neuroimaging may assist in identifying brain-based efficacy markers. This systematic review and position paper by the International Society for Bipolar Disorders Targeting Cognition Task Force examines the evidence from neuroimaging studies of pro-cognitive interventions. METHODS We included magnetic resonance imaging (MRI) studies of candidate interventions in people with mood disorders or healthy individuals, following the procedures of the Preferred Reporting Items for Systematic reviews and Meta-Analysis 2020 statement. Searches were conducted on PubMed/MEDLINE, PsycInfo, EMBASE, Cochrane Library, and Clinicaltrials.gov from inception to 30th April 2021. Two independent authors reviewed the studies using the National Heart, Lung, Blood Institutes of Health Quality Assessment Tool for Controlled Intervention Studies and the quality of neuroimaging methodology assessment checklist. RESULTS We identified 26 studies (N = 702). Six investigated cognitive remediation or pharmacological treatments in mood disorders (N = 190). In healthy individuals, 14 studies investigated pharmacological interventions (N = 319), 2 cognitive training (N = 73) and 4 neuromodulatory treatments (N = 120). Methodologies were mostly rated as 'fair'. 77% of studies investigated effects with task-based fMRI. Findings varied but most consistently involved treatment-associated cognitive control network (CCN) activity increases with cognitive improvements, or CCN activity decreases with no cognitive change, and increased functional connectivity. In mood disorders, treatment-related default mode network suppression occurred. CONCLUSIONS Modulation of CCN and DMN activity is a putative efficacy biomarker. Methodological recommendations are to pre-declare intended analyses and use task-based fMRI, paradigms probing the CCN, longitudinal assessments, mock scanning, and out-of-scanner tests.
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Affiliation(s)
- Kamilla W. Miskowiak
- Copenhagen Affective disorder Research Centre (CADIC), Psychiatric Centre CopenhagenCopenhagen University HospitalCopenhagenDenmark,Department of PsychologyUniversity of CopenhagenCopenhagenDenmark
| | - Nefize Yalin
- Department of Psychological MedicineInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Ida Seeberg
- Copenhagen Affective disorder Research Centre (CADIC), Psychiatric Centre CopenhagenCopenhagen University HospitalCopenhagenDenmark
| | - Katherine E. Burdick
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA,Department of PsychiatryBrigham and Women's HospitalBostonMassachusettsUSA
| | - Vicent Balanzá‐Martínez
- Teaching Unit of Psychiatry and Psychological Medicine, Department of MedicineUniversity of Valencia, CIBERSAMValenciaSpain
| | - Caterina del Mar Bonnin
- Clinical Institute of Neuroscience, Hospital ClinicUniversity of Barcelona, IDIBAPS, CIBERSAMBarcelonaSpain
| | | | - Andre F. Carvalho
- IMPACT Strategic Research Centre (Innovation in Mental and Physical Health and Clinical Treatment)Deakin UniversityGeelongVictoriaAustralia
| | - Annemieke Dols
- Department of Old Age Psychiatry, GGZ in Geest, Amsterdam UMC, location VUmc, Amsterdam NeuroscienceAmsterdam Public Health research instituteAmsterdamThe Netherlands
| | - Katie Douglas
- Department of Psychological MedicineUniversity of OtagoChristchurchNew Zealand
| | - Peter Gallagher
- Translational and Clinical Research Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle‐upon‐TyneUK
| | - Gregor Hasler
- Psychiatry Research UnitUniversity of FribourgFribourgSwitzerland
| | - Lars V. Kessing
- Copenhagen Affective disorder Research Centre (CADIC), Psychiatric Centre CopenhagenCopenhagen University HospitalCopenhagenDenmark,Department of Clinical MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Beny Lafer
- Bipolar Disorder Research Program, Institute of Psychiatry, Hospital das Clinicas, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Kathryn E. Lewandowski
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA,McLean HospitalSchizophrenia and Bipolar Disorder ProgramBelmontMassachusettsUSA
| | - Carlos López‐Jaramillo
- Research Group in Psychiatry, Department of PsychiatryUniversidad de AntioquiaMedellínColombia
| | - Anabel Martinez‐Aran
- Clinical Institute of Neuroscience, Hospital ClinicUniversity of Barcelona, IDIBAPS, CIBERSAMBarcelonaSpain
| | - Roger S. McIntyre
- Mood Disorders Psychopharmacology Unit, Brain and Cognition Discovery FoundationUniversity of TorontoTorontoCanada
| | - Richard J. Porter
- Department of Psychological MedicineUniversity of OtagoChristchurchNew Zealand
| | - Scot E. Purdon
- Department of PsychiatryUniversity of AlbertaEdmontonCanada
| | - Ayal Schaffer
- Department of PsychiatryUniversity of TorontoTorontoCanada
| | - Tomiki Sumiyoshi
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental HealthNational Center of Neurology and PsychiatryTokyoJapan
| | - Ivan J. Torres
- Department of PsychiatryUniversity of British ColumbiaVancouverCanada
| | - Tamsyn E. Van Rheenen
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of MelbourneCarltonAustralia,Centre for Mental Health, Faculty of Health, Arts and DesignSwinburne UniversityHawthornAustralia
| | - Lakshmi N. Yatham
- Department of PsychiatryUniversity of British ColumbiaVancouverCanada
| | - Allan H. Young
- Department of Psychological MedicineInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Eduard Vieta
- Clinical Institute of Neuroscience, Hospital ClinicUniversity of Barcelona, IDIBAPS, CIBERSAMBarcelonaSpain
| | - Paul R. A. Stokes
- Department of Psychological MedicineInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
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10
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Liu J, Liu Z, Wei Y, Zhang Y, Womer FY, Jia D, Wei S, Wu F, Kong L, Jiang X, Zhang L, Tang Y, Zhang X, Wang F. Combinatorial panel with endophenotypes from multilevel information of diffusion tensor imaging and lipid profile as predictors for depression. Aust N Z J Psychiatry 2022; 56:1187-1198. [PMID: 35632993 DOI: 10.1177/00048674211031477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
OBJECTIVE Clinical heterogeneity in major depressive disorder likely reflects the range of etiology and contributing factors in the disorder, such as genetic risk. Identification of more refined subgroups based on biomarkers such as white matter integrity and lipid-related metabolites could facilitate precision medicine in major depressive disorder. METHODS A total of 148 participants (15 genetic high-risk participants, 57 patients with first-episode major depressive disorder and 76 healthy controls) underwent diffusion tensor imaging and plasma lipid profiling. Alterations in white matter integrity and lipid metabolites were identified in genetic high-risk participants and patients with first-episode major depressive disorder. Then, shared alterations between genetic high-risk and first-episode major depressive disorder were used to develop an imaging x metabolite diagnostic panel for genetically based major depressive disorder via factor analysis and logistic regression. A fivefold cross-validation test was performed to evaluate the diagnostic panel. RESULTS Alterations of white matter integrity in corona radiata, superior longitudinal fasciculus and the body of corpus callosum and dysregulated unsaturated fatty acid metabolism were identified in both genetic high-risk participants and patients with first-episode major depressive disorder. An imaging x metabolite diagnostic panel, consisting of measures for white matter integrity and unsaturated fatty acid metabolism, was identified that achieved an area under the receiver operating characteristic curve of 0.86 and had a significantly higher diagnostic performance than that using either measure alone. And cross-validation confirmed the adequate reliability and accuracy of the diagnostic panel. CONCLUSION Combining white matter integrity in corpus callosum, superior longitudinal fasciculus and corona radiata, and unsaturated fatty acid profile may improve the identification of genetically based endophenotypes in major depressive disorder to advance precision medicine strategies.
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Affiliation(s)
- Juan Liu
- 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.,Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, PR China
| | - Zhuang Liu
- School of Public health, China Medical University, Shenyang, Liaoning, PR China
| | - Yange Wei
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China.,Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, PR China
| | - Yanbo Zhang
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Canada
| | - Fay Y Womer
- Department of Psychiatry, Washington University School of Medicine, St. Louis, USA
| | - Duan Jia
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China.,Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, PR China
| | - Shengnan Wei
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Feng Wu
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Lingtao Kong
- 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
| | - Luheng Zhang
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China.,Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, PR China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Xizhe Zhang
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, PR China.,Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, PR China.,Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, Jiangsu, PR China.,School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 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.,Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, PR China.,Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, Jiangsu, PR China
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11
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Interleukin-6-white matter network differences explained the susceptibility to depression after stressful life events. J Affect Disord 2022; 305:122-132. [PMID: 35271870 DOI: 10.1016/j.jad.2022.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 03/01/2022] [Accepted: 03/03/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND Stressful life events (SLEs) are well-established proximal predictors of the onset of depression. However, the fundamental causes of interindividual differences in depression outcomes are poorly understood. This study addressed this depression susceptibility mechanism using a well-powered sample of adults living in China. METHODS Healthy participants with SLEs (n = 185; mean = 47.51 years, 49.73% female), drawn from a longitudinal study on the development of depression, underwent diffusion tensor imaging, interleukin-6 (IL-6) level measurement, and trimonthly standardized clinical and scale evaluations within a two-year period. RESULTS Receiver operating characteristic analyses indicated that reduced feeder connection and HIP.R nodal efficiency improved the predictive accuracy of post-SLEs depression (ORfeeder = 0.623, AUC = 0.869, P < 0.001; ORHIP = 0.459, AUC = 0.855, P < 0.001). The successfully established path analysis model confirmed the significant partial effect of SLEs-IL-6-white matter (WM) network differences-depression (onset and severity) (x2/8 = 1.453, goodness-of-fit [GFI] = 0.935, standard root-mean-square error of approximation [SRMR] = 0.024). Females, individuals with lower exercise frequency (EF) or annual household income (AHI) were more likely to have higher IL-6 level after SLEs (βint-female⁎SLEs = -0.420, P < 0.001; βint-exercise⁎SLEs = -0.412, P < 0.001; βint-income⁎SLEs = -0.302, P = 0.005). LIMITATIONS The sample size was restricted due to the limited incidence rate and prospective follow-up design. CONCLUSIONS Our results suggested that among healthy adults after SLEs, those who exhibited abnormal IL-6-WM differences were susceptible to developing depression. Females, lower AHI or EF might account for an increased risk of developing these abnormal IL-6-WM differences.
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12
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Yu Q, Guo X, Zhu Z, Feng C, Jiang H, Zheng Z, Zhang J, Zhu J, Wu H. White Matter Tracts Associated With Deep Brain Stimulation Targets in Major Depressive Disorder: A Systematic Review. Front Psychiatry 2022; 13:806916. [PMID: 35573379 PMCID: PMC9095936 DOI: 10.3389/fpsyt.2022.806916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Abstract
Background Deep brain stimulation (DBS) has been proposed as a last-resort treatment for major depressive disorder (MDD) and has shown potential antidepressant effects in multiple clinical trials. However, the clinical effects of DBS for MDD are inconsistent and suboptimal, with 30-70% responder rates. The currently used DBS targets for MDD are not individualized, which may account for suboptimal effect. Objective We aim to review and summarize currently used DBS targets for MDD and relevant diffusion tensor imaging (DTI) studies. Methods A literature search of the currently used DBS targets for MDD, including clinical trials, case reports and anatomy, was performed. We also performed a literature search on DTI studies in MDD. Results A total of 95 studies are eligible for our review, including 51 DBS studies, and 44 DTI studies. There are 7 brain structures targeted for MDD DBS, and 9 white matter tracts with microstructural abnormalities reported in MDD. These DBS targets modulate different brain regions implicated in distinguished dysfunctional brain circuits, consistent with DTI findings in MDD. Conclusions In this review, we propose a taxonomy of DBS targets for MDD. These results imply that clinical characteristics and white matter tracts abnormalities may serve as valuable supplements in future personalized DBS for MDD.
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Affiliation(s)
| | | | | | | | | | | | | | - Junming Zhu
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hemmings Wu
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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13
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Luttenbacher I, Phillips A, Kazemi R, Hadipour AL, Sanghvi I, Martinez J, Adamson MM. Transdiagnostic role of glutamate and white matter damage in neuropsychiatric disorders: A Systematic Review. J Psychiatr Res 2022; 147:324-348. [PMID: 35151030 DOI: 10.1016/j.jpsychires.2021.12.042] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/08/2021] [Accepted: 12/19/2021] [Indexed: 12/09/2022]
Abstract
Neuropsychiatric disorders including generalized anxiety disorder (GAD), obsessive-compulsive disorder (OCD), major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ) have been considered distinct categories of diseases despite their overlapping characteristics and symptomatology. We aimed to provide an in-depth review elucidating the role of glutamate/Glx and white matter (WM) abnormalities in these disorders from a transdiagnostic perspective. The PubMed online database was searched for studies published between 2010 and 2021. After careful screening, 401 studies were included. The findings point to decreased levels of glutamate in the Anterior Cingulate Cortex in both SZ and BD, whereas Glx is elevated in the Hippocampus in SZ and MDD. With regard to WM abnormalities, the Corpus Callosum and superior Longitudinal Fascicle were the most consistently identified brain regions showing decreased fractional anisotropy (FA) across all the reviewed disorders, except GAD. Additionally, the Uncinate Fasciculus displayed decreased FA in all disorders, except OCD. Decreased FA was also found in the inferior Longitudinal Fasciculus, inferior Fronto-Occipital Fasciculus, Thalamic Radiation, and Corona Radiata in SZ, BD, and MDD. Decreased FA in the Fornix and Corticospinal Tract were found in BD and SZ patients. The Cingulum and Anterior Limb of Internal Capsule exhibited decreased FA in MDD and SZ patients. The results suggest a gradual increase in severity from GAD to SZ defined by the number of brain regions with WM abnormality which may be partially caused by abnormal glutamate levels. WM damage could thus be considered a potential marker of some of the main neuropsychiatric disorders.
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Affiliation(s)
- Ines Luttenbacher
- Department of Social & Behavioral Sciences, University of Amsterdam, Amsterdam, Netherlands; Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Angela Phillips
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Reza Kazemi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Abed L Hadipour
- Department of Cognitive Sciences, University of Messina, Messina, Italy
| | - Isha Sanghvi
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Neuroscience, University of Southern California, Los Angeles, CA, USA
| | - Julian Martinez
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Palo Alto University, Palo Alto, CA, USA
| | - Maheen M Adamson
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
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14
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The SACT Template: A Human Brain Diffusion Tensor Template for School-age Children. Neurosci Bull 2022; 38:607-621. [PMID: 35092576 DOI: 10.1007/s12264-022-00820-1] [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: 06/02/2021] [Accepted: 10/22/2021] [Indexed: 10/19/2022] Open
Abstract
School-age children are in a specific development stage corresponding to juvenility, when the white matter of the brain experiences ongoing maturation. Diffusion-weighted magnetic resonance imaging (DWI), especially diffusion tensor imaging (DTI), is extensively used to characterize the maturation by assessing white matter properties in vivo. In the analysis of DWI data, spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space. Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization. However, there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development. Here, we established the school-age children diffusion tensor (SACT) template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6-12 years. With an age-balanced design, the SACT template represented the entire age range well by showing high similarity to the age-specific templates. Compared with the tensor template of adults, the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of school-age children. A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template. Although similar spatial patterns were found, the SACT template showed significant effects on the distributions of the statistical results, which may be related to the performance of spatial normalization. Looking forward, the SACT template could contribute to future studies of white matter development in both healthy and clinical populations. The SACT template is publicly available now ( https://figshare.com/articles/dataset/SACT_template/14071283 ).
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15
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Den Ouden L, Suo C, Albertella L, Greenwood LM, Lee RSC, Fontenelle LF, Parkes L, Tiego J, Chamberlain SR, Richardson K, Segrave R, Yücel M. Transdiagnostic phenotypes of compulsive behavior and associations with psychological, cognitive, and neurobiological affective processing. Transl Psychiatry 2022; 12:10. [PMID: 35013101 PMCID: PMC8748429 DOI: 10.1038/s41398-021-01773-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 12/02/2021] [Accepted: 12/16/2021] [Indexed: 01/10/2023] Open
Abstract
Compulsivity is a poorly understood transdiagnostic construct thought to underlie multiple disorders, including obsessive-compulsive disorder, addictions, and binge eating. Our current understanding of the causes of compulsive behavior remains primarily based on investigations into specific diagnostic categories or findings relying on one or two laboratory measures to explain complex phenotypic variance. This proof-of-concept study drew on a heterogeneous sample of community-based individuals (N = 45; 18-45 years; 25 female) exhibiting compulsive behavioral patterns in alcohol use, eating, cleaning, checking, or symmetry. Data-driven statistical modeling of multidimensional markers was utilized to identify homogeneous subtypes that were independent of traditional clinical phenomenology. Markers were based on well-defined measures of affective processing and included psychological assessment of compulsivity, behavioral avoidance, and stress, neurocognitive assessment of reward vs. punishment learning, and biological assessment of the cortisol awakening response. The neurobiological validity of the subtypes was assessed using functional magnetic resonance imaging. Statistical modeling identified three stable, distinct subtypes of compulsivity and affective processing, which we labeled "Compulsive Non-Avoidant", "Compulsive Reactive" and "Compulsive Stressed". They differed meaningfully on validation measures of mood, intolerance of uncertainty, and urgency. Most importantly, subtypes captured neurobiological variance on amygdala-based resting-state functional connectivity, suggesting they were valid representations of underlying neurobiology and highlighting the relevance of emotion-related brain networks in compulsive behavior. Although independent larger samples are needed to confirm the stability of subtypes, these data offer an integrated understanding of how different systems may interact in compulsive behavior and provide new considerations for guiding tailored intervention decisions.
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Affiliation(s)
- Lauren Den Ouden
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia.
| | - Chao Suo
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Lucy Albertella
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Lisa-Marie Greenwood
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
- Research School of Psychology, ANU College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Rico S C Lee
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Leonardo F Fontenelle
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
- D'Or Institute for Research and Education and Anxiety, Obsessive, Compulsive Research Program, Institute of Psychiatry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Linden Parkes
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jeggan Tiego
- Neural Systems and Behaviour Lab, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Samuel R Chamberlain
- Department of Psychiatry, University of Southampton, Southampton, UK
- Southern Health NHS Foundation Trust, Southampton, UK
| | - Karyn Richardson
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Rebecca Segrave
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Murat Yücel
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
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16
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de la Fuente Revenga M, Zhu B, Guevara CA, Naler LB, Saunders JM, Zhou Z, Toneatti R, Sierra S, Wolstenholme JT, Beardsley PM, Huntley GW, Lu C, González-Maeso J. Prolonged epigenomic and synaptic plasticity alterations following single exposure to a psychedelic in mice. Cell Rep 2021; 37:109836. [PMID: 34686347 PMCID: PMC8582597 DOI: 10.1016/j.celrep.2021.109836] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 07/21/2021] [Accepted: 09/24/2021] [Indexed: 12/20/2022] Open
Abstract
Clinical evidence suggests that rapid and sustained antidepressant action can be attained with a single exposure to psychedelics. However, the biological substrates and key mediators of psychedelics' enduring action remain unknown. Here, we show that a single administration of the psychedelic DOI produces fast-acting effects on frontal cortex dendritic spine structure and acceleration of fear extinction via the 5-HT2A receptor. Additionally, a single dose of DOI leads to changes in chromatin organization, particularly at enhancer regions of genes involved in synaptic assembly that stretch for days after the psychedelic exposure. These DOI-induced alterations in the neuronal epigenome overlap with genetic loci associated with schizophrenia, depression, and attention deficit hyperactivity disorder. Together, these data support that epigenomic-driven changes in synaptic plasticity sustain psychedelics' long-lasting antidepressant action but also warn about potential substrate overlap with genetic risks for certain psychiatric conditions.
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MESH Headings
- Amphetamines/pharmacology
- Animals
- Behavior, Animal/drug effects
- Dendritic Spines/drug effects
- Dendritic Spines/metabolism
- Epigenesis, Genetic/drug effects
- Epigenome/drug effects
- Epigenomics
- Extinction, Psychological/drug effects
- Fear/drug effects
- Frontal Lobe/drug effects
- Frontal Lobe/metabolism
- Hallucinogens/pharmacology
- Male
- Mice, 129 Strain
- Mice, Inbred C57BL
- Mice, Knockout
- Neuronal Plasticity/drug effects
- Receptor, Serotonin, 5-HT2A/drug effects
- Receptor, Serotonin, 5-HT2A/genetics
- Receptor, Serotonin, 5-HT2A/metabolism
- Serotonin 5-HT2 Receptor Agonists/pharmacology
- Synapses/drug effects
- Synapses/metabolism
- Time Factors
- Mice
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Affiliation(s)
- Mario de la Fuente Revenga
- Department of Physiology and Biophysics, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA; Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Bohan Zhu
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA
| | - Christopher A Guevara
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lynette B Naler
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA
| | - Justin M Saunders
- Department of Physiology and Biophysics, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
| | - Zirui Zhou
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA
| | - Rudy Toneatti
- Department of Physiology and Biophysics, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
| | - Salvador Sierra
- Department of Physiology and Biophysics, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
| | - Jennifer T Wolstenholme
- Department of Pharmacology and Toxicology, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
| | - Patrick M Beardsley
- Department of Pharmacology and Toxicology, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA; Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - George W Huntley
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Chang Lu
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA.
| | - Javier González-Maeso
- Department of Physiology and Biophysics, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA.
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17
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Precise Modulation Strategies for Transcranial Magnetic Stimulation: Advances and Future Directions. Neurosci Bull 2021; 37:1718-1734. [PMID: 34609737 DOI: 10.1007/s12264-021-00781-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 06/23/2021] [Indexed: 10/20/2022] Open
Abstract
Transcranial magnetic stimulation (TMS) is a popular modulatory technique for the noninvasive diagnosis and therapy of neurological and psychiatric diseases. Unfortunately, current modulation strategies are only modestly effective. The literature provides strong evidence that the modulatory effects of TMS vary depending on device components and stimulation protocols. These differential effects are important when designing precise modulatory strategies for clinical or research applications. Developments in TMS have been accompanied by advances in combining TMS with neuroimaging techniques, including electroencephalography, functional near-infrared spectroscopy, functional magnetic resonance imaging, and positron emission tomography. Such studies appear particularly promising as they may not only allow us to probe affected brain areas during TMS but also seem to predict underlying research directions that may enable us to precisely target and remodel impaired cortices or circuits. However, few precise modulation strategies are available, and the long-term safety and efficacy of these strategies need to be confirmed. Here, we review the literature on possible technologies for precise modulation to highlight progress along with limitations with the goal of suggesting future directions for this field.
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18
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Peng Z, Li X, Li J, Dong Y, Gao Y, Liao Y, Yan M, Yuan Z, Cheng J. Dlg1 Knockout Inhibits Microglial Activation and Alleviates Lipopolysaccharide-Induced Depression-Like Behavior in Mice. Neurosci Bull 2021; 37:1671-1682. [PMID: 34490521 PMCID: PMC8643377 DOI: 10.1007/s12264-021-00765-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 05/23/2021] [Indexed: 12/17/2022] Open
Abstract
Microglia-mediated neuroinflammation is widely perceived as a contributor to numerous neurological diseases and mental disorders including depression. Discs large homolog 1 (Dlg1), an adaptor protein, regulates cell polarization and the function of K+ channels, which are reported to regulate the activation of microglia. However, little is known about the role of Dlg1 in microglia and the maintenance of central nervous system homeostasis. In this study, we found that Dlg1 knockdown suppressed lipopolysaccharide (LPS)-induced inflammation by down-regulating the activation of nuclear factor-κB signaling and the mitogen-activated protein kinase pathway in microglia. Moreover, using an inducible Dlg1 microglia-specific knockout (Dlg1flox/flox; CX3CR1CreER) mouse line, we found that microglial Dlg1 knockout reduced the activation of microglia and alleviated the LPS-induced depression-like behavior. In summary, our results demonstrated that Dlg1 plays a critical role in microglial activation and thus provides a potential therapeutic target for the clinical treatment of depression.
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Affiliation(s)
- Zhixin Peng
- Institute of Neuroscience, Hengyang Medical College, University of South China, Hengyang, 421001, China.,The Brain Science Center, Beijing Institute of Basic Medical Sciences, 27 Taiping Road, Haidian District, Beijing, 100850, China
| | - Xiaoheng Li
- The Brain Science Center, Beijing Institute of Basic Medical Sciences, 27 Taiping Road, Haidian District, Beijing, 100850, China
| | - Jun Li
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, 100069, China
| | - Yuan Dong
- Institutes of Brain Sciences and Disease, Medical College, Qingdao University, Qingdao, 266071, China
| | - Yuhao Gao
- The Brain Science Center, Beijing Institute of Basic Medical Sciences, 27 Taiping Road, Haidian District, Beijing, 100850, China
| | - Yajin Liao
- Center on Translational Neuroscience, College of Life and Environmental Science, Minzu University of China, Beijing, 100081, China
| | - Meichen Yan
- Center on Translational Neuroscience, College of Life and Environmental Science, Minzu University of China, Beijing, 100081, China
| | - Zengqiang Yuan
- Institute of Neuroscience, Hengyang Medical College, University of South China, Hengyang, 421001, China. .,The Brain Science Center, Beijing Institute of Basic Medical Sciences, 27 Taiping Road, Haidian District, Beijing, 100850, China. .,Center on Translational Neuroscience, College of Life and Environmental Science, Minzu University of China, Beijing, 100081, China.
| | - Jinbo Cheng
- Center on Translational Neuroscience, College of Life and Environmental Science, Minzu University of China, Beijing, 100081, China.
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19
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Berto S, Liu Y, Konopka G. Genomics at cellular resolution: insights into cognitive disorders and their evolution. Hum Mol Genet 2021; 29:R1-R9. [PMID: 32566943 DOI: 10.1093/hmg/ddaa117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 06/05/2020] [Accepted: 06/11/2020] [Indexed: 02/06/2023] Open
Abstract
High-throughput genomic sequencing approaches have held the promise of understanding and ultimately leading to treatments for cognitive disorders such as autism spectrum disorders, schizophrenia and Alzheimer's disease. Although significant progress has been made into identifying genetic variants associated with these diseases, these studies have also uncovered that these disorders are mostly genetically complex and thus challenging to model in non-human systems. Improvements in such models might benefit from understanding the evolution of the human genome and how such modifications have affected brain development and function. The intersection of genome-wide variant information with cell-type-specific expression and epigenetic information will further assist in resolving the contribution of particular cell types in evolution or disease. For example, the role of non-neuronal cells in brain evolution and cognitive disorders has gone mostly underappreciated until the recent availability of single-cell transcriptomic approaches. In this review, we discuss recent studies that carry out cell-type-specific assessments of gene expression in brain tissue across primates and between healthy and disease populations. The emerging results from these studies are beginning to elucidate how specific cell types in the evolved human brain are contributing to cognitive disorders.
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20
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Li Z, Ruan M, Chen J, Fang Y. Major Depressive Disorder: Advances in Neuroscience Research and Translational Applications. Neurosci Bull 2021; 37:863-880. [PMID: 33582959 PMCID: PMC8192601 DOI: 10.1007/s12264-021-00638-3] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 09/30/2020] [Indexed: 02/07/2023] Open
Abstract
Major depressive disorder (MDD), also referred to as depression, is one of the most common psychiatric disorders with a high economic burden. The etiology of depression is still not clear, but it is generally believed that MDD is a multifactorial disease caused by the interaction of social, psychological, and biological aspects. Therefore, there is no exact pathological theory that can independently explain its pathogenesis, involving genetics, neurobiology, and neuroimaging. At present, there are many treatment measures for patients with depression, including drug therapy, psychotherapy, and neuromodulation technology. In recent years, great progress has been made in the development of new antidepressants, some of which have been applied in the clinic. This article mainly reviews the research progress, pathogenesis, and treatment of MDD.
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Affiliation(s)
- Zezhi Li
- Clinical Research Center and Division of Mood Disorders of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.,Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Meihua Ruan
- Shanghai Institute of Nutrition and Health, Shanghai Information Center for Life Sciences, Chinese Academy of Science, Shanghai, 200031, China
| | - Jun Chen
- Clinical Research Center and Division of Mood Disorders of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 201108, China
| | - Yiru Fang
- Clinical Research Center and Division of Mood Disorders of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China. .,Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, 200031, China. .,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 201108, China.
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21
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Liang S, Deng W, Li X, Greenshaw AJ, Wang Q, Li M, Ma X, Bai TJ, Bo QJ, Cao J, Chen GM, Chen W, Cheng C, Cheng YQ, Cui XL, Duan J, Fang YR, Gong QY, Guo WB, Hou ZH, Hu L, Kuang L, Li F, Li KM, Liu YS, Liu ZN, Long YC, Luo QH, Meng HQ, Peng DH, Qiu HT, Qiu J, Shen YD, Shi YS, Si TM, Wang CY, Wang F, Wang K, Wang L, Wang X, Wang Y, Wu XP, Wu XR, Xie CM, Xie GR, Xie HY, Xie P, Xu XF, Yang H, Yang J, Yu H, Yao JS, Yao SQ, Yin YY, Yuan YG, Zang YF, Zhang AX, Zhang H, Zhang KR, Zhang ZJ, Zhao JP, Zhou RB, Zhou YT, Zou CJ, Zuo XN, Yan CG, Li T. Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns. NEUROIMAGE-CLINICAL 2020; 28:102514. [PMID: 33396001 PMCID: PMC7724374 DOI: 10.1016/j.nicl.2020.102514] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 11/20/2020] [Accepted: 11/22/2020] [Indexed: 02/05/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder. METHODS The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups. RESULTS Two MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables. CONCLUSIONS Findings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.
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Affiliation(s)
- Sugai Liang
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Wei Deng
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Xiaojing Li
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Andrew J Greenshaw
- Department of Psychiatry, University of Alberta, Edmonton T6G 2B7, AB, Canada
| | - Qiang Wang
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Mingli Li
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Xiaohong Ma
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Tong-Jian Bai
- Anhui Medical University, Hefei 230032, Anhui, China
| | - Qi-Jing Bo
- Beijing Anding Hospital, Capital Medical University, Beijing 100069, China
| | - Jun Cao
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Guan-Mao Chen
- The First Affiliated Hospital of Jinan University, Guangzhou 510630, Guangdong, China
| | - Wei Chen
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chang Cheng
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Yu-Qi Cheng
- First Affiliated Hospital of Kunming Medical University, Kunming 650211, Yunnan, China
| | - Xi-Long Cui
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Jia Duan
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang 110001, Liaoning, China
| | - Yi-Ru Fang
- Department of Psychiatry, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Qi-Yong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu 610040, Sichuan, China
| | - Wen-Bin Guo
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Zheng-Hua Hou
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210096, Jiangsu, China
| | - Lan Hu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Li Kuang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Feng Li
- Beijing Anding Hospital, Capital Medical University, Beijing 100069, China
| | - Kai-Ming Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China
| | - Yan-Song Liu
- Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou 215031, Jiangsu, China
| | - Zhe-Ning Liu
- The Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Yi-Cheng Long
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Qing-Hua Luo
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Hua-Qing Meng
- Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou 215031, Jiangsu, China
| | - Dai-Hui Peng
- Department of Psychiatry, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Hai-Tang Qiu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Yue-Di Shen
- Department of Diagnostics, Affiliated Hospital, Hangzhou Normal University Medical School, Hangzhou 311121, Zhejiang, China
| | - Yu-Shu Shi
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tian-Mei Si
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Chuan-Yue Wang
- Beijing Anding Hospital, Capital Medical University, Beijing 100069, China
| | - Fei Wang
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang 110001, Liaoning, China
| | - Kai Wang
- Beijing Anding Hospital, Capital Medical University, Beijing 100069, China
| | - Li Wang
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Xiang Wang
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Ying Wang
- The First Affiliated Hospital of Jinan University, Guangzhou 510630, Guangdong, China
| | - Xiao-Ping Wu
- Xi'an Central Hospital, Xi'an 710032, Shaanxi, China
| | - Xin-Ran Wu
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Chun-Ming Xie
- Department of Neurology, Affiliated Zhongda Hospital of Southeast University, Nanjing 210096, Jiangsu, China
| | - Guang-Rong Xie
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Hai-Yan Xie
- Department of Psychiatry, The Fourth Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Peng Xie
- Institute of Neuroscience, Chongqing Medical University, Chongqing 400016, China; Chongqing Key Laboratory of Neurobiology, Chongqing 400016, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xiu-Feng Xu
- First Affiliated Hospital of Kunming Medical University, Kunming 650211, Yunnan, China
| | - Hong Yang
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jian Yang
- The First Affiliated Hospital of Xi'an Jiaotong University, 710049 Shaanxi, China
| | - Hua Yu
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Jia-Shu Yao
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Shu-Qiao Yao
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Ying-Ying Yin
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210096, Jiangsu, China
| | - Yong-Gui Yuan
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210096, Jiangsu, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou 311121, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, Zhejiang, China
| | - Ai-Xia Zhang
- The First Affiliated Hospital of Xi'an Jiaotong University, 710049 Shaanxi, China
| | - Hong Zhang
- Xi'an Central Hospital, Xi'an 710032, Shaanxi, China
| | - Ke-Rang Zhang
- First Hospital of Shanxi Medical University, Taiyuan 030607, Shanxi, China
| | - Zhi-Jun Zhang
- Department of Neurology, Affiliated Zhongda Hospital of Southeast University, Nanjing 210096, Jiangsu, China
| | - Jing-Ping Zhao
- The Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Ru-Bai Zhou
- Department of Psychiatry, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Yi-Ting Zhou
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Chao-Jie Zou
- First Affiliated Hospital of Kunming Medical University, Kunming 650211, Yunnan, China
| | - Xi-Nian Zuo
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center and Research Center for Lifespan Development of Mind and Brain (CLIMB), Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center and Research Center for Lifespan Development of Mind and Brain (CLIMB), Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Tao Li
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
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22
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He B, Cao L, Xia X, Zhang B, Zhang D, You B, Fan L, Jiang T. Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles. Neurosci Bull 2020; 36:1454-1473. [PMID: 33108588 PMCID: PMC7719154 DOI: 10.1007/s12264-020-00589-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/30/2020] [Indexed: 11/25/2022] Open
Abstract
The frontal pole cortex (FPC) plays key roles in various higher-order functions and is highly developed in non-human primates. An essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer results. This is important for understanding the functional architecture of the cerebral cortex. Here, combining cross-validation and principal component analysis, we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC (2 males and 6 females) into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4T Bruker system, and then revealed their subregional connections. Furthermore, we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions, and found that the dorsolateral FPC, which contains an extension to the medial FPC, was mainly connected to regions of the default-mode network. The ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive network. These results enhance our understanding of the anatomy and circuitry of the macaque brain, and contribute to FPC-related clinical research.
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Affiliation(s)
- Bin He
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China
| | - Long Cao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xiaoluan Xia
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China
| | - Dan Zhang
- Core Facility, Center of Biomedical Analysis, Tsinghua University, Beijing, 100084, China
| | - Bo You
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China. .,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, CAS, Beijing, 100190, China. .,University of CAS, Beijing, 100049, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China. .,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, CAS, Beijing, 100190, China. .,Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China. .,The Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia. .,University of CAS, Beijing, 100049, China. .,Chinese Institute for Brain Research, Beijing, 102206, China.
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23
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Gryglewski G, Seiger R, Baldinger-Melich P, Unterholzner J, Spurny B, Vanicek T, Hahn A, Kasper S, Frey R, Lanzenberger R. Changes in White Matter Microstructure After Electroconvulsive Therapy for Treatment-Resistant Depression. Int J Neuropsychopharmacol 2020; 23:20-25. [PMID: 31740958 PMCID: PMC7064047 DOI: 10.1093/ijnp/pyz059] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 10/11/2019] [Accepted: 11/18/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Treatment-resistant depression is among the most debilitating conditions in psychiatry. Recent studies have associated alterations in white matter microstructure measured with magnetic resonance imaging with poor antidepressant response. Therefore, the extent to which electroconvulsive therapy, the most effective therapeutic option for treatment-resistant depression, affects white matter microstructure warrants investigation. METHODS A total 13 patients suffering from severe unipolar treatment-resistant depression underwent magnetic resonance imaging with a diffusion tensor imaging sequence before and after undergoing a series of right unilateral electroconvulsive therapy. Diffusivity metrics were compared voxel-wise using tract-based spatial statistics and repeated-measures ANOVA. RESULTS A total 12 patients responded to electroconvulsive therapy and 9 were classified as remitters. An increase in axial diffusivity was observed in the posterior limb of the internal capsule of the right hemisphere (PFWE ≤ .05). The increase in this area was higher in the right compared with the left hemisphere (P < .05). No correlation of this effect with treatment response could be found. CONCLUSIONS The strong lateralization of effects to the hemisphere of electrical stimulation suggests an effect of electroconvulsive therapy on diffusivity metrics which is dependent of electrode placement. Investigation in controlled studies is necessary to reveal to what extent the effects of electroconvulsive therapy on white matter microstructure are related to clinical outcomes and electrode placement.
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Affiliation(s)
- Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - René Seiger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Pia Baldinger-Melich
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Jakob Unterholzner
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Benjamin Spurny
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Thomas Vanicek
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Richard Frey
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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