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Zhou Y, Jing J, Zhang Z, Pan Y, Cai X, Zhu W, Li Z, Liu C, Liu H, Meng X, Cheng J, Wang Y, Li H, Wang S, Niu H, Wen W, Sachdev PS, Wei T, Liu T, Wang Y. Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study. Hum Brain Mapp 2024; 45:e26598. [PMID: 38339955 PMCID: PMC10839741 DOI: 10.1002/hbm.26598] [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: 04/28/2023] [Revised: 12/22/2023] [Accepted: 01/04/2024] [Indexed: 02/12/2024] Open
Abstract
The network nature of the brain is gradually becoming a consensus in the neuroscience field. A set of highly connected regions in the brain network called "rich-club" are crucial high efficiency communication hubs in the brain. The abnormal rich-club organization can reflect underlying abnormal brain function and metabolism, which receives increasing attention. Diabetes is one of the risk factors for neurological diseases, and most individuals with prediabetes will develop overt diabetes within their lifetime. However, the gradual impact of hyperglycemia on brain structures, including rich-club organization, remains unclear. We hypothesized that the brain follows a special disrupted pattern of rich-club organization in prediabetes and diabetes. We used cross-sectional baseline data from the population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study, which included 2218 participants with a mean age of 61.3 ± 6.6 years and 54.1% females comprising 1205 prediabetes, 504 diabetes, and 509 normal control subjects. The rich-club organization and network properties of the structural networks derived from diffusion tensor imaging data were investigated using a graph theory approach. Linear mixed models were used to assess associations between rich-club organization disruptions and the subjects' glucose status. Based on the graphical analysis methods, we observed the disrupted pattern of rich-club organization was from peripheral regions mainly located in frontal areas to rich-club regions mainly located in subcortical areas from prediabetes to diabetes. The rich-club organization disruptions were associated with elevated glucose levels. These findings provided more details of the process by which hyperglycemia affects the brain, contributing to a better understanding of the potential neurological consequences. Furthermore, the disrupted pattern observed in rich-club organization may serve as a potential neuroimaging marker for early detection and monitoring of neurological disorders in individuals with prediabetes or diabetes.
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Affiliation(s)
- Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Jing Jing
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Zhe Zhang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Yuesong Pan
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Xueli Cai
- Department of Neurology, Lishui HospitalZhejiang University School of MedicineLishuiZhejiangChina
| | - Wanlin Zhu
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Zixiao Li
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Xia Meng
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang UniversityBeijingChina
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Hao Li
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of MedicineLishuiZhejiangChina
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Wei Wen
- Division of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA)UNSWSydneyNew South WalesAustralia
- Neuropsychiatric Institute, Prince of Wales HospitalSydneyNew South WalesAustralia
| | - Perminder S. Sachdev
- Division of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA)UNSWSydneyNew South WalesAustralia
- Neuropsychiatric Institute, Prince of Wales HospitalSydneyNew South WalesAustralia
| | - Tiemin Wei
- Department of Cardiology, Lishui HospitalZhejiang University School of MedicineZhejiangChina
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Research Unit of Artificial Intelligence in Cerebrovascular DiseaseChinese Academy of Medical Sciences, 2019RU018BeijingChina
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Yu Y, Qiu M, Zou W, Zhao Y, Tang Y, Tian J, Chen X, Qiu W. Impaired rich-club connectivity in childhood absence epilepsy. Front Neurol 2023; 14:1135305. [PMID: 37251238 PMCID: PMC10213928 DOI: 10.3389/fneur.2023.1135305] [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: 12/31/2022] [Accepted: 04/12/2023] [Indexed: 05/31/2023] Open
Abstract
Introduction Childhood absence epilepsy (CAE) is a well-known pediatric epilepsy syndrome. Recent evidence has shown the presence of a disrupted structural brain network in CAE. However, little is known about the rich-club topology. This study aimed to explore the rich-club alterations in CAE and their association with clinical characteristics. Methods Diffusion tensor imaging (DTI) datasets were acquired in a sample of 30 CAE patients and 31 healthy controls. A structural network was derived from DTI data for each participant using probabilistic tractography. Then, the rich-club organization was examined, and the network connections were divided into rich-club connections, feeder connections, and local connections. Results Our results confirmed a less dense whole-brain structural network in CAE with lower network strength and global efficiency. In addition, the optimal organization of small-worldness was also damaged. A small number of highly connected and central brain regions were identified to form the rich-club organization in both patients and controls. However, patients exhibited a significantly reduced rich-club connectivity, while the other class of feeder and local connections was relatively spared. Moreover, the lower levels of rich-club connectivity strength were statistically correlated with disease duration. Discussion Our reports suggest that CAE is characterized by abnormal connectivity concentrated to rich-club organizations and might contribute to understanding the pathophysiological mechanism of CAE.
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Affiliation(s)
- Yadong Yu
- Department of Neurology, Lianshui County People's Hospital, Huai'an, China
| | - Mengdi Qiu
- Department of Neurology, The Fifth People's Hospital of Huai'an, Huai'an, China
| | - Wenwei Zou
- Department of Neurology, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, China
| | - Ying Zhao
- Department of Neurology, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, China
| | - Yan Tang
- Department of Neurology, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, China
| | - Jisha Tian
- Department of Neurology, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, China
| | - Xiaoyu Chen
- Department of Radiology, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, China
| | - Wenchao Qiu
- Department of Neurology, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, China
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3
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Kennedy E, Vadlamani S, Lindsey HM, Lei PW, Jo-Pugh M, Adamson M, Alda M, Alonso-Lana S, Ambrogi S, Anderson TJ, Arango C, Asarnow RF, Avram M, Ayesa-Arriola R, Babikian T, Banaj N, Bird LJ, Borgwardt S, Brodtmann A, Brosch K, Caeyenberghs K, Calhoun VD, Chiaravalloti ND, Cifu DX, Crespo-Facorro B, Dalrymple-Alford JC, Dams-O’Connor K, Dannlowski U, Darby D, Davenport N, DeLuca J, Diaz-Caneja CM, Disner SG, Dobryakova E, Ehrlich S, Esopenko C, Ferrarelli F, Frank LE, Franz C, Fuentes-Claramonte P, Genova H, Giza CC, Goltermann J, Grotegerd D, Gruber M, Gutierrez-Zotes A, Ha M, Haavik J, Hinkin C, Hoskinson KR, Hubl D, Irimia A, Jansen A, Kaess M, Kang X, Kenney K, Keřková B, Khlif MS, Kim M, Kindler J, Kircher T, Knížková K, Kolskår KK, Krch D, Kremen WS, Kuhn T, Kumari V, Kwon JS, Langella R, Laskowitz S, Lee J, Lengenfelder J, Liebel SW, Liou-Johnson V, Lippa SM, Løvstad M, Lundervold A, Marotta C, Marquardt CA, Mattos P, Mayeli A, McDonald CR, Meinert S, Melzer TR, Merchán-Naranjo J, Michel C, Morey RA, Mwangi B, Myall DJ, Nenadić I, Newsome MR, Nunes A, O’Brien T, Oertel V, Ollinger J, Olsen A, de la Foz VOG, Ozmen M, Pardoe H, Parent M, Piras F, Piras F, Pomarol-Clotet E, Repple J, Richard G, Rodriguez J, Rodriguez M, Rootes-Murdy K, Rowland J, Ryan NP, Salvador R, Sanders AM, Schmidt A, Soares JC, Spalleta G, Španiel F, Stasenko A, Stein F, Straube B, Thames A, Thomas-Odenthal F, Thomopoulos SI, Tone E, Torres I, Troyanskaya M, Turner JA, Ulrichsen KM, Umpierrez G, Vilella E, Vivash L, Walker WC, Werden E, Westlye LT, Wild K, Wroblewski A, Wu MJ, Wylie GR, Yatham LN, Zunta-Soares GB, Thompson PM, Tate DF, Hillary FG, Dennis EL, Wilde EA. Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.16.524331. [PMID: 36712107 PMCID: PMC9882238 DOI: 10.1101/2023.01.16.524331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. These efforts unveil new questions about integrating data arising from distinct sources and instruments. We focus on the most frequently assessed cognitive domain - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated global raw data from 53 studies totaling N = 10,505 individuals. A mega-analysis was conducted using empirical bayes harmonization to remove site effects, followed by linear models adjusting for common covariates. A continuous item response theory (IRT) model estimated each individual's latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance while preserving covariate effects, and our conversion tool is freely available online. This demonstrates that large-scale data sharing and harmonization initiatives can address reproducibility and integration challenges across the behavioral sciences.
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Affiliation(s)
- Eamonn Kennedy
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, 84132
- Division of Epidemiology, University of Utah, Salt Lake City, UT, 84132
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, 84132
| | - Shashank Vadlamani
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, 84132
| | - Hannah M Lindsey
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, 84132
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, 84132
| | - Pui-Wa Lei
- Department of Educational Psychology, Counseling, and Special Education, Pennsylvania State University, University Park, PA, 16801
| | - Mary Jo-Pugh
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, 84132
- Division of Epidemiology, University of Utah, Salt Lake City, UT, 84132
| | - Maheen Adamson
- WRIISC-WOMEN & Rehabilitation Department, VA Palo Alto, Palo Alto, CA, USA
- Neurosurgery, Stanford School of Medicine, Stanford, CA, USA
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, Canada
| | - Silvia Alonso-Lana
- FIDMAG Research Foundation, Barcelona, Spain
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), Madrid, Spain
| | - Sonia Ambrogi
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Tim J Anderson
- Department of Medicine, University of Otago, Christchurch, New Zealand
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Department of Neurology, Te Whatu Ora – Health New Zealand Waitaha Canterbury, Christchurch, New Zealand
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Robert F Asarnow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- Brain Research Institute, UCLA, Los Angeles, CA, USA
- Department of Psychology, UCLA, Los Angeles, CA, USA
| | - Mihai Avram
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Rosa Ayesa-Arriola
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), Madrid, Spain
- Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Talin Babikian
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Laura J Bird
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Melbourne, VIC, Australia
| | - Stefan Borgwardt
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
- Center of Brain, Behaviour and Metabolism (CBBM), University of Lübeck, Lübeck, Germany
| | - Amy Brodtmann
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University University, Atlanta, GA, USA
| | - Nancy D Chiaravalloti
- Centers for Neuropsychology, Neuroscience & Traumatic Brain Injury Research, Kessler Foundation, East Hanover, NJ, USA
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ, USA
| | - David X Cifu
- Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD
| | - Benedicto Crespo-Facorro
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), Madrid, Spain
- Department of Psychiatry, Virgen del Rocio University Hospital, School of Medicine, University of Seville, IBIS, Seville, Spain
| | - John C Dalrymple-Alford
- Department of Medicine, University of Otago, Christchurch, New Zealand
- New Zealand Brain Research Institute, Christchurch, New Zealand
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Kristen Dams-O’Connor
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Germany
| | - David Darby
- Department of Neuroscience, Monash University, Melbourne, Australia
- Department of Neurology, Alfred Health, Melbourne, Australia
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
| | - Nicholas Davenport
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN
- Minneapolis VA Health Care System, Minneapolis, MN
| | - John DeLuca
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ, USA
- Kessler Foundation, East Hanover, NJ, USA
| | - Covadonga M Diaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Seth G Disner
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN
- Minneapolis VA Health Care System, Minneapolis, MN
| | - Ekaterina Dobryakova
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ, USA
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Germany
- Eating Disorders Research and Treatment Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Germany
| | - Carrie Esopenko
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Fabio Ferrarelli
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lea E Frank
- Department of Psychology, University of Oregon, Eugene, OR, USA
| | - Carol Franz
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Paola Fuentes-Claramonte
- FIDMAG Research Foundation, Barcelona, Spain
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), Madrid, Spain
| | - Helen Genova
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ, USA
- Center for Autism Research, Kessler Foundation, East Hanover, NJ, USA
| | - Christopher C Giza
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
- Department of Pediatrics, Division of Neurology, UCLA Mattel Children’s Hospital, Los Angeles, CA, USA
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Alfonso Gutierrez-Zotes
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), Madrid, Spain
- Hospital Universitari Institut Pere Mata, Tarragona, Spain
- Institut d’Investiació Sanitària Pere Virgili-CERCA, Universitat Rovira i Virgili, Tarragona, Spain
| | - Minji Ha
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - Jan Haavik
- Department of Biomedicine, University of Bergen, Bergen, Norway
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Charles Hinkin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
| | - Kristen R Hoskinson
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH
- Section of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Daniela Hubl
- Translational Research Centre, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative & Computational Biology, Dornsife College of Arts & Sciences, University of Southern California, Los Angeles CA, USA
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Michael Kaess
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Clinic of Child and Adolescent Psychiatry, Centre of Psychosocial Medicine, University of Heidelberg, Heidelberg, Germany
| | - Xiaojian Kang
- WRIISC-WOMEN & Rehabilitation Department, VA Palo Alto, Palo Alto, CA, USA
| | - Kimbra Kenney
- Department of Neurology, Uniformed Services University, Bethesda, MD
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD
| | | | - Mohamed Salah Khlif
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - Jochen Kindler
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Karolina Knížková
- National Institute of Mental Health, Klecany, Czech Republic
- Department of Psychiatry, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Knut K Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital, Nesodden, Norway
| | - Denise Krch
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ, USA
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ
| | - William S Kremen
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Taylor Kuhn
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
| | - Veena Kumari
- Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge, United Kingdom
| | - Jun Soo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - Roberto Langella
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Sarah Laskowitz
- Brain Imaging and Analysis Center, Duke University, Durham, NC
| | - Jungha Lee
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - Jean Lengenfelder
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ, USA
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ
| | - Spencer W Liebel
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, 84132
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, 84132
| | | | - Sara M Lippa
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD
- Department of Neuroscience, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Marianne Løvstad
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital, Nesodden, Norway
| | - Astri Lundervold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Cassandra Marotta
- Department of Neuroscience, Monash University, Melbourne, Australia
- Department of Neurology, Alfred Health, Melbourne, Australia
| | - Craig A Marquardt
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN
- Minneapolis VA Health Care System, Minneapolis, MN
| | - Paulo Mattos
- Institute D’Or for Research and Education (IDOR), São Paulo, Brazil
| | - Ahmad Mayeli
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Carrie R McDonald
- Department of Radiation Medicine and Applied Sciences and Psychiatry, UC San Diego, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, UC San Diego, San Diego, CA, USA
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Tracy R Melzer
- Department of Medicine, University of Otago, Christchurch, New Zealand
- New Zealand Brain Research Institute, Christchurch, New Zealand
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Jessica Merchán-Naranjo
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Rajendra A Morey
- Brain Imaging and Analysis Center, Duke University, Durham, NC
- VISN 6 MIRECC, Durham VA, Durham, NC
| | - Benson Mwangi
- Center of Excellence on Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Daniel J Myall
- New Zealand Brain Research Institute, Christchurch, New Zealand
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Mary R Newsome
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX
| | - Abraham Nunes
- Department of Psychiatry, Dalhousie University, Halifax, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
| | - Terence O’Brien
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
- Department of Neuroscience, The Central Clinical School, Alfred Health, Monash University, Melbourne, VIC, Australia
| | - Viola Oertel
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapiey, Frankfurt University, Frankfurt, Germany
| | - John Ollinger
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- NorHEAD - Norwegian Centre for Headache Research, Trondheim, Norway
| | - Victor Ortiz García de la Foz
- Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Mustafa Ozmen
- Department of Electrical and Electronics Engineering, Antalya Bilim University, Antalya, Turkey
| | - Heath Pardoe
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
| | - Marise Parent
- Neuroscience Institute & Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, Rome, Italy
| | | | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Jonathan Rodriguez
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Mabel Rodriguez
- National Institute of Mental Health, Klecany, Czech Republic
| | - Kelly Rootes-Murdy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University University, Atlanta, GA, USA
| | - Jared Rowland
- W.G. (Bill) Hefner VA Medical Center, Salisbury, NC
- Department of Neurobiology & Anatomy, Wake Forest School of Medicine, Winston-Salem, NC
- VA Mid-Atlantic Mental Illness Research Education and Clinical Center (MA-MIRECC), Durham, NC
| | - Nicholas P Ryan
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia
| | - Raymond Salvador
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), Madrid, Spain
| | - Anne-Marthe Sanders
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital, Nesodden, Norway
| | - Andre Schmidt
- University of Basel, Department of Psychiatry (UPK), Basel, Switzerland
| | - Jair C Soares
- Center of Excellence on Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - Filip Španiel
- National Institute of Mental Health, Klecany, Czech Republic
- 3rd Faculty of Medicine Charles University, Prague, Czech Republic
| | - Alena Stasenko
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, UC San Diego, San Diego, CA, USA
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - April Thames
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
| | | | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90007
| | - Erin Tone
- Department of Psychology, Georgia State University, Atlanta, GA
| | - Ivan Torres
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
- British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC, Canada
| | - Maya Troyanskaya
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX
| | - Jessica A Turner
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, USA
| | - Kristine M Ulrichsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital, Nesodden, Norway
| | - Guillermo Umpierrez
- Division of Endocrinology, Emory University School of Medicine, Atlanta, GA, USA
| | - Elisabet Vilella
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), Madrid, Spain
- Hospital Universitari Institut Pere Mata, Tarragona, Spain
- Institut d’Investiació Sanitària Pere Virgili-CERCA, Universitat Rovira i Virgili, Tarragona, Spain
| | - Lucy Vivash
- Department of Neuroscience, Monash University, Melbourne, Australia
- Department of Neurology, Alfred Health, Melbourne, Australia
| | - William C Walker
- Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University, Richmond, VA
- Richmond Veterans Affairs (VA) Medical Center, Central Virginia VA Health Care System , Richmond, VA
| | - Emilio Werden
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Krista Wild
- Department of Psychology, Phoenix VA Health Care System, Phoenix, AZ, USA
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Mon-Ju Wu
- Center of Excellence on Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Glenn R Wylie
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ, USA
- Rocco Ortenzio Neuroimaging Center, Kessler Foundation, East Hanover, NJ, USA
| | - Lakshmi N Yatham
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Giovana B Zunta-Soares
- Center of Excellence on Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90007
- Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, USC, Los Angeles, CA, 90007
| | - David F Tate
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, 84132
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, 84132
| | - Frank G Hillary
- Department of Psychology, Penn State University, State College, PA, 16801
- Department of Neurology, Hershey Medical Center, State College, PA, 16801
- Social Life and Engineering Science Imaging Center, Penn State University, State College, PA, 16801
| | - Emily L Dennis
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, 84132
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, 84132
| | - Elisabeth A Wilde
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, 84132
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, 84132
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4
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Bayrakçı A, Zorlu N, Karakılıç M, Gülyüksel F, Yalınçetin B, Oral E, Gelal F, Bora E. Negative symptoms are associated with modularity and thalamic connectivity in schizophrenia. Eur Arch Psychiatry Clin Neurosci 2022; 273:565-574. [PMID: 35661912 DOI: 10.1007/s00406-022-01433-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 05/15/2022] [Indexed: 11/30/2022]
Abstract
Negative symptoms, including avolition, anhedonia, asociality, blunted affect and alogia are associated with poor long-term outcome and functioning. However, treatment options for negative symptoms are limited and neurobiological mechanisms underlying negative symptoms in schizophrenia are still poorly understood. Diffusion-weighted magnetic resonance imaging scans were acquired from 64 patients diagnosed with schizophrenia and 35 controls. Global and regional network properties and rich club organization were investigated using graph analytical methods. We found that the schizophrenia group had higher modularity, clustering coefficient and characteristic path length, and lower rich connections compared to controls, suggesting highly connected nodes within modules but less integrated with nodes in other modules in schizophrenia. We also found a lower nodal degree in the left thalamus and left putamen in schizophrenia relative to the control group. Importantly, higher modularity was associated with greater negative symptoms but not with cognitive deficits in patients diagnosed with schizophrenia suggesting an alteration in modularity might be specific to overall negative symptoms. The nodal degree of the left thalamus was associated with both negative and cognitive symptoms. Our findings are important for improving our understanding of abnormal white-matter network topology underlying negative symptoms in schizophrenia.
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Affiliation(s)
- Adem Bayrakçı
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Nabi Zorlu
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey.
| | - Merve Karakılıç
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Funda Gülyüksel
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Berna Yalınçetin
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Elif Oral
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Fazıl Gelal
- Department of Radiodiagnostics, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Emre Bora
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey.,Faculty of Medicine, Department of Psychiatry, Dokuz Eylul University, Izmir, Turkey.,Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, Australia
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5
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Westhoff MLS, Ladwig J, Heck J, Schülke R, Groh A, Deest M, Bleich S, Frieling H, Jahn K. Early Detection and Prevention of Schizophrenic Psychosis-A Review. Brain Sci 2021; 12:11. [PMID: 35053755 PMCID: PMC8774083 DOI: 10.3390/brainsci12010011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/16/2021] [Accepted: 12/20/2021] [Indexed: 01/04/2023] Open
Abstract
Psychotic disorders often run a chronic course and are associated with a considerable emotional and social impact for patients and their relatives. Therefore, early recognition, combined with the possibility of preventive intervention, is urgently warranted since the duration of untreated psychosis (DUP) significantly determines the further course of the disease. In addition to established diagnostic tools, neurobiological factors in the development of schizophrenic psychoses are increasingly being investigated. It is shown that numerous molecular alterations already exist before the clinical onset of the disease. As schizophrenic psychoses are not elicited by a single mutation in the deoxyribonucleic acid (DNA) sequence, epigenetics likely constitute the missing link between environmental influences and disease development and could potentially serve as a biomarker. The results from transcriptomic and proteomic studies point to a dysregulated immune system, likely evoked by epigenetic alterations. Despite the increasing knowledge of the neurobiological mechanisms involved in the development of psychotic disorders, further research efforts with large population-based study designs are needed to identify suitable biomarkers. In conclusion, a combination of blood examinations, functional imaging techniques, electroencephalography (EEG) investigations and polygenic risk scores should be considered as the basis for predicting how subjects will transition into manifest psychosis.
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Affiliation(s)
- Martin Lennart Schulze Westhoff
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Johannes Ladwig
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Johannes Heck
- Institute for Clinical Pharmacology, Hannover Medical School, D-30625 Hannover, Germany;
| | - Rasmus Schülke
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Adrian Groh
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Maximilian Deest
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Stefan Bleich
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Helge Frieling
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Kirsten Jahn
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
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6
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Associations between long-term psychosis risk, probabilistic category learning, and attenuated psychotic symptoms with cortical surface morphometry. Brain Imaging Behav 2021; 16:91-106. [PMID: 34218406 DOI: 10.1007/s11682-021-00479-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2021] [Indexed: 10/20/2022]
Abstract
Neuroimaging studies have consistently found structural cortical abnormalities in individuals with schizophrenia, especially in structural hubs. However, it is unclear what abnormalities predate psychosis onset and whether abnormalities are related to behavioral performance and symptoms associated with psychosis risk. Using surface-based morphometry, we examined cortical volume, gyrification, and thickness in a psychosis risk group at long-term risk for developing a psychotic disorder (n = 18; i.e., extreme positive schizotypy plus interview-rated attenuated psychotic symptoms [APS]) and control group (n = 19). Overall, the psychosis risk group exhibited cortical abnormalities in multiple structural hub regions, with abnormalities associated with poorer probabilistic category learning, a behavioral measure strongly associated with psychosis risk. For instance, the psychosis risk group had hypogyria in a right posterior midcingulate cortical hub and left superior parietal cortical hub, as well as decreased volume in a right pericalcarine hub. Morphometric measures in all of these regions were also associated with poorer probabilistic category learning. In addition to decreased right pericalcarine volume, the psychosis risk group exhibited a number of other structural abnormalities in visual network structural hub regions, consistent with previous evidence of visual perception deficits in psychosis risk. Further, severity of APS hallucinations, delusional ideation, and suspiciousness/persecutory ideas were associated with gyrification abnormalities, with all domains associated with hypogyria of the right lateral orbitofrontal cortex. Thus, current results suggest that structural abnormalities, especially in structural hubs, are present in psychosis risk and are associated both with poor learning on a psychosis risk-related task and with APS severity.
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7
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Taquet M, Smith SM, Prohl AK, Peters JM, Warfield SK, Scherrer B, Harrison PJ. A structural brain network of genetic vulnerability to psychiatric illness. Mol Psychiatry 2021; 26:2089-2100. [PMID: 32372008 PMCID: PMC7644622 DOI: 10.1038/s41380-020-0723-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Revised: 03/17/2020] [Accepted: 03/30/2020] [Indexed: 12/31/2022]
Abstract
Psychiatry is undergoing a paradigm shift from the acceptance of distinct diagnoses to a representation of psychiatric illness that crosses diagnostic boundaries. How this transition is supported by a shared neurobiology remains largely unknown. In this study, we first identify single nucleotide polymorphisms (SNPs) associated with psychiatric disorders based on 136 genome-wide association studies. We then conduct a joint analysis of these SNPs and brain structural connectomes in 678 healthy children in the PING study. We discovered a strong, robust, and transdiagnostic mode of genome-connectome covariation which is positively and specifically correlated with genetic risk for psychiatric illness at the level of individual SNPs. Similarly, this mode is also significantly positively correlated with polygenic risk scores for schizophrenia, alcohol use disorder, major depressive disorder, a combined bipolar disorder-schizophrenia phenotype, and a broader cross-disorder phenotype, and significantly negatively correlated with a polygenic risk score for educational attainment. The resulting "vulnerability network" is shown to mediate the influence of genetic risks onto behaviors related to psychiatric vulnerability (e.g., marijuana, alcohol, and caffeine misuse, perceived stress, and impulsive behavior). Its anatomy overlaps with the default-mode network, with a network of cognitive control, and with the occipital cortex. These findings suggest that the brain vulnerability network represents an endophenotype funneling genetic risks for various psychiatric illnesses through a common neurobiological root. It may form part of the neural underpinning of the well-recognized but poorly explained overlap and comorbidity between psychiatric disorders.
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Affiliation(s)
- Maxime Taquet
- Department of Psychiatry, University of Oxford, Oxford, UK.
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, UK
| | - Anna K Prohl
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jurriaan M Peters
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Benoit Scherrer
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul J Harrison
- Department of Psychiatry, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
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8
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Chen T, Chen Z, Gong Q. White Matter-Based Structural Brain Network of Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1305:35-55. [PMID: 33834393 DOI: 10.1007/978-981-33-6044-0_3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Major depressive disorder (MDD) is frequently characterized as a disorder of the disconnection syndrome. Diffusion tensor imaging (DTI) has played a critical role in supporting this view, with much investigation providing a large amount of evidence of structural connectivity abnormalities in the disorder. Recent research on the human connectome combined neuroimaging techniques with graph theoretic methods to highlight the disrupted topological properties of large-scale structural brain networks under depression, involving global metrics (e.g., global and local efficiencies), and local nodal properties (e.g., degree and betweenness), as well as other related metrics, including a modular structure, assortativity, and (rich) hubs. Here, we review the studies of white matter networks in the case of MDD with the application of these techniques, focusing principally on the consistent findings and the clinical significance of DTI-based network research, while discussing the key methodological issues that frequently arise in the field. The already published literature shows that MDD is associated with a widespread structural connectivity deficit. Topological alteration of structural brain networks in the case of MDD points to decreased overall connectivity strength and reduced global efficiency as well as decreased small-worldness and network resilience. These structural connectivity disturbances entail potential functional consequences, although the relationship between the two is very sophisticated and requires further investigation. In summary, the present study comprehensively maps the structural connectomic disturbances in patients with MDD across the entire brain, which adds important weight to the view suggesting connectivity abnormalities of this disorder and highlights the potential of network properties as diagnostic biomarkers in the psychoradiology field. Several common methodological issues of the study of DTI-based networks are discussed, involving sample heterogeneity and fiber crossing problems and the tractography algorithms. Finally, suggestions for future perspectives, including imaging multimodality, a longitudinal study and computational connectomics, in the further study of white matter networks under depression are given. Surmounting these challenges and advancing the research methods will be required to surpass the simple mapping of connectivity changes to illuminate the underlying psychiatric pathological mechanism.
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Affiliation(s)
- Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China
| | - Ziqi Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
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9
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Michielse S, Lange I, Bakker J, Goossens L, Verhagen S, Wichers M, Lieverse R, Schruers K, van Amelsvoort T, van Os J, Marcelis M. White matter microstructure and network-connectivity in emerging adults with subclinical psychotic experiences. Brain Imaging Behav 2020; 14:1876-1888. [PMID: 31183775 PMCID: PMC7572337 DOI: 10.1007/s11682-019-00129-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Group comparisons of individuals with psychotic disorder and controls have shown alterations in white matter microstructure. Whether white matter microstructure and network connectivity is altered in adolescents with subclinical psychotic experiences (PE) at the lowest end of the psychosis severity spectrum is less clear. DWI scan were acquired in 48 individuals with PE and 43 healthy controls (HC). Traditional tensor-derived indices: Fractional Anisotropy, Axial Diffusivity, Mean Diffusivity and Radial Diffusivity, as well as network connectivity measures (global/local efficiency and clustering coefficient) were compared between the groups. Subclinical psychopathology was assessed with the Community Assessment of Psychic Experiences (CAPE) and Montgomery-Åsberg Depression Rating Scale (MADRS) questionnaires and, in order to capture momentary subclinical expression of psychosis, the Experience Sampling Method (ESM) questionnaires. Within the PE-group, interactions between subclinical (momentary) symptoms and brain regions in the model of tensor-derived indices and network connectivity measures were investigated in a hypothesis-generating fashion. Whole brain analyses showed no group differences in tensor-derived indices and network connectivity measures. In the PE-group, a higher positive symptom distress score was associated with both higher local efficiency and clustering coefficient in the right middle temporal pole. The findings indicate absence of microstructural white matter differences between emerging adults with subclinical PE and controls. In the PE-group, attenuated symptoms were positively associated with network efficiency/cohesion, which requires replication and may indicate network alterations in emerging mild psychopathology.
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Affiliation(s)
- Stijn Michielse
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands.
| | - Iris Lange
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
| | - Jindra Bakker
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
- Department of Neuroscience, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium
| | - Liesbet Goossens
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
| | - Simone Verhagen
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
| | - Marieke Wichers
- University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, Groningen, The Netherlands
| | - Ritsaert Lieverse
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
| | - Koen Schruers
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
- Faculty of Psychology, Center for Experimental and Learning Psychology, University of Leuven, Leuven, Belgium
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
| | - Jim van Os
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
- King's Health Partners, Department of Psychosis Studies, Institute of Psychiatry, King's College London, London, England
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Machteld Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
- Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, the Netherlands
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10
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Collin G, Seidman LJ, Keshavan MS, Stone WS, Qi Z, Zhang T, Tang Y, Li H, Arnold Anteraper S, Niznikiewicz MA, McCarley RW, Shenton ME, Wang J, Whitfield-Gabrieli S. Functional connectome organization predicts conversion to psychosis in clinical high-risk youth from the SHARP program. Mol Psychiatry 2020; 25:2431-2440. [PMID: 30410064 PMCID: PMC6813871 DOI: 10.1038/s41380-018-0288-x] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 09/27/2018] [Accepted: 10/08/2018] [Indexed: 12/23/2022]
Abstract
The emergence of prodromal symptoms of schizophrenia and their evolution into overt psychosis may stem from an aberrant functional reorganization of the brain during adolescence. To examine whether abnormalities in connectome organization precede psychosis onset, we performed a functional connectome analysis in a large cohort of medication-naive youth at risk for psychosis from the Shanghai At Risk for Psychosis (SHARP) study. The SHARP program is a longitudinal study of adolescents and young adults at Clinical High Risk (CHR) for psychosis, conducted at the Shanghai Mental Health Center in collaboration with neuroimaging laboratories at Harvard and MIT. Our study involved a total of 251 subjects, including 158 CHRs and 93 age-, sex-, and education-matched healthy controls. During 1-year follow-up, 23 CHRs developed psychosis. CHRs who would go on to develop psychosis were found to show abnormal modular connectome organization at baseline, while CHR non-converters did not. In all CHRs, abnormal modular connectome organization at baseline was associated with a threefold conversion rate. A region-specific analysis showed that brain regions implicated in early-course schizophrenia, including superior temporal gyrus and anterior cingulate cortex, were most abnormal in terms of modular assignment. Our results show that functional changes in brain network organization precede the onset of psychosis and may drive psychosis development in at-risk youth.
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Affiliation(s)
- Guusje Collin
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. .,McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Larry J. Seidman
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA, Dr. Larry Seidman passed away on September 7, 2017 and Dr. Robert McCarley passed away on May 27, 2017. Professors Seidman and McCarley were two of the initiators and principal investigators of the Shanghai At Risk for Psychosis (SHARP) study
| | - Matcheri S. Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - William S. Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Zhenghan Qi
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA,Department of Linguistics and Cognitive Science, University of Delaware, Newark, DE, USA
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huijun Li
- Florida A&M University, Department of Psychology, Tallahassee, FL, USA
| | - Sheeba Arnold Anteraper
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA,Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital, Boston MA, USA
| | | | - Robert W. McCarley
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, USA, Dr. Larry Seidman passed away on September 7, 2017 and Dr. Robert McCarley passed away on May 27, 2017. Professors Seidman and McCarley were two of the initiators and principal investigators of the Shanghai At Risk for Psychosis (SHARP) study
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA,Research and Development, VA Boston Healthcare System, Brockton Division, Brockton, MA, USA,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Susan Whitfield-Gabrieli
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA,Poitras Center for Affective Disorders, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
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11
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Kim DJ, Min BK. Rich-club in the brain's macrostructure: Insights from graph theoretical analysis. Comput Struct Biotechnol J 2020; 18:1761-1773. [PMID: 32695269 PMCID: PMC7355726 DOI: 10.1016/j.csbj.2020.06.039] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 02/07/2023] Open
Abstract
The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.
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Key Words
- AD, Alzheimer’s disease
- ADHD, attention deficit hyperactivity disorder
- ASD, autism spectrum disorder
- BD, bipolar disorder
- Brain connectivity
- Brain network
- DTI, diffusion tensor imaging
- EEG, electroencephalography
- Graph theory
- MDD, major depressive disorder
- MEG, magnetoencephalography
- MRI, magnetic resonance imaging
- Neuroimaging
- Rich-club
- TBI, traumatic brain injury
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Affiliation(s)
- Dae-Jin Kim
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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12
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Zhang W, Lei D, Keedy SK, Ivleva EI, Eum S, Yao L, Tamminga CA, Clementz BA, Keshavan MS, Pearlson GD, Gershon ES, Bishop JR, Gong Q, Lui S, Sweeney JA. Brain gray matter network organization in psychotic disorders. Neuropsychopharmacology 2020; 45:666-674. [PMID: 31812151 PMCID: PMC7021697 DOI: 10.1038/s41386-019-0586-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 11/25/2019] [Accepted: 11/30/2019] [Indexed: 02/05/2023]
Abstract
Abnormal neuroanatomic brain networks have been reported in schizophrenia, but their characterization across patients with psychotic disorders, and their potential alterations in nonpsychotic relatives, remain to be clarified. Participants recruited by the Bipolar and Schizophrenia Network for Intermediate Phenotypes consortium included 326 probands with psychotic disorders (107 with schizophrenia (SZ), 87 with schizoaffective disorder (SAD), 132 with psychotic bipolar disorder (BD)), 315 of their nonpsychotic first-degree relatives and 202 healthy controls. Single-subject gray matter graphs were extracted from structural MRI scans, and whole-brain neuroanatomic organization was compared across the participant groups. Compared with healthy controls, psychotic probands showed decreased nodal efficiency mainly in bilateral superior temporal regions. These regions had altered morphological relationships primarily with frontal lobe regions, and their network-level alterations were associated with positive symptoms of psychosis. Nonpsychotic relatives showed lower nodal centrality metrics in the prefrontal cortex and subcortical regions, and higher nodal centrality metrics in the left cingulate cortex and left thalamus. Diagnosis-specific analysis indicated that individuals with SZ had lower nodal efficiency in bilateral superior temporal regions than controls, probands with SAD only exhibited lower nodal efficiency in the left superior and middle temporal gyrus, and individuals with psychotic BD did not show significant differences from healthy controls. Our findings provide novel evidence of clinically relevant disruptions in the anatomic association of the superior temporal lobe with other regions of whole-brain networks in patients with psychotic disorders, but not in their unaffected relatives, suggesting that it is a disease-related trait. Network disorganization primarily involving frontal lobe and subcortical regions in nonpsychotic relatives may be related to familial illness risk.
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Affiliation(s)
- Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Du Lei
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Sarah K Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Elena I Ivleva
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Seenae Eum
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN, USA
| | - Li Yao
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Brett A Clementz
- Department of Psychology, University of Georgia, Athens, GA, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA.
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13
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Yoviene Sykes LA, Ferrara M, Addington J, Bearden CE, Cadenhead KS, Cannon TD, Cornblatt BA, Perkins DO, Mathalon DH, Seidman LJ, Tsuang MT, Walker EF, McGlashan TH, Woodberry KA, Powers AR, Ponce AN, Cahill JD, Pollard JM, Srihari VH, Woods SW. Predictive validity of conversion from the clinical high risk syndrome to frank psychosis. Schizophr Res 2020; 216:184-191. [PMID: 31864837 PMCID: PMC7239715 DOI: 10.1016/j.schres.2019.12.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 12/04/2019] [Accepted: 12/05/2019] [Indexed: 02/08/2023]
Abstract
Although the clinical high risk for psychosis (CHR) paradigm has become well-established over the past two decades, one key component has received surprisingly little investigative attention: the predictive validity of the criteria for conversion or transition to frank psychosis. The current study evaluates the predictive validity of the transition to psychosis as measured by the Structured Interview for Psychosis-Risk Syndromes (SIPS) in CHR individuals. Participants included 33 SIPS converters and 399 CHR non-converters both from the North American Prodromal Longitudinal Study (NAPLS-2), as well as a sample of 67 separately ascertained first-episode psychosis (FEP) patients from the STEP program. Comparisons were made at baseline and one-year follow-up on demographic, diagnostic stability (SCID), and available measurement domains relating to severity of illness (psychotropic medication, psychosocial treatment, and resource utilization). Principal findings are: 1) a large majority of cases in both SIPS converters (n = 27/33, 81.8%) and FEP (n = 57/67, 85.1%) samples met criteria for continued psychosis at one-year follow-up; 2) follow-up prescription rates for current antipsychotic medication were higher in SIPS converters (n = 17/32, 53.1%) compared to SIPS non-converters (n = 81/397, 20.4%), and similar as compared to FEP cases (n = 39/65, 60%); and 3) at follow-up, SIPS converters had higher rates of resource utilization (psychiatric hospitalizations, day hospital admissions, and ER visits) than SIPS non-converters and were similar to FEP in most categories. The results suggest that the SIPS definition of psychosis onset carries substantial predictive validity. Limitations and future directions are discussed.
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Affiliation(s)
- Laura A Yoviene Sykes
- Department of Psychiatry and Connecticut Mental Health Center, Yale University, New Haven, CT, United States of America.
| | - Maria Ferrara
- Department of Psychiatry and Connecticut Mental Health Center, Yale University, New Haven, CT, United States of America; Dipartimento di Salute Mentale e Dipendenze Patologiche, AUSL Modena, Modena, Italy
| | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Carrie E Bearden
- Departments of Psychology and Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, CA, United States of America
| | | | - Tyrone D Cannon
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, United States of America
| | - Barbara A Cornblatt
- Department of Psychiatry, Zucker Hillside Hospital, Long Island, NY, United States of America
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States of America
| | - Daniel H Mathalon
- Department of Psychiatry, UCSF, San Francisco, CA, United States of America
| | - Larry J Seidman
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America
| | - Ming T Tsuang
- Department of Psychiatry, UCSD, San Diego, CA, United States of America
| | - Elaine F Walker
- Departments of Psychology and Psychiatry, Emory University, Atlanta, GA, United States of America
| | - Thomas H McGlashan
- Department of Psychiatry and Connecticut Mental Health Center, Yale University, New Haven, CT, United States of America
| | - Kristen A Woodberry
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America; Center for Psychiatric Research, Maine Medical Center, Portland, ME, United States of America
| | - Albert R Powers
- Department of Psychiatry and Connecticut Mental Health Center, Yale University, New Haven, CT, United States of America
| | - Allison N Ponce
- Department of Psychiatry and Connecticut Mental Health Center, Yale University, New Haven, CT, United States of America
| | - John D Cahill
- Department of Psychiatry and Connecticut Mental Health Center, Yale University, New Haven, CT, United States of America
| | - Jessica M Pollard
- Department of Psychiatry and Connecticut Mental Health Center, Yale University, New Haven, CT, United States of America
| | - Vinod H Srihari
- Department of Psychiatry and Connecticut Mental Health Center, Yale University, New Haven, CT, United States of America
| | - Scott W Woods
- Department of Psychiatry and Connecticut Mental Health Center, Yale University, New Haven, CT, United States of America
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14
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Wu Y, Mai N, Weng X, Liang J, Ning Y. Changes of Altruistic Behavior and Kynurenine Pathway in Late-Life Depression. Front Psychiatry 2020; 11:338. [PMID: 32425830 PMCID: PMC7212401 DOI: 10.3389/fpsyt.2020.00338] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 04/03/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Depressive patients show less altruistic behavior. While, older adults present higher tendencies for altruism than younger adults. Depression and age are two of the influencing factors of altruism, kynurenine (KYN), and its metabolites. However, the characteristics of altruism in late-life depression (LLD) and its possible underlying mechanism have not been studied. OBJECTIVE We aimed to explore the characteristics of altruism in LLD patients and its neurobiological mechanism and structural brain network. We investigated whether the levels of metabolites in kynurenine pathway (KP) and white matter (WM) network topological features would influence the altruistic behavior in LLD patients. METHODS Thirty-four LLD patients and 36 heathy controls (HCs) were included. Altruism was evaluated by the Dictator Game (DG) paradigm. Serum concentrations of KP metabolites were detected by the liquid chromatography-tandem mass spectrometry method. The topological features of the WM network were calculated from diffusion tensor imaging data in conjunction with graph-theoretical analysis. RESULTS The LLD participants exhibited a higher level of altruism and WM global network properties than the HCs. Kynurenic acid to kynurenine (KYNA/KYN) ratio was associated with the DG performance in LLD group. KYNA/KYN ratio was associated with the WM network properties in HC group. CONCLUSIONS KYN metabolism played an important role in altruistic behavior in LLD.
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Affiliation(s)
- Yujie Wu
- School of Psychology, South China Normal University, Guangzhou, China.,Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Naikeng Mai
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Xuchu Weng
- School of Psychology, South China Normal University, Guangzhou, China.,Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Jiuxing Liang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Yuping Ning
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.,The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
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15
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Cui LB, Wei Y, Xi YB, Griffa A, De Lange SC, Kahn RS, Yin H, Van den Heuvel MP. Connectome-Based Patterns of First-Episode Medication-Naïve Patients With Schizophrenia. Schizophr Bull 2019; 45:1291-1299. [PMID: 30926985 PMCID: PMC6811827 DOI: 10.1093/schbul/sbz014] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Emerging evidence indicates that a disruption in brain network organization may play an important role in the pathophysiology of schizophrenia. The neuroimaging fingerprint reflecting the pathophysiology of first-episode schizophrenia remains to be identified. Here, we aimed at characterizing the connectome organization of first-episode medication-naïve patients with schizophrenia. A cross-sectional structural and functional neuroimaging study using two independent samples (principal dataset including 42 medication-naïve, previously untreated patients and 48 healthy controls; replication dataset including 39 first-episode patients [10 untreated patients] and 66 healthy controls) was performed. Brain network architecture was assessed by means of white matter fiber integrity measures derived from diffusion-weighted imaging (DWI) and by means of structural-functional (SC-FC) coupling measured by combining DWI and resting-state functional magnetic resonance imaging. Connectome rich club organization was found to be significantly disrupted in medication-naïve patients as compared with healthy controls (P = .012, uncorrected), with rich club connection strength (P = .032, uncorrected) and SC-FC coupling (P < .001, corrected for false discovery rate) decreased in patients. Similar results were found in the replication dataset. Our findings suggest that a disruption of rich club organization and functional dynamics may reflect an early feature of schizophrenia pathophysiology. These findings add to our understanding of the neuropathological mechanisms of schizophrenia and provide new insights into the early stages of the disorder.
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Affiliation(s)
- Long-Biao Cui
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
- School of Medical Psychology, Fourth Military Medical University, Xi’an, China
| | - Yongbin Wei
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Yi-Bin Xi
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Alessandra Griffa
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Siemon C De Lange
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - René S Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Martijn P Van den Heuvel
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam University Medical Center, Amsterdam, The Netherlands
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16
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Michielse S, Rakijo K, Peeters S, Viechtbauer W, van Os J, Marcelis M. Microstructural white matter network-connectivity in individuals with psychotic disorder, unaffected siblings and controls. NEUROIMAGE-CLINICAL 2019; 23:101931. [PMID: 31491817 PMCID: PMC6658824 DOI: 10.1016/j.nicl.2019.101931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 06/08/2019] [Accepted: 07/10/2019] [Indexed: 02/08/2023]
Abstract
Background Altered structural network-connectivity has been reported in psychotic disorder but whether these alterations are associated with genetic vulnerability, and/or with phenotypic variation, has been less well examined. This study examined i) whether differences in network-connectivity exist between patients with psychotic disorder, siblings of patients with psychotic disorder and controls, and ii) whether network-connectivity alterations vary with (subclinical) symptomatology. Methods Network-connectivity measures (global efficiency (GE), density, local efficiency (LE), clustering coefficient (CC)) were derived from diffusion weighted imaging (DWI) and were compared between 85 patients with psychotic disorder, 93 siblings without psychotic disorder and 80 healthy comparison subjects using multilevel regression models. In patients, associations between Positive and Negative Syndrome Scale (PANSS) symptoms and topological measures were examined. In addition, interactions between subclinical psychopathology and sibling/healthy comparison subject status were examined in models of topological measures. Results While there was no main effect of group with respect to GE, density, LE and CC, siblings had a significantly higher CC compared to patients (B = 0.0039, p = .002). In patients, none of the PANSS symptom domains were significantly associated with any of the four network-connectivity measures. The two-way interaction between group and SIR-r positive score in the model of LE was significant (χ2 = 6.24, p = .01, df = 1). In the model of CC, the interactions between group and respectively SIS-r positive (χ2 = 5.59, p = .02, df = 1) and negative symptom scores (χ2 = 4.71, p = .03, df = 1) were significant. Stratified analysis showed that, in siblings, decreased LE and CC was significantly associated with increased SIS-r positive scores (LE: B = −0.0049, p = .003, CC: B = −0.0066, p = .01) and that decreased CC was significantly associated with increased SIS-r negative scores (B = −0.012, p = .003). There were no significant interactions between group and SIS-r scores in the models of GE and density. Conclusion The findings indicate absence of structural network-connectivity alterations in individuals with psychotic disorder and in individuals at higher than average genetic risk for psychotic disorder, in comparison with healthy subjects. The differential subclinical symptom-network connectivity associations in siblings with respect to controls may be a sign of psychosis vulnerability in the siblings. Patients with psychotic disorder had unchanged network efficiency and clustering. Siblings of patients had higher clustering coefficient compared to patients. Lower clustering/efficiency was associated with higher positive symptoms in siblings. Decreased clustering was associated with increased negative symptoms in siblings.
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Affiliation(s)
- Stijn Michielse
- Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, Maastricht 6200, MD, the Netherlands.
| | - Kimberley Rakijo
- Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, Maastricht 6200, MD, the Netherlands
| | - Sanne Peeters
- Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, Maastricht 6200, MD, the Netherlands; Faculty of Psychology and Educational Sciences, Open University of the Netherlands, Heerlen, the Netherlands
| | - Wolfgang Viechtbauer
- Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, Maastricht 6200, MD, the Netherlands
| | - Jim van Os
- Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, Maastricht 6200, MD, the Netherlands; King's College London, King's Health Partners, Department of Psychosis Studies, Institute of Psychiatry, London, UK; Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Machteld Marcelis
- Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, Maastricht 6200, MD, the Netherlands; Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, the Netherlands
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17
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Griffa A, Baumann PS, Klauser P, Mullier E, Cleusix M, Jenni R, van den Heuvel MP, Do KQ, Conus P, Hagmann P. Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging. Transl Psychiatry 2019; 9:62. [PMID: 30718455 PMCID: PMC6362225 DOI: 10.1038/s41398-019-0392-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 01/10/2019] [Indexed: 12/11/2022] Open
Abstract
Early in the course of psychosis, alterations in brain connectivity accompany the emergence of psychiatric symptoms and cognitive impairments, including processing speed. The clinical-staging model is a refined form of diagnosis that places the patient along a continuum of illness conditions, which allows stage-specific interventions with the potential of improving patient care and outcome. This cross-sectional study investigates brain connectivity features that characterize the clinical stages following a first psychotic episode. Structural brain networks were derived from diffusion-weighted MRI for 71 early-psychosis patients and 76 healthy controls. Patients were classified into stage II (first-episode), IIIa (incomplete remission), IIIb (one relapse), and IIIc (two or more relapses), according to the course of the illness until the time of scanning. Brain connectivity measures and diffusion parameters (fractional anisotropy, apparent diffusion coefficient) were investigated using general linear models and sparse linear discriminant analysis (sLDA), studying distinct subgroups of patients who were at specific stages of early psychosis. We found that brain connectivity impairments were more severe in clinical stages following the first-psychosis episode (stages IIIa, IIIb, IIIc) than in first-episode psychosis (stage II) patients. These alterations were spatially diffuse but converged on a set of vulnerable regions, whose inter-connectivity selectively correlated with processing speed in patients and controls. The sLDA suggested that relapsing-remitting (stages IIIb, IIIc) and non-remitting (stage IIIa) patients are characterized by distinct dysconnectivity profiles. Our results indicate that neuroimaging markers of brain dysconnectivity in early psychosis may reflect the heterogeneity of the illness and provide a connectomics signature of the clinical-staging model.
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Affiliation(s)
- Alessandra Griffa
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland. .,Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University, Amsterdam, The Netherlands.
| | - Philipp S. Baumann
- 0000 0001 0423 4662grid.8515.9Service of General Psychiatry and Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland ,0000 0001 0423 4662grid.8515.9Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Paul Klauser
- 0000 0001 0423 4662grid.8515.9Service of General Psychiatry and Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland ,0000 0001 0423 4662grid.8515.9Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Emeline Mullier
- 0000 0001 0423 4662grid.8515.9Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Martine Cleusix
- 0000 0001 0423 4662grid.8515.9Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Raoul Jenni
- 0000 0001 0423 4662grid.8515.9Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Martijn P. van den Heuvel
- grid.484519.5Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University, Amsterdam, The Netherlands
| | - Kim Q. Do
- 0000 0001 0423 4662grid.8515.9Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Philippe Conus
- 0000 0001 0423 4662grid.8515.9Service of General Psychiatry and Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Patric Hagmann
- 0000 0001 0423 4662grid.8515.9Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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18
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Bassett DS, Xia CH, Satterthwaite TD. Understanding the Emergence of Neuropsychiatric Disorders With Network Neuroscience. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:742-753. [PMID: 29729890 PMCID: PMC6119485 DOI: 10.1016/j.bpsc.2018.03.015] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 03/28/2018] [Accepted: 03/29/2018] [Indexed: 11/23/2022]
Abstract
Major neuropsychiatric disorders such as psychosis are increasingly acknowledged to be disorders of brain connectivity. Yet tools to map, model, predict, and change connectivity are difficult to develop, largely because of the complex, dynamic, and multivariate nature of interactions between brain regions. Network neuroscience (NN) provides a theoretical framework and mathematical toolset to address these difficulties. Building on areas of mathematics such as graph theory, NN in its simplest form summarizes neuroimaging data by treating brain regions as nodes in a graph and by treating interactions or connections between nodes as edges in the graph. Network metrics can then be used to quantitatively describe the architecture of the graph, which in turn reflects the network's function. We review evidence supporting the utility of NN in understanding psychiatric disorders, with a focus on normative brain network development and abnormalities associated with psychosis. We also emphasize relevant methodological challenges, such as motion artifact correction, which are particularly important to consider when applying network tools to developmental neuroimaging data. We close with a discussion of several emerging frontiers of NN in psychiatry, including generative network modeling and network control theory. We aim to offer an accessible introduction to this emerging field and motivate further work that uses NN to better understand the normative development of brain networks and alterations in that development that accompany or foreshadow psychiatric disease.
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Affiliation(s)
- Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Cedric Huchuan Xia
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
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19
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Das T, Borgwardt S, Hauke DJ, Harrisberger F, Lang UE, Riecher-Rössler A, Palaniyappan L, Schmidt A. Disorganized Gyrification Network Properties During the Transition to Psychosis. JAMA Psychiatry 2018; 75:613-622. [PMID: 29710118 PMCID: PMC6137528 DOI: 10.1001/jamapsychiatry.2018.0391] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
IMPORTANCE There is urgent need to improve the limited prognostic accuracy of clinical instruments to predict psychosis onset in individuals at clinical high risk (CHR) for psychosis. As yet, no reliable biological marker has been established to delineate CHR individuals who will develop psychosis from those who will not. OBJECTIVES To investigate abnormalities in a graph-based gyrification connectome in the early stages of psychosis and to test the accuracy of this systems-based approach to predict a transition to psychosis among CHR individuals. DESIGN, SETTING, AND PARTICIPANTS This investigation was a cross-sectional magnetic resonance imaging (MRI) study with follow-up assessment to determine the transition status of CHR individuals. Participants were recruited from a specialized clinic for the early detection of psychosis at the Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland. Participants included individuals in the following 4 study groups: 44 healthy controls (HC group), 63 at-risk mental state (ARMS) individuals without later transition to psychosis (ARMS-NT group), 16 ARMS individuals with later transition to psychosis (ARMS-T group), and 38 antipsychotic-free patients with first-episode psychosis (FEP group). The study dates were November 2008 to November 2014. The dates of analysis were March to November 2017. MAIN OUTCOMES AND MEASURES Gyrification-based structural covariance networks (connectomes) were constructed to quantify global integration, segregation, and small-worldness. Group differences in network measures were assessed using functional data analysis across a range of network densities. The extremely randomized trees algorithm with repeated 5-fold cross-validation was used to delineate ARMS-T individuals from ARMS-NT individuals. Permutation tests were conducted to assess the significance of classification performance measures. RESULTS The 4 study groups comprised 161 participants with mean (SD) ages ranging from 24.0 (4.7) to 25.9 (5.7) years. Small-worldness was reduced in the ARMS-T and FEP groups and was associated with decreased integration and increased segregation in both groups (Hedges g range, 0.666-1.050). Using the connectome properties as features, a good classification performance was obtained (accuracy, 90.49%; balanced accuracy, 81.34%; positive predictive value, 84.47%; negative predictive value, 92.18%; sensitivity, 66.11%; specificity, 96.58%; and area under the curve, 88.30%). CONCLUSIONS AND RELEVANCE These findings suggest that there is poor integration in the coordinated development of cortical folding in patients who develop psychosis. These results further suggest that gyrification-based connectomes might be a promising means to generate systems-based measures from anatomical data to improve individual prediction of a transition to psychosis in CHR individuals.
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Affiliation(s)
- Tushar Das
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada,Department of Psychiatry, University of Western Ontario, London, Ontario, Canada,Lawson Health Research Institute, London, Ontario, Canada
| | - Stefan Borgwardt
- Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland
| | - Daniel J. Hauke
- Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland
| | - Fabienne Harrisberger
- Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland
| | - Undine E. Lang
- Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland
| | - Anita Riecher-Rössler
- Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland
| | - Lena Palaniyappan
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada,Department of Psychiatry, University of Western Ontario, London, Ontario, Canada,Lawson Health Research Institute, London, Ontario, Canada,Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
| | - André Schmidt
- Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland
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20
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Müller F, Dolder PC, Schmidt A, Liechti ME, Borgwardt S. Altered network hub connectivity after acute LSD administration. Neuroimage Clin 2018; 18:694-701. [PMID: 29560311 PMCID: PMC5857492 DOI: 10.1016/j.nicl.2018.03.005] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 01/15/2018] [Accepted: 03/06/2018] [Indexed: 12/16/2022]
Abstract
LSD is an ambiguous substance, said to mimic psychosis and to improve mental health in people suffering from anxiety and depression. Little is known about the neuronal correlates of altered states of consciousness induced by this substance. Limited previous studies indicated profound changes in functional connectivity of resting state networks after the administration of LSD. The current investigation attempts to replicate and extend those findings in an independent sample. In a double-blind, randomized, cross-over study, 100 μg LSD and placebo were orally administered to 20 healthy participants. Resting state brain activity was assessed by functional magnetic resonance imaging. Within-network and between-network connectivity measures of ten established resting state networks were compared between drug conditions. Complementary analysis were conducted using resting state networks as sources in seed-to-voxel analyses. Acute LSD administration significantly decreased functional connectivity within visual, sensorimotor and auditory networks and the default mode network. While between-network connectivity was widely increased and all investigated networks were affected to some extent, seed-to-voxel analyses consistently indicated increased connectivity between networks and subcortical (thalamus, striatum) and cortical (precuneus, anterior cingulate cortex) hub structures. These latter observations are consistent with findings on the importance of hubs in psychopathological states, especially in psychosis, and could underlay therapeutic effects of hallucinogens as proposed by a recent model.
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Affiliation(s)
- Felix Müller
- University of Basel, Department of Psychiatry (UPK), Basel 4012, Switzerland
| | - Patrick C Dolder
- University of Basel, Division of Clinical Pharmacology and Toxicology, Department of Biomedicine and Department of Clinical Research, University Hospital Basel, Basel 4031, Switzerland
| | - André Schmidt
- University of Basel, Department of Psychiatry (UPK), Basel 4012, Switzerland
| | - Matthias E Liechti
- University of Basel, Division of Clinical Pharmacology and Toxicology, Department of Biomedicine and Department of Clinical Research, University Hospital Basel, Basel 4031, Switzerland
| | - Stefan Borgwardt
- University of Basel, Department of Psychiatry (UPK), Basel 4012, Switzerland.
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21
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Alloza C, Bastin ME, Cox SR, Gibson J, Duff B, Semple SI, Whalley HC, Lawrie SM. Central and non-central networks, cognition, clinical symptoms, and polygenic risk scores in schizophrenia. Hum Brain Mapp 2017; 38:5919-5930. [PMID: 28881417 DOI: 10.1002/hbm.23798] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 08/02/2017] [Accepted: 08/24/2017] [Indexed: 12/25/2022] Open
Abstract
Schizophrenia is a complex disorder that may be the result of aberrant connections between specific brain regions rather than focal brain abnormalities. Here, we investigate the relationships between brain structural connectivity as described by network analysis, intelligence, symptoms, and polygenic risk scores (PGRS) for schizophrenia in a group of patients with schizophrenia and a group of healthy controls. Recently, researchers have shown an interest in the role of high centrality networks in the disorder. However, the importance of non-central networks still remains unclear. Thus, we specifically examined network-averaged fractional anisotropy (mean edge weight) in central and non-central subnetworks. Connections with the highest betweenness centrality within the average network (>75% of centrality values) were selected to represent the central subnetwork. The remaining connections were assigned to the non-central subnetwork. Additionally, we calculated graph theory measures from the average network (connections that occur in at least 2/3 of participants). Density, strength, global efficiency, and clustering coefficient were significantly lower in patients compared with healthy controls for the average network (pFDR < 0.05). All metrics across networks were significantly associated with intelligence (pFDR < 0.05). There was a tendency towards significance for a correlation between intelligence and PGRS for schizophrenia (r = -0.508, p = 0.052) that was significantly mediated by central and non-central mean edge weight and every graph metric from the average network. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. Hum Brain Mapp 38:5919-5930, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Clara Alloza
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Clinical Brain Sciences, Western General Hospital, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, United Kingdom
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, United Kingdom
| | - Jude Gibson
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Barbara Duff
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Scott I Semple
- Clinical Research Imaging Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Heather C Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
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22
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Mai N, Zhong X, Chen B, Peng Q, Wu Z, Zhang W, Ouyang C, Ning Y. Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits. Front Aging Neurosci 2017; 9:279. [PMID: 28878666 PMCID: PMC5572942 DOI: 10.3389/fnagi.2017.00279] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 08/07/2017] [Indexed: 11/13/2022] Open
Abstract
Patients with late-life depression (LLD) have a higher incident of developing dementia, especially individuals with memory deficits. However, little is known about the white matter characteristics of LLD with memory deficits (LLD-MD) in the human connectome, especially for the rich-club coefficient, which is an indicator that describes the organization pattern of hub in the network. To address this question, diffusion tensor imaging of 69 participants [15 LLD-MD patients; 24 patients with LLD with intact memory (LLD-IM); and 30 healthy controls (HC)] was applied to construct a brain network for each individual. A full-scale battery of neuropsychological tests were used for grouping, and evaluating executive function, processing speed and memory. Rich-club analysis and global network properties were utilized to describe the topological features in each group. Network-based statistics (NBS) were calculated to identify the impaired subnetwork in the LLD-MD group relative to that in the LLD-IM group. We found that compared with HC participants, patients with LLD (LLD-MD and LLD-IM) had relatively impaired rich-club organizations and rich-club connectivity. In addition, LLD-MD group exhibited lower feeder and local connective average strength than LLD-IM group. Furthermore, global network properties, such as the shortest path length, connective strength, efficiency and fault tolerant efficiency, were significantly decreased in the LLD-MD group relative to those in the LLD-IM and HC groups. According to NBS analysis, a subnetwork, including right cognitive control network (CCN) and corticostriatal circuits, were disrupted in LLD-MD patients. In conclusion, the disease effects of LLD were prevalent in rich-club organization. Feeder and local connections, especially in the subnetwork including right CCN and corticostriatal circuits, were further impaired in those with memory deficits. Global network properties were disrupted in LLD-MD patients relative to those in LLD-IM patients.
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Affiliation(s)
- Naikeng Mai
- Department of Neurology, Southern Medical UniversityGuangdong, China.,Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Xiaomei Zhong
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Ben Chen
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Qi Peng
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Zhangying Wu
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Weiru Zhang
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Cong Ouyang
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Yuping Ning
- Department of Neurology, Southern Medical UniversityGuangdong, China.,Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
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23
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Zhao X, Tian L, Yan J, Yue W, Yan H, Zhang D. Abnormal Rich-Club Organization Associated with Compromised Cognitive Function in Patients with Schizophrenia and Their Unaffected Parents. Neurosci Bull 2017. [PMID: 28646350 DOI: 10.1007/s12264-017-0151-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Schizophrenia is considered to be a disorder of brain connectivity, which might result from a disproportionally impaired rich-club organization. The rich-club is composed of highly interconnected hub regions that play crucial roles in integrating information between different brain regions. Few studies have yet investigated whether the structural rich-club organization is impaired in patients and their first-degree relatives. In this study, we established a weighted network model of white matter connections using diffusion tensor imaging of 19 patients and 39 unaffected parents, 22 young healthy controls for the patients, and 25 old healthy controls for the parents. Feeder edges between rich-club nodes and non-rich-club nodes were significantly decreased in both schizophrenic patients and their unaffected parents compared with controls. Furthermore, the feeder edges showed significant positive correlations with the scores in Category Fluency Test-animal naming in the unaffected parents. Specific feeder edges exhibited discriminative power with accuracy of 84.4% in distinguishing unaffected parents from old healthy controls. Our findings suggest that impaired rich-club organization, especially impaired feeder edges, may be related to familial vulnerability to schizophrenia, possibly reflecting a genetic predisposition for schizophrenia.
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Affiliation(s)
- Xin Zhao
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, China.,National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health (Ministry of Health), Peking University, Beijing, 100191, China
| | - Lin Tian
- Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, 214151, China.,Wuxi Tongren International Rehabilitation Hospital, Wuxi, 214151, China
| | - Jun Yan
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, China.,National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health (Ministry of Health), Peking University, Beijing, 100191, China
| | - Weihua Yue
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, China.,National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health (Ministry of Health), Peking University, Beijing, 100191, China
| | - Hao Yan
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, China. .,National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health (Ministry of Health), Peking University, Beijing, 100191, China.
| | - Dai Zhang
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, China. .,National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health (Ministry of Health), Peking University, Beijing, 100191, China. .,Peking University-Tsinghua University Joint Center for Life Sciences/ PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China.
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24
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Aberrant Temporal Connectivity in Persons at Clinical High Risk for Psychosis. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 2:696-705. [PMID: 29202110 DOI: 10.1016/j.bpsc.2016.12.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Background Schizophrenia, a neurodevelopmental disorder, involves abnormalities in functional connectivity (FC) across distributed neural networks, which are thought to antedate the emergence of psychosis. In a cohort of adolescents and young adults at clinical high risk (CHR) for psychosis, we applied data-driven approaches to resting-state fMRI data so as to systematically characterize FC abnormalities during this period and determine whether these abnormalities are associated with psychosis risk and severity of psychotic symptoms. Methods Fifty-one CHR participants and 47 matched healthy controls (HCs) were included in our analyses. Twelve of these CHR participants developed psychosis within 3.9 years. We estimated one multivariate measure of FC and studied its relationship to CHR status, conversion to psychosis and positive symptom severity. Results Multivariate analyses revealed between-group differences in whole-brain connectivity patterns of bilateral temporal areas, mostly affecting their functional connections to the thalamus. Further, more severe positive symptoms were associated with greater connectivity abnormalities in the anterior cingulate and frontal cortex. Conclusions Our study demonstrates that the well-established FC abnormalities of the thalamus and temporal areas observed in schizophrenia are also present in the CHR period, with aberrant connectivity of the temporal cortex most associated with psychosis risk.
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25
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Crossley NA, Marques TR, Taylor H, Chaddock C, Dell'Acqua F, Reinders AATS, Mondelli V, DiForti M, Simmons A, David AS, Kapur S, Pariante CM, Murray RM, Dazzan P. Connectomic correlates of response to treatment in first-episode psychosis. Brain 2016; 140:487-496. [PMID: 28007987 DOI: 10.1093/brain/aww297] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 09/21/2016] [Accepted: 09/28/2016] [Indexed: 11/14/2022] Open
Abstract
Connectomic approaches using diffusion tensor imaging have contributed to our understanding of brain changes in psychosis, and could provide further insights into the neural mechanisms underlying response to antipsychotic treatment. We here studied the brain network organization in patients at their first episode of psychosis, evaluating whether connectome-based descriptions of brain networks predict response to treatment, and whether they change after treatment. Seventy-six patients with a first episode of psychosis and 74 healthy controls were included. Thirty-three patients were classified as responders after 12 weeks of antipsychotic treatment. Baseline brain structural networks were built using whole-brain diffusion tensor imaging tractography, and analysed using graph analysis and network-based statistics to explore baseline characteristics of patients who subsequently responded to treatment. A subgroup of 43 patients was rescanned at the 12-week follow-up, to study connectomic changes over time in relation to treatment response. At baseline, those subjects who subsequently responded to treatment, compared to those that did not, showed higher global efficiency in their structural connectomes, a network configuration that theoretically facilitates the flow of information. We did not find specific connectomic changes related to treatment response after 12 weeks of treatment. Our data suggest that patients who have an efficiently-wired connectome at first onset of psychosis show a better subsequent response to antipsychotics. However, response is not accompanied by specific structural changes over time detectable with this method.
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Affiliation(s)
- Nicolas A Crossley
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK .,Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, Santiago 8330077, Chile
| | - Tiago Reis Marques
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK .,MRC Clinical Sciences Centre, Faculty of Medicine, Imperial College, Du Cane Road, London, W12 0NN, UK
| | - Heather Taylor
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Chris Chaddock
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Flavio Dell'Acqua
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Antje A T S Reinders
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Valeria Mondelli
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Marta DiForti
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Andrew Simmons
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Anthony S David
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Shitij Kapur
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Carmine M Pariante
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK.,National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, De Crespigny Park, London SE5 8AF, UK
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26
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Zhao Y, Du M, Gao X, Xiao Y, Shah C, Sun H, Chen F, Yang L, Yan Z, Fu Y, Lui S. Altered brain network topology in left-behind children: A resting-state functional magnetic resonance imaging study. CHILD ABUSE & NEGLECT 2016; 62:89-99. [PMID: 27794245 DOI: 10.1016/j.chiabu.2016.10.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Revised: 10/16/2016] [Accepted: 10/17/2016] [Indexed: 06/06/2023]
Abstract
Whether a lack of direct parental care affects brain function in children is an important question, particularly in developing countries where hundreds of millions of children are left behind when their parents migrate for economic or political reasons. In this study, we investigated changes in the topological architectures of brain functional networks in left-behind children (LBC). Resting-state functional magnetic resonance imaging data were obtained from 26 LBC and 21 children living within their nuclear family (non-LBC). LBC showed a significant increase in the normalized characteristic path length (λ), suggesting a decrease in efficiency in information access, and altered nodal centralities in the fronto-limbic regions and motor and sensory systems. Moreover, a decreased nodal degree and the nodal betweenness of the right rectus gyrus were positively correlated with annual family income. The present study provides the first empirical evidence that suggests that a lack of direct parental care could affect brain functional development in children, particularly involving emotional networks.
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Affiliation(s)
- Youjin Zhao
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang 325035, PR China; Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Meimei Du
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang 325035, PR China
| | - Xin Gao
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang 325035, PR China; Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Yuan Xiao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Chandan Shah
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Huaiqiang Sun
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Fuqin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Lili Yang
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang 325035, PR China
| | - Zhihan Yan
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang 325035, PR China
| | - Yuchuan Fu
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang 325035, PR China
| | - Su Lui
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang 325035, PR China; Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China.
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27
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Carpenter WT, Regier D. Diagnostic Categories: Provisional, Not Otherwise Classified, or Place-holder? Schizophr Bull 2016; 42:1305-1306. [PMID: 27566842 PMCID: PMC5049540 DOI: 10.1093/schbul/sbw127] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- William T. Carpenter
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD;,*To whom correspondence should be addressed; Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, PO Box 21247, Baltimore, MD 21228, US; tel: 410-402-7101, fax: 410-788-3837, e-mail:
| | - Darrel Regier
- Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University, Bethesda, MD
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